From 0c188b87c07e16a1571eaf65bf9498b0898822d2 Mon Sep 17 00:00:00 2001 From: Tools Platform Ecosystem bot Date: Sun, 29 Jan 2023 01:27:13 +0000 Subject: [PATCH] biotools-import on Sun Jan 29 01:27:12 UTC 2023 --- data/3.6.3/3.6.3.biotools.json | 77 ++++ data/3dpolys-le/3dpolys-le.biotools.json | 98 ++++++ data/4accpred/4accpred.biotools.json | 102 ++++++ data/4d-fed-gnn/4d-fed-gnn.biotools.json | 70 ++++ data/acinetobase/acinetobase.biotools.json | 98 ++++++ data/acp_ms/acp_ms.biotools.json | 85 +++++ data/airrscape/airrscape.biotools.json | 129 +++++++ data/alphafill/alphafill.biotools.json | 129 +++++++ data/alvascience/alvascience.biotools.json | 100 ++++++ data/ampbenchmark/ampbenchmark.biotools.json | 147 ++++++++ data/antiage-db/antiage-db.biotools.json | 78 ++++ .../antibody_registry.biotools.json | 108 ++++++ data/apache_trino/apache_trino.biotools.json | 67 ++++ .../apache_zeppelin.biotools.json | 46 +++ .../apis-wings-eu/apis-wings-eu.biotools.json | 58 +++ 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data/wenda_gpu/wenda_gpu.biotools.json create mode 100644 data/wgdtree/wgdtree.biotools.json create mode 100644 data/white_rabbit/white_rabbit.biotools.json create mode 100644 data/wrmxpress/wrmxpress.biotools.json diff --git a/data/3.6.3/3.6.3.biotools.json b/data/3.6.3/3.6.3.biotools.json new file mode 100644 index 0000000000000..e280b40d641eb --- /dev/null +++ b/data/3.6.3/3.6.3.biotools.json @@ -0,0 +1,77 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T10:45:29.244472Z", + "biotoolsCURIE": "biotools:3.6.3", + "biotoolsID": "3.6.3", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "issa.alkhonain@gmail.com", + "name": "Issa M Alkhonain", + "typeEntity": "Person" + }, + { + "name": "Abdulrhman A. Alsharqi" + }, + { + "name": "Feras O. Alsulami" + }, + { + "name": "Omalkhaire M. Alshaikh" + } + ], + "description": "Assessing the Degree of Gastroesophageal Reflux Disease (GERD) Knowledge Among the Riyadh Population.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://cran.r-project.org/bin/windows/base/old/3.6.3/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-26T10:45:29.246877Z", + "license": "Not licensed", + "name": "3.6.3", + "operatingSystem": [ + "Linux", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.7759/CUREUS.19569", + "pmcid": "PMC8670576", + "pmid": "34917444" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Medical informatics", + "uri": "http://edamontology.org/topic_3063" + }, + { + "term": "Nutritional science", + "uri": "http://edamontology.org/topic_3390" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/3dpolys-le/3dpolys-le.biotools.json b/data/3dpolys-le/3dpolys-le.biotools.json new file mode 100644 index 0000000000000..f854a668ddbc1 --- /dev/null +++ b/data/3dpolys-le/3dpolys-le.biotools.json @@ -0,0 +1,98 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T22:10:21.312699Z", + "biotoolsCURIE": "biotools:3dpolys-le", + "biotoolsID": "3dpolys-le", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daniel.jost@ens-lyon.fr", + "name": "Daniel Jost", + "typeEntity": "Person" + }, + { + "name": "Gabriel Zala" + }, + { + "name": "Peter Meister" + }, + { + "name": "Todor Gitchev" + } + ], + "description": "An accessible simulation framework to model the interplay between chromatin and loop extrusion.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Loop modelling", + "uri": "http://edamontology.org/operation_0481" + } + ] + } + ], + "homepage": "https://gitlab.com/togop/3DPolyS-LE", + "language": [ + "Fortran", + "Python" + ], + "lastUpdate": "2023-01-18T22:10:21.315528Z", + "license": "MIT", + "name": "3DPolyS-LE", + "operatingSystem": [ + "Linux" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac705", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Recent studies suggest that the loop extrusion activity of Structural Maintenance of Chromosomes complexes is central to proper organization of genomes in vivo. Polymer physics-based modeling of chromosome structure has been instrumental to assess which structures such extrusion can create. Only few laboratories however have the technical and computational expertise to create in silico models combining dynamic features of chromatin and loop extruders. Here, we present 3DPolyS-LE, a self-contained, easy to use modeling and simulation framework allowing non-specialists to ask how specific properties of loop extruders and boundary elements impact on 3D chromosome structure. 3DPolyS-LE also provides algorithms to compare predictions with experimental Hi-C data. AVAILABILITY AND IMPLEMENTATION: Software available at https://gitlab.com/togop/3DPolyS-LE; implemented in Python and Fortran 2003 and supported on any Unix-based operating system (Linux and Mac OS). SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.", + "authors": [ + { + "name": "Gitchev T." + }, + { + "name": "Jost D." + }, + { + "name": "Meister P." + }, + { + "name": "Zala G." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "3DPolyS-LE: an accessible simulation framework to model the interplay between chromatin and loop extrusion" + }, + "pmcid": "PMC9750120", + "pmid": "36355469" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Chromosome conformation capture", + "uri": "http://edamontology.org/topic_3940" + }, + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + } + ] +} diff --git a/data/4accpred/4accpred.biotools.json b/data/4accpred/4accpred.biotools.json new file mode 100644 index 0000000000000..ecc3e00a4f277 --- /dev/null +++ b/data/4accpred/4accpred.biotools.json @@ -0,0 +1,102 @@ +{ + "additionDate": "2023-01-25T09:56:08.039558Z", + "biotoolsCURIE": "biotools:4accpred", + "biotoolsID": "4accpred", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daiyun.huang@liverpool.ac.uk", + "name": "Daiyun Huang", + "typeEntity": "Person" + } + ], + "description": "Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + }, + { + "term": "Sequence motif discovery", + "uri": "http://edamontology.org/operation_0238" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "http://www.rnamd.org/4accpred", + "lastUpdate": "2023-01-25T09:56:08.042167Z", + "license": "Other", + "name": "4acCPred", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.OMTN.2022.10.004", + "metadata": { + "abstract": "© 2022 The AuthorsDNA methylation is one of the earliest epigenetic regulation mechanisms studied extensively, and it is critical for normal development, diseases, and gene expression. As a recently identified chemical modification of DNA, N4-acetyldeoxycytosine (4acC) was shown to be abundant in Arabidopsis and highly associated with gene expression and actively transcribed genes. Precise identification of 4acC is essential for studying its biological function. We proposed the 4acCPred, the first computational framework for predicting 4acC-carrying regions from Arabidopsis genomic DNA sequences. Since the existing 4acC data are not precise for a specific base but only report regions that are hundreds of bases long, we formulated the task as a weakly supervised learning problem and built 4acCPred using a multi-instance-based deep neural network. Both cross-validation and independent testing on the four datasets under different conditions show promising performance, with mean areas under the receiver operating characteristic curve (AUCs) of 0.9877 and 0.9899, respectively. 4acCPred also provides motif mining through model interpretation. The motifs found by 4acCPred are consistent with existing knowledge, indicating that the model successfully captured real biological signals. In addition, a user-friendly web server was built to facilitate 4acC prediction, motif visualization, and data access. Our framework and web server should serve as useful tools for 4acC research.", + "authors": [ + { + "name": "Huang D." + }, + { + "name": "Meng J." + }, + { + "name": "Wang X." + }, + { + "name": "Wei Z." + }, + { + "name": "Zhou J." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Molecular Therapy - Nucleic Acids", + "title": "4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences" + }, + "pmcid": "PMC9636570", + "pmid": "36381577" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Methylated DNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3674" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/4d-fed-gnn/4d-fed-gnn.biotools.json b/data/4d-fed-gnn/4d-fed-gnn.biotools.json new file mode 100644 index 0000000000000..4dae3fc003a17 --- /dev/null +++ b/data/4d-fed-gnn/4d-fed-gnn.biotools.json @@ -0,0 +1,70 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T10:05:03.802136Z", + "biotoolsCURIE": "biotools:4d-fed-gnn", + "biotoolsID": "4d-fed-gnn", + "confidence_flag": "tool", + "credit": [ + { + "name": "Islem Rekik" + }, + { + "name": "Zeynep Gurler" + } + ], + "description": "Federated Brain Graph Evolution Prediction using Decentralized Connectivity Datasets with Temporally-varying Acquisitions.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + } + ] + } + ], + "homepage": "http://github.com/basiralab/4D-FedGNN-Plus", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T10:05:03.807367Z", + "license": "Not licensed", + "name": "4D-FED-GNN++", + "operatingSystem": [ + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1109/TMI.2022.3225083", + "metadata": { + "abstract": "IEEEForeseeing the evolution of brain connectivity between anatomical regions from a baseline observation can propel early disease diagnosis and clinical decision making. Such task becomes challenging when learning from multiple decentralized datasets with missing timepoints (e.g., datasets collected from different hospitals with a varying sequence of acquisitions). Federated learning (FL) is an emerging paradigm that enables collaborative learning among multiple clients (i.e., hospitals) in a fully privacy-preserving fashion. However, to the best of our knowledge, there is no FL work that foresees the time-dependent brain connectivity evolution from a single timepoint –let alone learning from non-iid decentralized longitudinal datasets with varying acquisition timepoints. In this paper, we propose the first FL framework to significantly boost the predictive performance of local hospitals with missing acquisition timepoints while benefiting from other hospitals with available data at those timepoints without sharing data. Specifically, we introduce 4D-FED-GNN+, a novel longitudinal federated GNN framework that works in (i) a uni-mode, where it acts as a graph self-encoder if the next timepoint is locally missing or (ii) in a dual-mode, where it concurrently acts as a graph generator and a self-encoder if the local follow-up data is available. Further, we propose a dual federation strategy, where (i) GNN layer-wise weight aggregation and (ii) pairwise GNN weight exchange between hospitals in a random order. To improve the performance of the poorly-conditioned hospitals (e.g., consecutive missing timepoints, intermediate missing timepoint), we further propose a second variant, namely 4D-FED-GNN++, which federates based on an ordering of the local hospitals computed using their incomplete sequential patterns. Our comprehensive experiments on real longitudinal datasets show that overall 4D-FED-GNN+ and 4D-FED-GNN++ significantly outperform benchmark methods. Our source code is available at https: //github.com/basiralab/4D-FedGNN-Plus.", + "authors": [ + { + "name": "Gurler Z." + }, + { + "name": "Rekik I." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE Transactions on Medical Imaging", + "title": "Federated Brain Graph Evolution Prediction using Decentralized Connectivity Datasets with Temporally-varying Acquisitions" + }, + "pmid": "36441899" + } + ], + "toolType": [ + "Script", + "Workbench" + ], + "topic": [ + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + } + ] +} diff --git a/data/acinetobase/acinetobase.biotools.json b/data/acinetobase/acinetobase.biotools.json new file mode 100644 index 0000000000000..7d9821f69307f --- /dev/null +++ b/data/acinetobase/acinetobase.biotools.json @@ -0,0 +1,98 @@ +{ + "additionDate": "2023-01-25T10:13:57.511222Z", + "biotoolsCURIE": "biotools:acinetobase", + "biotoolsID": "acinetobase", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "charles.vanderhenst@vub.vib.be", + "name": "Charles Van der Henst", + "typeEntity": "Person" + } + ], + "description": "A database and repository of Acinetobacter strains", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + } + ] + } + ], + "homepage": "https://acinetobase.vib.be/", + "lastUpdate": "2023-01-25T10:13:57.513756Z", + "license": "GPL-3.0", + "name": "Acinetobase", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/DATABASE/BAAC099", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Acinetobacter baumannii is one of the most problematic nosocomial pathogens that can efficiently thrive within hospital settings, mainly due to resistances toward antibiotics, desiccation, disinfectants, human serum and oxidative stress. Recently, increased resistance against last-resort antibiotics earns this bacterium the highest priority concern classified by the Centers for Disease Control and Prevention and the World Health Organization. An obvious hallmark of this bacterium is the high heterogeneity observed among A. baumannii isolates, with a limited core genome. This feature complexifies the study of A. baumannii bacteria as an entity, subsequently reflected in a diversity of phenotypes of not only antimicrobial and environmental resistance but also virulence. A high degree of genome plasticity, along with the use of a limited subset of established strains, can lead to strain-specific observations, decreasing the global understanding of this pathogenic agent. Phenotypic variability of A. baumannii strains is easily observable such as with the macrocolony morphologies, in vitro and in vivo virulence, natural competence level, production of different capsular polysaccharide structures and cellular densities. Some strains encode an extensive amount of virulence factors, while others, including the established strains, lack several key ones. The lack/excess of genes or specific physiological processes might interfere with in vivo and in vitro experiments, thus providing a limited impact on the global understanding of Acinetobacter bacteria. As an answer to the high heterogeneity among A. baumannii strains, we propose a first comprehensive database that includes the bacterial strains and the associated phenotypic and genetic data. This new repository, freely accessible to the entire scientific community, allows selecting the best bacterial isolate(s) related to any biological question, using an efficient and fast exchange platform. Database URL: https://acinetobase.vib.be/", + "authors": [ + { + "name": "Botzki A." + }, + { + "name": "Collier J." + }, + { + "name": "Valcek A." + }, + { + "name": "Van Der Henst C." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Database", + "title": "Acinetobase: The comprehensive database and repository of Acinetobacter strains" + }, + "pmid": "36412325" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + } + ] +} diff --git a/data/acp_ms/acp_ms.biotools.json b/data/acp_ms/acp_ms.biotools.json new file mode 100644 index 0000000000000..26ed1db888828 --- /dev/null +++ b/data/acp_ms/acp_ms.biotools.json @@ -0,0 +1,85 @@ +{ + "additionDate": "2023-01-25T10:19:15.207217Z", + "biotoolsCURIE": "biotools:acp_ms", + "biotoolsID": "acp_ms", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dragonbw@163.com", + "name": "Bo Liao", + "typeEntity": "Person" + } + ], + "description": "A prediction model of anticancer peptides based on feature extraction.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Peptide identification", + "uri": "http://edamontology.org/operation_3631" + } + ] + } + ], + "homepage": "https://github.com/Zhoucaimao1998/Zc", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T10:19:15.210506Z", + "name": "ACP_MS", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC462", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.", + "authors": [ + { + "name": "Jia R." + }, + { + "name": "Liao B." + }, + { + "name": "Peng D." + }, + { + "name": "Wu F." + }, + { + "name": "Zhou C." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "ACP_MS: prediction of anticancer peptides based on feature extraction" + }, + "pmid": "36326080" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/airrscape/airrscape.biotools.json b/data/airrscape/airrscape.biotools.json new file mode 100644 index 0000000000000..3ab0ed566f846 --- /dev/null +++ b/data/airrscape/airrscape.biotools.json @@ -0,0 +1,129 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T00:16:20.718687Z", + "biotoolsCURIE": "biotools:airrscape", + "biotoolsID": "airrscape", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "eric.waltari@czbiohub.org", + "name": "Eric Waltari", + "orcidid": "http://orcid.org/0000-0001-6930-9645", + "typeEntity": "Person" + }, + { + "email": "john.pak@czbiohub.org", + "name": "John E. Pak", + "orcidid": "http://orcid.org/0000-0002-2998-9735", + "typeEntity": "Person" + }, + { + "name": "Joan Wong", + "orcidid": "http://orcid.org/0000-0002-7849-6320" + }, + { + "name": "Krista M. McCutcheon", + "orcidid": "http://orcid.org/0000-0003-1942-5175" + }, + { + "name": "Saba Nafees", + "orcidid": "http://orcid.org/0000-0002-3292-7703" + } + ], + "description": "An interactive tool for exploring B-cell receptor repertoires and antibody responses.\n\nTo run AIRRscape, clone the repo and open the app.R file in your RStudio, then click \"Run App\". As a Shiny app, it can run as a window of RStudio, or as a tab in a web browser (recommended).", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Peptide immunogenicity prediction", + "uri": "http://edamontology.org/operation_0252" + }, + { + "term": "Side chain modelling", + "uri": "http://edamontology.org/operation_0480" + } + ] + } + ], + "homepage": "https://ewaltari.shinyapps.io/airrscape2/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T00:16:20.722128Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/czbiohub/AIRRscape" + } + ], + "name": "AIRRscape", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pcbi.1010052", + "metadata": { + "abstract": "© 2022 Waltari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.", + "authors": [ + { + "name": "McCutcheon K.M." + }, + { + "name": "Nafees S." + }, + { + "name": "Pak J.E." + }, + { + "name": "Waltari E." + }, + { + "name": "Wong J." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses" + }, + "pmcid": "PMC9524643", + "pmid": "36126074" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "Immunogenetics", + "uri": "http://edamontology.org/topic_3930" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Vaccinology", + "uri": "http://edamontology.org/topic_3966" + } + ] +} diff --git a/data/alphafill/alphafill.biotools.json b/data/alphafill/alphafill.biotools.json new file mode 100644 index 0000000000000..c368e2f3dba70 --- /dev/null +++ b/data/alphafill/alphafill.biotools.json @@ -0,0 +1,129 @@ +{ + "additionDate": "2023-01-25T10:39:37.145358Z", + "biotoolsCURIE": "biotools:alphafill", + "biotoolsID": "alphafill", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Ida de Vries" + }, + { + "name": "Maarten L Hekkelman" + } + ], + "description": "AlphaFill is an algorithm based on sequence and structure similarity that “transplants” missing compounds to the AlphaFold models. By adding the molecular context to the protein structures, the models can be more easily appreciated in terms of function and structure integrity.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Protein modelling", + "uri": "http://edamontology.org/operation_0477" + }, + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + }, + { + "term": "Residue distance calculation", + "uri": "http://edamontology.org/operation_2950" + }, + { + "term": "Sequence alignment editing", + "uri": "http://edamontology.org/operation_3081" + } + ] + } + ], + "homepage": "http://alphafill.eu", + "language": [ + "C++" + ], + "lastUpdate": "2023-01-25T10:39:37.148046Z", + "license": "BSD-2-Clause", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://rsync.alphafill.eu/alphafill" + }, + { + "type": [ + "Other" + ], + "url": "https://alphafill.eu/alphafill.json.schema" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/PDB-REDO/alphafill" + }, + { + "type": [ + "Repository" + ], + "url": "https://zenodo.org/record/6706668#.Y2EXV3bP2Uk" + } + ], + "name": "AlphaFill", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41592-022-01685-Y", + "metadata": { + "abstract": "© 2022, The Author(s).Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to ‘transplant’ such ‘missing’ small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.", + "authors": [ + { + "name": "Hekkelman M.L." + }, + { + "name": "Joosten R.P." + }, + { + "name": "Perrakis A." + }, + { + "name": "de Vries I." + } + ], + "citationCount": 2, + "date": "2022-01-01T00:00:00Z", + "journal": "Nature Methods", + "title": "AlphaFill: enriching AlphaFold models with ligands and cofactors" + }, + "pmid": "36424442" + } + ], + "toolType": [ + "Command-line tool", + "Database portal", + "Script" + ], + "topic": [ + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + }, + { + "term": "Surgery", + "uri": "http://edamontology.org/topic_3421" + } + ] +} diff --git a/data/alvascience/alvascience.biotools.json b/data/alvascience/alvascience.biotools.json new file mode 100644 index 0000000000000..5f6a9a878a05a --- /dev/null +++ b/data/alvascience/alvascience.biotools.json @@ -0,0 +1,100 @@ +{ + "additionDate": "2023-01-25T10:49:03.531191Z", + "biotoolsCURIE": "biotools:alvascience", + "biotoolsID": "alvascience", + "confidence_flag": "tool", + "cost": "Commercial", + "credit": [ + { + "email": "andrea.mauri@alvascience.com", + "name": "Andrea Mauri", + "orcidid": "https://orcid.org/0000-0002-1966-4347", + "typeEntity": "Person" + }, + { + "name": "Matteo Bertola" + } + ], + "description": "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood-Brain Barrier Permeability", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://www.alvascience.com", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T10:49:03.533590Z", + "license": "Proprietary", + "name": "Alvascience", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/IJMS232112882", + "metadata": { + "abstract": "© 2022 by the authors.Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) are established techniques to relate endpoints to molecular features. We present the Alvascience software suite that takes care of the whole QSAR/QSPR workflow necessary to use models to predict endpoints for untested molecules. The first step, data curation, is covered by alvaMolecule. Features such as molecular descriptors and fingerprints are generated by using alvaDesc. Models are built and validated with alvaModel. The models can then be deployed and used on new molecules by using alvaRunner. We use these software tools on a real case scenario to predict the blood–brain barrier (BBB) permeability. The resulting predictive models have accuracy equal or greater than 0.8. The models are bundled in an alvaRunner project available on the Alvascience website.", + "authors": [ + { + "name": "Bertola M." + }, + { + "name": "Mauri A." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "International Journal of Molecular Sciences", + "title": "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability" + }, + "pmcid": "PMC9655980", + "pmid": "36361669" + } + ], + "toolType": [ + "Desktop application", + "Suite" + ], + "topic": [ + { + "term": "Data submission, annotation and curation", + "uri": "http://edamontology.org/topic_0219" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/ampbenchmark/ampbenchmark.biotools.json b/data/ampbenchmark/ampbenchmark.biotools.json new file mode 100644 index 0000000000000..81725a2300032 --- /dev/null +++ b/data/ampbenchmark/ampbenchmark.biotools.json @@ -0,0 +1,147 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T02:22:08.626953Z", + "biotoolsCURIE": "biotools:ampbenchmark", + "biotoolsID": "ampbenchmark", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "michalburdukiewicz@gmail.com", + "name": "Michał Burdukiewicz", + "orcidid": "http://orcid.org/0000-0001-8926-582X", + "typeEntity": "Person" + }, + { + "name": "Katarzyna Sidorczuk", + "orcidid": "http://orcid.org/0000-0001-6576-9054" + }, + { + "name": "Paweł Mackiewicz", + "orcidid": "http://orcid.org/0000-0003-4855-497X" + }, + { + "name": "Przemysław Gagat", + "orcidid": "http://orcid.org/0000-0001-9077-439X" + } + ], + "description": "Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Adhesin prediction", + "uri": "http://edamontology.org/operation_3968" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Virulence prediction", + "uri": "http://edamontology.org/operation_3461" + } + ] + } + ], + "homepage": "http://BioGenies.info/AMPBenchmark", + "language": [ + "R" + ], + "lastUpdate": "2023-01-20T02:22:08.630175Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/BioGenies/AMPBenchmark" + } + ], + "name": "AMPBenchmark", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac343", + "metadata": { + "abstract": "© 2022 The Author(s).Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.", + "authors": [ + { + "name": "Bakala L." + }, + { + "name": "Burdukiewicz M." + }, + { + "name": "Cooke I.R." + }, + { + "name": "Fingerhut L.C.H.W." + }, + { + "name": "Gagat P." + }, + { + "name": "Kala J." + }, + { + "name": "Kolenda R." + }, + { + "name": "MacKiewicz P." + }, + { + "name": "Pietluch F." + }, + { + "name": "Rafacz D." + }, + { + "name": "Rodiger S." + }, + { + "name": "Sidorczuk K." + }, + { + "name": "Slowik J." + } + ], + "citationCount": 4, + "date": "2022-09-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data" + }, + "pmcid": "PMC9487607", + "pmid": "35988923" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Microbiology", + "uri": "http://edamontology.org/topic_3301" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/antiage-db/antiage-db.biotools.json b/data/antiage-db/antiage-db.biotools.json new file mode 100644 index 0000000000000..46f0d730c9a5b --- /dev/null +++ b/data/antiage-db/antiage-db.biotools.json @@ -0,0 +1,78 @@ +{ + "additionDate": "2023-01-25T10:55:42.436257Z", + "biotoolsCURIE": "biotools:antiage-db", + "biotoolsID": "antiage-db", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "atzakos@uoi.gr", + "name": "Andreas G. Tzakos", + "orcidid": "https://orcid.org/0000-0001-6391-0288", + "typeEntity": "Person" + }, + { + "email": "hperez@ucam.edu", + "name": "Horacio Pérez-Sánchez", + "orcidid": "https://orcid.org/0000-0003-4468-7898", + "typeEntity": "Person" + } + ], + "description": "A database and server termed ANTIAGE-DB that allows the prediction of the anti-aging potential of target compounds.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://bio-hpc.ucam.edu/anti-age-db", + "lastUpdate": "2023-01-25T10:55:42.438658Z", + "license": "Other", + "name": "ANTIAGE-DB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/ANTIOX11112268", + "pmcid": "PMC9686885", + "pmid": "36421454" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Enzymes", + "uri": "http://edamontology.org/topic_0821" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/antibody_registry/antibody_registry.biotools.json b/data/antibody_registry/antibody_registry.biotools.json new file mode 100644 index 0000000000000..6ca22f8b67e71 --- /dev/null +++ b/data/antibody_registry/antibody_registry.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T11:04:55.050911Z", + "biotoolsCURIE": "biotools:antibody_registry", + "biotoolsID": "antibody_registry", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "abandrowski@ucsd.edu", + "name": "Anita Bandrowski", + "orcidid": "https://orcid.org/0000-0002-5497-0243", + "typeEntity": "Person" + } + ], + "description": "The Antibody Registry is a public, open database that enables citation of antibodies by providing a persistent record for any antibody-based reagent used in a publication.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://antibodyregistry.org", + "language": [ + "JavaScript", + "Python" + ], + "lastUpdate": "2023-01-25T11:04:55.053288Z", + "license": "Apache-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/MetaCell/scicrunch-antibody-registry" + } + ], + "name": "Antibody Registry", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC927", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Antibodies are ubiquitous key biological research resources yet are tricky to use as they are prone to performance issues and represent a major source of variability across studies. Understanding what antibody was used in a published study is therefore necessary to repeat and/or interpret a given study. However, antibody reagents are still frequently not cited with sufficient detail to determine which antibody was used in experiments. The Antibody Registry is a public, open database that enables citation of antibodies by providing a persistent record for any antibody-based reagent used in a publication. The registry is the authority for antibody Research Resource Identifiers, or RRIDs, which are requested or required by hundreds of journals seeking to improve the citation of these key resources. The registry is the most comprehensive listing of persistently identified antibody reagents used in the scientific literature. Data contributors span individual authors who use antibodies to antibody companies, which provide their entire catalogs including discontinued items. Unlike many commercial antibody listing sites which tend to remove reagents no longer sold, registry records persist, providing an interface between a fast-moving commercial marketplace and the static scientific literature. The Antibody Registry (RRID:SCR_006397) https://antibodyregistry.org.", + "authors": [ + { + "name": "Bandrowski A." + }, + { + "name": "Eckmann P." + }, + { + "name": "Grethe J." + }, + { + "name": "Martone M.E." + }, + { + "name": "Pairish M." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "The Antibody Registry: ten years of registering antibodies" + }, + "pmcid": "PMC9825422", + "pmid": "36370112" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Biotechnology", + "uri": "http://edamontology.org/topic_3297" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + } + ] +} diff --git a/data/apache_trino/apache_trino.biotools.json b/data/apache_trino/apache_trino.biotools.json new file mode 100644 index 0000000000000..e317520ad8cd6 --- /dev/null +++ b/data/apache_trino/apache_trino.biotools.json @@ -0,0 +1,67 @@ +{ + "additionDate": "2023-01-27T06:55:53.986823Z", + "biotoolsCURIE": "biotools:apache_trino", + "biotoolsID": "apache_trino", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "url": "https://trino.io/community.html#contributors" + } + ], + "description": "Trino is a distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources.\n\nTrino is a tool designed to efficiently query vast amounts of data using distributed queries. If you work with terabytes or petabytes of data, you are likely using tools that interact with Hadoop and HDFS. Trino was designed as an alternative to tools that query HDFS using pipelines of MapReduce jobs, such as Hive or Pig, but Trino is not limited to accessing HDFS. Trino can be and has been extended to operate over different kinds of data sources, including traditional relational databases and other data sources such as Cassandra.\n\nTrino was designed to handle data warehousing and analytics: data analysis, aggregating large amounts of data and producing reports. These workloads are often classified as Online Analytical Processing (OLAP).", + "documentation": [ + { + "note": "Guidelines for participants with corporate interests", + "type": [ + "Other" + ], + "url": "https://trino.io/guidelines-corporate.html" + }, + { + "type": [ + "Installation instructions" + ], + "url": "https://trino.io/docs/current/" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/trinodb/trino" + }, + { + "type": "Software package", + "url": "https://trino.io/download.html" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://trino.io/", + "lastUpdate": "2023-01-27T06:55:53.989377Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/trinodb/trino" + } + ], + "name": "Apache Trino", + "owner": "iacs-biocomputacion", + "version": [ + "Release 406 (25 Jan 2023)" + ] +} diff --git a/data/apache_zeppelin/apache_zeppelin.biotools.json b/data/apache_zeppelin/apache_zeppelin.biotools.json new file mode 100644 index 0000000000000..e190584480f3e --- /dev/null +++ b/data/apache_zeppelin/apache_zeppelin.biotools.json @@ -0,0 +1,46 @@ +{ + "additionDate": "2023-01-27T13:21:31.125788Z", + "biotoolsCURIE": "biotools:apache_zeppelin", + "biotoolsID": "apache_zeppelin", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "url": "https://www.apache.org/foundation/how-it-works.html" + } + ], + "description": "Web-based notebook that enables data-driven,\ninteractive data analytics and collaborative documents with SQL, Scala, Python, R and more.", + "documentation": [ + { + "type": [ + "Installation instructions" + ], + "url": "https://zeppelin.apache.org/docs/0.10.1/" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://zeppelin.apache.org/download.html" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "https://zeppelin.apache.org/", + "lastUpdate": "2023-01-27T13:21:31.128277Z", + "link": [ + { + "type": [ + "Mailing list" + ], + "url": "https://zeppelin.apache.org/community.html" + } + ], + "name": "Apache Zeppelin", + "owner": "iacs-biocomputacion", + "version": [ + "0.10.1" + ] +} diff --git a/data/apis-wings-eu/apis-wings-eu.biotools.json b/data/apis-wings-eu/apis-wings-eu.biotools.json new file mode 100644 index 0000000000000..27b6d6bd68f05 --- /dev/null +++ b/data/apis-wings-eu/apis-wings-eu.biotools.json @@ -0,0 +1,58 @@ +{ + "additionDate": "2022-12-30T06:46:43.806950Z", + "biotoolsCURIE": "biotools:apis-wings-eu", + "biotoolsID": "apis-wings-eu", + "description": "Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe.\nWe provide 26,481 forewing images of honey bee workers. They represent 1,725 samples from 13 European countries. The shape of the wings was described using the coordinates for 19 landmarks at wing veins’ intersections. The whole dataset, including the wing images, landmark coordinates, geographic coordinates of sampling locations, and other data, is available on the Zenodo website under a Public Domain licence.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Statistical calculation", + "uri": "http://edamontology.org/operation_2238" + } + ] + } + ], + "homepage": "https://zenodo.org/record/7244070", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T08:20:22.981177Z", + "license": "CC0-1.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://zenodo.org/record/7244070" + } + ], + "name": "Apis-wings-EU", + "owner": "tofilski", + "publication": [ + { + "doi": "10.5281/zenodo.7244070", + "version": "2" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/appinetwork/appinetwork.biotools.json b/data/appinetwork/appinetwork.biotools.json new file mode 100644 index 0000000000000..8f4eda828e882 --- /dev/null +++ b/data/appinetwork/appinetwork.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T13:20:25.399305Z", + "biotoolsCURIE": "biotools:appinetwork", + "biotoolsID": "appinetwork", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "simon.gosset1@universite-paris-saclay.fr", + "name": "Simon Gosset", + "typeEntity": "Person" + } + ], + "description": "An R package for building and computational analysis of protein-protein interaction networks.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Protein-protein interaction analysis", + "uri": "http://edamontology.org/operation_2949" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "https://forgemia.inra.fr/GNet/appinetwork", + "language": [ + "C", + "Python", + "R" + ], + "lastUpdate": "2023-01-25T13:20:25.403441Z", + "license": "BSD-3-Clause", + "name": "APPINetwork", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.7717/PEERJ.14204", + "metadata": { + "abstract": "Copyright 2022 Gosset et al.Background. Protein–protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein–protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods. APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/ GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results. APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion. More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.", + "authors": [ + { + "name": "Gallopin M." + }, + { + "name": "Glatigny A." + }, + { + "name": "Gosset S." + }, + { + "name": "Mucchielli-Giorgi M.-H." + }, + { + "name": "Sale M." + }, + { + "name": "Yi Z." + } + ], + "date": "2022-11-04T00:00:00Z", + "journal": "PeerJ", + "title": "APPINetwork: an R package for building and computational analysis of protein–protein interaction networks" + }, + "pmcid": "PMC9639416", + "pmid": "36353604" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Protein interaction experiment", + "uri": "http://edamontology.org/topic_3957" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/aptamat/aptamat.biotools.json b/data/aptamat/aptamat.biotools.json new file mode 100644 index 0000000000000..b3ed005cc7be6 --- /dev/null +++ b/data/aptamat/aptamat.biotools.json @@ -0,0 +1,116 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T13:42:25.343432Z", + "biotoolsCURIE": "biotools:aptamat", + "biotoolsID": "aptamat", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "irene.maffucci@utc.fr", + "name": "Irene Maffucci", + "orcidid": "https://orcid.org/0000-0002-4524-1137", + "typeEntity": "Person" + }, + { + "email": "miraine.davila-felipe@utc.fr", + "name": "Miraine Dávila Felipe", + "typeEntity": "Person" + } + ], + "description": "AptaMat is a simple script which aims to measure differences between DNA or RNA secondary structures. The method is based on the comparison of the matrices representing the two secondary structures to analyze, assimilable to dotplots.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Nucleic acid structure comparison", + "uri": "http://edamontology.org/operation_2518" + }, + { + "term": "Protein secondary structure comparison", + "uri": "http://edamontology.org/operation_2488" + }, + { + "term": "RNA inverse folding", + "uri": "http://edamontology.org/operation_0483" + }, + { + "term": "RNA secondary structure alignment", + "uri": "http://edamontology.org/operation_0502" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + } + ] + } + ], + "homepage": "https://github.com/GEC-git/AptaMat.git", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T13:42:25.346038Z", + "license": "MIT", + "name": "AptaMat", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC752", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures. RESULTS: In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure. AVAILABILITY AND IMPLEMENTATION: The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Avalle B." + }, + { + "name": "Binet T." + }, + { + "name": "Davila Felipe M." + }, + { + "name": "Maffucci I." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures" + }, + "pmcid": "PMC9805580", + "pmid": "36440922" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Nucleic acid structure analysis", + "uri": "http://edamontology.org/topic_0097" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + } + ] +} diff --git a/data/ascancer_atlas/ascancer_atlas.biotools.json b/data/ascancer_atlas/ascancer_atlas.biotools.json new file mode 100644 index 0000000000000..b19a2c4326355 --- /dev/null +++ b/data/ascancer_atlas/ascancer_atlas.biotools.json @@ -0,0 +1,136 @@ +{ + "additionDate": "2023-01-25T13:47:12.706159Z", + "biotoolsCURIE": "biotools:ascancer_atlas", + "biotoolsID": "ascancer_atlas", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "baoym@big.ac.cn", + "name": "Yiming Bao", + "orcidid": "https://orcid.org/0000-0002-9922-9723", + "typeEntity": "Person" + }, + { + "email": "lirj@big.ac.cn", + "name": "Zhaoqi Liu", + "typeEntity": "Person" + }, + { + "email": "liuzq@big.ac.cn", + "name": "Rujiao Li", + "typeEntity": "Person" + } + ], + "description": "A comprehensive knowledgebase of alternative splicing in human cancers.", + "download": [ + { + "type": "Downloads page", + "url": "https://ngdc.cncb.ac.cn/ascancer/download" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Alternative splicing prediction", + "uri": "http://edamontology.org/operation_0264" + }, + { + "term": "Exonic splicing enhancer prediction", + "uri": "http://edamontology.org/operation_0446" + }, + { + "term": "Splice site prediction", + "uri": "http://edamontology.org/operation_0433" + } + ] + } + ], + "homepage": "https://ngdc.cncb.ac.cn/ascancer", + "lastUpdate": "2023-01-25T13:47:12.708582Z", + "license": "CC-BY-NC-3.0", + "name": "ASCancer Atlas", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC955", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Alternative splicing (AS) is a fundamental process that governs almost all aspects of cellular functions, and dysregulation in this process has been implicated in tumor initiation, progression and treatment resistance. With accumulating studies of carcinogenic mis-splicing in cancers, there is an urgent demand to integrate cancer-associated splicing changes to better understand their internal cross-talks and functional consequences from a global view. However, a resource of key functional AS events in human cancers is still lacking. To fill the gap, we developed ASCancer Atlas (https://ngdc.cncb.ac.cn/ascancer), a comprehensive knowledgebase of aberrant splicing in human cancers. Compared to extant databases, ASCancer Atlas features a high-confidence collection of 2006 cancer-associated splicing events experimentally proved to promote tumorigenesis, a systematic splicing regulatory network, and a suit of multi-scale online analysis tools. For each event, we manually curated the functional axis including upstream splicing regulators, splicing event annotations, downstream oncogenic effects, and possible therapeutic strategies. ASCancer Atlas also houses about 2 million computationally putative splicing events. Additionally, a user-friendly web interface was built to enable users to easily browse, search, visualize, analyze, and download all splicing events. Overall, ASCancer Atlas provides a unique resource to study the functional roles of splicing dysregulation in human cancers.", + "authors": [ + { + "name": "Bao Y." + }, + { + "name": "Gong Z." + }, + { + "name": "Huang Y." + }, + { + "name": "Li R." + }, + { + "name": "Liu Z." + }, + { + "name": "Wang G." + }, + { + "name": "Wu S." + }, + { + "name": "Xing P." + }, + { + "name": "Zhang M." + }, + { + "name": "Zhao W." + }, + { + "name": "Zheng X." + }, + { + "name": "Zong W." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "ASCancer Atlas: a comprehensive knowledgebase of alternative splicing in human cancers" + }, + "pmcid": "PMC9825479", + "pmid": "36318242" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/ascept/ascept.biotools.json b/data/ascept/ascept.biotools.json new file mode 100644 index 0000000000000..365c0596b5cfe --- /dev/null +++ b/data/ascept/ascept.biotools.json @@ -0,0 +1,91 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T13:54:24.587002Z", + "biotoolsCURIE": "biotools:ascept", + "biotoolsID": "ascept", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kimberly.glass@channing.harvard.edu", + "name": "Kimberly Glass", + "orcidid": "https://orcid.org/0000-0003-4394-5779", + "typeEntity": "Person" + } + ], + "description": "Automated Selection of Changepoints using Empirical P-values and Trimming (ASCEPT), to select an optimal set of changepoints in mobile health data", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/matthewquinn1/changepointSelect", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T13:54:24.589589Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/matthewquinn1/changepointSelect.

Results 1% and three genotypes and further aggregates the rest SNPs using SKAT. CauchyGM then combines the correlated P-values across multiple SNPs and different genetic models within the set using Cauchy Combination Test. To further accommodate both sparse and dense signal patterns, we also propose an omnibus association test (CauchyGM-O) by combining CauchyGM with SKAT and the burden test. Our extensive simulations show that both CauchyGM and CauchyGM-O maintain the type I error well at the genome-wide significance level and provide substantial power improvement compared to existing methods. We apply our methods to a pharmacogenomic GWAS data from a large cardiovascular randomized clinical trial. Both CauchyGM and CauchyGM-O identify several novel genome-wide significant genes. AVAILABILITY AND IMPLEMENTATION: The R package CauchyGM is publicly available on github: https://github.com/ykim03517/CauchyGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Chi Y.-Y." + }, + { + "name": "Kim Y." + }, + { + "name": "Shen J." + }, + { + "name": "Zou F." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Robust genetic model-based SNP-set association test using CauchyGM" + }, + "pmid": "36383169" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Pharmacogenomics", + "uri": "http://edamontology.org/topic_0208" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + } + ] +} diff --git a/data/celldrift/celldrift.biotools.json b/data/celldrift/celldrift.biotools.json new file mode 100644 index 0000000000000..8a92b7cdd3340 --- /dev/null +++ b/data/celldrift/celldrift.biotools.json @@ -0,0 +1,123 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T22:28:32.895559Z", + "biotoolsCURIE": "biotools:celldrift", + "biotoolsID": "celldrift", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "bruce.aronow@cchmc.org", + "name": "Bruce J. Aronow", + "orcidid": "http://orcid.org/0000-0001-9689-2469", + "typeEntity": "Person" + }, + { + "name": "Daniel Schnell" + }, + { + "name": "Guangyuan Li", + "orcidid": "http://orcid.org/0000-0002-0628-2454" + }, + { + "name": "Kang Jin", + "orcidid": "http://orcid.org/0000-0001-5638-040X" + } + ], + "description": "Inferring Perturbation Responses in Temporally-Sampled Single Cell Data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Enrichment analysis", + "uri": "http://edamontology.org/operation_3501" + }, + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + } + ] + } + ], + "homepage": "https://github.com/KANG-BIOINFO/CellDrift", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T22:28:32.898176Z", + "license": "MIT", + "name": "CellDrift", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac324", + "metadata": { + "abstract": "© 2022 The Author(s).Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.", + "authors": [ + { + "name": "Aronow B.J." + }, + { + "name": "Jin K." + }, + { + "name": "Li G." + }, + { + "name": "Prasath V.B.S." + }, + { + "name": "Salomonis N." + }, + { + "name": "Schnell D." + }, + { + "name": "Szczesniak R." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "CellDrift: Inferring perturbation responses in temporally sampled single-cell data" + }, + "pmcid": "PMC9487655", + "pmid": "35998893" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/cellsium/cellsium.biotools.json b/data/cellsium/cellsium.biotools.json new file mode 100644 index 0000000000000..9488a023cad15 --- /dev/null +++ b/data/cellsium/cellsium.biotools.json @@ -0,0 +1,78 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T00:52:19.023875Z", + "biotoolsCURIE": "biotools:cellsium", + "biotoolsID": "cellsium", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Christian Carsten Sachs" + }, + { + "name": "Karina Ruzaeva," + }, + { + "name": "Katharina Nöh", + "orcidid": "https://orcid.org/0000-0002-5407-2275" + } + ], + "description": "CellSium is a cell simulator developed for the primary application of generating realistically looking images of bacterial microcolonies, which may serve as ground truth for machine learning training processes.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://cellsium.readthedocs.io/en/latest/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + } + ] + } + ], + "homepage": "https://github.com/modsim/cellsium", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T00:52:19.026395Z", + "license": "BSD-2-Clause", + "name": "CellSium", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioadv/vbac053" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/cf-seq/cf-seq.biotools.json b/data/cf-seq/cf-seq.biotools.json new file mode 100644 index 0000000000000..40b1edbd61977 --- /dev/null +++ b/data/cf-seq/cf-seq.biotools.json @@ -0,0 +1,148 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T01:07:55.838578Z", + "biotoolsCURIE": "biotools:cf-seq", + "biotoolsID": "cf-seq", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Bruce.A.Stanton@dartmouth.edu", + "name": "Bruce A. Stanton", + "orcidid": "http://orcid.org/0000-0002-1661-407X", + "typeEntity": "Person" + }, + { + "name": "Samuel L. Neff", + "orcidid": "http://orcid.org/0000-0002-5993-8445" + }, + { + "name": "Thomas H. Hampton", + "orcidid": "http://orcid.org/0000-0003-0543-402X" + } + ], + "description": "Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Species name", + "uri": "http://edamontology.org/data_1045" + } + } + ], + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Expression data visualisation", + "uri": "http://edamontology.org/operation_0571" + }, + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + } + ] + } + ], + "homepage": "http://scangeo.dartmouth.edu/CFSeq/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T01:07:55.840993Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/samlo777/cf-seq.git" + } + ], + "name": "CF-Seq", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/s41597-022-01431-1", + "metadata": { + "abstract": "© 2022, The Author(s).Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 128 studies and 1,322 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CF-Seq: http://scangeo.dartmouth.edu/CFSeq/)", + "authors": [ + { + "name": "Cengher L." + }, + { + "name": "Cheung A.L." + }, + { + "name": "Cramer R.A." + }, + { + "name": "Doing G." + }, + { + "name": "Hampton T.H." + }, + { + "name": "Hogan D.A." + }, + { + "name": "Koeppen K." + }, + { + "name": "Lee A.J." + }, + { + "name": "Neff S.L." + }, + { + "name": "Puerner C." + }, + { + "name": "Stanton B.A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "CF-Seq, an accessible web application for rapid re-analysis of cystic fibrosis pathogen RNA sequencing studies" + }, + "pmcid": "PMC9203545", + "pmid": "35710652" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/cg-diva/cg-diva.biotools.json b/data/cg-diva/cg-diva.biotools.json new file mode 100644 index 0000000000000..50036a794ccce --- /dev/null +++ b/data/cg-diva/cg-diva.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T14:08:34.564803Z", + "biotoolsCURIE": "biotools:cg-diva", + "biotoolsID": "cg-diva", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Manuel Eichenlaub" + } + ], + "description": "A collection of software packages for the statistical performance assessment of CGM systems.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/IfDTUlm/CGM_Performance_Assessment", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-27T14:08:34.567291Z", + "license": "MIT", + "name": "CG-DIVA", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1177/19322968221134639", + "metadata": { + "abstract": "© 2022 Diabetes Technology Society.Background: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for “integrated” CGM systems. Methods: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. Results: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment). Conclusions: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.", + "authors": [ + { + "name": "Diem P." + }, + { + "name": "Eichenlaub M." + }, + { + "name": "Freckmann G." + }, + { + "name": "Haug C." + }, + { + "name": "Hinzmann R." + }, + { + "name": "Jendle J." + }, + { + "name": "Pleus S." + }, + { + "name": "Rothenbuhler M." + }, + { + "name": "Stephan P." + }, + { + "name": "Thomas A." + }, + { + "name": "Waldenmaier D." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Journal of Diabetes Science and Technology", + "title": "Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems" + }, + "pmid": "36329636" + } + ], + "toolType": [ + "Desktop application" + ], + "topic": [ + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + } + ] +} diff --git a/data/cgar/cgar.biotools.json b/data/cgar/cgar.biotools.json index 8ec7b2e58d408..52f9573147da2 100644 --- a/data/cgar/cgar.biotools.json +++ b/data/cgar/cgar.biotools.json @@ -3,7 +3,54 @@ "biotoolsCURIE": "biotools:CGAR", "biotoolsID": "CGAR", "confidence_flag": "tool", + "credit": [ + { + "email": "carles.hernandez-ferrer@childrens.harvard.edu", + "name": "Carles Hernandez-Ferrer", + "orcidid": "https://orcid.org/0000-0002-8029-7160", + "typeEntity": "Person", + "typeRole": [ + "Contributor" + ] + }, + { + "email": "SekWon.Kong@childrens.harvard.edu", + "name": "Sek-Won Kong", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ] + }, + { + "name": "In-Hee Lee", + "typeEntity": "Person", + "typeRole": [ + "Developer" + ] + }, + { + "name": "Jose A. Negron", + "typeEntity": "Person", + "typeRole": [ + "Developer" + ] + } + ], "description": "An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition.\n\nClinical Genome & Ancestry Report (CGAR) enables users to identify and prioritize phenotype-associated variants from genome sequencing with a user-friendly and interactive online platform.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://cgar-doc.readthedocs.io/en/latest/" + } + ], + "download": [ + { + "type": "Source code", + "url": "https://bitbucket.org/gnome_pipeline/cgar_pub" + } + ], "editPermission": { "type": "public" }, @@ -30,13 +77,19 @@ } ], "homepage": "https://tom.tch.harvard.edu/apps/cgar/", - "lastUpdate": "2020-12-10T16:04:07Z", + "lastUpdate": "2023-01-24T17:00:33.105076Z", "link": [ { "type": [ "Repository" ], "url": "https://bitbucket.org/gnome_pipeline/cgar_pub" + }, + { + "type": [ + "Service" + ], + "url": "https://tom.tch.harvard.edu/apps/cgar/" } ], "name": "Clinical Genome and Ancestry Report (CGAR)", diff --git a/data/chempert/chempert.biotools.json b/data/chempert/chempert.biotools.json new file mode 100644 index 0000000000000..d64870cfb813d --- /dev/null +++ b/data/chempert/chempert.biotools.json @@ -0,0 +1,128 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-11T13:17:44.504617Z", + "biotoolsCURIE": "biotools:chempert", + "biotoolsID": "chempert", + "collectionID": [ + "LCSB-CBG" + ], + "credit": [ + { + "email": "antonio.delsol@uni.lu", + "name": "Antonio del Sol", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ], + "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa" + } + ], + "description": "Mapping between chemical perturbation and transcriptional response for non-cancer cells", + "documentation": [ + { + "type": [ + "Quick start guide" + ], + "url": "https://chempert.uni.lu/information" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://git-r3lab.uni.lu/CBG/chempert" + } + ], + "editPermission": { + "type": "group" + }, + "elixirNode": [ + "Luxembourg" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Compound name", + "uri": "http://edamontology.org/data_0990" + }, + "format": [ + { + "term": "plain text format (unformatted)", + "uri": "http://edamontology.org/format_1964" + } + ] + }, + { + "data": { + "term": "Expression data", + "uri": "http://edamontology.org/data_2603" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + } + ], + "operation": [ + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + }, + { + "term": "Prediction and recognition", + "uri": "http://edamontology.org/operation_2423" + } + ] + } + ], + "homepage": "https://chempert.uni.lu/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-11T13:17:44.507334Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://git-r3lab.uni.lu/CBG/chempert" + } + ], + "name": "ChemPert", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "KartikeyaS", + "publication": [ + { + "doi": "10.1093/nar/gkac862", + "pmcid": "PMC9825489", + "pmid": "36200827", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Data identity and mapping", + "uri": "http://edamontology.org/topic_3345" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/chipbase/chipbase.biotools.json b/data/chipbase/chipbase.biotools.json new file mode 100644 index 0000000000000..637e7b9ba0dd7 --- /dev/null +++ b/data/chipbase/chipbase.biotools.json @@ -0,0 +1,157 @@ +{ + "additionDate": "2023-01-27T14:13:11.402325Z", + "biotoolsCURIE": "biotools:chipbase", + "biotoolsID": "chipbase", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kzhou@coh.org", + "name": "Keren Zhou", + "orcidid": "https://orcid.org/0000-0001-7905-3399", + "typeEntity": "Person" + }, + { + "email": "yangjh7@mail.sysu.edu.cn", + "name": "Jianhua Yang", + "orcidid": "https://orcid.org/0000-0003-3863-2786", + "typeEntity": "Person" + }, + { + "email": "libin73@mail.sysu.edu.cn", + "name": "Bin Li", + "typeEntity": "Person" + }, + { + "email": "liushr27@mail.sysu.edu.cn", + "name": "Shurong Liu", + "typeEntity": "Person" + }, + { + "name": "Lianghu Qu", + "orcidid": "https://orcid.org/0000-0003-3657-2863", + "typeEntity": "Person" + } + ], + "description": "The encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Gene regulatory network analysis", + "uri": "http://edamontology.org/operation_1781" + }, + { + "term": "Gene regulatory network prediction", + "uri": "http://edamontology.org/operation_2437" + } + ] + } + ], + "homepage": "https://rnasysu.com/chipbase3/", + "lastUpdate": "2023-01-27T14:13:11.404763Z", + "license": "GPL-1.0", + "name": "ChIPBase", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1067", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Non-coding RNAs (ncRNAs) are emerging as key regulators of various biological processes. Although thousands of ncRNAs have been discovered, the transcriptional mechanisms and networks of the majority of ncRNAs have not been fully investigated. In this study, we updated ChIPBase to version 3.0 (https://rnasysu.com/chipbase3/) to provide the most comprehensive transcriptional regulation atlas of ncRNAs and protein-coding genes (PCGs). ChIPBase has identified ∼151 187 000 regulatory relationships between ∼171 600 genes and ∼3000 regulators by analyzing ∼55 000 ChIP-seq datasets, which represent a 30-fold expansion. Moreover, we de novo identified ∼29 000 motif matrices of transcription factors. In addition, we constructed a novel 'Enhancer' module to predict ∼1 837 200 regulation regions functioning as poised, active or super enhancers under ∼1300 conditions. Importantly, we constructed exhaustive coexpression maps between regulators and their target genes by integrating expression profiles of ∼65 000 normal and ∼15 000 tumor samples. We built a 'Disease' module to obtain an atlas of the disease-associated variations in the regulation regions of genes. We also constructed an 'EpiInter' module to explore potential interactions between epitranscriptome and epigenome. Finally, we designed 'Network' module to provide extensive and gene-centred regulatory networks. ChIPBase will serve as a useful resource to facilitate integrative explorations and expand our understanding of transcriptional regulation.", + "authors": [ + { + "name": "Chen Z." + }, + { + "name": "Huang J." + }, + { + "name": "Huang Q." + }, + { + "name": "Li B." + }, + { + "name": "Lin Q." + }, + { + "name": "Liu C." + }, + { + "name": "Liu S." + }, + { + "name": "Qu L." + }, + { + "name": "Wu D." + }, + { + "name": "Xuan J." + }, + { + "name": "Yang J." + }, + { + "name": "Zhang P." + }, + { + "name": "Zheng L." + }, + { + "name": "Zheng W." + }, + { + "name": "Zhou K." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "ChIPBase v3.0: the encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes" + }, + "pmcid": "PMC9825553", + "pmid": "36399495" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + } + ], + "version": [ + "3.0" + ] +} diff --git a/data/chromdmm/chromdmm.biotools.json b/data/chromdmm/chromdmm.biotools.json new file mode 100644 index 0000000000000..a304fe94498c0 --- /dev/null +++ b/data/chromdmm/chromdmm.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T01:20:28.986399Z", + "biotoolsCURIE": "biotools:chromdmm", + "biotoolsID": "chromdmm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Gökçen Eraslan" + }, + { + "name": "Harri Lähdesmäki" + }, + { + "name": "Maria Osmala", + "orcidid": "http://orcid.org/0000-0003-0128-4896" + } + ], + "description": "A Dirichlet-Multinomial Mixture Model For Clustering Heterogeneous Epigenetic Data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Transcriptional regulatory element prediction", + "uri": "http://edamontology.org/operation_0438" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/MariaOsmala/ChromDMM", + "language": [ + "C++", + "R" + ], + "lastUpdate": "2023-01-17T01:20:28.989666Z", + "license": "LGPL-3.0", + "name": "ChromDMM", + "operatingSystem": [ + "Linux", + "Mac" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac444", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Motivation: Research on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements. Results: We introduce ChromDMM, a product Dirichlet-multinomial mixture model for clustering genomic regions that are characterized by multiple chromatin features. ChromDMM extends the mixture model framework by profile shifting and flipping that can probabilistically account for inaccuracies in the position and strand-orientation of the genomic regions. Owing to hyper-parameter optimization, ChromDMM can also regularize the smoothness of the epigenetic profiles across the consecutive genomic regions. With simulated data, we demonstrate that ChromDMM clusters, shifts and strand-orients the profiles more accurately than previous methods. With ENCODE data, we show that the clustering of enhancer regions in the human genome reveals distinct patterns in several chromatin features. We further validate the enhancer clusters by their enrichment for transcriptional regulatory factor binding sites.", + "authors": [ + { + "name": "Eraslan G." + }, + { + "name": "Lahdesmaki H." + }, + { + "name": "Osmala M." + } + ], + "date": "2022-08-15T00:00:00Z", + "journal": "Bioinformatics", + "title": "ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data" + }, + "pmcid": "PMC9364382", + "pmid": "35786716" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Gene regulation", + "uri": "http://edamontology.org/topic_0204" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/cirdataset/cirdataset.biotools.json b/data/cirdataset/cirdataset.biotools.json new file mode 100644 index 0000000000000..deb422b4d3e94 --- /dev/null +++ b/data/cirdataset/cirdataset.biotools.json @@ -0,0 +1,71 @@ +{ + "additionDate": "2023-01-08T15:00:03.610431Z", + "biotoolsCURIE": "biotools:cirdataset", + "biotoolsID": "cirdataset", + "confidence_flag": "tool", + "credit": [ + { + "email": "nadeems@mskcc.org", + "name": "Saad Nadeem", + "typeEntity": "Person" + }, + { + "email": "wookjin.choi@jefferson.edu", + "name": "Wookjin Choi", + "typeEntity": "Person" + } + ], + "description": "A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction.", + "editPermission": { + "type": "public" + }, + "homepage": "https://github.com/nadeemlab/CIR", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-08T15:00:03.613987Z", + "license": "Not licensed", + "name": "CIRDataset", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1007/978-3-031-16443-9_2", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956 radiologist QA/QC’ed spiculation/lobulation annotations on segmented lung nodules from two public datasets, LIDC-IDRI (N = 883) and LUNGx (N = 73). We also present an end-to-end deep learning model based on multi-class Voxel2Mesh extension to segment nodules (while preserving spikes), classify spikes (sharp/spiculation and curved/lobulation), and perform malignancy prediction. Previous methods have performed malignancy prediction for LIDC and LUNGx datasets but without robust attribution to any clinically reported/actionable features (due to known hyperparameter sensitivity issues with general attribution schemes). With the release of this comprehensively-annotated CIRDataset and end-to-end deep learning baseline, we hope that malignancy prediction methods can validate their explanations, benchmark against our baseline, and provide clinically-actionable insights. Dataset, code, pretrained models, and docker containers are available at https://github.com/nadeemlab/CIR.", + "authors": [ + { + "name": "Choi W." + }, + { + "name": "Dahiya N." + }, + { + "name": "Nadeem S." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", + "title": "CIRDataset: A Large-Scale Dataset for Clinically-Interpretable Lung Nodule Radiomics and Malignancy Prediction" + }, + "pmcid": "PMC9527770", + "pmid": "36198166" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Probes and primers", + "uri": "http://edamontology.org/topic_0632" + } + ] +} diff --git a/data/citedb/citedb.biotools.json b/data/citedb/citedb.biotools.json new file mode 100644 index 0000000000000..9530a383c9691 --- /dev/null +++ b/data/citedb/citedb.biotools.json @@ -0,0 +1,130 @@ +{ + "additionDate": "2023-01-08T15:04:58.707694Z", + "biotoolsCURIE": "biotools:citedb", + "biotoolsID": "citedb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "houl@tsinghua.edu.cn", + "name": "Lin Hou", + "orcidid": "https://orcid.org/0000-0002-4283-8501", + "typeEntity": "Person" + } + ], + "description": "A manually curated database of cell-cell interactions in human.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Text annotation", + "uri": "http://edamontology.org/operation_3778" + } + ] + } + ], + "homepage": "https://citedb.cn/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-08T15:04:58.710536Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/shanny01/benchmark" + } + ], + "name": "CITEdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC654", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS: We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION: CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Gao J." + }, + { + "name": "Guo H." + }, + { + "name": "Hou L." + }, + { + "name": "Jiang J." + }, + { + "name": "Li D." + }, + { + "name": "Lu Y." + }, + { + "name": "Ren Y." + }, + { + "name": "Shan N." + }, + { + "name": "Yan L." + }, + { + "name": "Zhao X." + } + ], + "date": "2022-11-15T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "CITEdb: a manually curated database of cell-cell interactions in human" + }, + "pmcid": "PMC9665858", + "pmid": "36179089" + } + ], + "toolType": [ + "Database portal", + "Library", + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Data mining", + "uri": "http://edamontology.org/topic_3473" + }, + { + "term": "Data submission, annotation and curation", + "uri": "http://edamontology.org/topic_0219" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + } + ] +} diff --git a/data/citrus/citrus.biotools.json b/data/citrus/citrus.biotools.json new file mode 100644 index 0000000000000..1f062a91a8a31 --- /dev/null +++ b/data/citrus/citrus.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-08T15:10:03.022697Z", + "biotoolsCURIE": "biotools:citrus", + "biotoolsID": "citrus", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "osmanbeyogluhu@pitt.edu", + "name": "Hatice Ulku Osmanbeyoglu", + "orcidid": "https://orcid.org/0000-0002-3175-1777", + "typeEntity": "Person" + } + ], + "description": "Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + } + ] + } + ], + "homepage": "https://github.com/osmanbeyoglulab/CITRUS", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-08T15:10:03.027091Z", + "license": "MIT", + "name": "CITRUS", + "operatingSystem": [ + "Linux", + "Mac" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC881", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.", + "authors": [ + { + "name": "Lu X." + }, + { + "name": "Ma X." + }, + { + "name": "Osmanbeyoglu H.U." + }, + { + "name": "Palmer D." + }, + { + "name": "Schwartz R." + }, + { + "name": "Tao Y." + } + ], + "date": "2022-10-28T00:00:00Z", + "journal": "Nucleic Acids Research", + "title": "Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers" + }, + "pmcid": "PMC9638905", + "pmid": "36243974" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/civic/civic.biotools.json b/data/civic/civic.biotools.json new file mode 100644 index 0000000000000..8928b84c3745b --- /dev/null +++ b/data/civic/civic.biotools.json @@ -0,0 +1,332 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T14:20:17.081666Z", + "biotoolsCURIE": "biotools:civic", + "biotoolsID": "civic", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kkrysiak@wustl.edu", + "name": "Kilannin Krysiak", + "typeEntity": "Person" + }, + { + "email": "mgriffit@wustl.edu", + "name": "Malachi Griffith", + "typeEntity": "Person" + }, + { + "email": "obigriffith@wustl.edu", + "name": "Obi L Griffith", + "typeEntity": "Person" + } + ], + "description": "CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Text mining", + "uri": "http://edamontology.org/operation_0306" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Variant filtering", + "uri": "http://edamontology.org/operation_3675" + } + ] + } + ], + "homepage": "http://civicdb.org", + "language": [ + "Ruby" + ], + "lastUpdate": "2023-01-27T14:20:17.084081Z", + "license": "MIT", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://bionlp.bcgsc.ca/civicmine/" + }, + { + "type": [ + "Other" + ], + "url": "https://civicdb.org/releases" + }, + { + "type": [ + "Other" + ], + "url": "https://civicdb.org/releases/main" + }, + { + "type": [ + "Other" + ], + "url": "https://griffithlab.github.io/civic-v2/" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/griffithlab/civic-v2" + } + ], + "name": "CIViC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC979", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.", + "authors": [ + { + "name": "Ainscough B.J." + }, + { + "name": "Andric V." + }, + { + "name": "Barnell E.K." + }, + { + "name": "Campbell K.M." + }, + { + "name": "Chiorean A." + }, + { + "name": "Clark K.A." + }, + { + "name": "Coffman A.C." + }, + { + "name": "Corson L.B." + }, + { + "name": "Cotto K.C." + }, + { + "name": "Danos A.M." + }, + { + "name": "Delong S." + }, + { + "name": "Evans M." + }, + { + "name": "Farncombe K.M." + }, + { + "name": "Giles R.H." + }, + { + "name": "Griffith M." + }, + { + "name": "Griffith O.L." + }, + { + "name": "Grisdale C.J." + }, + { + "name": "Hoang M.H." + }, + { + "name": "Horak P." + }, + { + "name": "Jani P." + }, + { + "name": "Ji J." + }, + { + "name": "Jones S.J.M." + }, + { + "name": "Kanagal-Shamanna R." + }, + { + "name": "Kesserwan C." + }, + { + "name": "Khanfar M." + }, + { + "name": "Kim R.H." + }, + { + "name": "King I." + }, + { + "name": "Kiwala S." + }, + { + "name": "Krysiak K." + }, + { + "name": "Kujan L." + }, + { + "name": "Lamping M." + }, + { + "name": "Lever J." + }, + { + "name": "Li B.V." + }, + { + "name": "Lin W.-H." + }, + { + "name": "Madhavan S." + }, + { + "name": "Mardis E.R." + }, + { + "name": "Marr A.R." + }, + { + "name": "McMichael J.F." + }, + { + "name": "Milosavljevic A." + }, + { + "name": "Patel R.Y." + }, + { + "name": "Pema S." + }, + { + "name": "Raca G." + }, + { + "name": "Rao S." + }, + { + "name": "Reisle C." + }, + { + "name": "Ridd S." + }, + { + "name": "Rieke D.T." + }, + { + "name": "Ritter D.I." + }, + { + "name": "Salama Y." + }, + { + "name": "Saliba J." + }, + { + "name": "Schriml L.M." + }, + { + "name": "Shen H." + }, + { + "name": "Sheta L." + }, + { + "name": "Singhal K." + }, + { + "name": "Skidmore Z.L." + }, + { + "name": "Spies N.C." + }, + { + "name": "Suda A." + }, + { + "name": "Takahashi H." + }, + { + "name": "Terraf P." + }, + { + "name": "Venigalla A.C." + }, + { + "name": "Wagner A.H." + }, + { + "name": "Walker J.R." + }, + { + "name": "Xu X." + }, + { + "name": "Zhang J." + }, + { + "name": "Zhao X." + }, + { + "name": "Zhou X." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase" + }, + "pmcid": "PMC9825608", + "pmid": "36373660" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + } + ] +} diff --git a/data/clair3-trio/clair3-trio.biotools.json b/data/clair3-trio/clair3-trio.biotools.json new file mode 100644 index 0000000000000..6143cc8ed167d --- /dev/null +++ b/data/clair3-trio/clair3-trio.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T01:12:25.794991Z", + "biotoolsCURIE": "biotools:clair3-trio", + "biotoolsID": "clair3-trio", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Syed Shakeel Ahmed" + }, + { + "name": "Junhao Su", + "orcidid": "http://orcid.org/0000-0002-8560-3999" + }, + { + "name": "Ruibang Luo", + "orcidid": "http://orcid.org/0000-0001-9711-6533" + }, + { + "name": "Tak-Wah Lam", + "orcidid": "http://orcid.org/0000-0003-4676-8587" + }, + { + "name": "Zhenxian Zheng", + "orcidid": "http://orcid.org/0000-0002-6546-2324" + } + ], + "description": "High-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "https://github.com/HKU-BAL/Clair3-Trio", + "language": [ + "Python", + "Shell" + ], + "lastUpdate": "2023-01-22T01:12:25.799862Z", + "license": "BSD-3-Clause", + "name": "Clair3-Trio", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac301", + "metadata": { + "abstract": "© 2022 The Author(s).Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio's predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.", + "authors": [ + { + "name": "Ahmed S.S." + }, + { + "name": "Lam T.-W." + }, + { + "name": "Luo R." + }, + { + "name": "Su J." + }, + { + "name": "Zheng Z." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "Clair3-trio: High-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks" + }, + "pmcid": "PMC9487642", + "pmid": "35849103" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Genetics", + "uri": "http://edamontology.org/topic_3053" + }, + { + "term": "Laboratory techniques", + "uri": "http://edamontology.org/topic_3361" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/clampfish/clampfish.biotools.json b/data/clampfish/clampfish.biotools.json new file mode 100644 index 0000000000000..eba9329b6442f --- /dev/null +++ b/data/clampfish/clampfish.biotools.json @@ -0,0 +1,123 @@ +{ + "additionDate": "2023-01-08T15:16:37.330819Z", + "biotoolsCURIE": "biotools:clampfish", + "biotoolsID": "clampfish", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Ian Dardani" + } + ], + "description": "clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Amplification detection", + "uri": "http://edamontology.org/operation_3965" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Primer and probe design", + "uri": "http://edamontology.org/operation_2419" + } + ] + } + ], + "homepage": "https://github.com/iandarr/clampFISH2allcode", + "language": [ + "MATLAB" + ], + "lastUpdate": "2023-01-08T15:16:37.333501Z", + "license": "Not licensed", + "name": "clampFISH", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41592-022-01653-6", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection to detect 10 different RNA species in more than 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect that the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.", + "authors": [ + { + "name": "Alicea G.M." + }, + { + "name": "Dardani I." + }, + { + "name": "Emert B.L." + }, + { + "name": "Fane M.E." + }, + { + "name": "Goyal Y." + }, + { + "name": "Herlyn M." + }, + { + "name": "Jiang C.L." + }, + { + "name": "Kaur A." + }, + { + "name": "Lee J." + }, + { + "name": "Raj A." + }, + { + "name": "Rouhanifard S.H." + }, + { + "name": "Weeraratna A.T." + }, + { + "name": "Xiao M." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Nature Methods", + "title": "ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ" + }, + "pmid": "36280724" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Probes and primers", + "uri": "http://edamontology.org/topic_0632" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ], + "version": [ + "2.0" + ] +} diff --git a/data/clarion/clarion.biotools.json b/data/clarion/clarion.biotools.json new file mode 100644 index 0000000000000..479826b75f3ff --- /dev/null +++ b/data/clarion/clarion.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T14:25:48.166787Z", + "biotoolsCURIE": "biotools:clarion", + "biotoolsID": "clarion", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Jiangning Song" + }, + { + "name": "Yue Bi" + } + ], + "description": "Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://monash.bioweb.cloud.edu.au/Clarion/center.php?page=help" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "DNA transcription", + "uri": "http://edamontology.org/operation_0372" + }, + { + "term": "Gene regulatory network prediction", + "uri": "http://edamontology.org/operation_2437" + }, + { + "term": "Subcellular localisation prediction", + "uri": "http://edamontology.org/operation_2489" + } + ] + } + ], + "homepage": "http://monash.bioweb.cloud.edu.au/Clarion/", + "lastUpdate": "2023-01-27T14:25:48.169340Z", + "name": "Clarion", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC467", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.", + "authors": [ + { + "name": "Bi Y." + }, + { + "name": "Guo X." + }, + { + "name": "Guo Y." + }, + { + "name": "Jia C." + }, + { + "name": "Li F." + }, + { + "name": "Pan T." + }, + { + "name": "Song J." + }, + { + "name": "Wang Z." + }, + { + "name": "Webb G.I." + }, + { + "name": "Yao J." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations" + }, + "pmid": "36341591" + } + ], + "toolType": [ + "Desktop application", + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/clevrvis/clevrvis.biotools.json b/data/clevrvis/clevrvis.biotools.json new file mode 100644 index 0000000000000..00e3267d6ce85 --- /dev/null +++ b/data/clevrvis/clevrvis.biotools.json @@ -0,0 +1,134 @@ +{ + "additionDate": "2023-01-18T09:58:02.321637Z", + "biotoolsCURIE": "biotools:clevrvis", + "biotoolsID": "clevrvis", + "collectionID": [ + "Bioconductor" + ], + "credit": [ + { + "email": "sarah.sandmann@uni-muenster.de", + "name": "Sarah Sandmann", + "orcidid": "https://orcid.org/0000-0002-5011-0641" + } + ], + "description": "clevRvis provides an extensive set of visualization techniques for clonal evolution. Three types of plots are available: 1) shark plots (basic trees, showing the phylogeny and optionally the cancer cell fraction CCF); 2) dolphin plots (advanced visualization, showing the phylogeny and the development of CCFs over time); 3) plaice plots (novel visualization, showing the phylogeny, the development of CCFs and the development of remaining healthy alleles, influenced by bi-allelic events, over time). Moreover, the tool provides algorithms for fully automatic interpolation of time points and estimation of therapy effect to approximate a tumor's development in the presence of few measured time points, as well as exploring alternative trees.", + "documentation": [ + { + "note": "Detailed documentation of the functions can be found in the manuals. A detailed walk-through is provided in the vignette.", + "type": [ + "Quick start guide" + ], + "url": "https://github.com/sandmanns/clevRvis" + } + ], + "download": [ + { + "note": "clevRvis is an R package. It can be easily downloaded by executing\n\nif (!requireNamespace(\"devtools\", quietly=TRUE))\n install.packages(\"devtools\")\ndevtools::install_github(\"sandmanns/clevRvis\")\n\nin R.", + "type": "Source code", + "url": "https://github.com/sandmanns/clevRvis", + "version": "0.99.5" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Phylogenetic data", + "uri": "http://edamontology.org/data_2523" + }, + "format": [ + { + "term": "CSV", + "uri": "http://edamontology.org/format_3752" + } + ] + }, + { + "data": { + "term": "Phylogenetic data", + "uri": "http://edamontology.org/data_2523" + }, + "format": [ + { + "term": "xls", + "uri": "http://edamontology.org/format_3468" + } + ] + }, + { + "data": { + "term": "Phylogenetic data", + "uri": "http://edamontology.org/data_2523" + }, + "format": [ + { + "term": "xlsx", + "uri": "http://edamontology.org/format_3620" + } + ] + } + ], + "operation": [ + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ], + "output": [ + { + "data": { + "term": "Phylogenetic tree", + "uri": "http://edamontology.org/data_0872" + } + }, + { + "data": { + "term": "Plot", + "uri": "http://edamontology.org/data_2884" + } + } + ] + } + ], + "homepage": "https://github.com/sandmanns/clevRvis", + "language": [ + "R" + ], + "lastUpdate": "2023-01-18T10:20:59.143492Z", + "license": "LGPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/sandmanns/clevRvis" + } + ], + "maturity": "Mature", + "name": "clevRvis", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "sandmanns", + "toolType": [ + "Command-line tool", + "Library", + "Workflow" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + } + ], + "version": [ + "0.99.5" + ] +} diff --git a/data/clin_skat/clin_skat.biotools.json b/data/clin_skat/clin_skat.biotools.json new file mode 100644 index 0000000000000..416ebd9dedd31 --- /dev/null +++ b/data/clin_skat/clin_skat.biotools.json @@ -0,0 +1,112 @@ +{ + "additionDate": "2023-01-08T15:20:47.053374Z", + "biotoolsCURIE": "biotools:clin_skat", + "biotoolsID": "clin_skat", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tplu@ntu.edu.tw", + "name": "Tzu-Pin Lu", + "orcidid": "https://orcid.org/0000-0003-3697-0386", + "typeEntity": "Person" + } + ], + "description": "CLIN_SKAT is a package within the R programming language to (i) first extract clinically relevant variants (rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Collapsing methods", + "uri": "http://edamontology.org/operation_3791" + }, + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + }, + { + "term": "Haplotype mapping", + "uri": "http://edamontology.org/operation_0487" + } + ] + } + ], + "homepage": "https://github.com/ShihChingYu/CLIN_SKAT", + "language": [ + "R" + ], + "lastUpdate": "2023-01-08T15:20:47.055837Z", + "license": "GPL-2.0", + "name": "CLIN_SKAT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1186/S12859-022-04987-2", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants. Results: CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github(\"ShihChingYu/CLIN_SKAT\", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats. Conclusions: Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.", + "authors": [ + { + "name": "Chattopadhyay A." + }, + { + "name": "Chuang E.Y." + }, + { + "name": "Hsu Y.-C." + }, + { + "name": "Juang J.-M.J." + }, + { + "name": "Lu T.-P." + }, + { + "name": "Shih C.-Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants" + }, + "pmcid": "PMC9590128", + "pmid": "36274122" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "Exome sequencing", + "uri": "http://edamontology.org/topic_3676" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + } + ] +} diff --git a/data/clustercad/clustercad.biotools.json b/data/clustercad/clustercad.biotools.json new file mode 100644 index 0000000000000..62e434f1a2b88 --- /dev/null +++ b/data/clustercad/clustercad.biotools.json @@ -0,0 +1,130 @@ +{ + "additionDate": "2023-01-27T14:30:52.208554Z", + "biotoolsCURIE": "biotools:clustercad", + "biotoolsID": "clustercad", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tbackman@lbl.gov", + "name": "Tyler W H Backman", + "orcidid": "https://orcid.org/0000-0002-6056-353X", + "typeEntity": "Person" + } + ], + "description": "ClusterCAD provides a database and web-based toolkit designed to enable researchers to harness the potential of type I modular polyketide synthases and nonribosomal peptide synthetases for combinatorial biosynthesis.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://clustercad.jbei.org", + "language": [ + "JavaScript", + "Python", + "Scheme" + ], + "lastUpdate": "2023-01-27T14:30:52.211341Z", + "license": "Other", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/JBEI/clusterCAD" + } + ], + "name": "ClusterCAD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1075", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce novel chemicals. If we can understand the design rules to reshuffle these parts, PKSs and NRPSs will provide a systematic and modular way to synthesize millions of molecules including pharmaceuticals, biomaterials, and biofuels. However, PKS and NRPS engineering remains difficult due to a limited understanding of the determinants of PKS and NRPS fold and function. We developed ClusterCAD to streamline and simplify the process of designing and testing engineered PKS variants. Here, we present the highly improved ClusterCAD 2.0 release, available at https://clustercad.jbei.org. ClusterCAD 2.0 boasts support for PKS-NRPS hybrid and NRPS clusters in addition to PKS clusters; a vastly enlarged database of curated PKS, PKS-NRPS hybrid, and NRPS clusters; a diverse set of chemical 'starters' and loading modules; the new Domain Architecture Cluster Search Tool; and an offline Jupyter Notebook workspace, among other improvements. Together these features massively expand the chemical space that can be accessed by enzymes engineered with ClusterCAD.", + "authors": [ + { + "name": "Backman T.W.H." + }, + { + "name": "Keasling J.D." + }, + { + "name": "LaFrance S." + }, + { + "name": "Martin H.G." + }, + { + "name": "Nava A.A." + }, + { + "name": "Tao X.B." + }, + { + "name": "Xing Y." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design" + }, + "pmcid": "PMC9825560", + "pmid": "36416273" + } + ], + "toolType": [ + "Database portal", + "Suite", + "Web application" + ], + "topic": [ + { + "term": "Biomaterials", + "uri": "http://edamontology.org/topic_3368" + }, + { + "term": "Enzymes", + "uri": "http://edamontology.org/topic_0821" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ], + "version": [ + "2.0" + ] +} diff --git a/data/clustmmra/clustmmra.biotools.json b/data/clustmmra/clustmmra.biotools.json new file mode 100644 index 0000000000000..87c85cb225e17 --- /dev/null +++ b/data/clustmmra/clustmmra.biotools.json @@ -0,0 +1,94 @@ +{ + "additionDate": "2023-01-27T14:34:54.539532Z", + "biotoolsCURIE": "biotools:clustmmra", + "biotoolsID": "clustmmra", + "confidence_flag": "tool", + "credit": [ + { + "email": "Loredana.Martignetti@curie.fr", + "typeEntity": "Person" + } + ], + "description": "A scalable version of the clustMMRA pipeline for the identification of genomically co-clustered microRNAs driving cancer subtypes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "miRNA expression analysis", + "uri": "http://edamontology.org/operation_3792" + }, + { + "term": "miRNA target prediction", + "uri": "http://edamontology.org/operation_0463" + } + ] + } + ], + "homepage": "https://github.com/sysbio-curie/clustMMRA_v2", + "language": [ + "Perl", + "R", + "Shell" + ], + "lastUpdate": "2023-01-27T14:34:54.542050Z", + "license": "Not licensed", + "name": "clustMMRA", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1007/978-3-031-08356-3_10", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.In recent cancer genomics programs, large-scale profiling of microRNAs has been routinely used in order to better understand the role of microRNAs in gene regulation and disease. To support the analysis of such amount of data, scalability of bioinformatics pipelines is increasingly important to handle larger datasets. Here, we describe a scalable implementation of the clustered miRNA Master Regulator Analysis (clustMMRA) pipeline, developed to search for genomic clusters of microRNAs potentially driving cancer molecular subtyping. Genomically clustered microRNAs can be simultaneously expressed to work in a combined manner and jointly regulate cell phenotypes. However, the majority of computational approaches for the identification of microRNA master regulators are typically designed to detect the regulatory effect of a single microRNA. We have applied the clustMMRA pipeline to multiple pediatric tumor datasets, up to a hundred samples in size, demonstrating very satisfying performances of the software on large datasets. Results have highlighted genomic clusters of microRNAs potentially involved in several subgroups of the different pediatric cancers or specifically involved in the phenotype of a subgroup. In particular, we confirmed the cluster of microRNAs at the 14q32 locus to be involved in multiple pediatric cancers, showing its specific downregulation in tumor subgroups with aggressive phenotype.", + "authors": [ + { + "name": "Ayrault O." + }, + { + "name": "Cancila G." + }, + { + "name": "Hernandez C." + }, + { + "name": "Martignetti L." + }, + { + "name": "Zinovyev A." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Advances in Experimental Medicine and Biology", + "title": "ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively on Tumor Molecular Subgroups" + }, + "pmid": "36352218" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene regulation", + "uri": "http://edamontology.org/topic_0204" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/cnnarginineme/cnnarginineme.biotools.json b/data/cnnarginineme/cnnarginineme.biotools.json new file mode 100644 index 0000000000000..fdaaa602f8bba --- /dev/null +++ b/data/cnnarginineme/cnnarginineme.biotools.json @@ -0,0 +1,106 @@ +{ + "additionDate": "2023-01-27T14:37:59.015853Z", + "biotoolsCURIE": "biotools:cnnarginineme", + "biotoolsID": "cnnarginineme", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "leinama@gmail.com", + "name": "Leina Ma", + "typeEntity": "Person" + } + ], + "description": "A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene methylation analysis", + "uri": "http://edamontology.org/operation_3207" + }, + { + "term": "PCR primer design", + "uri": "http://edamontology.org/operation_0308" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + } + ] + } + ], + "homepage": "https://github.com/guoyangzou/CNNArginineMe", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-27T14:37:59.018295Z", + "license": "Not licensed", + "name": "CNNArginineMe", + "owner": "Chan019", + "publication": [ + { + "doi": "10.3389/FGENE.2022.1036862", + "metadata": { + "abstract": "Copyright © 2022 Zhao, Jiang, Zou, Lin, Wang, Liu and Ma.Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.", + "authors": [ + { + "name": "Jiang H." + }, + { + "name": "Lin Q." + }, + { + "name": "Liu J." + }, + { + "name": "Ma L." + }, + { + "name": "Wang Q." + }, + { + "name": "Zhao J." + }, + { + "name": "Zou G." + } + ], + "citationCount": 1, + "date": "2022-10-17T00:00:00Z", + "journal": "Frontiers in Genetics", + "title": "CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence" + }, + "pmcid": "PMC9618650", + "pmid": "36324513" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Methylated DNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3674" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/coadti/coadti.biotools.json b/data/coadti/coadti.biotools.json new file mode 100644 index 0000000000000..559fec4ed3c77 --- /dev/null +++ b/data/coadti/coadti.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-08T15:27:21.005340Z", + "biotoolsCURIE": "biotools:coadti", + "biotoolsID": "coadti", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kc.w@cityu.edu.hk", + "name": "Xiangtao Li", + "typeEntity": "Person" + }, + { + "email": "lixt314@jlu.edu.cn", + "name": "Ka-Chun Wong", + "typeEntity": "Person" + } + ], + "description": "A multi-modal co-attention based framework for drug-target interaction annotation.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://github.com/Layne-Huang/CoaDTI", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-08T15:27:21.009117Z", + "license": "Apache-2.0", + "name": "CoaDTI", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC446", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.MOTIVATION: The identification of drug-target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug-target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors. RESULTS: Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights. AVAILABILITY: Source code are publicly available at https://github.com/Layne-Huang/CoaDTI.", + "authors": [ + { + "name": "Chen X." + }, + { + "name": "Huang L." + }, + { + "name": "Li X." + }, + { + "name": "Lin J." + }, + { + "name": "Liu R." + }, + { + "name": "Meng L." + }, + { + "name": "Wong K.-C." + }, + { + "name": "Zheng Z." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation" + }, + "pmid": "36274236" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/cocktail/cocktail.biotools.json b/data/cocktail/cocktail.biotools.json new file mode 100644 index 0000000000000..0031612bf223c --- /dev/null +++ b/data/cocktail/cocktail.biotools.json @@ -0,0 +1,63 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T14:42:31.024157Z", + "biotoolsCURIE": "biotools:cocktail", + "biotoolsID": "cocktail", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Saija Johanna Kiljunen" + } + ], + "description": "Cocktail is a program for mathematical modelling of bacteriophage (phage) infection kinetics. Cocktail is a Windows 64-bit program and the source code can be developed in the directions that users see fit.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + } + ] + } + ], + "homepage": "https://github.com/ASNilsson/Cocktail-phage-infection-kinetics", + "lastUpdate": "2023-01-27T14:42:31.027040Z", + "license": "CC-BY-NC-SA-4.0", + "name": "Cocktail", + "operatingSystem": [ + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/V14112483", + "pmcid": "PMC9695944", + "pmid": "36366581" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Mathematics", + "uri": "http://edamontology.org/topic_3315" + }, + { + "term": "Mobile genetic elements", + "uri": "http://edamontology.org/topic_0798" + }, + { + "term": "Software engineering", + "uri": "http://edamontology.org/topic_3372" + } + ] +} diff --git a/data/coda/coda.biotools.json b/data/coda/coda.biotools.json new file mode 100644 index 0000000000000..b7476d0127816 --- /dev/null +++ b/data/coda/coda.biotools.json @@ -0,0 +1,138 @@ +{ + "additionDate": "2023-01-08T15:31:23.037960Z", + "biotoolsCURIE": "biotools:coda", + "biotoolsID": "coda", + "confidence_flag": "tool", + "credit": [ + { + "name": "Ashley L. Kiemen" + } + ], + "description": "A tool for quantitative 3D reconstruction of large tissues at cellular resolution.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://github.com/ashleylk/CODA", + "language": [ + "MATLAB" + ], + "lastUpdate": "2023-01-08T15:31:23.040823Z", + "license": "Not licensed", + "name": "CODA", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41592-022-01650-9", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA’s ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.", + "authors": [ + { + "name": "Amoa F." + }, + { + "name": "Babu J.M." + }, + { + "name": "Braxton A.M." + }, + { + "name": "Cornish T.C." + }, + { + "name": "Grahn M.P." + }, + { + "name": "Han K.S." + }, + { + "name": "Hong S.-M." + }, + { + "name": "Hruban R.H." + }, + { + "name": "Hsu J." + }, + { + "name": "Huang P." + }, + { + "name": "Jiang A.C." + }, + { + "name": "Kiemen A.L." + }, + { + "name": "Kim B." + }, + { + "name": "Reddy S." + }, + { + "name": "Reichel R." + }, + { + "name": "Thompson E.D." + }, + { + "name": "Wirtz D." + }, + { + "name": "Wood L.D." + }, + { + "name": "Wu P.-H." + } + ], + "citationCount": 1, + "date": "2022-11-01T00:00:00Z", + "journal": "Nature Methods", + "title": "CODA: quantitative 3D reconstruction of large tissues at cellular resolution" + }, + "pmid": "36280719" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + } + ] +} diff --git a/data/combatdb/combatdb.biotools.json b/data/combatdb/combatdb.biotools.json new file mode 100644 index 0000000000000..a22923ffb5b98 --- /dev/null +++ b/data/combatdb/combatdb.biotools.json @@ -0,0 +1,115 @@ +{ + "additionDate": "2023-01-27T14:46:37.506706Z", + "biotoolsCURIE": "biotools:combatdb", + "biotoolsID": "combatdb", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "credit": [ + { + "email": "julian@well.ox.ac.uk", + "name": "Julian C Knight", + "orcidid": "https://orcid.org/0000-0002-0377-5536", + "typeEntity": "Person" + }, + { + "email": "brian.marsden@cmd.ox.ac.uk", + "name": "Brian D Marsden", + "typeEntity": "Person" + } + ], + "description": "COMBATdb is a multi-omics database for the human blood response in SARS-CoV-2 infection generated by the COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://db.combat.ox.ac.uk", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T14:46:37.509283Z", + "license": "Not licensed", + "name": "COMBATdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1019", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb (https://db.combat.ox.ac.uk). This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.", + "authors": [ + { + "name": "Burnham K.L." + }, + { + "name": "Knight J.C." + }, + { + "name": "Kumar V." + }, + { + "name": "Marsden B.D." + }, + { + "name": "Mentzer A.J." + }, + { + "name": "Wang D." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas" + }, + "pmcid": "PMC9825482", + "pmid": "36353986" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Cytometry", + "uri": "http://edamontology.org/topic_3934" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/commpath/commpath.biotools.json b/data/commpath/commpath.biotools.json new file mode 100644 index 0000000000000..f7a87eb222949 --- /dev/null +++ b/data/commpath/commpath.biotools.json @@ -0,0 +1,127 @@ +{ + "additionDate": "2023-01-27T14:51:08.556561Z", + "biotoolsCURIE": "biotools:commpath", + "biotoolsID": "commpath", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "quanc1989@163.com", + "name": "Cheng Quan", + "typeEntity": "Person" + }, + { + "email": "ylu.phd@gmail.com", + "name": "Yiming Lu", + "typeEntity": "Person" + }, + { + "email": "zhougq114@126.com", + "name": "Gangqiao Zhou", + "typeEntity": "Person" + } + ], + "description": "An webserver and R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Network visualisation", + "uri": "http://edamontology.org/operation_3925" + }, + { + "term": "Pathway visualisation", + "uri": "http://edamontology.org/operation_3926" + }, + { + "term": "Scatter plot plotting", + "uri": "http://edamontology.org/operation_2940" + } + ] + } + ], + "homepage": "https://commpath.omic.tech/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T14:51:08.559036Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/yingyonghui/CommPath" + } + ], + "name": "CommPath", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.CSBJ.2022.10.028", + "metadata": { + "abstract": "© 2022 The AuthorsSingle-cell transcriptomics offers opportunities to investigate ligand-receptor (LR) interactions between heterogeneous cell populations within tissues. However, most existing tools for the inference of intercellular communication do not allow prioritization of functional LR associations that provoke certain biological responses in the receiver cells. In addition, current tools do not enable the identification of the impact on the downstream cell types of the receiver cells. We present CommPath, an open-source R package and webserver, to analyze and visualize the LR interactions and pathway-mediated intercellular communication chain with single-cell transcriptomic data. CommPath curates a comprehensive signaling pathway database to interpret the consequences of LR associations and therefore infers functional LR interactions. Furthermore, CommPath determines cell-cell communication chain by considering both the upstream and downstream cells of user-defined cell populations. Applying CommPath to human hepatocellular carcinoma dataset shows its ability to decipher complex LR interaction patterns and the associated intercellular communication chain, as well as their changes in disease versus homeostasis.", + "authors": [ + { + "name": "Gao W." + }, + { + "name": "Jiang Y." + }, + { + "name": "Lu H." + }, + { + "name": "Lu Y." + }, + { + "name": "Ping J." + }, + { + "name": "Quan C." + }, + { + "name": "Zhao Z." + }, + { + "name": "Zhou G." + }, + { + "name": "Zhou G." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Computational and Structural Biotechnology Journal", + "title": "CommPath: An R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics" + }, + "pmcid": "PMC9647193", + "pmid": "36382188" + } + ], + "toolType": [ + "Library", + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/comutdb/comutdb.biotools.json b/data/comutdb/comutdb.biotools.json new file mode 100644 index 0000000000000..4a388424bf100 --- /dev/null +++ b/data/comutdb/comutdb.biotools.json @@ -0,0 +1,91 @@ +{ + "additionDate": "2023-01-27T14:55:39.518372Z", + "biotoolsCURIE": "biotools:comutdb", + "biotoolsID": "comutdb", + "confidence_flag": "tool", + "credit": [ + { + "email": "yanguo1978@gmail.com", + "name": "Yan Guo", + "orcidid": "https://orcid.org/0000-0001-5252-3960", + "typeEntity": "Person" + } + ], + "description": "CoMutDB: the landscape of somatic mutation co-occurrence in cancers", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "http://www.innovebioinfo.com/Database/CoMutDB/Home.php", + "language": [ + "JavaScript", + "PHP" + ], + "lastUpdate": "2023-01-27T14:55:39.521036Z", + "license": "Not licensed", + "name": "CoMutDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC725", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Somatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking. RESULTS: Using multi-omics data from over 30 000 subjects and 1747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the online database CoMutDB: http://www.innovebioinfo.com/Database/CoMutDB/Home.php.", + "authors": [ + { + "name": "Guo Y." + }, + { + "name": "Jiang L." + }, + { + "name": "Tang J." + }, + { + "name": "Yu H." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "CoMutDB: the landscape of somatic mutation co-occurrence in cancers" + }, + "pmcid": "PMC9805589", + "pmid": "36355452" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/continuousflex/continuousflex.biotools.json b/data/continuousflex/continuousflex.biotools.json index bef7349ee3dd4..dcb5d10050948 100644 --- a/data/continuousflex/continuousflex.biotools.json +++ b/data/continuousflex/continuousflex.biotools.json @@ -9,6 +9,9 @@ "name": "Slavica Jonić", "orcidid": "https://orcid.org/0000-0001-5112-2743", "typeEntity": "Person" + }, + { + "name": "Mohamad Harastani" } ], "description": "Hybrid Electron Microscopy Normal Mode Analysis with Scipion.\nPlugin to use continuousflex protocols within the Scipion framework.\nThis plugin provides HEMNMA and StructMap protocols and is frequently updated.", @@ -42,8 +45,16 @@ "MATLAB", "Python" ], - "lastUpdate": "2020-12-16T17:49:50Z", + "lastUpdate": "2023-01-08T15:43:54.750894Z", "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/scipion-em/scipion-em-continuousflex" + } + ], "name": "ContinuousFlex", "owner": "Pub2Tools", "publication": [ @@ -62,13 +73,41 @@ "name": "Sorzano C.O.S." } ], - "citationCount": 4, + "citationCount": 15, "date": "2020-01-01T00:00:00Z", "journal": "Protein Science", "title": "Hybrid Electron Microscopy Normal Mode Analysis with Scipion" }, "pmcid": "PMC6933837", "pmid": "31693263" + }, + { + "doi": "10.1016/J.JSB.2022.107906", + "metadata": { + "abstract": "© 2022 Elsevier Inc.ContinuousFlex is a user-friendly open-source software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET) data. In 2019, ContinuousFlex became available as a plugin for Scipion, an image processing software package extensively used in the cryo-EM field. Currently, ContinuousFlex contains software for running (1) recently published methods HEMNMA-3D, TomoFlow, and NMMD; (2) earlier published methods HEMNMA and StructMap; and (3) methods for simulating cryo-EM and cryo-ET data with conformational variability and methods for data preprocessing. It also includes external software for molecular dynamics simulation (GENESIS) and normal mode analysis (ElNemo), used in some of the mentioned methods. The HEMNMA software has been presented in the past, but not the software of other methods. Besides, ContinuousFlex currently also offers a deep learning extension of HEMNMA, named DeepHEMNMA. In this article, we review these methods in the context of the ContinuousFlex package, developed to facilitate their use by the community.", + "authors": [ + { + "name": "Hamitouche I." + }, + { + "name": "Harastani M." + }, + { + "name": "Jonic S." + }, + { + "name": "Moghadam N.B." + }, + { + "name": "Vuillemot R." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Structural Biology", + "title": "ContinuousFlex: Software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy and tomography data" + }, + "pmid": "36244611" } ], "toolType": [ diff --git a/data/cordial/cordial.biotools.json b/data/cordial/cordial.biotools.json new file mode 100644 index 0000000000000..32abf9f4278dd --- /dev/null +++ b/data/cordial/cordial.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T15:00:19.694843Z", + "biotoolsCURIE": "biotools:cordial", + "biotoolsID": "cordial", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "p.cutillas@qmul.ac.uk", + "name": "Pedro R Cutillas", + "orcidid": "https://orcid.org/0000-0002-3426-2274", + "typeEntity": "Person" + } + ], + "description": "A R package for convenient correlation analysis", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Enrichment analysis", + "uri": "http://edamontology.org/operation_3501" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + }, + { + "term": "Weighted correlation network analysis", + "uri": "http://edamontology.org/operation_3766" + } + ] + } + ], + "homepage": "https://github.com/CutillasLab/cordial", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T15:00:19.697248Z", + "license": "MIT", + "name": "cordial", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC769", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Pathway inference methods are important for annotating the genome, for providing insights into the mechanisms of biochemical processes and allow the discovery of signalling members and potential new drug targets. Here, we tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus providing a conceptual basis for a pathway inference method based on correlated anti-proliferative gene properties. METHODS: To test this concept, we used recently available large-scale RNAi screens to develop a method, termed functional pathway inference analysis (FPIA), to systemically identify correlated gene dependencies. RESULTS: To assess FPIA, we initially focused on PI3K/AKT/MTOR signalling, a prototypic oncogenic pathway for which we have a good sense of ground truth. Dependencies for AKT1, MTOR and PDPK1 were among the most correlated with those for PIK3CA (encoding PI3Kα), as returned by FPIA, whereas negative regulators of PI3K/AKT/MTOR signalling, such as PTEN were anti-correlated. Following FPIA, MTOR, PIK3CA and PIK3CB produced significantly greater correlations for genes in the PI3K-Akt pathway versus other pathways. Application of FPIA to two additional pathways (p53 and MAPK) returned expected associations (e.g. MDM2 and TP53BP1 for p53 and MAPK1 and BRAF for MEK1). Over-representation analysis of FPIA-returned genes enriched the respective pathway, and FPIA restricted to specific tumour lineages uncovered cell type-specific networks. Overall, our study demonstrates the ability of FPIA to identify members of pro-survival biochemical pathways in cancer cells. AVAILABILITY AND IMPLEMENTATION: FPIA is implemented in a new R package named 'cordial' freely available from https://github.com/CutillasLab/cordial. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Badshah I.I." + }, + { + "name": "Cutillas P.R." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis" + }, + "pmcid": "PMC9805595", + "pmid": "36448701" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/cormap/cormap.biotools.json b/data/cormap/cormap.biotools.json new file mode 100644 index 0000000000000..f5d029b087a65 --- /dev/null +++ b/data/cormap/cormap.biotools.json @@ -0,0 +1,123 @@ +{ + "accessibility": "Restricted access", + "additionDate": "2023-01-08T15:55:05.061869Z", + "biotoolsCURIE": "biotools:cormap", + "biotoolsID": "cormap", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "aheyland@uoguelph.ca", + "name": "Andreas Heyland", + "orcidid": "https://orcid.org/0000-0002-7592-4473", + "typeEntity": "Person" + } + ], + "description": "Comparative Meta RNA-Seq Data Standardized Analysis Pipeline (CMRP) is a processing frame for the standardized analysis of Meta RNA-Seq raw data from wide-ranged species.", + "documentation": [ + { + "type": [ + "Installation instructions" + ], + "url": "https://github.com/rubysheng/CoRMAP/blob/mus_comparison/doc/Install.md" + }, + { + "type": [ + "User manual" + ], + "url": "https://github.com/rubysheng/CoRMAP/blob/mus_comparison/doc/Usage.md" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "De-novo assembly", + "uri": "http://edamontology.org/operation_0524" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "RNA-Seq analysis", + "uri": "http://edamontology.org/operation_3680" + }, + { + "term": "RNA-Seq quantification", + "uri": "http://edamontology.org/operation_3800" + } + ] + } + ], + "homepage": "https://github.com/rubysheng/CoRMAP.git", + "language": [ + "Bash", + "R", + "Shell" + ], + "lastUpdate": "2023-01-08T15:55:05.064473Z", + "license": "GPL-3.0", + "name": "CoRMAP", + "operatingSystem": [ + "Linux" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1186/S12859-022-04972-9", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Transcriptional regulation is a fundamental mechanism underlying biological functions. In recent years, a broad array of RNA-Seq tools have been used to measure transcription levels in biological experiments, in whole organisms, tissues, and at the single cell level. Collectively, this is a vast comparative dataset on transcriptional processes across organisms. Yet, due to technical differences between the studies (sequencing, experimental design, and analysis) extracting usable comparative information and conducting meta-analyses remains challenging. Results: We introduce Comparative RNA-Seq Metadata Analysis Pipeline (CoRMAP), a meta-analysis tool to retrieve comparative gene expression data from any RNA-Seq dataset using de novo assembly, standardized gene expression tools and the implementation of OrthoMCL, a gene orthology search algorithm. It employs the use of orthogroup assignments to ensure the accurate comparison of gene expression levels between experiments and species. Here we demonstrate the use of CoRMAP on two mouse brain transcriptomes with similar scope, that were collected several years from each other using different sequencing technologies and analysis methods. We also compare the performance of CoRMAP with a functional mapping tool, previously published. Conclusion: CoRMAP provides a framework for the meta-analysis of RNA-Seq data from divergent taxonomic groups. This method facilitates the retrieval and comparison of gene expression levels from published data sets using standardized assembly and analysis. CoRMAP does not rely on reference genomes and consequently facilitates direct comparison between diverse studies on a range of organisms.", + "authors": [ + { + "name": "Ali R.A." + }, + { + "name": "Heyland A." + }, + { + "name": "Sheng Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP)" + }, + "pmcid": "PMC9547434", + "pmid": "36207678" + } + ], + "toolType": [ + "Script", + "Workflow" + ], + "topic": [ + { + "term": "Gene regulation", + "uri": "http://edamontology.org/topic_0204" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/cosbin/cosbin.biotools.json b/data/cosbin/cosbin.biotools.json new file mode 100644 index 0000000000000..eef8890646dda --- /dev/null +++ b/data/cosbin/cosbin.biotools.json @@ -0,0 +1,78 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T15:03:02.294541Z", + "biotoolsCURIE": "biotools:cosbin", + "biotoolsID": "cosbin", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yuewang@vt.edu", + "name": "Yue Wang", + "orcidid": "https://orcid.org/0000-0002-5197-5874", + "typeEntity": "Person" + } + ], + "description": "Cosine score-based iterative normalization of biologically diverse samples.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/MinjieSh/Cosbin", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T15:03:02.297035Z", + "license": "MIT", + "name": "Cosbin", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOADV/VBAC076", + "pmcid": "PMC9614059", + "pmid": "36330358" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Sample collections", + "uri": "http://edamontology.org/topic_3277" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/cottonmd/cottonmd.biotools.json b/data/cottonmd/cottonmd.biotools.json new file mode 100644 index 0000000000000..7694e14efd68d --- /dev/null +++ b/data/cottonmd/cottonmd.biotools.json @@ -0,0 +1,88 @@ +{ + "additionDate": "2023-01-09T08:01:40.610810Z", + "biotoolsCURIE": "biotools:cottonmd", + "biotoolsID": "cottonmd", + "confidence_flag": "tool", + "credit": [ + { + "email": "yangzuoren@caas.cn", + "name": "Zuoren Yang", + "typeEntity": "Person" + }, + { + "email": "yqy@mail.hzau.edu.cn", + "name": "Qing-Yong Yang", + "typeEntity": "Person" + } + ], + "description": "CottonMD is a curated and integrated multi-omics resource for cotton. In this database, we integrated and analyzed datasets from genomics, epigenomics, transcriptomics, metabolomics and phenomics, and offer multiple tools for users to make it easy to utilize datasets.", + "download": [ + { + "type": "Downloads page", + "url": "http://yanglab.hzau.edu.cn/CottonMD/download" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Gene expression QTL analysis", + "uri": "http://edamontology.org/operation_3232" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + } + ] + } + ], + "homepage": "http://yanglab.hzau.edu.cn/CottonMD/", + "lastUpdate": "2023-01-09T08:01:40.614016Z", + "license": "Other", + "name": "CottonMD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC863", + "pmid": "36215030" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Phenomics", + "uri": "http://edamontology.org/topic_3298" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/cov2clusters/cov2clusters.biotools.json b/data/cov2clusters/cov2clusters.biotools.json new file mode 100644 index 0000000000000..a6b87678fab62 --- /dev/null +++ b/data/cov2clusters/cov2clusters.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T08:05:52.842609Z", + "biotoolsCURIE": "biotools:cov2clusters", + "biotoolsID": "cov2clusters", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "benjamin_sobkowiak@sfu.ca", + "name": "Benjamin Sobkowiak", + "typeEntity": "Person" + } + ], + "description": "Stable clustering of SARS-CoV-2 sequences from phylogenetic trees.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Phylogenetic inference", + "uri": "http://edamontology.org/operation_0323" + }, + { + "term": "Phylogenetic tree editing", + "uri": "http://edamontology.org/operation_0326" + }, + { + "term": "Tree dating", + "uri": "http://edamontology.org/operation_3942" + } + ] + } + ], + "homepage": "http://github.com/bensobkowiak/cov2clusters", + "language": [ + "R" + ], + "lastUpdate": "2023-01-09T08:06:22.039471Z", + "license": "MIT", + "name": "cov2clusters", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1186/S12864-022-08936-4", + "metadata": { + "abstract": "© 2022, The Author(s).Background: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. Results: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. Conclusions: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.", + "authors": [ + { + "name": "Colijn C." + }, + { + "name": "Hoang L.M.N." + }, + { + "name": "Kamelian K." + }, + { + "name": "Prystajecky N." + }, + { + "name": "Silva A.G." + }, + { + "name": "Sobkowiak B." + }, + { + "name": "Tyson J." + }, + { + "name": "Zlosnik J.E.A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Genomics", + "title": "Cov2clusters: genomic clustering of SARS-CoV-2 sequences" + }, + "pmcid": "PMC9579665", + "pmid": "36258173" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + }, + { + "term": "Virology", + "uri": "http://edamontology.org/topic_0781" + }, + { + "term": "Whole genome sequencing", + "uri": "http://edamontology.org/topic_3673" + } + ] +} diff --git a/data/covid-19_serohub/covid-19_serohub.biotools.json b/data/covid-19_serohub/covid-19_serohub.biotools.json new file mode 100644 index 0000000000000..eb47b29ccc32e --- /dev/null +++ b/data/covid-19_serohub/covid-19_serohub.biotools.json @@ -0,0 +1,135 @@ +{ + "additionDate": "2023-01-27T16:28:51.494147Z", + "biotoolsCURIE": "biotools:covid-19_serohub", + "biotoolsID": "covid-19_serohub", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "credit": [ + { + "email": "freedmanne@mail.nih.gov", + "name": "Neal D. Freedman", + "typeEntity": "Person" + } + ], + "description": "the COVID-19 Seroprevalence Studies Hub, known as COVID-19 SeroHub, is an online dashboard intended to help researchers and policymakers monitor studies of Severe Acute Respiratory", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Epitope mapping", + "uri": "http://edamontology.org/operation_0416" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://covid19serohub.nih.gov/", + "lastUpdate": "2023-01-27T16:30:01.297485Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://covid19serohub.nih.gov/public/COVID-19_SeroHub_Submission_Template.xlsx" + } + ], + "name": "COVID-19 SeroHub", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41597-022-01830-4", + "metadata": { + "abstract": "© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.", + "authors": [ + { + "name": "Averhoff F." + }, + { + "name": "Bayrak K." + }, + { + "name": "Benoit T.J." + }, + { + "name": "Brown L." + }, + { + "name": "Bu X." + }, + { + "name": "Chanock S.J." + }, + { + "name": "Coffey B." + }, + { + "name": "Freedman N.D." + }, + { + "name": "Jackson L." + }, + { + "name": "Jones J.M." + }, + { + "name": "Kerlavage A.R." + }, + { + "name": "Lu A." + }, + { + "name": "Newman L.M." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "COVID-19 SeroHub, an online repository of SARS-CoV-2 seroprevalence studies in the United States" + }, + "pmcid": "PMC9701211", + "pmid": "36435936" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Allergy, clinical immunology and immunotherapeutics", + "uri": "http://edamontology.org/topic_3400" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + } + ] +} diff --git a/data/covid-gwab/covid-gwab.biotools.json b/data/covid-gwab/covid-gwab.biotools.json new file mode 100644 index 0000000000000..93d7893349404 --- /dev/null +++ b/data/covid-gwab/covid-gwab.biotools.json @@ -0,0 +1,98 @@ +{ + "additionDate": "2023-01-09T08:11:13.800423Z", + "biotoolsCURIE": "biotools:covid-gwab", + "biotoolsID": "covid-gwab", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "credit": [ + { + "email": "insuklee@yonsei.ac.kr", + "name": "Insuk Lee", + "orcidid": "https://orcid.org/0000-0003-3146-6180", + "typeEntity": "Person" + } + ], + "description": "A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene prediction", + "uri": "http://edamontology.org/operation_2454" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Network visualisation", + "uri": "http://edamontology.org/operation_3925" + } + ] + } + ], + "homepage": "https://inetbio.org/covidgwab/", + "lastUpdate": "2023-01-09T08:11:13.803176Z", + "license": "Not licensed", + "name": "COVID-GWAB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/BIOM12101446", + "metadata": { + "abstract": "© 2022 by the authors.Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.", + "authors": [ + { + "name": "Baek S." + }, + { + "name": "Lee I." + }, + { + "name": "Yang S." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "Biomolecules", + "title": "COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data" + }, + "pmcid": "PMC9599684", + "pmid": "36291657" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genetics", + "uri": "http://edamontology.org/topic_3053" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/covinter/covinter.biotools.json b/data/covinter/covinter.biotools.json new file mode 100644 index 0000000000000..64a488ca0e29c --- /dev/null +++ b/data/covinter/covinter.biotools.json @@ -0,0 +1,94 @@ +{ + "additionDate": "2023-01-09T08:16:47.748755Z", + "biotoolsCURIE": "biotools:covinter", + "biotoolsID": "covinter", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "hanlianyi@ipm-gba.org.cn", + "name": "Lianyi Han", + "typeEntity": "Person" + }, + { + "email": "taolin@hznu.edu.cn", + "name": "Lin Tao", + "typeEntity": "Person" + }, + { + "email": "zhufeng@zju.edu.cn", + "name": "Feng Zhu", + "typeEntity": "Person" + } + ], + "description": "Database of SARS-COV-2, SARS-COV, MERS-CoV, HCoV-229E and HCoV-OC43, etc.7 human pathogenic coronaviruses RNAs and host proteins interactions, which are critical for viral infection.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Protein-protein interaction analysis", + "uri": "http://edamontology.org/operation_2949" + }, + { + "term": "RNA-binding protein prediction", + "uri": "http://edamontology.org/operation_3901" + } + ] + } + ], + "homepage": "https://idrblab.org/covinter/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T08:16:47.751397Z", + "license": "Other", + "name": "CovInter", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC834", + "pmid": "36200814" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Protein expression", + "uri": "http://edamontology.org/topic_0108" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/cox/cox.biotools.json b/data/cox/cox.biotools.json new file mode 100644 index 0000000000000..dcf875ac688c9 --- /dev/null +++ b/data/cox/cox.biotools.json @@ -0,0 +1,122 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T10:54:21.203478Z", + "biotoolsCURIE": "biotools:cox", + "biotoolsID": "cox", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ruilinli@stanford.edu", + "name": "Ruilin Li", + "orcidid": "https://orcid.org/0000-0002-5152-7086", + "typeEntity": "Person" + }, + { + "name": "Yosuke Tanigawa", + "orcidid": "https://orcid.org/0000-0001-9759-157X" + }, + { + "name": "Manuel A Rivas", + "orcidid": "https://orcid.org/0000-0003-1457-9925", + "typeEntity": "Person" + }, + { + "name": "Robert Tibshirani", + "typeEntity": "Person" + } + ], + "description": "Multi-snpnet-Cox (mrcox) Efficient Group-Sparse Lasso solver for multi-response Cox model.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/rivas-lab/multisnpnet-Cox", + "language": [ + "C++", + "R" + ], + "lastUpdate": "2023-01-26T10:54:21.206017Z", + "license": "MIT", + "name": "Cox", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAB095", + "metadata": { + "abstract": "© 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.Motivation: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. Results: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. Availabilityandimplementation: https://github.com/rivas-lab/multisnpnet-Cox", + "authors": [ + { + "name": "Hastie T." + }, + { + "name": "Justesen J.M." + }, + { + "name": "Li R." + }, + { + "name": "Rivas M.A." + }, + { + "name": "Tanigawa Y." + }, + { + "name": "Taylor J." + }, + { + "name": "Tibshirani R." + } + ], + "citationCount": 3, + "date": "2021-12-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "Survival analysis on rare events using group-regularized multi-response Cox regression" + }, + "pmcid": "PMC8652035", + "pmid": "33560296" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "Mathematics", + "uri": "http://edamontology.org/topic_3315" + }, + { + "term": "Rare diseases", + "uri": "http://edamontology.org/topic_3325" + } + ] +} diff --git a/data/cplot/cplot.biotools.json b/data/cplot/cplot.biotools.json new file mode 100644 index 0000000000000..8a679848d1152 --- /dev/null +++ b/data/cplot/cplot.biotools.json @@ -0,0 +1,104 @@ +{ + "additionDate": "2023-01-09T08:22:34.438026Z", + "biotoolsCURIE": "biotools:cplot", + "biotoolsID": "cplot", + "confidence_flag": "tool", + "credit": [ + { + "email": "gangman@dongguk.edu", + "name": "Gangman Yi", + "typeEntity": "Person" + } + ], + "description": "A visualized contig plotting application for analysis of short read alignment of nucleotide sequences", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Read mapping", + "uri": "http://edamontology.org/operation_3198" + }, + { + "term": "Reverse complement", + "uri": "http://edamontology.org/operation_0363" + }, + { + "term": "Sequence alignment", + "uri": "http://edamontology.org/operation_0292" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + }, + { + "term": "Sequence visualisation", + "uri": "http://edamontology.org/operation_0564" + } + ] + } + ], + "homepage": "https://datalab.dongguk.edu/cPlot", + "language": [ + "JavaScript", + "Python" + ], + "lastUpdate": "2023-01-09T08:22:34.440625Z", + "license": "Not licensed", + "name": "cPlot", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/IJMS231911484", + "metadata": { + "abstract": "© 2022 by the authors.Advances in the next-generation sequencing technology have led to a dramatic decrease in read-generation cost and an increase in read output. Reconstruction of short DNA sequence reads generated by next-generation sequencing requires a read alignment method that reconstructs a reference genome. In addition, it is essential to analyze the results of read alignments for a biologically meaningful inference. However, read alignment from vast amounts of genomic data from various organisms is challenging in that it involves repeated automatic and manual analysis steps. We, here, devised cPlot software for read alignment of nucleotide sequences, with automated read alignment and position analysis, which allows visual assessment of the analysis results by the user. cPlot compares sequence similarity of reads by performing multiple read alignments, with FASTA format files as the input. This application provides a web-based interface for the user for facile implementation, without the need for a dedicated computing environment. cPlot identifies the location and order of the sequencing reads by comparing the sequence to a genetically close reference sequence in a way that is effective for visualizing the assembly of short reads generated by NGS and rapid gene map construction.", + "authors": [ + { + "name": "Ji M." + }, + { + "name": "Jung J." + }, + { + "name": "Kan Y." + }, + { + "name": "Kim D." + }, + { + "name": "Yi G." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "International Journal of Molecular Sciences", + "title": "cPlot: Contig-Plotting Visualization for the Analysis of Short-Read Nucleotide Sequence Alignments" + }, + "pmcid": "PMC9570162", + "pmid": "36232783" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/cramdb/cramdb.biotools.json b/data/cramdb/cramdb.biotools.json new file mode 100644 index 0000000000000..0c5253091e0a8 --- /dev/null +++ b/data/cramdb/cramdb.biotools.json @@ -0,0 +1,145 @@ +{ + "additionDate": "2023-01-27T16:33:26.779150Z", + "biotoolsCURIE": "biotools:cramdb", + "biotoolsID": "cramdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chent@nrc.ac.cn", + "name": "Tong Chen", + "typeEntity": "Person" + }, + { + "email": "hushengwei@163.com", + "name": "Wei Ni", + "typeEntity": "Person" + }, + { + "name": "Shengwei Hu", + "orcidid": "https://orcid.org/0000-0001-8849-265X", + "typeEntity": "Person" + } + ], + "description": "CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Genome annotation", + "uri": "http://edamontology.org/operation_0362" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "http://www.ehbio.com/CRAMdb/", + "language": [ + "Shell" + ], + "lastUpdate": "2023-01-27T16:33:26.781744Z", + "license": "Other", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/Tong-Chen/CRAMdb" + } + ], + "name": "CRAMdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC973", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals. It focuses on the composition and roles of the microbiome in various animal species. The main goal of the CRAMdb is to facilitate the reuse of animal metagenomic data, and enable cross-host and cross-phenotype comparisons. To this end, we consistently annotated microbiomes (including 16S, 18S, ITS and metagenomics sequencing data) of 516 animals from 475 projects spanning 43 phenotype pairs to construct the database that is equipped with 9430 bacteria, 278 archaea, 2216 fungi and 458 viruses. CRAMdb provides two main contents: microbiome composition data, illustrating the landscape of the microbiota (bacteria, archaea, fungi, and viruses) in various animal species, and microbiome association data, revealing the relationships between the microbiota and various phenotypes across different animal species. More importantly, users can quickly compare the composition of the microbiota of interest cross-host or body site and the associated taxa that differ between phenotype pairs cross-host or cross-phenotype. CRAMdb is freely available at (http://www.ehbio.com/CRAMdb).", + "authors": [ + { + "name": "Chen T." + }, + { + "name": "Cui C." + }, + { + "name": "Hu S." + }, + { + "name": "Lei B." + }, + { + "name": "Lei Y." + }, + { + "name": "Li C." + }, + { + "name": "Li F." + }, + { + "name": "Li X." + }, + { + "name": "Liu C." + }, + { + "name": "Ni W." + }, + { + "name": "Wang L." + }, + { + "name": "Xu Y." + }, + { + "name": "Yang Q." + }, + { + "name": "Zhou P." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "CRAMdb: a comprehensive database for composition and roles of microbiome in animals" + }, + "pmcid": "PMC9825719", + "pmid": "36318246" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/creammist/creammist.biotools.json b/data/creammist/creammist.biotools.json new file mode 100644 index 0000000000000..cc2117a07bf4b --- /dev/null +++ b/data/creammist/creammist.biotools.json @@ -0,0 +1,90 @@ +{ + "additionDate": "2023-01-09T08:27:59.495695Z", + "biotoolsCURIE": "biotools:creammist", + "biotoolsID": "creammist", + "confidence_flag": "tool", + "credit": [ + { + "email": "hatairat.y@cmu.ac.th", + "name": "Hatairat Yingtaweesittikul", + "typeEntity": "Person" + }, + { + "email": "suphavilaic@gis.a-star.edu.sg", + "name": "Chayaporn Suphavilai", + "typeEntity": "Person" + } + ], + "description": "CREAMMIST is an integrated cancer drug sensitivity database for in vitro pharmacogenomics analysis, providing an integrative dose-response curve across five widely used cancer cell-line drug-response datasets (CCLE, GDSC1, GDSC2, CTRP1, CTRP2).", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://creammist.mtms.dev/doc/dose_response_curve/" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://creammist.mtms.dev", + "lastUpdate": "2023-01-09T08:27:59.498248Z", + "license": "Not licensed", + "name": "CREAMMIST", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC911", + "pmid": "36259664" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pharmacogenomics", + "uri": "http://edamontology.org/topic_0208" + } + ] +} diff --git a/data/cresil/cresil.biotools.json b/data/cresil/cresil.biotools.json new file mode 100644 index 0000000000000..d93cce61ea069 --- /dev/null +++ b/data/cresil/cresil.biotools.json @@ -0,0 +1,81 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T08:34:18.011208Z", + "biotoolsCURIE": "biotools:cresil", + "biotoolsID": "cresil", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "INookaew@uams.edu", + "name": "Intawat Nookaew", + "typeEntity": "Person" + } + ], + "description": "A tool for detecting eccDNA from Nanopore reads", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Read mapping", + "uri": "http://edamontology.org/operation_3198" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/visanuwan/cresil", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T08:34:18.013910Z", + "license": "MIT", + "name": "CReSIL", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC422", + "pmid": "36198068" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Whole genome sequencing", + "uri": "http://edamontology.org/topic_3673" + } + ] +} diff --git a/data/crisprbase/crisprbase.biotools.json b/data/crisprbase/crisprbase.biotools.json new file mode 100644 index 0000000000000..44a8da72b986d --- /dev/null +++ b/data/crisprbase/crisprbase.biotools.json @@ -0,0 +1,143 @@ +{ + "additionDate": "2023-01-27T16:40:43.658251Z", + "biotoolsCURIE": "biotools:crisprbase", + "biotoolsID": "crisprbase", + "confidence_flag": "tool", + "credit": [ + { + "email": "fengbiaomao@bjmu.edu.cn", + "name": "Fengbiao Mao", + "orcidid": "https://orcid.org/0000-0003-0852-4266", + "typeEntity": "Person" + }, + { + "email": "lixiangxue@hsc.pku.edu.cn", + "name": "Lixiang Xue", + "typeEntity": "Person" + }, + { + "email": "sunlichao@cicams.ac.cn", + "name": "Lichao Sun", + "typeEntity": "Person" + } + ], + "description": "CRISPRbase is a comprehensive database curating the outcome and evaluating off-target effects of base editors on various cell types and tissues in dozens of species", + "documentation": [ + { + "type": [ + "General" + ], + "url": "http://crisprbase.beyondthe.top:580/welcome/tutorial.html" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "http://crisprbase.maolab.org", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-27T16:40:43.660718Z", + "license": "Not licensed", + "name": "CRISPRbase", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC967", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CRISPR-Cas base editing (BE) system is a powerful tool to expand the scope and efficiency of genome editing with single-nucleotide resolution. The editing efficiency, product purity, and off-target effect differ among various BE systems. Herein, we developed CRISPRbase (http://crisprbase.maolab.org), by integrating 1 252 935 records of base editing outcomes in more than 50 cell types from 17 species. CRISPRbase helps to evaluate the putative editing precision of different BE systems by integrating multiple annotations, functional predictions and a blasting system for single-guide RNA sequences. We systematically assessed the editing window, editing efficiency and product purity of various BE systems. Intensive efforts were focused on increasing the editing efficiency and product purity of base editors since the byproduct could be detrimental in certain applications. Remarkably, more than half of cancer-related off-target mutations were non-synonymous and extremely damaging to protein functions in most common tumor types. Luckily, most of these cancer-related mutations were passenger mutations (4840/5703, 84.87%) rather than cancer driver mutations (863/5703, 15.13%), indicating a weak effect of off-target mutations on carcinogenesis. In summary, CRISPRbase is a powerful and convenient tool to study the outcomes of different base editors and help researchers choose appropriate BE designs for functional studies.", + "authors": [ + { + "name": "Chen X." + }, + { + "name": "Fan J." + }, + { + "name": "Li K." + }, + { + "name": "Liu Q." + }, + { + "name": "Mao F." + }, + { + "name": "Shi L." + }, + { + "name": "Song R." + }, + { + "name": "Su J." + }, + { + "name": "Sun L." + }, + { + "name": "Wang F." + }, + { + "name": "Xue L." + }, + { + "name": "Zhou D." + }, + { + "name": "Zhu Z." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase" + }, + "pmcid": "PMC9825451", + "pmid": "36350608" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Genetic engineering", + "uri": "http://edamontology.org/topic_3912" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/crispron_off/crispron_off.biotools.json b/data/crispron_off/crispron_off.biotools.json new file mode 100644 index 0000000000000..c3386feb72d90 --- /dev/null +++ b/data/crispron_off/crispron_off.biotools.json @@ -0,0 +1,89 @@ +{ + "additionDate": "2023-01-09T08:43:09.554581Z", + "biotoolsCURIE": "biotools:crispron_off", + "biotoolsID": "crispron_off", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "gorodkin@rth.dk", + "name": "Jan Gorodkin", + "typeEntity": "Person" + } + ], + "description": "Webservers for CRISPR Cas9 on- and off-target predictions.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + } + ] + } + ], + "homepage": "https://rth.dk/resources/crispr/crispron/", + "language": [ + "C", + "JavaScript", + "Python" + ], + "lastUpdate": "2023-01-09T08:43:09.557404Z", + "license": "Other", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://rth.dk/resources/crispr/crisproff/" + }, + { + "type": [ + "Other" + ], + "url": "https://rth.dk/resources/crispr/crispron/" + } + ], + "name": "CRISPRon_off", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC697", + "pmid": "36271848" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Genetic engineering", + "uri": "http://edamontology.org/topic_3912" + }, + { + "term": "Probes and primers", + "uri": "http://edamontology.org/topic_0632" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/crisprverse/crisprverse.biotools.json b/data/crisprverse/crisprverse.biotools.json new file mode 100644 index 0000000000000..38a7160d700a5 --- /dev/null +++ b/data/crisprverse/crisprverse.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T16:49:56.869024Z", + "biotoolsCURIE": "biotools:crisprverse", + "biotoolsID": "crisprverse", + "confidence_flag": "tool", + "credit": [ + { + "email": "fortin946@gmail.com", + "name": "Jean-Philippe Fortin", + "orcidid": "https://orcid.org/0000-0001-9328-3852", + "typeEntity": "Person" + } + ], + "description": "A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies.", + "documentation": [ + { + "type": [ + "Training material" + ], + "url": "https://github.com/crisprVerse/Tutorials" + } + ], + "download": [ + { + "type": "Container file", + "url": "https://github.com/crisprVerse/Docker" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Genome indexing", + "uri": "http://edamontology.org/operation_3211" + }, + { + "term": "SNP annotation", + "uri": "http://edamontology.org/operation_3661" + } + ] + } + ], + "homepage": "https://github.com/crisprVerse", + "language": [ + "Python", + "R", + "Shell" + ], + "lastUpdate": "2023-01-27T16:49:56.871525Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/crisprVerse/crisprVersePaper" + } + ], + "name": "crisprVerse", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41467-022-34320-7", + "metadata": { + "abstract": "© 2022, The Author(s).The success of CRISPR-mediated gene perturbation studies is highly dependent on the quality of gRNAs, and several tools have been developed to enable optimal gRNA design. However, these tools are not all adaptable to the latest CRISPR modalities or nucleases, nor do they offer comprehensive annotation methods for advanced CRISPR applications. Here, we present a new ecosystem of R packages, called crisprVerse, that enables efficient gRNA design and annotation for a multitude of CRISPR technologies. This includes CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe) and CRISPR knockdown (CRISPRkd). The core package, crisprDesign, offers a user-friendly and unified interface to add off-target annotations, rich gene and SNP annotations, and on- and off-target activity scores. These functionalities are enabled for any RNA- or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project (https://github.com/crisprVerse).", + "authors": [ + { + "name": "Fortin J.-P." + }, + { + "name": "Hoberecht L." + }, + { + "name": "Lun A." + }, + { + "name": "Perampalam P." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Nature Communications", + "title": "A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies" + }, + "pmcid": "PMC9630310", + "pmid": "36323688" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/crit/crit.biotools.json b/data/crit/crit.biotools.json new file mode 100644 index 0000000000000..b8a146cede7a5 --- /dev/null +++ b/data/crit/crit.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T16:54:03.079843Z", + "biotoolsCURIE": "biotools:crit", + "biotoolsID": "crit", + "confidence_flag": "tool", + "credit": [ + { + "email": "jzxu01@stu.edu.cn", + "name": "Jianzhen Xu", + "typeEntity": "Person" + } + ], + "description": "CRIT (CircRNA Regulator Identification Tool) is a pipeline based on a non-negative matrix factorization method to integrate various omics information and to identify regulating RBPs.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "RNA binding site prediction", + "uri": "http://edamontology.org/operation_3902" + }, + { + "term": "RNA-binding protein prediction", + "uri": "http://edamontology.org/operation_3901" + } + ] + } + ], + "homepage": "https://github.com/BioinformaticsSTU/CRIT", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T16:54:03.082258Z", + "license": "Not licensed", + "name": "CRIT", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.OMTN.2022.10.015", + "metadata": { + "abstract": "© 2022 The AuthorsCircular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers.", + "authors": [ + { + "name": "Cai Y." + }, + { + "name": "Chen Q." + }, + { + "name": "Gao X." + }, + { + "name": "Hao S." + }, + { + "name": "Jiang L." + }, + { + "name": "Shao M." + }, + { + "name": "Xu J." + }, + { + "name": "Zhao X." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Molecular Therapy - Nucleic Acids", + "title": "CRIT: Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization" + }, + "pmcid": "PMC9664520", + "pmid": "36420213" + } + ], + "toolType": [ + "Workflow" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3794" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/cronos/cronos.biotools.json b/data/cronos/cronos.biotools.json new file mode 100644 index 0000000000000..3633b68796d55 --- /dev/null +++ b/data/cronos/cronos.biotools.json @@ -0,0 +1,74 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T08:47:52.132055Z", + "biotoolsCURIE": "biotools:cronos", + "biotoolsID": "cronos", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ilias.lagkouvardos@tum.de", + "name": "Ilias Lagkouvardos", + "typeEntity": "Person" + } + ], + "description": "Cronos, an analytical pipeline written in R. Cronos' inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "RNA-seq time series data analysis", + "uri": "http://edamontology.org/operation_3565" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/Lagkouvardos/Cronos", + "language": [ + "R" + ], + "lastUpdate": "2023-01-09T08:47:52.134686Z", + "license": "MIT", + "name": "Cronos", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3389/FBINF.2022.866902", + "pmcid": "PMC9580867", + "pmid": "36304308" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/cross-attention_phv/cross-attention_phv.biotools.json b/data/cross-attention_phv/cross-attention_phv.biotools.json new file mode 100644 index 0000000000000..96fa230706e0e --- /dev/null +++ b/data/cross-attention_phv/cross-attention_phv.biotools.json @@ -0,0 +1,94 @@ +{ + "additionDate": "2023-01-09T08:53:20.585589Z", + "biotoolsCURIE": "biotools:cross-attention_phv", + "biotoolsID": "cross-attention_phv", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kurata@bio.kyutech.ac.jp", + "name": "Hiroyuki Kurata", + "typeEntity": "Person" + } + ], + "description": "Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + }, + { + "term": "Protein secondary structure prediction", + "uri": "http://edamontology.org/operation_0267" + } + ] + } + ], + "homepage": "https://github.com/kuratahiroyuki/Cross-Attention_PHV", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T08:53:20.588118Z", + "license": "Apache-2.0", + "name": "cross-attention PHV", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.CSBJ.2022.10.012", + "metadata": { + "abstract": "© 2022 The Author(s)Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein–protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human–SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.", + "authors": [ + { + "name": "Kurata H." + }, + { + "name": "Tsukiyama S." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Computational and Structural Biotechnology Journal", + "title": "Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention–based neural networks" + }, + "pmcid": "PMC9546503", + "pmid": "36249566" + } + ], + "toolType": [ + "Command-line tool", + "Library" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein interaction experiment", + "uri": "http://edamontology.org/topic_3957" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/cscs/cscs.biotools.json b/data/cscs/cscs.biotools.json new file mode 100644 index 0000000000000..4c52363fe3a9d --- /dev/null +++ b/data/cscs/cscs.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T01:05:18.374509Z", + "biotoolsCURIE": "biotools:cscs", + "biotoolsID": "cscs", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhangyijing@cemps.ac.cn", + "name": "Yijing Zhang", + "orcidid": "https://orcid.org/0000-0001-9568-9389", + "typeEntity": "Person" + }, + { + "email": "zhaofei@cemps.ac.cn", + "name": "Fei Zhao", + "typeEntity": "Person" + }, + { + "name": "Tengfei Tang" + }, + { + "name": "Xiaojuan Ran" + } + ], + "description": "A chromatin state interface for Chinese Spring bread wheat.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome assembly", + "uri": "http://edamontology.org/operation_0525" + }, + { + "term": "Map drawing", + "uri": "http://edamontology.org/operation_0573" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + } + ] + } + ], + "homepage": "http://bioinfo.cemps.ac.cn/CSCS/", + "lastUpdate": "2023-01-10T01:05:18.377036Z", + "name": "CSCS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1007/S42994-021-00048-Z", + "metadata": { + "abstract": "© 2021, Agricultural Information Institute, Chinese Academy of Agricultural Sciences.A chromosome-level genome assembly of the bread wheat variety Chinese Spring (CS) has recently been published. Genome-wide identification of regulatory elements (REs) responsible for regulating gene activity is key to further mechanistic studies. Because epigenetic activity can reflect RE activity, defining chromatin states based on epigenomic features is an effective way to detect REs. Here, we present the web-based platform Chinese Spring chromatin state (CSCS), which provides CS chromatin signature information. CSCS includes 15 recently published epigenomic data sets including open chromatin and major chromatin marks, which are further partitioned into 15 distinct chromatin states. CSCS curates detailed information about these chromatin states, with trained self-organization mapping (SOM) for segments in all chromatin states and JBrowse visualization for genomic regions or genes. Motif analysis for genomic regions or genes, GO analysis for genes and SOM analysis for new epigenomic data sets are also integrated into CSCS. In summary, the CSCS database contains the combinatorial patterns of chromatin signatures in wheat and facilitates the detection of functional elements and further clarification of regulatory activities. We illustrate how CSCS enables biological insights using one example, demonstrating that CSCS is a highly useful resource for intensive data mining. CSCS is available at http://bioinfo.cemps.ac.cn/CSCS/.", + "authors": [ + { + "name": "Ran X." + }, + { + "name": "Tang T." + }, + { + "name": "Wang M." + }, + { + "name": "Ye L." + }, + { + "name": "Zhang Y." + }, + { + "name": "Zhao F." + }, + { + "name": "Zhuang Y." + } + ], + "citationCount": 1, + "date": "2021-12-01T00:00:00Z", + "journal": "aBIOTECH", + "title": "CSCS: a chromatin state interface for Chinese Spring bread wheat" + }, + "pmcid": "PMC9590471", + "pmid": "36311809" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/csrep/csrep.biotools.json b/data/csrep/csrep.biotools.json new file mode 100644 index 0000000000000..c9cabb0bf43f1 --- /dev/null +++ b/data/csrep/csrep.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T01:22:02.894147Z", + "biotoolsCURIE": "biotools:csrep", + "biotoolsID": "csrep", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jason.ernst@ucla.edu", + "name": "Jason Ernst", + "typeEntity": "Person" + }, + { + "name": "Petko Fiziev" + }, + { + "name": "Zane Koch" + }, + { + "name": "Ha Vu", + "orcidid": "http://orcid.org/0000-0002-1131-7375" + } + ], + "description": "A framework for summarizing chromatin state annotations within and identifying differential annotations across groups of samples.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://github.com/ernstlab/csrep/blob/master/tutorial.md" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome annotation", + "uri": "http://edamontology.org/operation_0362" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "http://github.com/ernstlab/csrep", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-22T01:22:02.896591Z", + "license": "Not licensed", + "name": "CSREP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac722", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Genome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution. RESULTS: We developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference in CSREP's probability assignments for the two groups can be used to identify genomic locations with differential chromatin state assignments. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution. AVAILABILITY AND IMPLEMENTATION: The CSREP source code and generated data are available at http://github.com/ernstlab/csrep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Ernst J." + }, + { + "name": "Fiziev P." + }, + { + "name": "Koch Z." + }, + { + "name": "Vu H." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "A framework for group-wise summarization and comparison of chromatin state annotations" + }, + "pmcid": "PMC9805555", + "pmid": "36342196" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + } + ] +} diff --git a/data/ctd_r/ctd_r.biotools.json b/data/ctd_r/ctd_r.biotools.json new file mode 100644 index 0000000000000..1c7c1401f0c3c --- /dev/null +++ b/data/ctd_r/ctd_r.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T11:02:17.003087Z", + "biotoolsCURIE": "biotools:ctd_r", + "biotoolsID": "ctd_r", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "amilosav@bcm.edu", + "name": "Aleksandar Milosavljevic", + "typeEntity": "Person" + }, + { + "name": "Lillian R Thistlethwaite" + }, + { + "name": "Varduhi Petrosyan" + }, + { + "name": "Xiqi Li" + } + ], + "description": "An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Metabolic network modelling", + "uri": "http://edamontology.org/operation_3660" + }, + { + "term": "Network visualisation", + "uri": "http://edamontology.org/operation_3925" + } + ] + } + ], + "homepage": "https://www.rdocumentation.org/packages/CTD/versions/1.1.0", + "language": [ + "R" + ], + "lastUpdate": "2023-01-26T11:02:17.005652Z", + "license": "MIT", + "name": "CTD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1008550", + "metadata": { + "abstract": "© 2021 Thistlethwaite et al.We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, \"Connect the Dots\", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.", + "authors": [ + { + "name": "Elsea S.H." + }, + { + "name": "Li X." + }, + { + "name": "Miller M.J." + }, + { + "name": "Milosavljevic A." + }, + { + "name": "Petrosyan V." + }, + { + "name": "Thistlethwaite L.R." + } + ], + "citationCount": 6, + "date": "2021-01-29T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models" + }, + "pmcid": "PMC7875364", + "pmid": "33513132" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/ctdquerier/ctdquerier.biotools.json b/data/ctdquerier/ctdquerier.biotools.json index 4119573460e73..78e4d993f6d07 100644 --- a/data/ctdquerier/ctdquerier.biotools.json +++ b/data/ctdquerier/ctdquerier.biotools.json @@ -3,6 +3,15 @@ "biotoolsCURIE": "biotools:ctdquerier", "biotoolsID": "ctdquerier", "credit": [ + { + "email": "carles.hernandez@isglobal.org", + "name": "Carles Hernandez-Ferrer", + "orcidid": "https://orcid.org/0000-0002-8029-7160", + "typeEntity": "Person", + "typeRole": [ + "Developer" + ] + }, { "email": "juanr.gonzalez@isglobal.org", "name": "Juan R Gonzalez", @@ -10,10 +19,24 @@ "typeRole": [ "Primary contact" ] + }, + { + "email": "xavier.escriba@isglobal.org", + "name": "Xavier Escribà Montagut", + "typeEntity": "Person", + "typeRole": [ + "Maintainer" + ] } ], "description": "Comparative Toxicogenomics Database data extraction, visualization and enrichment of environmental and toxicological studies.", "documentation": [ + { + "type": [ + "General" + ], + "url": "https://bioconductor.org/packages/release/bioc/html/CTDquerier.html" + }, { "type": [ "General" @@ -21,9 +44,17 @@ "url": "https://github.com/isglobal-brge/CTDquerier" } ], + "download": [ + { + "type": "Software package", + "url": "https://bioconductor.org/packages/release/bioc/src/contrib/CTDquerier_2.6.0.tar.gz", + "version": "2.6.0" + } + ], "editPermission": { "authors": [ - "brgelab" + "brgelab", + "chernan3" ], "type": "group" }, @@ -41,7 +72,15 @@ "language": [ "R" ], - "lastUpdate": "2021-01-11T10:41:10Z", + "lastUpdate": "2023-01-24T16:51:35.569103Z", + "link": [ + { + "type": [ + "Mirror" + ], + "url": "https://bioconductor.org/packages/CTDquerier" + } + ], "name": "CTDquerier", "operatingSystem": [ "Linux", @@ -62,13 +101,19 @@ "name": "Hernandez-Ferrer C." } ], - "citationCount": 2, + "citationCount": 7, "date": "2018-09-15T00:00:00Z", "journal": "Bioinformatics", "title": "CTDquerier: A bioconductor R package for comparative toxicogenomics database™ data extraction, visualization and enrichment of environmental and toxicological studies" } } ], + "relation": [ + { + "biotoolsID": "rexposome", + "type": "usedBy" + } + ], "toolType": [ "Library" ], diff --git a/data/ctpathway/ctpathway.biotools.json b/data/ctpathway/ctpathway.biotools.json new file mode 100644 index 0000000000000..180d9f6ee423e --- /dev/null +++ b/data/ctpathway/ctpathway.biotools.json @@ -0,0 +1,167 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T01:01:49.472079Z", + "biotoolsCURIE": "biotools:ctpathway", + "biotoolsID": "ctpathway", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "christine.eischen@jefferson.edu", + "name": "Christine M. Eischen", + "typeEntity": "Person" + }, + { + "email": "weijiang@nuaa.edu.cn", + "name": "Wei Jiang", + "typeEntity": "Person" + }, + { + "name": "Haizhou Liu" + }, + { + "name": "Mengqin Yuan" + } + ], + "description": "A CrossTalk-based pathway enrichment analysis method for cancer research.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene ID", + "uri": "http://edamontology.org/data_2295" + } + }, + { + "data": { + "term": "Gene symbol", + "uri": "http://edamontology.org/data_1026" + } + }, + { + "data": { + "term": "Locus ID (EntrezGene)", + "uri": "http://edamontology.org/data_1904" + } + } + ], + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Expression profile pathway mapping", + "uri": "http://edamontology.org/operation_0533" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + }, + { + "term": "Pathway visualisation", + "uri": "http://edamontology.org/operation_3926" + } + ] + } + ], + "homepage": "http://www.jianglab.cn/CTpathway/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T01:01:49.474651Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/Bioccjw/CTpathway" + } + ], + "name": "CTpathway", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13073-022-01119-6", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. Methods: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. Results: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. Conclusions: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway.", + "authors": [ + { + "name": "Eischen C.M." + }, + { + "name": "Hou F." + }, + { + "name": "Huang Y.-E." + }, + { + "name": "Jiang W." + }, + { + "name": "Lei W." + }, + { + "name": "Liu H." + }, + { + "name": "Long M." + }, + { + "name": "Mitra R." + }, + { + "name": "Yuan M." + }, + { + "name": "Zhou S." + }, + { + "name": "Zhou X." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Genome Medicine", + "title": "CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research" + }, + "pmcid": "PMC9563764", + "pmid": "36229842" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/ctrr-ncrna/ctrr-ncrna.biotools.json b/data/ctrr-ncrna/ctrr-ncrna.biotools.json new file mode 100644 index 0000000000000..d24eeb149e95a --- /dev/null +++ b/data/ctrr-ncrna/ctrr-ncrna.biotools.json @@ -0,0 +1,66 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:55:17.724836Z", + "biotoolsCURIE": "biotools:ctrr-ncrna", + "biotoolsID": "ctrr-ncrna", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Bairong Shen", + "orcidid": "https://orcid.org/0000-0003-2899-1531" + }, + { + "name": "Shumin Ren", + "orcidid": "https://orcid.org/0000-0002-1376-1891" + }, + { + "name": "Tong Tang", + "orcidid": "https://orcid.org/0000-0003-1657-612X" + }, + { + "name": "Xingyun Liu", + "orcidid": "https://orcid.org/0000-0002-9295-2767" + } + ], + "description": "A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs.", + "editPermission": { + "type": "private" + }, + "homepage": "http://ctrr.bioinf.org.cn/", + "lastUpdate": "2023-01-10T00:55:17.727542Z", + "name": "CTRR-ncRNA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.GPB.2022.10.003", + "pmid": "36265769" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/cubids/cubids.biotools.json b/data/cubids/cubids.biotools.json new file mode 100644 index 0000000000000..125608cf6299f --- /dev/null +++ b/data/cubids/cubids.biotools.json @@ -0,0 +1,171 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T01:37:14.628233Z", + "biotoolsCURIE": "biotools:cubids", + "biotoolsID": "cubids", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Matthew Cieslak", + "orcidid": "http://orcid.org/0000-0002-1931-4734" + }, + { + "name": "Sydney Covitz", + "orcidid": "http://orcid.org/0000-0002-7430-4125" + }, + { + "name": "Theodore D. Satterthwaite", + "orcidid": "http://orcid.org/0000-0001-7072-9399" + }, + { + "name": "Tinashe M. Tapera", + "orcidid": "http://orcid.org/0000-0001-9080-5010" + } + ], + "description": "A workflow and software package for streamlining reproducible curation of large BIDS datasets.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://cubids.readthedocs.io/en/latest/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://github.com/PennLINC/CuBIDS", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-22T01:37:14.630846Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://pypi.org/project/cubids/" + } + ], + "name": "CuBIDS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.neuroimage.2022.119609", + "metadata": { + "abstract": "© 2022The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.", + "authors": [ + { + "name": "Adebimpe A." + }, + { + "name": "Alexander-Bloch A.F." + }, + { + "name": "Bertolero M.A." + }, + { + "name": "Cieslak M." + }, + { + "name": "Covitz S." + }, + { + "name": "Fair D.A." + }, + { + "name": "Feczko E." + }, + { + "name": "Franco A.R." + }, + { + "name": "Gur R.C." + }, + { + "name": "Gur R.E." + }, + { + "name": "Hendrickson T." + }, + { + "name": "Houghton A." + }, + { + "name": "Mehta K." + }, + { + "name": "Milham M.P." + }, + { + "name": "Murtha K." + }, + { + "name": "Perrone A.J." + }, + { + "name": "Robert-Fitzgerald T." + }, + { + "name": "Satterthwaite T.D." + }, + { + "name": "Schabdach J.M." + }, + { + "name": "Shinohara R.T." + }, + { + "name": "Tapera T.M." + }, + { + "name": "Vogel J.W." + }, + { + "name": "Zhao C." + } + ], + "citationCount": 1, + "date": "2022-11-01T00:00:00Z", + "journal": "NeuroImage", + "title": "Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets" + }, + "pmid": "36064140" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Data submission, annotation and curation", + "uri": "http://edamontology.org/topic_0219" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/cysmoddb/cysmoddb.biotools.json b/data/cysmoddb/cysmoddb.biotools.json new file mode 100644 index 0000000000000..d60ee41820047 --- /dev/null +++ b/data/cysmoddb/cysmoddb.biotools.json @@ -0,0 +1,147 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:42:23.608490Z", + "biotoolsCURIE": "biotools:cysmoddb", + "biotoolsID": "cysmoddb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "lileime@hotmail.com", + "name": "Lei Li", + "orcidid": "https://orcid.org/0000-0003-0266-8939", + "typeEntity": "Person" + }, + { + "email": "bio_shangsp@hotmail.com", + "name": "Shipeng Shang", + "typeEntity": "Person" + }, + { + "name": "Lin Zhang", + "orcidid": "https://orcid.org/0000-0003-3902-6083" + }, + { + "name": "Yanzheng Meng", + "orcidid": "https://orcid.org/0000-0002-1357-9635" + } + ], + "description": "A comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene name", + "uri": "http://edamontology.org/data_2299" + } + }, + { + "data": { + "term": "Protein name", + "uri": "http://edamontology.org/data_1009" + } + }, + { + "data": { + "term": "UniProt ID", + "uri": "http://edamontology.org/data_2291" + } + } + ], + "operation": [ + { + "term": "Free cysteine detection", + "uri": "http://edamontology.org/operation_1830" + }, + { + "term": "PTM localisation", + "uri": "http://edamontology.org/operation_3755" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + } + ] + } + ], + "homepage": "https://cysmoddb.bioinfogo.org/", + "lastUpdate": "2023-01-10T00:42:23.610987Z", + "name": "CysModDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC460", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.", + "authors": [ + { + "name": "Chen Y." + }, + { + "name": "Li C." + }, + { + "name": "Li L." + }, + { + "name": "Meng Y." + }, + { + "name": "Shang S." + }, + { + "name": "Wang X." + }, + { + "name": "Wang Z." + }, + { + "name": "Zhang L." + }, + { + "name": "Zhang L." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications" + }, + "pmcid": "PMC9677505", + "pmid": "36305460" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Protein binding sites", + "uri": "http://edamontology.org/topic_3534" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Protein modifications", + "uri": "http://edamontology.org/topic_0601" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ] +} diff --git a/data/dadapy/dadapy.biotools.json b/data/dadapy/dadapy.biotools.json new file mode 100644 index 0000000000000..d049dfa20bcb0 --- /dev/null +++ b/data/dadapy/dadapy.biotools.json @@ -0,0 +1,138 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:35:58.369280Z", + "biotoolsCURIE": "biotools:dadapy", + "biotoolsID": "dadapy", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "aldo.glielmo@bancaditalia.it", + "name": "Aldo Glielmo", + "orcidid": "https://orcid.org/0000-0002-4737-2878", + "typeEntity": "Person" + }, + { + "email": "laio@sissa.it", + "name": "Alessandro Laio", + "typeEntity": "Person" + }, + { + "name": "Alex Rodriguez" + }, + { + "name": "Iuri Macocco" + } + ], + "description": "Distance-based analysis of data-manifolds in Python.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://dadapy.readthedocs.io/en/latest/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Dendrogram visualisation", + "uri": "http://edamontology.org/operation_2938" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Feature selection", + "uri": "http://edamontology.org/operation_3936" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/sissa-data-science/DADApy", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T00:36:52.838158Z", + "license": "Apache-2.0", + "name": "DADApy", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.PATTER.2022.100589", + "metadata": { + "abstract": "© 2022 The Author(s)DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.", + "authors": [ + { + "name": "Carli M." + }, + { + "name": "Doimo D." + }, + { + "name": "Glielmo A." + }, + { + "name": "Laio A." + }, + { + "name": "Macocco I." + }, + { + "name": "Rodriguez A." + }, + { + "name": "Wild R." + }, + { + "name": "Zeni C." + }, + { + "name": "d'Errico M." + } + ], + "date": "2022-10-14T00:00:00Z", + "journal": "Patterns", + "title": "DADApy: Distance-based analysis of data-manifolds in Python" + }, + "pmcid": "PMC9583186", + "pmid": "36277821" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Physics", + "uri": "http://edamontology.org/topic_3318" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/dartr/dartr.biotools.json b/data/dartr/dartr.biotools.json new file mode 100644 index 0000000000000..fcfc1c43c93ef --- /dev/null +++ b/data/dartr/dartr.biotools.json @@ -0,0 +1,153 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T22:42:25.401698Z", + "biotoolsCURIE": "biotools:dartr", + "biotoolsID": "dartr", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "luis.mijangos@gmail.com", + "name": "Jose Luis Mijangos", + "orcidid": "http://orcid.org/0000-0001-6121-4860", + "typeEntity": "Person" + }, + { + "name": "Arthur Georges", + "orcidid": "http://orcid.org/0000-0003-2428-0361" + }, + { + "name": "Bernd Gruber", + "orcidid": "http://orcid.org/0000-0003-0078-8179" + }, + { + "name": "Carlo Pacioni", + "orcidid": "http://orcid.org/0000-0001-5115-4120" + }, + { + "name": "Oliver Berry", + "orcidid": "http://orcid.org/0000-0001-7545-5083" + } + ], + "description": "An accessible genetic analysis platform for conservation, ecology, and agriculture.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://cran.r-project.org/web/packages/dartR/dartR.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Principal component visualisation", + "uri": "http://edamontology.org/operation_2939" + } + ] + } + ], + "homepage": "http://georges.biomatix.org/dartR", + "language": [ + "R" + ], + "lastUpdate": "2023-01-18T22:42:25.404988Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Discussion forum" + ], + "url": "https://groups.google.com/g/dartr" + }, + { + "type": [ + "Repository" + ], + "url": "https://cran.r-project.org/web/packages/dartR/index.html" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/green-striped-gecko/dartR" + } + ], + "name": "dartR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1111/2041-210X.13918", + "metadata": { + "abstract": "© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.Innumerable approaches to analyse genetic data are now available to guide conservation, ecological and agricultural projects. However, streamlined and accessible tools are needed to bring these approaches within the reach of a broader user base. dartR was released in 2018 to lessen the intrinsic complexity of analysing single nucleotide polymorphisms (SNPs) and dominant markers (presence/absence of amplified sequence tags) by providing user-friendly data quality control and marker selection functions. dartR users have grown steadily since its release and provided valuable feedback on their interaction with the package allowing us to enhance dartR capabilities. Here, we present Version 2 of dartR. In this version, we substantially increased the number of available functions from 45 to 144. In addition to improved functionality, we focused on enhancing the user experience by extending plot customisation, function standardisation, increasing user support and function speed. dartR provides functions for various stages in analysing genetic data, from data manipulation to reporting. dartR provides many functions for importing, exporting and linking to other packages, to provide an easy-to-navigate conduit between data generation and analysis options already available via other packages. We also implemented simulation functions whose results can be analysed seamlessly with several other dartR functions. As more methods and approaches mature to inform conservation, we envision that accessible platforms to analyse genetic data will play a crucial role in translating science into practice.", + "authors": [ + { + "name": "Berry O." + }, + { + "name": "Georges A." + }, + { + "name": "Gruber B." + }, + { + "name": "Mijangos J.L." + }, + { + "name": "Pacioni C." + } + ], + "citationCount": 1, + "date": "2022-01-01T00:00:00Z", + "journal": "Methods in Ecology and Evolution", + "title": "dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture" + } + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + } + ] +} diff --git a/data/data_virtuality/data_virtuality.biotools.json b/data/data_virtuality/data_virtuality.biotools.json new file mode 100644 index 0000000000000..55bd2093afdc2 --- /dev/null +++ b/data/data_virtuality/data_virtuality.biotools.json @@ -0,0 +1,55 @@ +{ + "additionDate": "2023-01-26T12:20:30.937327Z", + "biotoolsCURIE": "biotools:data_virtuality", + "biotoolsID": "data_virtuality", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "url": "https://support.datavirtuality.com/hc/en-us" + } + ], + "description": "Data Virtualization for Flexible Data Architectures\n\nBuilt by data integration professionals for data integration professionals allowing you to meet ever-changing business needs\n\nPipes automatically gets data from 200+ available sources in your data warehouse. With just a few clicks and without any coding.\n\nPipes Professional enables you to build custom data pipelines with the best-in-class SQL editor", + "documentation": [ + { + "note": "In our documentation you find all things Data Virtuality Server, from general information on how it works to step-by-step guides for specific operations. The documentation consists of three parts:\n\n Administration Guide – which is mostly aimed at administrators\n User Guide – which includes mostly theoretical information on how the Data Virtuality Server works\n Reference Guide – which contains technical information on the SQL dialect, procedures, commands, schemas, and other elements used in the Data Virtuality Serve", + "type": [ + "User manual" + ], + "url": "https://datavirtuality.com/en/docs-and-support/" + } + ], + "download": [ + { + "note": "Acces to free trial", + "type": "Other", + "url": "https://eu.pipes.datavirtuality.com/#/start-trial" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://datavirtuality.com/en/", + "lastUpdate": "2023-01-26T13:41:31.822004Z", + "link": [ + { + "type": [ + "Service" + ], + "url": "https://datavirtuality.com/en/" + } + ], + "name": "Data Virtuality", + "owner": "iacs-biocomputacion" +} diff --git a/data/dbcan-seq/dbcan-seq.biotools.json b/data/dbcan-seq/dbcan-seq.biotools.json new file mode 100644 index 0000000000000..8ee4fe6662f4d --- /dev/null +++ b/data/dbcan-seq/dbcan-seq.biotools.json @@ -0,0 +1,121 @@ +{ + "additionDate": "2023-01-27T17:17:43.449911Z", + "biotoolsCURIE": "biotools:dbcan-seq", + "biotoolsID": "dbcan-seq", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yyin@unl.edu", + "name": "Yanbin Yin", + "orcidid": "https://orcid.org/0000-0001-7667-881X", + "typeEntity": "Person" + } + ], + "description": "A online database dbCAN-seq to provide pre-computed carbohydrate-active enzyme (CAZymes) sequence and annotation data for 5,349 bacterial genomes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + } + ] + } + ], + "homepage": "https://bcb.unl.edu/dbCAN_seq", + "lastUpdate": "2023-01-27T17:17:43.452400Z", + "license": "Other", + "name": "dbCAN-seq", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/nar/gkx894", + "metadata": { + "abstract": "© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.Carbohydrate-active enzyme (CAZymes) are not only the most important enzymes for bioenergy and agricultural industries, but also very important for human health, in that human gut microbiota encode hundreds of CAZyme genes in their genomes for degrading various dietary and host carbohydrates. We have built an online database dbCAN-seq (http://cys.bios.niu.edu/dbCAN-seq) to provide pre-computed CAZyme sequence and annotation data for 5,349 bacterial genomes. Compared to the other CAZyme resources, dbCAN-seq has the following new features: (i) a convenient download page to allow batch download of all the sequence and annotation data; (ii) an annotation page for every CAZyme to provide the most comprehensive annotation data; (iii) a metadata page to organize the bacterial genomes according to species metadata such as disease, habitat, oxygen requirement, temperature, metabolism; (iv) a very fast tool to identify physically linked CAZyme gene clusters (CGCs) and (v) a powerful search function to allow fast and efficient data query. With these unique utilities, dbCAN-seq will become a valuable web resource for CAZyme research, with a focus complementary to dbCAN (automated CAZyme annotation server) and CAZy (CAZyme family classification and reference database).", + "authors": [ + { + "name": "Entwistle S." + }, + { + "name": "Huang L." + }, + { + "name": "Li X." + }, + { + "name": "Wu P." + }, + { + "name": "Yang Z." + }, + { + "name": "Yi H." + }, + { + "name": "Yin Y." + }, + { + "name": "Yohe T." + }, + { + "name": "Zhang H." + } + ], + "citationCount": 133, + "date": "2018-01-01T00:00:00Z", + "journal": "Nucleic Acids Research", + "title": "DbCAN-seq: A database of carbohydrate-active enzyme (CAZyme) sequence and annotation" + }, + "pmcid": "PMC5753378", + "pmid": "30053267" + }, + { + "doi": "10.1093/NAR/GKAC1068", + "pmcid": "PMC9825555", + "pmid": "36399503" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Carbohydrates", + "uri": "http://edamontology.org/topic_0152" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + } + ] +} diff --git a/data/dcgn/dcgn.biotools.json b/data/dcgn/dcgn.biotools.json new file mode 100644 index 0000000000000..eba8846b8bd41 --- /dev/null +++ b/data/dcgn/dcgn.biotools.json @@ -0,0 +1,123 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:27:30.089586Z", + "biotoolsCURIE": "biotools:dcgn", + "biotoolsID": "dcgn", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "luojunwei@hpu.edu.cn", + "name": "Junwei Luo", + "typeEntity": "Person" + }, + { + "name": "Huimin Luo" + }, + { + "name": "Jiawei Shi" + }, + { + "name": "Jiquan Shen" + } + ], + "description": "Deep learning approach for cancer subtype classification using high-dimensional gene expression data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/shijwe/DCGN", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T00:27:30.092432Z", + "license": "Not licensed", + "name": "DCGN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-04980-9", + "metadata": { + "abstract": "© 2022, The Author(s).Motivation: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. Results: In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.", + "authors": [ + { + "name": "Liu X." + }, + { + "name": "Luo H." + }, + { + "name": "Luo J." + }, + { + "name": "Shen J." + }, + { + "name": "Shi J." + }, + { + "name": "Wu Z." + }, + { + "name": "Yan C." + }, + { + "name": "Zhai H." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Deep learning approach for cancer subtype classification using high-dimensional gene expression data" + }, + "pmcid": "PMC9575247", + "pmid": "36253710" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Taxonomy", + "uri": "http://edamontology.org/topic_0637" + } + ] +} diff --git a/data/dchic/dchic.biotools.json b/data/dchic/dchic.biotools.json new file mode 100644 index 0000000000000..5bfaf508e604a --- /dev/null +++ b/data/dchic/dchic.biotools.json @@ -0,0 +1,102 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T17:22:19.132216Z", + "biotoolsCURIE": "biotools:dchic", + "biotoolsID": "dchic", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "abhijit@lji.org", + "name": "Abhijit Chakraborty", + "orcidid": "https://orcid.org/0000-0002-1500-3699", + "typeEntity": "Person" + }, + { + "email": "ferhatay@lji.org", + "name": "Ferhat Ay", + "orcidid": "https://orcid.org/0000-0002-0708-6914", + "typeEntity": "Person" + } + ], + "description": "A tool for differential compartment analysis of Hi-C datasets", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + } + ] + } + ], + "homepage": "https://github.com/ay-lab/dcHiC", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T17:22:19.134688Z", + "license": "MIT", + "name": "dcHiC", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41467-022-34626-6", + "metadata": { + "abstract": "© 2022, The Author(s).The compartmental organization of mammalian genomes and its changes play important roles in distinct biological processes. Here, we introduce dcHiC, which utilizes a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on four collections of bulk and single-cell contact maps from in vitro mouse neural differentiation (n = 3), mouse hematopoiesis (n = 10), human LCLs (n = 20) and post-natal mouse brain development (n = 3 stages), we show its effectiveness and sensitivity in detecting biologically relevant changes, including those orthogonally validated. dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, subcompartments, replication timing and lamin association. With its efficient implementation, dcHiC enables high-resolution compartment analysis as well as standalone browser visualization, differential interaction identification and time-series clustering. dcHiC is an essential addition to the Hi-C analysis toolbox for the ever-growing number of bulk and single-cell contact maps. Available at: https://github.com/ay-lab/dcHiC.", + "authors": [ + { + "name": "Ay F." + }, + { + "name": "Chakraborty A." + }, + { + "name": "Wang J.G." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Nature Communications", + "title": "dcHiC detects differential compartments across multiple Hi-C datasets" + }, + "pmcid": "PMC9652325", + "pmid": "36369226" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/dcifer/dcifer.biotools.json b/data/dcifer/dcifer.biotools.json new file mode 100644 index 0000000000000..2868763a7fc5c --- /dev/null +++ b/data/dcifer/dcifer.biotools.json @@ -0,0 +1,102 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T22:59:27.936164Z", + "biotoolsCURIE": "biotools:dcifer", + "biotoolsID": "dcifer", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Boris Gerlovin" + }, + { + "name": "Bryan Greenhouse" + }, + { + "name": "Isabel Rodríguez-Barraquer" + }, + { + "name": "Inna Gerlovina", + "orcidid": "http://orcid.org/0000-0002-7772-7473" + } + ], + "description": "an IBD-based method to calculate genetic distance between polyclonal infections.", + "documentation": [ + { + "type": [ + "Other" + ], + "url": "https://cran.r-project.org/web/packages/dcifer/vignettes/vignetteDcifer.pdf" + }, + { + "type": [ + "User manual" + ], + "url": "https://cran.r-project.org/web/packages/dcifer/dcifer.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + } + ] + } + ], + "homepage": "https://eppicenter.github.io/dcifer/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-18T22:59:27.938753Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://cran.r-project.org/web/packages/dcifer/index.html" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/EPPIcenter/dcifer" + } + ], + "name": "Dcifer", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/genetics/iyac126", + "pmcid": "PMC9526043", + "pmid": "36000888" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + }, + { + "term": "Structure analysis", + "uri": "http://edamontology.org/topic_0081" + } + ] +} diff --git a/data/deep_ksuccsite/deep_ksuccsite.biotools.json b/data/deep_ksuccsite/deep_ksuccsite.biotools.json new file mode 100644 index 0000000000000..92b80073e6347 --- /dev/null +++ b/data/deep_ksuccsite/deep_ksuccsite.biotools.json @@ -0,0 +1,115 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:20:40.161230Z", + "biotoolsCURIE": "biotools:deep_ksuccsite", + "biotoolsID": "deep_ksuccsite", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "liuxin@xzhmu.edu.cn", + "name": "Xin Liu", + "typeEntity": "Person" + }, + { + "email": "liuymito@xzhmu.edu.cn", + "name": "Yong Liu", + "typeEntity": "Person" + }, + { + "name": "Lin-Lin Xu" + }, + { + "name": "Liang Wang", + "typeEntity": "Person" + } + ], + "description": "A novel deep learning method for the identification of lysine succinylation sites.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "PTM localisation", + "uri": "http://edamontology.org/operation_3755" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + } + ] + } + ], + "homepage": "https://github.com/flyinsky6/Deep_KsuccSite", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T00:20:40.163712Z", + "license": "Not licensed", + "name": "Deep_KsuccSite", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FGENE.2022.1007618", + "metadata": { + "abstract": "Copyright © 2022 Liu, Xu, Lu, Yang, Gu, Wang and Liu.Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.", + "authors": [ + { + "name": "Gu X.-Y." + }, + { + "name": "Liu X." + }, + { + "name": "Liu Y." + }, + { + "name": "Lu Y.-P." + }, + { + "name": "Wang L." + }, + { + "name": "Xu L.-L." + }, + { + "name": "Yang T." + } + ], + "date": "2022-09-29T00:00:00Z", + "journal": "Frontiers in Genetics", + "title": "Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites" + }, + "pmcid": "PMC9557156", + "pmid": "36246655" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein modifications", + "uri": "http://edamontology.org/topic_0601" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/deepbrainipp/deepbrainipp.biotools.json b/data/deepbrainipp/deepbrainipp.biotools.json new file mode 100644 index 0000000000000..c911fc8244214 --- /dev/null +++ b/data/deepbrainipp/deepbrainipp.biotools.json @@ -0,0 +1,85 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:15:44.025632Z", + "biotoolsCURIE": "biotools:deepbrainipp", + "biotoolsID": "deepbrainipp", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "khaled.khairy@stjude.org", + "name": "Khaled Khairy", + "typeEntity": "Person" + }, + { + "email": "shahinur.alam@stjude.org", + "name": "Shahinur Alam", + "typeEntity": "Person" + }, + { + "name": "Stanislav S. Zakharenko" + }, + { + "name": "Tae-Yeon Eom" + } + ], + "description": "An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://github.com/stjude/DeepBrainIPP", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T00:15:44.028070Z", + "license": "Apache-2.0", + "name": "DeepBrainIPP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2022.865443", + "pmcid": "PMC9580949", + "pmid": "36304320" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Computer science", + "uri": "http://edamontology.org/topic_3316" + }, + { + "term": "MRI", + "uri": "http://edamontology.org/topic_3444" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Neurobiology", + "uri": "http://edamontology.org/topic_3304" + } + ] +} diff --git a/data/deeplncpro/deeplncpro.biotools.json b/data/deeplncpro/deeplncpro.biotools.json new file mode 100644 index 0000000000000..4d37f573c2416 --- /dev/null +++ b/data/deeplncpro/deeplncpro.biotools.json @@ -0,0 +1,116 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-10T00:01:53.657080Z", + "biotoolsCURIE": "biotools:deeplncpro", + "biotoolsID": "deeplncpro", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhaoqi@lnu.edu.cn", + "name": "Qi Zhao", + "orcidid": "https://orcid.org/0000-0001-9713-1864", + "typeEntity": "Person" + }, + { + "email": "greatchen@ncst.edu.cn", + "name": "Wei Chen", + "typeEntity": "Person" + }, + { + "name": "Fulei Nie" + }, + { + "name": "Qiang Tang" + }, + { + "name": "Tianyang Zhang" + } + ], + "description": "An interpretable convolutional neural network model for identifying long non-coding RNA promoters.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + } + ] + } + ], + "homepage": "https://github.com/zhangtian-yang/DeepLncPro", + "language": [ + "JavaScript", + "Python" + ], + "lastUpdate": "2023-01-10T00:01:53.660093Z", + "license": "MIT", + "name": "DeepLncPro", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC447", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Long non-coding RNA (lncRNA) plays important roles in a series of biological processes. The transcription of lncRNA is regulated by its promoter. Hence, accurate identification of lncRNA promoter will be helpful to understand its regulatory mechanisms. Since experimental techniques remain time consuming for gnome-wide promoter identification, developing computational tools to identify promoters are necessary. However, only few computational methods have been proposed for lncRNA promoter prediction and their performances still have room to be improved. In the present work, a convolutional neural network based model, called DeepLncPro, was proposed to identify lncRNA promoters in human and mouse. Comparative results demonstrated that DeepLncPro was superior to both state-of-the-art machine learning methods and existing models for identifying lncRNA promoters. Furthermore, DeepLncPro has the ability to extract and analyze transcription factor binding motifs from lncRNAs, which made it become an interpretable model. These results indicate that the DeepLncPro can server as a powerful tool for identifying lncRNA promoters. An open-source tool for DeepLncPro was provided at https://github.com/zhangtian-yang/DeepLncPro.", + "authors": [ + { + "name": "Chen W." + }, + { + "name": "Nie F." + }, + { + "name": "Tang Q." + }, + { + "name": "Zhang T." + }, + { + "name": "Zhao Q." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters" + }, + "pmid": "36209437" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/deepmr/deepmr.biotools.json b/data/deepmr/deepmr.biotools.json new file mode 100644 index 0000000000000..107c3098522b0 --- /dev/null +++ b/data/deepmr/deepmr.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:54:22.048627Z", + "biotoolsCURIE": "biotools:deepmr", + "biotoolsID": "deepmr", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "stephenmalina@gmail.com", + "name": "Stephen Malina", + "orcidid": "https://orcid.org/0000-0002-7383-0094", + "typeEntity": "Person" + }, + { + "name": "Daniel Cizin" + }, + { + "name": "David A. Knowles" + } + ], + "description": "Investigating the causal knowledge of genomic deep learning models.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Sequence motif analysis", + "uri": "http://edamontology.org/operation_2404" + } + ] + } + ], + "homepage": "https://github.com/an1lam/deepmr", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T23:54:22.051389Z", + "license": "Not licensed", + "name": "DeepMR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pcbi.1009880", + "metadata": { + "abstract": "Copyright: © 2022 Malina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the ‘true’ global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR’s causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.", + "authors": [ + { + "name": "Cizin D." + }, + { + "name": "Knowles D.A." + }, + { + "name": "Malina S." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models" + }, + "pmcid": "PMC9624391", + "pmid": "36265006" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/deeppervar/deeppervar.biotools.json b/data/deeppervar/deeppervar.biotools.json new file mode 100644 index 0000000000000..36f5667907633 --- /dev/null +++ b/data/deeppervar/deeppervar.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T23:06:10.846569Z", + "biotoolsCURIE": "biotools:deeppervar", + "biotoolsID": "deeppervar", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chen61@iu.edu", + "name": "Li Chen", + "orcidid": "http://orcid.org/0000-0001-9372-5606", + "typeEntity": "Person" + }, + { + "name": "Ye Wang" + } + ], + "description": "A multimodal deep learning framework for functional interpretation of genetic variants in personal genome.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Collapsing methods", + "uri": "http://edamontology.org/operation_3791" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + }, + { + "term": "Variant prioritisation", + "uri": "http://edamontology.org/operation_3226" + } + ] + } + ], + "homepage": "https://github.com/lichen-lab/DeepPerVar", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T23:06:10.849797Z", + "license": "Not licensed", + "name": "DeepPerVar", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac696", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease. RESULTS: By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/DeepPerVar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Chen L." + }, + { + "name": "Wang Y." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome" + }, + "pmcid": "PMC9750124", + "pmid": "36271868" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + } + ] +} diff --git a/data/deepprotacs/deepprotacs.biotools.json b/data/deepprotacs/deepprotacs.biotools.json new file mode 100644 index 0000000000000..bd143b86c10d6 --- /dev/null +++ b/data/deepprotacs/deepprotacs.biotools.json @@ -0,0 +1,147 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T10:20:01.252252Z", + "biotoolsCURIE": "biotools:deepprotacs", + "biotoolsID": "deepprotacs", + "confidence_flag": "tool", + "credit": [ + { + "email": "baifang@shanghaitech.edu.cn", + "name": "Fang Bai", + "orcidid": "https://orcid.org/0000-0003-1468-5568", + "typeEntity": "Person" + }, + { + "email": "gaoshh@shanghaitech.edu.cn", + "name": "Shenghua Gao", + "orcidid": "https://orcid.org/0000-0003-1626-2040", + "typeEntity": "Person" + }, + { + "email": "yang.xiaobao@gluetacs.com", + "name": "Xiaobao Yang", + "orcidid": "https://orcid.org/0000-0001-5266-7673", + "typeEntity": "Person" + } + ], + "description": "DeepPROTACs is a deep learning-based targeted degradation predictor for proteolysis targeting chimera (PROTACs).", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T10:20:01.255819Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/fenglei104/DeepPROTACs" + } + ], + "name": "DeepPROTACs", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41467-022-34807-3", + "metadata": { + "abstract": "© 2022, The Author(s).The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server (https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/) and at github (https://github.com/fenglei104/DeepPROTACs).", + "authors": [ + { + "name": "Bai F." + }, + { + "name": "Dai Z." + }, + { + "name": "Gao S." + }, + { + "name": "Hu Q." + }, + { + "name": "Li F." + }, + { + "name": "Liu Z." + }, + { + "name": "Ma X." + }, + { + "name": "Sun R." + }, + { + "name": "Tian S." + }, + { + "name": "Wu S." + }, + { + "name": "Yang X." + }, + { + "name": "Zhang X." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Nature Communications", + "title": "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs" + }, + "pmcid": "PMC9681730", + "pmid": "36414666" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "Computational chemistry", + "uri": "http://edamontology.org/topic_3332" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/deeppseudomsi/deeppseudomsi.biotools.json b/data/deeppseudomsi/deeppseudomsi.biotools.json new file mode 100644 index 0000000000000..30b9e5e5a1d6c --- /dev/null +++ b/data/deeppseudomsi/deeppseudomsi.biotools.json @@ -0,0 +1,141 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T01:44:44.863292Z", + "biotoolsCURIE": "biotools:deeppseudomsi", + "biotoolsID": "deeppseudomsi", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mirabela.rusu@stanford.edu", + "name": "Mirabela Rusu", + "typeEntity": "Person" + }, + { + "email": "mpsnyder@stanford.edu", + "name": "Michael P. Snyder", + "typeEntity": "Person" + }, + { + "name": "Wei Shao" + }, + { + "name": "Xiaotao Shen", + "orcidid": "http://orcid.org/0000-0002-9608-9964" + } + ], + "description": "Deep Learning-based Pseudo-Mass Spectrometry Imaging Analysis for Precision Medicine.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://www.deeppseudomsi.org/tutorial/use_deeppseudomsi/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Natural product identification", + "uri": "http://edamontology.org/operation_3803" + } + ] + } + ], + "homepage": "https://www.deeppseudomsi.org/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-22T01:44:44.866484Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/jaspershen/deepPseudoMSI" + } + ], + "name": "deepPseudoMSI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac331", + "metadata": { + "abstract": "© 2022 The Author(s).Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.", + "authors": [ + { + "name": "Chen S." + }, + { + "name": "Liang L." + }, + { + "name": "Rusu M." + }, + { + "name": "Shao W." + }, + { + "name": "Shen X." + }, + { + "name": "Snyder M.P." + }, + { + "name": "Wang C." + }, + { + "name": "Zhang S." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine" + }, + "pmid": "35947990" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + } + ] +} diff --git a/data/deepscm/deepscm.biotools.json b/data/deepscm/deepscm.biotools.json new file mode 100644 index 0000000000000..e3b09edbb3484 --- /dev/null +++ b/data/deepscm/deepscm.biotools.json @@ -0,0 +1,80 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T01:32:11.520410Z", + "biotoolsCURIE": "biotools:deepscm", + "biotoolsID": "deepscm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Pin-Kuang Lai", + "orcidid": "http://orcid.org/0000-0003-2894-3900" + } + ], + "description": "An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + } + ] + } + ], + "homepage": "https://github.com/Lailabcode/DeepSCM", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T01:32:11.523818Z", + "license": "CC-BY-NC-3.0", + "name": "DeepSCM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.csbj.2022.04.035", + "metadata": { + "abstract": "© 2022 The Author(s)Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43–48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N = 1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.", + "authors": [ + { + "name": "Lai P.-K." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Computational and Structural Biotechnology Journal", + "title": "DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity" + }, + "pmcid": "PMC9092385", + "pmid": "35832619" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/topic_0176" + } + ] +} diff --git a/data/deepst/deepst.biotools.json b/data/deepst/deepst.biotools.json new file mode 100644 index 0000000000000..e72f024efbea4 --- /dev/null +++ b/data/deepst/deepst.biotools.json @@ -0,0 +1,83 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:43:31.608438Z", + "biotoolsCURIE": "biotools:deepst", + "biotoolsID": "deepst", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "qhjiang@hit.edu.cn", + "name": "Qinghua Jiang", + "typeEntity": "Person" + }, + { + "name": "Chang Xu" + }, + { + "name": "Guohua Wang" + }, + { + "name": "Xiyun Jin", + "orcidid": "https://orcid.org/0000-0003-2795-6451" + } + ], + "description": "Identifying spatial domains in spatial transcriptomics by deep learning.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/JiangBioLab/DeepST", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T23:43:31.611123Z", + "license": "MIT", + "name": "DeepST", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC901", + "pmid": "36250636" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/deeptoa/deeptoa.biotools.json b/data/deeptoa/deeptoa.biotools.json new file mode 100644 index 0000000000000..3895ffba6d611 --- /dev/null +++ b/data/deeptoa/deeptoa.biotools.json @@ -0,0 +1,104 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T23:15:04.647499Z", + "biotoolsCURIE": "biotools:deeptoa", + "biotoolsID": "deeptoa", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daniel.huson@uni-tuebingen.de", + "name": "Daniel H. Huson", + "typeEntity": "Person" + }, + { + "name": "Anupam Gautam" + }, + { + "name": "Wenhuan Zeng" + } + ], + "description": "An Ensemble Deep-Learning Approach to Predicting the Theater of Activity of a Microbiome.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Taxonomic classification", + "uri": "http://edamontology.org/operation_3460" + } + ] + } + ], + "homepage": "https://plabase.cs.uni-tuebingen.de/deeptoa/", + "lastUpdate": "2023-01-18T23:15:04.650085Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://ab.inf.uni-tuebingen.de/software/deeptoa" + } + ], + "name": "DeepToA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac584", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Gautam A." + }, + { + "name": "Huson D.H." + }, + { + "name": "Zeng W." + } + ], + "date": "2022-10-14T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome" + }, + "pmid": "36029249" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Metagenomic sequencing", + "uri": "http://edamontology.org/topic_3837" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + } + ] +} diff --git a/data/defined-proteins/defined-proteins.biotools.json b/data/defined-proteins/defined-proteins.biotools.json new file mode 100644 index 0000000000000..69e8f9188df12 --- /dev/null +++ b/data/defined-proteins/defined-proteins.biotools.json @@ -0,0 +1,93 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:36:26.057526Z", + "biotoolsCURIE": "biotools:defined-proteins", + "biotoolsID": "defined-proteins", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "igorb@bii.a-star.edu.sg", + "name": "Igor N. Berezovsky", + "typeEntity": "Person" + }, + { + "name": "Alexander Goncearenco" + }, + { + "name": "Melvin Yin" + } + ], + "description": "Deriving and Using Descriptors of Elementary Functions in Rational Protein Design.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Protein design", + "uri": "http://edamontology.org/operation_4008" + }, + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + } + ] + } + ], + "homepage": "https://github.com/MelvinYin/Defined_Proteins", + "language": [ + "C++", + "Python" + ], + "lastUpdate": "2023-01-09T23:36:26.061606Z", + "license": "Not licensed", + "name": "DEFINED-PROTEINS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2021.657529", + "pmcid": "PMC9581014", + "pmid": "36303771" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Protein folding, stability and design", + "uri": "http://edamontology.org/topic_0130" + }, + { + "term": "Protein folds and structural domains", + "uri": "http://edamontology.org/topic_0736" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/denodo/denodo.biotools.json b/data/denodo/denodo.biotools.json new file mode 100644 index 0000000000000..6021c94e98257 --- /dev/null +++ b/data/denodo/denodo.biotools.json @@ -0,0 +1,58 @@ +{ + "additionDate": "2023-01-26T12:00:23.194508Z", + "biotoolsCURIE": "biotools:denodo", + "biotoolsID": "denodo", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "email": "support@denodo.com", + "name": "Angel Viña", + "note": "In 1999, Angel Viña, who was then a professor at the University of A Coruña, recognized that data repositories were likely to grow unsustainably large if businesses continued to integrate data in the traditional manner. He envisioned a data integration strategy based on data virtualization, a modern strategy in which users could integrate data without replicating it, and thus, Denodo was born.", + "typeEntity": "Person", + "url": "https://support.denodo.com/?utm_source=Denodo-web&utm_medium=Try-Denodo" + } + ], + "description": "For every organization data and its related infrastructure is constantly evolving. As a result, enterprise data will always remain distributed. The Denodo Platform gives IT organizations the flexibility to evolve their data strategies, migrating to the cloud, or logically unifying data warehouses and data lakes, without affecting business. The Denodo Platform also accelerates data provisioning through reduced data replication, it enables consistent security and governance across multiple systems, and it gives your business users the flexibility to choose their preferred applications. The only way you can accomplish this is through a logical data fabric powered by data virtualization. The Denodo Platform is the only solution that can meet this need. Read about the benefits of the Denodo Platform in this Forrester TEI Repo", + "documentation": [ + { + "note": "Resources Denodo offers an extensive library of data virtualization resources aimed at helping you unleash the true value of your data.", + "type": [ + "Other" + ], + "url": "https://www.denodo.com/en/resources?utm_source=Denodo-web&utm_medium=Try-Denodo" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://www.denodo.com/en/denodo-platform/denodo-express?utm_source=Denodo-web&utm_medium=Try-Denodo" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://www.denodo.com/en", + "lastUpdate": "2023-01-26T12:02:12.037211Z", + "link": [ + { + "type": [ + "Technical monitoring" + ], + "url": "https://www.denodo.com/en/denodo-platform/services/overview" + } + ], + "name": "Denodo", + "owner": "iacs-biocomputacion" +} diff --git a/data/denvis/denvis.biotools.json b/data/denvis/denvis.biotools.json new file mode 100644 index 0000000000000..0f86413fa11b2 --- /dev/null +++ b/data/denvis/denvis.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T01:45:10.917049Z", + "biotoolsCURIE": "biotools:denvis", + "biotoolsID": "denvis", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "a.krasoulis@deeplab.ai", + "name": "Agamemnon Krasoulis", + "orcidid": "http://orcid.org/0000-0002-0468-0627", + "typeEntity": "Person" + }, + { + "name": "Stavros Theodorakis" + }, + { + "name": "Nick Antonopoulos", + "orcidid": "http://orcid.org/0000-0002-3175-8338" + }, + { + "name": "Vassilis Pitsikalis", + "orcidid": "http://orcid.org/0000-0002-1593-7491" + } + ], + "description": "Scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://github.com/deeplab-ai/denvis", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T01:45:10.919752Z", + "license": "GPL-3.0", + "name": "DENVIS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/acs.jcim.2c01057", + "metadata": { + "abstract": "© 2022 American Chemical Society.Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estimating the binding orientation of a query protein-ligand pair and a corresponding binding affinity score. Over the recent years, classical and modern machine learning architectures have shown potential for outperforming traditional docking algorithms. Nevertheless, most learning-based algorithms still rely on the availability of the protein-ligand complex binding pose, typically estimated via docking simulations, which leads to a severe slowdown of the overall virtual screening process. A family of algorithms processing target information at the amino acid sequence level avoid this requirement, however, at the cost of processing protein data at a higher representation level. We introduce deep neural virtual screening (DENVIS), an end-to-end pipeline for virtual screening using graph neural networks (GNNs). By performing experiments on two benchmark databases, we show that our method performs competitively to several docking-based, machine learning-based, and hybrid docking/machine learning-based algorithms. By avoiding the intermediate docking step, DENVIS exhibits several orders of magnitude faster screening times (i.e., higher throughput) than both docking-based and hybrid models. When compared to an amino acid sequence-based machine learning model with comparable screening times, DENVIS achieves dramatically better performance. Some key elements of our approach include protein pocket modeling using a combination of atomic and surface features, the use of model ensembles, and data augmentation via artificial negative sampling during model training. In summary, DENVIS achieves competitive to state-of-the-art virtual screening performance, while offering the potential to scale to billions of molecules using minimal computational resources.", + "authors": [ + { + "name": "Antonopoulos N." + }, + { + "name": "Krasoulis A." + }, + { + "name": "Pitsikalis V." + }, + { + "name": "Theodorakis S." + } + ], + "date": "2022-10-10T00:00:00Z", + "journal": "Journal of Chemical Information and Modeling", + "title": "DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features" + }, + "pmid": "36154119" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/detectimports/detectimports.biotools.json b/data/detectimports/detectimports.biotools.json new file mode 100644 index 0000000000000..e56072fb78966 --- /dev/null +++ b/data/detectimports/detectimports.biotools.json @@ -0,0 +1,95 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T10:22:15.826620Z", + "biotoolsCURIE": "biotools:detectimports", + "biotoolsID": "detectimports", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "xavier.didelot@warwick.ac.uk", + "name": "Xavier Didelot", + "orcidid": "https://orcid.org/0000-0003-1885-500X", + "typeEntity": "Person" + } + ], + "description": "DetectImports is a R package aimed at distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Phylogenetic inference", + "uri": "http://edamontology.org/operation_0323" + }, + { + "term": "Statistical inference", + "uri": "http://edamontology.org/operation_3658" + }, + { + "term": "Tree dating", + "uri": "http://edamontology.org/operation_3942" + } + ] + } + ], + "homepage": "https://github.com/xavierdidelot/DetectImports", + "language": [ + "R" + ], + "lastUpdate": "2023-01-28T10:22:15.830446Z", + "license": "MIT", + "name": "DetectImports", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC761", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. RESULTS: Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. AVAILABILITY AND IMPLEMENTATION: The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Didelot X." + }, + { + "name": "Helekal D." + }, + { + "name": "Kendall M." + }, + { + "name": "Ribeca P." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease" + }, + "pmcid": "PMC9805578", + "pmid": "36440957" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Phylogeny", + "uri": "http://edamontology.org/topic_0084" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + } + ] +} diff --git a/data/diadpredictor/diadpredictor.biotools.json b/data/diadpredictor/diadpredictor.biotools.json new file mode 100644 index 0000000000000..854c642247cbc --- /dev/null +++ b/data/diadpredictor/diadpredictor.biotools.json @@ -0,0 +1,110 @@ +{ + "additionDate": "2023-01-28T10:28:33.762748Z", + "biotoolsCURIE": "biotools:diadpredictor", + "biotoolsID": "diadpredictor", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "lixiao1688@163.com", + "name": "Xiao Li", + "typeEntity": "Person" + } + ], + "description": "DIADpredictor: in silico prediction for drug-induced autoimmune diseases (DIAD) with machine learning.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Nucleic acid structure prediction", + "uri": "http://edamontology.org/operation_0475" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "http://diad.sapredictor.cn/", + "lastUpdate": "2023-01-28T10:28:33.765346Z", + "license": "Other", + "name": "DIADpredictor", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3389/FIMMU.2022.1015409", + "metadata": { + "abstract": "Copyright © 2022 Guo, Zhang, Zhang, Hua, Zhang, Cui, Huang and Li.The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.", + "authors": [ + { + "name": "Cui X." + }, + { + "name": "Guo H." + }, + { + "name": "Hua Y." + }, + { + "name": "Huang X." + }, + { + "name": "Li X." + }, + { + "name": "Zhang P." + }, + { + "name": "Zhang P." + }, + { + "name": "Zhang R." + } + ], + "date": "2022-10-24T00:00:00Z", + "journal": "Frontiers in Immunology", + "title": "Modeling and insights into the structural characteristics of drug-induced autoimmune diseases" + }, + "pmcid": "PMC9637949", + "pmid": "36353637" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Toxicology", + "uri": "http://edamontology.org/topic_2840" + } + ] +} diff --git a/data/diamin/diamin.biotools.json b/data/diamin/diamin.biotools.json new file mode 100644 index 0000000000000..71e52b9f28af2 --- /dev/null +++ b/data/diamin/diamin.biotools.json @@ -0,0 +1,101 @@ +{ + "additionDate": "2023-01-28T10:38:36.212207Z", + "biotoolsCURIE": "biotools:diamin", + "biotoolsID": "diamin", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "simona.rombo@unipa.it", + "name": "Simona E. Rombo", + "orcidid": "https://orcid.org/0000-0003-3833-835X", + "typeEntity": "Person" + }, + { + "email": "umberto.ferraro@uniroma1.it", + "name": "Umberto Ferraro Petrillo", + "typeEntity": "Person" + } + ], + "description": "DIAMIN is a high-level software library to facilitate the development of distributed applications for the efficient analysis of large-scale molecular interaction networks.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Protein-protein interaction analysis", + "uri": "http://edamontology.org/operation_2949" + } + ] + } + ], + "homepage": "https://github.com/ldirocco/DIAMIN", + "language": [ + "Java", + "Scala" + ], + "lastUpdate": "2023-01-28T10:38:36.215212Z", + "license": "GPL-3.0", + "name": "DIAMIN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1186/S12859-022-05026-W", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale. Results: We propose DIAMIN, that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks. DIAMIN relies on distributed computing, and it is implemented in Java upon the framework Apache Spark. It delivers a set of functionalities implementing different tasks on an abstract representation of very large graphs, providing a built-in support for methods and algorithms commonly used to analyze these networks. DIAMIN has been tested on data retrieved from two of the most used molecular interactions databases, resulting to be highly efficient and scalable. As shown by different provided examples, DIAMIN can be exploited by users without any distributed programming experience, in order to perform various types of data analysis, and to implement new algorithms based on its primitives. Conclusions: The proposed DIAMIN has been proved to be successful in allowing users to solve specific biological problems that can be modeled relying on biological networks, by using its functionalities. The software is freely available and this will hopefully allow its rapid diffusion through the scientific community, to solve both specific data analysis and more complex tasks.", + "authors": [ + { + "name": "Di Rocco L." + }, + { + "name": "Ferraro Petrillo U." + }, + { + "name": "Rombo S.E." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks" + }, + "pmcid": "PMC9652854", + "pmid": "36368948" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/diffbrainnet/diffbrainnet.biotools.json b/data/diffbrainnet/diffbrainnet.biotools.json new file mode 100644 index 0000000000000..00b25ee667379 --- /dev/null +++ b/data/diffbrainnet/diffbrainnet.biotools.json @@ -0,0 +1,156 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T01:56:00.789649Z", + "biotoolsCURIE": "biotools:diffbrainnet", + "biotoolsID": "diffbrainnet", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "arloth@psych.mpg.de", + "name": "Janine Knauer-Arloth", + "orcidid": "http://orcid.org/0000-0003-3825-4279", + "typeEntity": "Person" + }, + { + "email": "binder@psych.mpg.de", + "name": "Elisabeth B. Binder", + "orcidid": "http://orcid.org/0000-0001-7088-6618", + "typeEntity": "Person" + }, + { + "name": "Anthi C. Krontira", + "orcidid": "http://orcid.org/0000-0003-0125-0215" + }, + { + "name": "Nathalie Gerstner", + "orcidid": "http://orcid.org/0000-0002-3111-5949" + } + ], + "description": "Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions.", + "download": [ + { + "type": "Container file", + "url": "https://hub.docker.com/r/ngerst/diffbrainnet" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Gene regulatory network analysis", + "uri": "http://edamontology.org/operation_1781" + } + ] + } + ], + "homepage": "http://diffbrainnet.psych.mpg.de/app/diffbrainnet", + "language": [ + "R" + ], + "lastUpdate": "2023-01-22T01:56:00.792204Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://github.molgen.mpg.de/mpip/DiffBrainNet_ShinyApp" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.molgen.mpg.de/mpip/DiffBrainNet" + } + ], + "name": "DiffBrainNet", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.ynstr.2022.100496", + "metadata": { + "abstract": "© 2022 The AuthorsGenome-wide gene expression analyses are invaluable tools for studying biological and disease processes, allowing a hypothesis-free comparison of expression profiles. Traditionally, transcriptomic analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders. Here, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress receptor agonist dexamethasone in 8 mouse brain regions important for stress processing. By applying a combination of differential network- and expression-analyses, we find that these explain distinct but complementary biological mechanisms of the glucocorticoid responses. Additionally, network analysis identifies new differentially connected partners of risk genes and can be used to generate hypotheses on molecular pathways affected. With DiffBrainNet (http://diffbrainnet.psych.mpg.de), we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.", + "authors": [ + { + "name": "Binder E.B." + }, + { + "name": "Cruceanu C." + }, + { + "name": "Gerstner N." + }, + { + "name": "Knauer-Arloth J." + }, + { + "name": "Krontira A.C." + }, + { + "name": "Putz B." + }, + { + "name": "Rex-Haffner M." + }, + { + "name": "Roeh S." + }, + { + "name": "Sauer S." + }, + { + "name": "Schmidt M.V." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Neurobiology of Stress", + "title": "DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions" + }, + "pmcid": "PMC9755029", + "pmid": "36532379" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/dira/dira.biotools.json b/data/dira/dira.biotools.json new file mode 100644 index 0000000000000..1d9979d30ac1a --- /dev/null +++ b/data/dira/dira.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:21:01.200222Z", + "biotoolsCURIE": "biotools:dira", + "biotoolsID": "dira", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "fhaghigh@asu.edu", + "name": "Fatemeh Haghighi", + "typeEntity": "Person" + }, + { + "name": "Jianming Liang" + }, + { + "name": "Michael B Gotway" + }, + { + "name": "Mohammad Reza Hosseinzadeh Taher", + "typeEntity": "Person" + } + ], + "description": "Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + } + ] + } + ], + "homepage": "https://github.com/JLiangLab/DiRA", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T23:21:01.203814Z", + "license": "Other", + "name": "DiRA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/CVPR52688.2022.02016", + "metadata": { + "abstract": "© 2022 IEEE.Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can sig-nificantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, thefirstframework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA.", + "authors": [ + { + "name": "Gotway M.B." + }, + { + "name": "Haghighi F." + }, + { + "name": "Liang J." + }, + { + "name": "Taher M.R.H." + } + ], + "citationCount": 3, + "date": "2022-01-01T00:00:00Z", + "journal": "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition", + "title": "DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis" + }, + "pmcid": "PMC9615927", + "pmid": "36313959" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + } + ] +} diff --git a/data/directrmdb/directrmdb.biotools.json b/data/directrmdb/directrmdb.biotools.json new file mode 100644 index 0000000000000..7ac25092366bf --- /dev/null +++ b/data/directrmdb/directrmdb.biotools.json @@ -0,0 +1,134 @@ +{ + "additionDate": "2023-01-28T10:44:32.704567Z", + "biotoolsCURIE": "biotools:directrmdb", + "biotoolsID": "directrmdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kunqi.chen@fjmu.edu.cn", + "name": "Kunqi Chen", + "orcidid": "https://orcid.org/0000-0002-6025-8957", + "typeEntity": "Person" + }, + { + "email": "daiyun.huang@liverpool.ac.uk", + "name": "Daiyun Hang", + "typeEntity": "Person" + }, + { + "email": "zhen.wei01@xjtlu.edu.cn", + "name": "Zhen Wei", + "typeEntity": "Person" + } + ], + "description": "A database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + } + ] + } + ], + "homepage": "http://www.rnamd.org/directRMDB/", + "lastUpdate": "2023-01-28T10:44:32.708677Z", + "license": "Other", + "name": "DirectRMDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1061", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.With advanced technologies to map RNA modifications, our understanding of them has been revolutionized, and they are seen to be far more widespread and important than previously thought. Current next-generation sequencing (NGS)-based modification profiling methods are blind to RNA modifications and thus require selective chemical treatment or antibody immunoprecipitation methods for particular modification types. They also face the problem of short read length, isoform ambiguities, biases and artifacts. Direct RNA sequencing (DRS) technologies, commercialized by Oxford Nanopore Technologies (ONT), enable the direct interrogation of any given modification present in individual transcripts and promise to address the limitations of previous NGS-based methods. Here, we present the first ONT-based database of quantitative RNA modification profiles, DirectRMDB, which includes 16 types of modification and a total of 904,712 modification sites in 25 species identified from 39 independent studies. In addition to standard functions adopted by existing databases, such as gene annotations and post-transcriptional association analysis, we provide a fresh view of RNA modifications, which enables exploration of the epitranscriptome in an isoform-specific manner. The DirectRMDB database is freely available at: http://www.rnamd.org/directRMDB/.", + "authors": [ + { + "name": "Chen K." + }, + { + "name": "Hang D." + }, + { + "name": "Jia G." + }, + { + "name": "Jiang J." + }, + { + "name": "Ma J." + }, + { + "name": "Meng J." + }, + { + "name": "Rigden D.J." + }, + { + "name": "Song B." + }, + { + "name": "Wang Y." + }, + { + "name": "Wei Z." + }, + { + "name": "Zhang Y." + }, + { + "name": "de Magalhaes J.P." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "DirectRMDB: a database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology" + }, + "pmcid": "PMC9825532", + "pmid": "36382409" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/divcom/divcom.biotools.json b/data/divcom/divcom.biotools.json new file mode 100644 index 0000000000000..d322538ff2198 --- /dev/null +++ b/data/divcom/divcom.biotools.json @@ -0,0 +1,77 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:13:54.707010Z", + "biotoolsCURIE": "biotools:divcom", + "biotoolsID": "divcom", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ilias.lagkouvardos@tum.de", + "name": "Ilias Lagkouvardos", + "typeEntity": "Person" + }, + { + "name": "Evangelia Intze" + } + ], + "description": "A Tool for Systematic Partition of Groups of Microbial Profiles Into Intrinsic Subclusters and Distance-Based Subgroup Comparisons.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Statistical calculation", + "uri": "http://edamontology.org/operation_2238" + } + ] + } + ], + "homepage": "https://github.com/Lagkouvardos/DivCom", + "language": [ + "R" + ], + "lastUpdate": "2023-01-09T23:13:54.711087Z", + "license": "MIT", + "name": "DivCom", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2022.864382", + "pmcid": "PMC9580884", + "pmid": "36304338" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + } + ] +} diff --git a/data/dla-ranker/dla-ranker.biotools.json b/data/dla-ranker/dla-ranker.biotools.json new file mode 100644 index 0000000000000..f15911196a7c6 --- /dev/null +++ b/data/dla-ranker/dla-ranker.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T23:41:33.879010Z", + "biotoolsCURIE": "biotools:dla-ranker", + "biotoolsID": "dla-ranker", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "alessandra.carbone@sorbonne-universite.fr", + "name": "Alessandra Carbone", + "orcidid": "http://orcid.org/0000-0003-2098-5743", + "typeEntity": "Person" + }, + { + "email": "elodie.laine@sorbonne-universite.fr", + "name": "Elodie Laine", + "orcidid": "http://orcid.org/0000-0003-4870-6304", + "typeEntity": "Person" + }, + { + "name": "Simon Crouzet", + "orcidid": "http://orcid.org/0000-0002-5012-4621" + }, + { + "name": "Yasser Mohseni Behbahani", + "orcidid": "http://orcid.org/0000-0003-0254-6595" + } + ], + "description": "Deep Local Analysis evaluates protein docking conformations with Locally oriented Cubes.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + }, + { + "term": "Protein structure prediction", + "uri": "http://edamontology.org/operation_0474" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + } + ] + } + ], + "homepage": "http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T23:41:33.881381Z", + "license": "Not licensed", + "name": "DLA-Ranker", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac551", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: With the recent advances in protein 3D structure prediction, protein interactions are becoming more central than ever before. Here, we address the problem of determining how proteins interact with one another. More specifically, we investigate the possibility of discriminating near-native protein complex conformations from incorrect ones by exploiting local environments around interfacial residues. RESULTS: Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. We assessed its performance on three docking benchmarks made of half a million acceptable and incorrect conformations. We show that DLA-Ranker successfully identifies near-native conformations from ensembles generated by molecular docking. It surpasses or competes with other deep learning-based scoring functions. We also showcase its usefulness to discover alternative interfaces. AVAILABILITY AND IMPLEMENTATION: http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Carbone A." + }, + { + "name": "Crouzet S." + }, + { + "name": "Laine E." + }, + { + "name": "Mohseni Behbahani Y." + } + ], + "date": "2022-09-30T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Deep Local Analysis evaluates protein docking conformations with locally oriented cubes" + }, + "pmcid": "PMC9525006", + "pmid": "35962985" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Protein targeting and localisation", + "uri": "http://edamontology.org/topic_0140" + } + ] +} diff --git a/data/dloopcaller/dloopcaller.biotools.json b/data/dloopcaller/dloopcaller.biotools.json new file mode 100644 index 0000000000000..63c739379146b --- /dev/null +++ b/data/dloopcaller/dloopcaller.biotools.json @@ -0,0 +1,125 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T23:10:22.353243Z", + "biotoolsCURIE": "biotools:dloopcaller", + "biotoolsID": "dloopcaller", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dshuang@eias.ac.cn", + "name": "De-Shuang Huang", + "orcidid": "https://orcid.org/0000-0002-6759-2691", + "typeEntity": "Person" + }, + { + "name": "Kyungsook Han", + "orcidid": "https://orcid.org/0000-0001-9900-6741" + }, + { + "name": "Siguo Wang", + "orcidid": "https://orcid.org/0000-0002-3244-3629" + }, + { + "name": "Qinhu Zhang", + "orcidid": "https://orcid.org/0000-0002-4232-7736", + "typeEntity": "Person" + } + ], + "description": "A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Loop modelling", + "uri": "http://edamontology.org/operation_0481" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + }, + { + "term": "Residue contact prediction", + "uri": "http://edamontology.org/operation_0272" + } + ] + } + ], + "homepage": "https://github.com/wangguoguoa/DLoopCaller", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T23:10:22.357322Z", + "license": "Not licensed", + "name": "DLoopCaller", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1010572", + "metadata": { + "abstract": "Copyright: © 2022 Wang et al.In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.", + "authors": [ + { + "name": "Cui Z." + }, + { + "name": "Guo Z." + }, + { + "name": "Han K." + }, + { + "name": "He Y." + }, + { + "name": "Huang D.-S." + }, + { + "name": "Wang S." + }, + { + "name": "Zhang Q." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes" + }, + "pmcid": "PMC9581407", + "pmid": "36206320" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Chromosome conformation capture", + "uri": "http://edamontology.org/topic_3940" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Tomography", + "uri": "http://edamontology.org/topic_3452" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/dmfpred/dmfpred.biotools.json b/data/dmfpred/dmfpred.biotools.json new file mode 100644 index 0000000000000..4adc36af2f9f9 --- /dev/null +++ b/data/dmfpred/dmfpred.biotools.json @@ -0,0 +1,102 @@ +{ + "additionDate": "2023-01-28T10:48:27.016998Z", + "biotoolsCURIE": "biotools:dmfpred", + "biotoolsID": "dmfpred", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "bliu@bliulab.net", + "name": "Bin Liu", + "orcidid": "https://orcid.org/0000-0001-6314-0762", + "typeEntity": "Person" + } + ], + "description": "Predicting protein disorder molecular functions based on protein cubic language model.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Protein disorder prediction", + "uri": "http://edamontology.org/operation_3904" + }, + { + "term": "Protein function prediction", + "uri": "http://edamontology.org/operation_1777" + }, + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + }, + { + "term": "Sequence motif recognition", + "uri": "http://edamontology.org/operation_0239" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "http://bliulab.net/DMFpred/", + "lastUpdate": "2023-01-28T10:48:27.019761Z", + "license": "Not licensed", + "name": "DMFpred", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1010668", + "metadata": { + "abstract": "© 2022 Pang, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Intrinsically disordered proteins and regions (IDP/IDRs) are widespread in living organisms and perform various essential molecular functions. These functions are summarized as six general categories, including entropic chain, assembler, scavenger, effector, display site, and chaperone. The alteration of IDP functions is responsible for many human diseases. Therefore, identifying the function of disordered proteins is helpful for the studies of drug target discovery and rational drug design. Experimental identification of the molecular functions of IDP in the wet lab is an expensive and laborious procedure that is not applicable on a large scale. Some computational methods have been proposed and mainly focus on predicting the entropic chain function of IDRs, while the computational predictive methods for the remaining five important categories of disordered molecular functions are desired. Motivated by the growing numbers of experimental annotated functional sequences and the need to expand the coverage of disordered protein function predictors, we proposed DMFpred for disordered molecular functions prediction, covering disordered assembler, scavenger, effector, display site and chaperone. DMFpred employs the Protein Cubic Language Model (PCLM), which incorporates three protein language models for characterizing sequences, structural and functional features of proteins, and attention-based alignment for understanding the relationship among three captured features and generating a joint representation of proteins. The PCLM was pre-trained with large-scaled IDR sequences and finetuned with functional annotation sequences for molecular function prediction. The predictive performance evaluation on five categories of functional and multi-functional residues suggested that DMFpred provides high-quality predictions. The web-server of DMFpred can be freely accessed from http://bliulab.net/DMFpred/. Copyright:", + "authors": [ + { + "name": "Liu B." + }, + { + "name": "Pang Y." + } + ], + "citationCount": 1, + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "DMFpred: Predicting protein disorder molecular functions based on protein cubic language model" + }, + "pmcid": "PMC9674156", + "pmid": "36315580" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Protein disordered structure", + "uri": "http://edamontology.org/topic_3538" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/dmiso/dmiso.biotools.json b/data/dmiso/dmiso.biotools.json new file mode 100644 index 0000000000000..bd047a19797c7 --- /dev/null +++ b/data/dmiso/dmiso.biotools.json @@ -0,0 +1,120 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T23:52:58.666563Z", + "biotoolsCURIE": "biotools:dmiso", + "biotoolsID": "dmiso", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "xiaoman@mail.ucf.edu", + "name": "Xiaoman Li", + "typeEntity": "Person" + }, + { + "name": "Amlan Talukder" + }, + { + "name": "Haiyan Hu" + }, + { + "name": "Wencai Zhang" + } + ], + "description": "A Deep Learning Method for MiRNA/IsomiR Target Detection.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "http://hulab.ucf.edu/research/projects/DMISO/README.md" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Chimera detection", + "uri": "http://edamontology.org/operation_0450" + }, + { + "term": "miRNA expression analysis", + "uri": "http://edamontology.org/operation_3792" + }, + { + "term": "miRNA target prediction", + "uri": "http://edamontology.org/operation_0463" + } + ] + } + ], + "homepage": "http://hulab.ucf.edu/research/projects/DMISO", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T23:52:58.669177Z", + "license": "Not licensed", + "name": "DMISO", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/s41598-022-14890-8", + "metadata": { + "abstract": "© 2022, The Author(s).Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.", + "authors": [ + { + "name": "Hu H." + }, + { + "name": "Li X." + }, + { + "name": "Talukder A." + }, + { + "name": "Zhang W." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Reports", + "title": "A deep learning method for miRNA/isomiR target detection" + }, + "pmcid": "PMC9226005", + "pmid": "35739186" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/dnadna/dnadna.biotools.json b/data/dnadna/dnadna.biotools.json new file mode 100644 index 0000000000000..a25c5d37ad02d --- /dev/null +++ b/data/dnadna/dnadna.biotools.json @@ -0,0 +1,147 @@ +{ + "additionDate": "2023-01-28T10:53:22.677732Z", + "biotoolsCURIE": "biotools:dnadna", + "biotoolsID": "dnadna", + "confidence_flag": "tool", + "credit": [ + { + "email": "flora.jay@lri.fr", + "name": "Flora Jay", + "orcidid": "https://orcid.org/0000-0001-5884-4730", + "typeEntity": "Person" + }, + { + "email": "jean.cury@normalesup.org", + "name": "Jean Cury", + "orcidid": "https://orcid.org/0000-0002-6462-8783", + "typeEntity": "Person" + } + ], + "description": "A deep learning framework for population genetics inference.", + "documentation": [ + { + "type": [ + "API documentation" + ], + "url": "http://mlgenetics.gitlab.io/dnadna/api.html" + }, + { + "type": [ + "Training material" + ], + "url": "http://mlgenetics.gitlab.io/dnadna/tutorials.html" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Document clustering", + "uri": "http://edamontology.org/operation_3279" + }, + { + "term": "Information extraction", + "uri": "http://edamontology.org/operation_3907" + }, + { + "term": "Nucleic acid melting curve plotting", + "uri": "http://edamontology.org/operation_0458" + }, + { + "term": "Nucleic acid stitch profile plotting", + "uri": "http://edamontology.org/operation_0457" + } + ] + } + ], + "homepage": "http://mlgenetics.gitlab.io/dnadna/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T10:53:22.682283Z", + "license": "CECILL-C", + "link": [ + { + "type": [ + "Repository" + ], + "url": "http://gitlab.com/mlgenetics/dnadna" + }, + { + "type": [ + "Repository" + ], + "url": "http://pypi.org/project/dnadna/" + } + ], + "name": "dnadna", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC765", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS: dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION: dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.", + "authors": [ + { + "name": "Bray E.M." + }, + { + "name": "Charpiat G." + }, + { + "name": "Cury J." + }, + { + "name": "Guez J." + }, + { + "name": "Jay F." + }, + { + "name": "Jobic P." + }, + { + "name": "Letournel A.-C." + }, + { + "name": "Sanchez T." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "dnadna: a deep learning framework for population genetics inference" + }, + "pmcid": "PMC9825738", + "pmid": "36445000" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Population genetics", + "uri": "http://edamontology.org/topic_3056" + } + ] +} diff --git a/data/dockground/dockground.biotools.json b/data/dockground/dockground.biotools.json new file mode 100644 index 0000000000000..478c3241e54e5 --- /dev/null +++ b/data/dockground/dockground.biotools.json @@ -0,0 +1,109 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T22:59:50.815808Z", + "biotoolsCURIE": "biotools:dockground", + "biotoolsID": "dockground", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pkundro@ku.edu", + "name": "Petras J. Kundrotas" + }, + { + "email": "vakser@ku.edu", + "name": "Ilya A. Vakser", + "orcidid": "https://orcid.org/0000-0002-5743-2934" + }, + { + "name": "Keeley W. Collins" + }, + { + "name": "Matthew M. Copeland" + } + ], + "description": "DOCKGROUND resource for protein recognition studies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein modelling", + "uri": "http://edamontology.org/operation_0477" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://dockground.compbio.ku.edu", + "language": [ + "SQL" + ], + "lastUpdate": "2023-01-09T22:59:50.818815Z", + "name": "Dockground", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1002/PRO.4481", + "metadata": { + "abstract": "© 2022 The Protein Society.Structural information of protein–protein interactions is essential for characterization of life processes at the molecular level. While a small fraction of known protein interactions has experimentally determined structures, computational modeling of protein complexes (protein docking) has to fill the gap. The Dockground resource (http://dockground.compbio.ku.edu) provides a collection of datasets for the development and testing of protein docking techniques. Currently, Dockground contains datasets for the bound and the unbound (experimentally determined and simulated) protein structures, model–model complexes, docking decoys of experimentally determined and modeled proteins, and templates for comparative docking. The Dockground bound proteins dataset is a core set, from which other Dockground datasets are generated. It is devised as a relational PostgreSQL database containing information on experimentally determined protein–protein complexes. This report on the Dockground resource describes current status of the datasets, new automated update procedures and further development of the core datasets. We also present a new Dockground interactive web interface, which allows search by various parameters, such as release date, multimeric state, complex type, structure resolution, and so on, visualization of the search results with a number of customizable parameters, as well as downloadable datasets with predefined levels of sequence and structure redundancy.", + "authors": [ + { + "name": "Collins K.W." + }, + { + "name": "Copeland M.M." + }, + { + "name": "Kotthoff I." + }, + { + "name": "Kundrotas P.J." + }, + { + "name": "Singh A." + }, + { + "name": "Vakser I.A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Protein Science", + "title": "Dockground resource for protein recognition studies" + }, + "pmcid": "PMC9667896", + "pmid": "36281025" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Protein structure analysis", + "uri": "http://edamontology.org/topic_2814" + } + ] +} diff --git a/data/domainmapper/domainmapper.biotools.json b/data/domainmapper/domainmapper.biotools.json new file mode 100644 index 0000000000000..1024e8ddaff23 --- /dev/null +++ b/data/domainmapper/domainmapper.biotools.json @@ -0,0 +1,99 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T22:53:42.593550Z", + "biotoolsCURIE": "biotools:domainmapper", + "biotoolsID": "domainmapper", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "emanriq1@jhu.edu", + "name": "Edgar Manriquez‐Sandoval", + "orcidid": "https://orcid.org/0000-0001-7284-1237", + "typeEntity": "Person" + }, + { + "email": "sdfried@jhu.edu", + "name": "Stephen D. Fried", + "orcidid": "https://orcid.org/0000-0003-2494-2193", + "typeEntity": "Person" + } + ], + "description": "Accurate domain structure annotation including those with non-contiguous topologies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://github.com/FriedLabJHU/DomainMapper", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T22:53:42.596610Z", + "license": "Apache-2.0", + "name": "DomainMapper", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1002/PRO.4465", + "metadata": { + "abstract": "© 2022 The Protein Society.Automated domain annotation is an important tool for structural informatics. These pipelines typically involve searching query sequences against hidden Markov model (HMM) profiles, yielding matches to profiles for various domains. However, domain annotation can be ambiguous or inaccurate when proteins contain domains with non-contiguous residue ranges, and especially when insertional domains are hosted within them. Here, we present DomainMapper, an algorithm that accurately assigns a unique domain structure annotation to a query sequence, including those with complex topologies. We validate our domain assignments using the AlphaFold database and confirm that non-contiguity is pervasive (10.74% of all domains in yeast and 4.52% in human). Using this resource, we find that certain folds have strong propensities to be non-contiguous or insertional across the Tree of Life. DomainMapper is freely available and can be ran as a single command-line function.", + "authors": [ + { + "name": "Fried S.D." + }, + { + "name": "Manriquez-Sandoval E." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Protein Science", + "title": "DomainMapper: Accurate domain structure annotation including those with non-contiguous topologies" + }, + "pmcid": "PMC9601794", + "pmid": "36208126" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Cladistics", + "uri": "http://edamontology.org/topic_3944" + }, + { + "term": "Informatics", + "uri": "http://edamontology.org/topic_0605" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Protein folds and structural domains", + "uri": "http://edamontology.org/topic_0736" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/doric_12.0/doric_12.0.biotools.json b/data/doric_12.0/doric_12.0.biotools.json new file mode 100644 index 0000000000000..3ef5eff508634 --- /dev/null +++ b/data/doric_12.0/doric_12.0.biotools.json @@ -0,0 +1,85 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T22:47:22.737262Z", + "biotoolsCURIE": "biotools:doric_12.0", + "biotoolsID": "doric_12.0", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "fgao@tju.edu.cn", + "name": "Feng Gao", + "orcidid": "https://orcid.org/0000-0002-9563-3841", + "typeEntity": "Person" + }, + { + "name": "Mei-Jing Dong" + }, + { + "name": "Hao Luo", + "orcidid": "https://orcid.org/0000-0003-2714-8817" + } + ], + "description": "An updated database of replication origins in both complete and draft prokaryotic genomes.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://tubic.org/doric/", + "lastUpdate": "2023-01-09T22:47:22.741531Z", + "name": "DoriC 12.0", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC964", + "pmid": "36305822" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ] +} diff --git a/data/dreamm/dreamm.biotools.json b/data/dreamm/dreamm.biotools.json new file mode 100644 index 0000000000000..b3016671484c7 --- /dev/null +++ b/data/dreamm/dreamm.biotools.json @@ -0,0 +1,92 @@ +{ + "additionDate": "2023-01-28T10:57:52.584573Z", + "biotoolsCURIE": "biotools:dreamm", + "biotoolsID": "dreamm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zcournia@bioacademy.gr", + "name": "Zoe Cournia", + "typeEntity": "Person" + } + ], + "description": "A web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Binding site prediction", + "uri": "http://edamontology.org/operation_2575" + }, + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://dreamm.ni4os.eu", + "lastUpdate": "2023-01-28T10:57:52.587805Z", + "license": "Not licensed", + "name": "DREAMM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC680", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM. AVAILABILITY AND IMPLEMENTATION: DREAMM web server is accessible via https://dreamm.ni4os.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Chatzigoulas A." + }, + { + "name": "Cournia Z." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design" + }, + "pmcid": "PMC9750117", + "pmid": "36355565" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Membrane and lipoproteins", + "uri": "http://edamontology.org/topic_0820" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/dresis/dresis.biotools.json b/data/dresis/dresis.biotools.json new file mode 100644 index 0000000000000..3faf43933178b --- /dev/null +++ b/data/dresis/dresis.biotools.json @@ -0,0 +1,69 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:59:16.618787Z", + "biotoolsCURIE": "biotools:dresis", + "biotoolsID": "dresis", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhufeng@zju.edu.cn", + "name": "Feng Zhu", + "orcidid": "https://orcid.org/0000-0001-8069-0053", + "typeEntity": "Person" + }, + { + "name": "Xiuna Sun" + }, + { + "name": "Yintao Zhang" + }, + { + "name": "Yunqing Qiu" + } + ], + "description": "The first comprehensive landscape of drug resistance information.", + "editPermission": { + "type": "private" + }, + "homepage": "https://idrblab.org/dresis/", + "lastUpdate": "2023-01-09T01:59:16.621263Z", + "name": "DRESIS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC812", + "pmid": "36243960" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Drug metabolism", + "uri": "http://edamontology.org/topic_3375" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + } + ] +} diff --git a/data/drnet/drnet.biotools.json b/data/drnet/drnet.biotools.json new file mode 100644 index 0000000000000..8babcba4d5a74 --- /dev/null +++ b/data/drnet/drnet.biotools.json @@ -0,0 +1,104 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:54:44.962073Z", + "biotoolsCURIE": "biotools:drnet", + "biotoolsID": "drnet", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Shuicheng Yan" + }, + { + "name": "Zhiyuan Fang" + }, + { + "name": "Guangyu Gao", + "orcidid": "https://orcid.org/0000-0002-0083-3016" + } + ], + "description": "Double Recalibration Network for Few-Shot Semantic Segmentation.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://github.com/fangzy97/drnet", + "language": [ + "Pascal" + ], + "lastUpdate": "2023-01-09T01:54:44.964682Z", + "license": "Not licensed", + "name": "DRNet", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/TIP.2022.3215905", + "metadata": { + "abstract": "© 1992-2012 IEEE.Few-shot segmentation aims at learning to segment query images guided by only a few annotated images from the support set. Previous methods rely on mining the feature embedding similarity across the query and the support images to achieve successful segmentation. However, these models tend to perform badly in cases where the query instances have a large variance from the support ones. To enhance model robustness against such intra-class variance, we propose a Double Recalibration Network (DRNet) with two recalibration modules, i.e., the Self-adapted Recalibration (SR) module and the Cross-attended Recalibration (CR) module. In particular, beyond learning robust feature embedding for pixel-wise comparison between support and query as in conventional methods, the DRNet further exploits semantic-aware knowledge embedded in the query image to help segment itself, which we call 'self-adapted recalibration'. More specifically, DRNet first employs guidance from the support set to roughly predict an incomplete but correct initial object region for the query image, and then reversely uses the feature embedding extracted from the incomplete object region to segment the query image. Also, we devise a CR module to refine the feature representation of the query image by propagating the underlying knowledge embedded in the support image's foreground to the query. Instead of foreground global pooling, we refine the response at each pixel in the query feature map by attending to all foreground pixels in the support feature map and taking the weighted average by their similarity; meanwhile, feature maps of the query image are also added back to weighted feature maps as a residual connection. Our DRNet can effectively address the intra-class variance under the few-shot setting with such two recalibration modules, and mine more accurate target regions for query images. We conduct extensive experiments on the popular benchmarks PASCAL- 5i and COCO- 20i. The DRNet with the best configuration achieves the mIoU of 63.6% and 64.9% on PASCAL- 5i and 44.7% and 49.6% on COCO- 20i for 1-shot and 5-shot settings respectively, significantly outperforming the state-of-the-arts without any bells and whistles. Code is available at: https://github.com/fangzy97/drnet.", + "authors": [ + { + "name": "Fang Z." + }, + { + "name": "Gao G." + }, + { + "name": "Han C." + }, + { + "name": "Liu C.H." + }, + { + "name": "Wei Y." + }, + { + "name": "Yan S." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE Transactions on Image Processing", + "title": "DRNet: Double Recalibration Network for Few-Shot Semantic Segmentation" + }, + "pmid": "36282824" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + } + ] +} diff --git a/data/drugmap/drugmap.biotools.json b/data/drugmap/drugmap.biotools.json new file mode 100644 index 0000000000000..2bbedb1845095 --- /dev/null +++ b/data/drugmap/drugmap.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:48:18.201517Z", + "biotoolsCURIE": "biotools:drugmap", + "biotoolsID": "drugmap", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chenyuzong@sz.tsinghua.edu.cn", + "name": "Yuzong Chen", + "orcidid": "https://orcid.org/0000-0002-5473-8022", + "typeEntity": "Person" + }, + { + "email": "zhufeng@zju.edu.cn", + "name": "Feng Zhu", + "orcidid": "https://orcid.org/0000-0001-8069-0053", + "typeEntity": "Person" + }, + { + "email": "zengsu@zju.edu.cn", + "name": "Su Zeng", + "typeEntity": "Person" + } + ], + "description": "Molecular atlas and pharma-information of all drugs.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Keyword", + "uri": "http://edamontology.org/data_0968" + } + } + ], + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://idrblab.org/drugmap/", + "lastUpdate": "2023-01-09T01:48:18.204080Z", + "name": "DrugMAP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC813", + "pmid": "36243961" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Drug metabolism", + "uri": "http://edamontology.org/topic_3375" + }, + { + "term": "Pharmacology", + "uri": "http://edamontology.org/topic_0202" + }, + { + "term": "Pharmacovigilance", + "uri": "http://edamontology.org/topic_3378" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/drugnomeai/drugnomeai.biotools.json b/data/drugnomeai/drugnomeai.biotools.json new file mode 100644 index 0000000000000..adaef53cce171 --- /dev/null +++ b/data/drugnomeai/drugnomeai.biotools.json @@ -0,0 +1,141 @@ +{ + "additionDate": "2023-01-28T11:05:45.743090Z", + "biotoolsCURIE": "biotools:drugnomeai", + "biotoolsID": "drugnomeai", + "confidence_flag": "tool", + "credit": [ + { + "email": "dimitrios.vitsios@astrazeneca.com", + "name": "Dimitrios Vitsios", + "orcidid": "https://orcid.org/0000-0002-8939-5445", + "typeEntity": "Person" + } + ], + "description": "DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "http://drugnomeai.public.cgr.astrazeneca.com", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T11:06:10.118415Z", + "license": "MPL-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release/blob/master/drugnome_ai/conf/" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release/blob/master/drugnome_ai/conf/.config" + } + ], + "name": "DrugnomeAI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S42003-022-04245-4", + "metadata": { + "abstract": "© 2022, The Author(s).The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10−308) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10−5) and quantitative traits (p value = 1.6 × 10−7). We accompany our method with a web application (http://drugnomeai.public.cgr.astrazeneca.com) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.", + "authors": [ + { + "name": "Dhindsa R.S." + }, + { + "name": "Engkvist O." + }, + { + "name": "Harper A.R." + }, + { + "name": "Hill P." + }, + { + "name": "Middleton L." + }, + { + "name": "Petrovski S." + }, + { + "name": "Raies A." + }, + { + "name": "Stainer J." + }, + { + "name": "Tulodziecka E." + }, + { + "name": "Vitsios D." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Communications Biology", + "title": "DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets" + }, + "pmcid": "PMC9700683", + "pmid": "36434048" + } + ], + "toolType": [ + "Script", + "Web application", + "Workbench" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "Exome sequencing", + "uri": "http://edamontology.org/topic_3676" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/drugrep/drugrep.biotools.json b/data/drugrep/drugrep.biotools.json new file mode 100644 index 0000000000000..e6be37af5c571 --- /dev/null +++ b/data/drugrep/drugrep.biotools.json @@ -0,0 +1,134 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:43:06.492127Z", + "biotoolsCURIE": "biotools:drugrep", + "biotoolsID": "drugrep", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "cao@scu.edu.cn", + "name": "Yang Cao", + "typeEntity": "Person" + }, + { + "name": "Ji-xiang Liu" + }, + { + "name": "Jian-hong Gan" + } + ], + "description": "DrugRep is a computer-aided drug discovery online tool for virtual screening of drugs, particularly for drug repurposing.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "PDB ID", + "uri": "http://edamontology.org/data_1127" + } + }, + { + "data": { + "term": "Expression data", + "uri": "http://edamontology.org/data_2603" + }, + "format": [ + { + "term": "PDB", + "uri": "http://edamontology.org/format_1476" + } + ] + } + ], + "operation": [ + { + "term": "Binding site prediction", + "uri": "http://edamontology.org/operation_2575" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "http://cao.labshare.cn/drugrep/", + "lastUpdate": "2023-01-09T01:43:06.494676Z", + "name": "DrugRep", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/S41401-022-00996-2", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive licence to Shanghai Institute of Materia Medica, Chinese Academy of Sciences and Chinese Pharmacological Society.Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.", + "authors": [ + { + "name": "Cao Y." + }, + { + "name": "Chen S.-W." + }, + { + "name": "Dai W.-T." + }, + { + "name": "Gan J.-H." + }, + { + "name": "Liu J.-X." + }, + { + "name": "Liu Y." + }, + { + "name": "Xiao Z.-X." + } + ], + "citationCount": 1, + "date": "2022-01-01T00:00:00Z", + "journal": "Acta Pharmacologica Sinica", + "title": "DrugRep: an automatic virtual screening server for drug repurposing" + }, + "pmcid": "PMC9549438", + "pmid": "36216900" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Drug metabolism", + "uri": "http://edamontology.org/topic_3375" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/drugtax/drugtax.biotools.json b/data/drugtax/drugtax.biotools.json new file mode 100644 index 0000000000000..5e27114c61834 --- /dev/null +++ b/data/drugtax/drugtax.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:37:11.716358Z", + "biotoolsCURIE": "biotools:drugtax", + "biotoolsID": "drugtax", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "irina.moreira@cnc.uc.pt", + "name": "Irina S. Moreira", + "typeEntity": "Person" + }, + { + "name": "A. J. Preto" + }, + { + "name": "Paulo C. Correia" + } + ], + "description": "Package for drug taxonomy identification and explainable feature extraction.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://pypi.org/project/DrugTax/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T01:37:11.718952Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/MoreiraLAB/DrugTax" + } + ], + "name": "DrugTax", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13321-022-00649-W", + "metadata": { + "abstract": "© 2022, The Author(s).DrugTax is an easy-to-use Python package for small molecule detailed characterization. It extends a previously explored chemical taxonomy making it ready-to-use in any Artificial Intelligence approach. DrugTax leverages small molecule representations as input in one of their most accessible and simple forms (SMILES) and allows the simultaneously extraction of taxonomy information and key features for big data algorithm deployment. In addition, it delivers a set of tools for bulk analysis and visualization that can also be used for chemical space representation and molecule similarity assessment. DrugTax is a valuable tool for chemoinformatic processing and can be easily integrated in drug discovery pipelines. DrugTax can be effortlessly installed via PyPI (https://pypi.org/project/DrugTax/) or GitHub (https://github.com/MoreiraLAB/DrugTax).", + "authors": [ + { + "name": "Correia P.C." + }, + { + "name": "Moreira I.S." + }, + { + "name": "Preto A.J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Cheminformatics", + "title": "DrugTax: package for drug taxonomy identification and explainable feature extraction" + }, + "pmcid": "PMC9609197", + "pmid": "36303244" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cheminformatics", + "uri": "http://edamontology.org/topic_2258" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Taxonomy", + "uri": "http://edamontology.org/topic_0637" + } + ] +} diff --git a/data/dsmzcelldive/dsmzcelldive.biotools.json b/data/dsmzcelldive/dsmzcelldive.biotools.json new file mode 100644 index 0000000000000..67dfbfc4fc3ac --- /dev/null +++ b/data/dsmzcelldive/dsmzcelldive.biotools.json @@ -0,0 +1,137 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-19T00:01:13.848337Z", + "biotoolsCURIE": "biotools:dsmzcelldive", + "biotoolsID": "dsmzcelldive", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Claudia Pommerenke", + "orcidid": "https://orcid.org/0000-0002-9448-416X" + }, + { + "name": "Sonja Eberth", + "orcidid": "https://orcid.org/0000-0002-5796-2089" + }, + { + "name": "Julia Koblitz", + "orcidid": "https://orcid.org/0000-0002-7260-2129", + "typeEntity": "Person" + }, + { + "name": "Laura Steenpass", + "typeEntity": "Person" + } + ], + "description": "Diving into high-throughput cell line data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "DNA barcoding", + "uri": "http://edamontology.org/operation_3200" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://celldive.dsmz.de", + "language": [ + "JavaScript", + "PHP" + ], + "lastUpdate": "2023-01-19T00:01:13.851628Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/JKoblitz/DSMZCellDive" + } + ], + "name": "DSMZCellDive", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.12688/f1000research.111175.2", + "metadata": { + "abstract": "© 2022 Koblitz J et al.Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data.", + "authors": [ + { + "name": "Dirks W.G." + }, + { + "name": "Eberth S." + }, + { + "name": "Koblitz J." + }, + { + "name": "Nagel S." + }, + { + "name": "Pommerenke C." + }, + { + "name": "Steenpass L." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "F1000Research", + "title": "DSMZCellDive: Diving into high-throughput cell line data" + }, + "pmcid": "PMC9334839", + "pmid": "35949917" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/dxformer/dxformer.biotools.json b/data/dxformer/dxformer.biotools.json new file mode 100644 index 0000000000000..12770b215aa26 --- /dev/null +++ b/data/dxformer/dxformer.biotools.json @@ -0,0 +1,56 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T11:12:41.839763Z", + "biotoolsCURIE": "biotools:dxformer", + "biotoolsID": "dxformer", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jiajiepeng@nwpu.edu.cn", + "name": "Jiajie Peng", + "typeEntity": "Person" + }, + { + "email": "zywei@fudan.edu.cn", + "name": "Zhongyu Wei", + "typeEntity": "Person" + } + ], + "description": "A decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.", + "editPermission": { + "type": "public" + }, + "homepage": "https://github.com/lemuria-wchen/DxFormer", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T11:12:41.842434Z", + "license": "MIT", + "name": "DxFormer", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC744", + "pmcid": "PMC9825744", + "pmid": "36409016" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Respiratory medicine", + "uri": "http://edamontology.org/topic_3322" + } + ] +} diff --git a/data/e-pix_web/e-pix_web.biotools.json b/data/e-pix_web/e-pix_web.biotools.json new file mode 100644 index 0000000000000..82b780f20f939 --- /dev/null +++ b/data/e-pix_web/e-pix_web.biotools.json @@ -0,0 +1,79 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T23:31:37.298031Z", + "biotoolsCURIE": "biotools:e-pix_web", + "biotoolsID": "e-pix_web", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Lukas Arnecke" + }, + { + "name": "Martin Bialke" + }, + { + "name": "Wolfgang Hoffmann" + }, + { + "name": "Christopher Hampf", + "orcidid": "https://orcid.org/0000-0002-4557-4783" + } + ], + "description": "Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Anonymisation", + "uri": "http://edamontology.org/operation_3283" + }, + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + } + ] + } + ], + "homepage": "https://demo.ths-greifswald.de/epix-web/", + "lastUpdate": "2023-01-26T23:31:37.301468Z", + "name": "E-PIX", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.21203/RS.3.RS-1053445/V1" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Data security", + "uri": "http://edamontology.org/topic_3263" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + } + ] +} diff --git a/data/e-snps_and_go/e-snps_and_go.biotools.json b/data/e-snps_and_go/e-snps_and_go.biotools.json new file mode 100644 index 0000000000000..7d3d1498082e0 --- /dev/null +++ b/data/e-snps_and_go/e-snps_and_go.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:23:56.169620Z", + "biotoolsCURIE": "biotools:e-snps_and_go", + "biotoolsID": "e-snps_and_go", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pierluigi.martelli@unibo.it", + "name": "Pier Luigi Martelli", + "orcidid": "https://orcid.org/0000-0002-0274-5669", + "typeEntity": "Person" + }, + { + "name": "Matteo Manfredi" + }, + { + "name": "Castrense Savojardo", + "orcidid": "https://orcid.org/0000-0002-7359-0633" + }, + { + "name": "Rita Casadio", + "orcidid": "https://orcid.org/0000-0002-7462-7039" + } + ], + "description": "Embedding of protein sequence and function improves the annotation of human pathogenic variants.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], + "operation": [ + { + "term": "Gene functional annotation", + "uri": "http://edamontology.org/operation_3672" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + }, + { + "term": "Virulence prediction", + "uri": "http://edamontology.org/operation_3461" + } + ] + } + ], + "homepage": "https://esnpsandgo.biocomp.unibo.it", + "lastUpdate": "2023-01-09T01:23:56.172153Z", + "name": "E-SNPs and GO", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC678", + "pmid": "36227117" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "Protein variants", + "uri": "http://edamontology.org/topic_3120" + } + ] +} diff --git a/data/e-snpsgo/e-snpsgo.biotools.json b/data/e-snpsgo/e-snpsgo.biotools.json new file mode 100644 index 0000000000000..82d65ec2b97e2 --- /dev/null +++ b/data/e-snpsgo/e-snpsgo.biotools.json @@ -0,0 +1,131 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-05T08:42:10.922859Z", + "biotoolsCURIE": "biotools:E-SNPsGO", + "biotoolsID": "E-SNPsGO", + "description": "E-SNPs&GO is a machine-learning method the pathogenicity of human variations. E-SNPs&GO classify input variations into pathogenic or benign.", + "editPermission": { + "authors": [ + "ELIXIR-ITA-BOLOGNA" + ], + "type": "group" + }, + "elixirCommunity": [ + "Rare Diseases" + ], + "elixirNode": [ + "Italy" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + }, + { + "data": { + "term": "Sequence variations", + "uri": "http://edamontology.org/data_3498" + }, + "format": [ + { + "term": "Textual format", + "uri": "http://edamontology.org/format_2330" + } + ] + } + ], + "operation": [ + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + } + ], + "output": [ + { + "data": { + "term": "Score", + "uri": "http://edamontology.org/data_1772" + }, + "format": [ + { + "term": "HTML", + "uri": "http://edamontology.org/format_2331" + }, + { + "term": "JSON", + "uri": "http://edamontology.org/format_3464" + }, + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + } + ] + } + ], + "homepage": "https://esnpsandgo.biocomp.unibo.it/", + "language": [ + "Other" + ], + "lastUpdate": "2023-01-05T09:18:20.176934Z", + "name": "E-SNPs and GO", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "PierLuigiMartelli", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac678", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. RESULTS: E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Casadio R." + }, + { + "name": "Manfredi M." + }, + { + "name": "Martelli P.L." + }, + { + "name": "Savojardo C." + } + ], + "date": "2022-11-30T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants" + }, + "pmcid": "PMC9710551", + "pmid": "36227117", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Protein variants", + "uri": "http://edamontology.org/topic_3120" + } + ] +} diff --git a/data/e-tsn/e-tsn.biotools.json b/data/e-tsn/e-tsn.biotools.json new file mode 100644 index 0000000000000..797ff4b834231 --- /dev/null +++ b/data/e-tsn/e-tsn.biotools.json @@ -0,0 +1,101 @@ +{ + "additionDate": "2023-01-28T11:21:03.788494Z", + "biotoolsCURIE": "biotools:e-tsn", + "biotoolsID": "e-tsn", + "confidence_flag": "tool", + "credit": [ + { + "email": "shiliangli@ecust.edu.cn", + "name": "Shiliang Li", + "orcidid": "https://orcid.org/0000-0003-4414-237X", + "typeEntity": "Person" + } + ], + "description": "An interactive visual exploration platform for target-disease knowledge mapping from literature.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Text mining", + "uri": "http://edamontology.org/operation_0306" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://www.lilab-ecust.cn/etsn/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T11:21:03.791066Z", + "license": "Other", + "name": "e-TSN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC465", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.", + "authors": [ + { + "name": "Feng Z." + }, + { + "name": "Li H." + }, + { + "name": "Li S." + }, + { + "name": "Shen Z." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature" + }, + "pmcid": "PMC9677481", + "pmid": "36347537" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/e.page/e.page.biotools.json b/data/e.page/e.page.biotools.json new file mode 100644 index 0000000000000..1e8cc53a09db5 --- /dev/null +++ b/data/e.page/e.page.biotools.json @@ -0,0 +1,129 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T10:28:44.717607Z", + "biotoolsCURIE": "biotools:e.page", + "biotoolsID": "e.page", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "a.mehdi@uq.edu.au", + "name": "Ahmed M. Mehdi", + "typeEntity": "Person" + } + ], + "description": "Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + } + ] + } + ], + "homepage": "https://github.com/AhmedMehdiLab/E.PAGE", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T10:28:44.720956Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/AhmedMehdiLab/E.PATH" + } + ], + "name": "E.PAGE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41598-022-21988-6", + "metadata": { + "abstract": "© 2022, The Author(s).The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.", + "authors": [ + { + "name": "Ali R.A." + }, + { + "name": "Ali S." + }, + { + "name": "Badshah J." + }, + { + "name": "Chandra J." + }, + { + "name": "Frazer I.H." + }, + { + "name": "Mehdi A.M." + }, + { + "name": "Muralidharan S." + }, + { + "name": "Thomas R." + }, + { + "name": "Yang L." + }, + { + "name": "Zahir S.F." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Reports", + "title": "Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene–environment associations" + }, + "pmcid": "PMC9636158", + "pmid": "36333579" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Immunology", + "uri": "http://edamontology.org/topic_0804" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/ecgxai/ecgxai.biotools.json b/data/ecgxai/ecgxai.biotools.json new file mode 100644 index 0000000000000..24ddb17a6b285 --- /dev/null +++ b/data/ecgxai/ecgxai.biotools.json @@ -0,0 +1,72 @@ +{ + "additionDate": "2023-01-27T17:04:09.252092Z", + "biotoolsCURIE": "biotools:ecgxai", + "biotoolsID": "ecgxai", + "confidence_flag": "tool", + "credit": [ + { + "email": "p.wouters@umcutrecht.nl", + "name": "Philippe C Wouters", + "typeEntity": "Person" + } + ], + "description": "Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://crt.ecgx.ai", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-27T17:04:09.254521Z", + "license": "AGPL-3.0", + "name": "ECGxAI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/EURHEARTJ/EHAC617", + "pmid": "36342291" + } + ], + "toolType": [ + "Library", + "Web application" + ], + "topic": [ + { + "term": "Cardiology", + "uri": "http://edamontology.org/topic_3335" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Surgery", + "uri": "http://edamontology.org/topic_3421" + } + ] +} diff --git a/data/echtvar/echtvar.biotools.json b/data/echtvar/echtvar.biotools.json new file mode 100644 index 0000000000000..9b7008d9d94dd --- /dev/null +++ b/data/echtvar/echtvar.biotools.json @@ -0,0 +1,68 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-19T10:20:35.375199Z", + "biotoolsCURIE": "biotools:echtvar", + "biotoolsID": "echtvar", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Brent S. Pedersen", + "orcidid": "http://orcid.org/0000-0003-1786-2216" + }, + { + "name": "Jeroen de Ridder", + "orcidid": "http://orcid.org/0000-0002-0828-3477" + } + ], + "description": "Compressed variant representation for rapid annotation and filtering of SNPs and indels.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genetic variation analysis", + "uri": "http://edamontology.org/operation_3197" + }, + { + "term": "SNP annotation", + "uri": "http://edamontology.org/operation_3661" + }, + { + "term": "Variant filtering", + "uri": "http://edamontology.org/operation_3675" + } + ] + } + ], + "homepage": "https://github.com/brentp/echtvar", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-19T10:20:35.377823Z", + "license": "MIT", + "name": "echtvar", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/nar/gkac931", + "pmid": "36300617" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + } + ] +} diff --git a/data/ecotranslearn/ecotranslearn.biotools.json b/data/ecotranslearn/ecotranslearn.biotools.json new file mode 100644 index 0000000000000..f1037e03e9b10 --- /dev/null +++ b/data/ecotranslearn/ecotranslearn.biotools.json @@ -0,0 +1,92 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:13:11.464340Z", + "biotoolsCURIE": "biotools:ecotranslearn", + "biotoolsID": "ecotranslearn", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Guillaume.Wacquet@ifremer.fr", + "name": "Guillaume Wacquet", + "orcidid": "https://orcid.org/0000-0002-3325-5136", + "typeEntity": "Person" + }, + { + "name": "Alain Lefebvre" + } + ], + "description": "An R-package to easily use Transfer Learning for Ecological Studies. A plankton case study.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Ecological modelling", + "uri": "http://edamontology.org/operation_3946" + } + ] + } + ], + "homepage": "https://github.com/IFREMER-LERBL/EcoTransLearn", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-09T01:13:11.466989Z", + "license": "GPL-3.0", + "name": "EcoTransLearn", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC703", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of 'easy-to-use' software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in the classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset. AVAILABILITY AND IMPLEMENTATION: EcoTransLearn is an open-source package. It is implemented in R and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Lefebvre A." + }, + { + "name": "Wacquet G." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study" + }, + "pmid": "36282847" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/edomics/edomics.biotools.json b/data/edomics/edomics.biotools.json new file mode 100644 index 0000000000000..47711918a7903 --- /dev/null +++ b/data/edomics/edomics.biotools.json @@ -0,0 +1,139 @@ +{ + "additionDate": "2023-01-28T11:24:50.168141Z", + "biotoolsCURIE": "biotools:edomics", + "biotoolsID": "edomics", + "confidence_flag": "tool", + "credit": [ + { + "email": "bodong@ouc.edu.cn", + "name": "Bo Dong", + "orcidid": "https://orcid.org/0000-0003-1616-5363", + "typeEntity": "Person" + }, + { + "email": "liyuli@ouc.edu.cn", + "name": "Yuli Li", + "orcidid": "https://orcid.org/0000-0002-8112-1730", + "typeEntity": "Person" + }, + { + "email": "swang@ouc.edu.cn", + "name": "Shi Wang", + "orcidid": "https://orcid.org/0000-0002-9571-9864", + "typeEntity": "Person" + } + ], + "description": "A comprehensive and comparative multi-omics database for animal evo-devo.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Transcriptome assembly", + "uri": "http://edamontology.org/operation_3258" + }, + { + "term": "Weighted correlation network analysis", + "uri": "http://edamontology.org/operation_3766" + } + ] + } + ], + "homepage": "http://edomics.qnlm.ac", + "lastUpdate": "2023-01-28T11:24:50.171203Z", + "license": "Other", + "name": "EDomics", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC944", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.", + "authors": [ + { + "name": "Dong B." + }, + { + "name": "Jia D." + }, + { + "name": "Jiang A." + }, + { + "name": "Li Y." + }, + { + "name": "Liu F." + }, + { + "name": "Liu P." + }, + { + "name": "Pu Z." + }, + { + "name": "Qiao J." + }, + { + "name": "Wang B." + }, + { + "name": "Wang S." + }, + { + "name": "Wei J." + }, + { + "name": "Zhang J." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "EDomics: a comprehensive and comparative multi-omics database for animal evo-devo" + }, + "pmcid": "PMC9825439", + "pmid": "36318263" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Developmental biology", + "uri": "http://edamontology.org/topic_3064" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/efmsdti/efmsdti.biotools.json b/data/efmsdti/efmsdti.biotools.json new file mode 100644 index 0000000000000..deedc80b2dd95 --- /dev/null +++ b/data/efmsdti/efmsdti.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:07:21.921693Z", + "biotoolsCURIE": "biotools:efmsdti", + "biotoolsID": "efmsdti", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yyzhang1217@163.com", + "name": "Yuanyuan Zhang", + "typeEntity": "Person" + }, + { + "name": "Mengjie Wu" + }, + { + "name": "Shudong Wang" + }, + { + "name": "Wei Chen" + } + ], + "description": "Drug-target interaction prediction based on an efficient fusion of multi-source data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Subcellular localisation prediction", + "uri": "http://edamontology.org/operation_2489" + } + ] + } + ], + "homepage": "https://github.com/meng-jie/EFMSDTI", + "language": [ + "MATLAB", + "Python" + ], + "lastUpdate": "2023-01-09T01:07:21.924162Z", + "license": "Not licensed", + "name": "EFMSDTI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FPHAR.2022.1009996", + "metadata": { + "abstract": "Copyright © 2022 Zhang, Wu, Wang and Chen.Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.", + "authors": [ + { + "name": "Chen W." + }, + { + "name": "Wang S." + }, + { + "name": "Wu M." + }, + { + "name": "Zhang Y." + } + ], + "date": "2022-09-23T00:00:00Z", + "journal": "Frontiers in Pharmacology", + "title": "EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data" + }, + "pmcid": "PMC9538487", + "pmid": "36210804" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Pharmacology", + "uri": "http://edamontology.org/topic_0202" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/eggnog/eggnog.biotools.json b/data/eggnog/eggnog.biotools.json index de0bfe8dc2bbf..f280afbce62da 100644 --- a/data/eggnog/eggnog.biotools.json +++ b/data/eggnog/eggnog.biotools.json @@ -135,7 +135,7 @@ "language": [ "Python" ], - "lastUpdate": "2020-10-07T22:54:52Z", + "lastUpdate": "2023-01-28T11:27:56.183348Z", "license": "GPL-3.0", "maturity": "Mature", "name": "eggNOG", @@ -191,7 +191,7 @@ "name": "Walter M.C." } ], - "citationCount": 992, + "citationCount": 1251, "date": "2016-01-01T00:00:00Z", "journal": "Nucleic Acids Research", "title": "EGGNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences" @@ -199,6 +199,11 @@ "pmcid": "PMC4702882", "pmid": "26582926" }, + { + "doi": "10.1093/NAR/GKAC1022", + "pmcid": "PMC9825578", + "pmid": "36399505" + }, { "metadata": { "abstract": "Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains nonsupervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%). © The Author(s) 2011. Published by Oxford University Press.", @@ -243,7 +248,7 @@ "name": "Von Mering C." } ], - "citationCount": 347, + "citationCount": 382, "date": "2012-01-01T00:00:00Z", "journal": "Nucleic Acids Research", "title": "eggNOG v3.0: Orthologous groups covering 1133 organisms at 41 different taxonomic ranges" @@ -276,7 +281,7 @@ "name": "von Mering C." } ], - "citationCount": 281, + "citationCount": 347, "date": "2008-01-01T00:00:00Z", "journal": "Nucleic Acids Research", "title": "eggNOG: Automated construction and annotation of orthologous groups of genes" @@ -321,7 +326,7 @@ "name": "Von Mering C." } ], - "citationCount": 167, + "citationCount": 175, "date": "2009-11-07T00:00:00Z", "journal": "Nucleic Acids Research", "title": "eggNOG v2.0: Extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations" @@ -372,7 +377,7 @@ "name": "Von Mering C." } ], - "citationCount": 343, + "citationCount": 394, "date": "2014-01-01T00:00:00Z", "journal": "Nucleic Acids Research", "title": "EggNOG v4.0: Nested orthology inference across 3686 organisms" @@ -420,7 +425,7 @@ "name": "Von Mering C." } ], - "citationCount": 373, + "citationCount": 1145, "date": "2019-01-08T00:00:00Z", "journal": "Nucleic Acids Research", "title": "EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses" @@ -446,6 +451,6 @@ ], "validated": 1, "version": [ - "5.0" + "6.0" ] } diff --git a/data/em-hiv/em-hiv.biotools.json b/data/em-hiv/em-hiv.biotools.json new file mode 100644 index 0000000000000..334d1f0f5fdec --- /dev/null +++ b/data/em-hiv/em-hiv.biotools.json @@ -0,0 +1,107 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T01:02:05.663512Z", + "biotoolsCURIE": "biotools:em-hiv", + "biotoolsID": "em-hiv", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "hupengwei@hotmail.com", + "name": "Pengwei Hu", + "typeEntity": "Person" + }, + { + "email": "zhouxi@ms.xjb.ac.cn", + "name": "Xi Zhou", + "typeEntity": "Person" + }, + { + "name": "Lun Hu" + }, + { + "name": "Zhenfeng Li" + } + ], + "description": "Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Protein cleavage site prediction", + "uri": "http://edamontology.org/operation_0422" + } + ] + } + ], + "homepage": "https://github.com/AllenV5/EM-HIV", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T01:02:05.666060Z", + "license": "Not licensed", + "name": "EM-HIV", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-04999-Y", + "metadata": { + "abstract": "© 2022, The Author(s).Background: The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. Results: In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.", + "authors": [ + { + "name": "Hu L." + }, + { + "name": "Hu P." + }, + { + "name": "Li Z." + }, + { + "name": "Tang Z." + }, + { + "name": "Zhao C." + }, + { + "name": "Zhou X." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach" + }, + "pmcid": "PMC9608884", + "pmid": "36303135" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Laboratory techniques", + "uri": "http://edamontology.org/topic_3361" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/ematlas/ematlas.biotools.json b/data/ematlas/ematlas.biotools.json new file mode 100644 index 0000000000000..be0ffec48664c --- /dev/null +++ b/data/ematlas/ematlas.biotools.json @@ -0,0 +1,82 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T00:56:36.124520Z", + "biotoolsCURIE": "biotools:ematlas", + "biotoolsID": "ematlas", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yczuo@imu.edu.cn", + "name": "Yongchun Zuo", + "orcidid": "https://orcid.org/0000-0002-6065-7835", + "typeEntity": "Person" + }, + { + "email": "xingyongqiang1984@163.com", + "name": "Yongqiang Xing", + "typeEntity": "Person" + }, + { + "name": "Pengfei Liang" + }, + { + "name": "Lei Zheng", + "orcidid": "https://orcid.org/0000-0002-8531-6949" + } + ], + "description": "A comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Differential gene expression profiling", + "uri": 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b/data/ensemblesplice/ensemblesplice.biotools.json new file mode 100644 index 0000000000000..adb3521eeb64b --- /dev/null +++ b/data/ensemblesplice/ensemblesplice.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T00:47:43.598302Z", + "biotoolsCURIE": "biotools:ensemblesplice", + "biotoolsID": "ensemblesplice", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ooluwada@uccs.edu", + "name": "Oluwatosin Oluwadare", + "typeEntity": "Person" + }, + { + "name": "Trevor Martin" + }, + { + "name": "Victor Akpokiro" + } + ], + "description": "Ensemble deep learning model for splice site prediction.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Sequence merging", + "uri": "http://edamontology.org/operation_0232" + }, + { + "term": "Splice site prediction", + "uri": 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Within this research purview, efficient and accurate splice site detection is highly desirable, and a variety of computational models have been developed toward this end. Neural network architectures have recently been shown to outperform classical machine learning approaches for the task of splice site prediction. Despite these advances, there is still considerable potential for improvement, especially regarding model prediction accuracy, and error rate. Results: Given these deficits, we propose EnsembleSplice, an ensemble learning architecture made up of four (4) distinct convolutional neural networks (CNN) model architecture combination that outperform existing splice site detection methods in the experimental evaluation metrics considered including the accuracies and error rates. We trained and tested a variety of ensembles made up of CNNs and DNNs using the five-fold cross-validation method to identify the model that performed the best across the evaluation and diversity metrics. As a result, we developed our diverse and highly effective splice site (SS) detection model, which we evaluated using two (2) genomic Homo sapiens datasets and the Arabidopsis thaliana dataset. The results showed that for of the Homo sapiens EnsembleSplice achieved accuracies of 94.16% for one of the acceptor splice sites and 95.97% for donor splice sites, with an error rate for the same Homo sapiens dataset, 4.03% for the donor splice sites and 5.84% for the acceptor splice sites datasets. Conclusions: Our five-fold cross validation ensured the prediction accuracy of our models are consistent. For reproducibility, all the datasets used, models generated, and results in our work are publicly available in our GitHub repository here: https://github.com/OluwadareLab/EnsembleSplice", + "authors": [ + { + "name": "Akpokiro V." + }, + { + "name": "Martin T." + }, + { + "name": "Oluwadare O." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "EnsembleSplice: ensemble deep learning model for splice site prediction" + }, + "pmcid": "PMC9535948", + "pmid": "36203144" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/envemind/envemind.biotools.json b/data/envemind/envemind.biotools.json new file mode 100644 index 0000000000000..a28b7ce66bd09 --- /dev/null +++ b/data/envemind/envemind.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T00:37:28.128249Z", + "biotoolsCURIE": "biotools:envemind", + "biotoolsID": "envemind", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pmradzinski@mimuw.edu.pl", + "name": "Piotr Radziński", + "orcidid": "https://orcid.org/0000-0001-5107-7487", + "typeEntity": "Person" + }, + { + "name": "Anna Gambin" + }, + { + "name": "Dirk Valkenborg" + }, + { + "name": "Michał Piotr Startek", + "orcidid": "https://orcid.org/0000-0001-5227-3447" + } + ], + "description": "Accurate Monoisotopic Mass Determination Based On Isotopic Envelope.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deisotoping", + "uri": "http://edamontology.org/operation_3629" + }, + { + "term": "Protein identification", + "uri": "http://edamontology.org/operation_3767" + }, + { + "term": "Spectrum calculation", + "uri": "http://edamontology.org/operation_3860" + } + ] + } + ], + "homepage": "https://github.com/PiotrRadzinski/envemind", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T00:37:28.131465Z", + "license": "MIT", + "name": "envemind", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/JASMS.2C00176", + "metadata": { + "abstract": "© 2022 American Chemical Society.Nowadays, monoisotopic mass is used as an important feature in top-down proteomics. Knowing the exact monoisotopic mass is helpful for precise and quick protein identification in large protein databases. However, only in spectra of small molecules the monoisotopic peak is visible. For bigger molecules like proteins, it is hidden in noise or undetected at all, and therefore its position has to be predicted. By improving the prediction of the peak, we contribute to a more accurate identification of molecules, which is crucial in fields such as chemistry and medicine. In this work, we present the envemind algorithm, which is a two-step procedure to predict monoisotopic masses of proteins. The prediction is based on an isotopic envelope. Therefore, envemind is dedicated to spectra where we are able to resolve the one dalton separated isotopic variants. Furthermore, only single-molecule spectra are allowed, that is, spectra that do not require prior deconvolution. The algorithm deals with the problem of off-by-one dalton errors, which are common in monoisotopic mass prediction. A novel aspect of this work is a mathematical exploration of the space of molecules, where we equate chemical formulas and their theoretical spectrum. Since the space of molecules consists of all possible chemical formulas, this approach is not limited to known substances only. This makes optimization processes faster and enables to approximate theoretical spectrum for a given experimental one. The algorithm is available as a Python package envemind on our GitHub page https://github.com/PiotrRadzinski/envemind.", + "authors": [ + { + "name": "Gambin A." + }, + { + "name": "Radzinski P." + }, + { + "name": "Startek M.P." + }, + { + "name": "Valkenborg D." + } + ], + "date": "2022-11-02T00:00:00Z", + "journal": "Journal of the American Society for Mass Spectrometry", + "title": "Envemind: Accurate Monoisotopic Mass Determination Based on Isotopic Envelope" + }, + "pmcid": "PMC9634886", + "pmid": "36223196" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Chemistry", + "uri": "http://edamontology.org/topic_3314" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ] +} diff --git a/data/epiphany/epiphany.biotools.json b/data/epiphany/epiphany.biotools.json new file mode 100644 index 0000000000000..1316e712e834c --- /dev/null +++ b/data/epiphany/epiphany.biotools.json @@ -0,0 +1,95 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-09T00:29:02.574112Z", + "biotoolsCURIE": "biotools:epiphany", + "biotoolsID": "epiphany", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "scott.napper@usask.ca", + "name": "Scott Napper", + "typeEntity": "Person" + }, + { + "name": "Anthony J Kusalik" + }, + { + "name": "Antonio Facciuolo" + }, + { + "name": "Zoe Parker Cates" + } + ], + "description": "Platform for Analysis and Visualization of Peptide Immunoarray Data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dendrogram visualisation", + "uri": "http://edamontology.org/operation_2938" + }, + { + "term": "Epitope mapping", + "uri": "http://edamontology.org/operation_0416" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Principal component visualisation", + "uri": "http://edamontology.org/operation_2939" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://epiphany.usask.ca/epiphany/", + "lastUpdate": "2023-01-09T00:29:02.577571Z", + "name": "EPIphany", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2021.694324", + "pmcid": "PMC9581008", + "pmid": "36303765" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Allergy, clinical immunology and immunotherapeutics", + "uri": "http://edamontology.org/topic_3400" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/espaloma/espaloma.biotools.json b/data/espaloma/espaloma.biotools.json new file mode 100644 index 0000000000000..939010beb532c --- /dev/null +++ b/data/espaloma/espaloma.biotools.json @@ -0,0 +1,129 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T11:33:39.723315Z", + "biotoolsCURIE": "biotools:espaloma", + "biotoolsID": "espaloma", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "John D. Chodera", + "orcidid": "https://orcid.org/0000-0003-0542-119X", + "typeEntity": "Person" + }, + { + "name": "Yuanqing Wang", + "orcidid": "https://orcid.org/0000-0003-4403-2015", + "typeEntity": "Person" + } + ], + "description": "End-to-end differentiable construction of molecular mechanics force fields.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Forcefield parameterisation", + "uri": "http://edamontology.org/operation_3893" + }, + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/choderalab/espaloma", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T11:33:39.726083Z", + "license": "MIT", + "name": "espaloma", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1039/D2SC02739A", + "metadata": { + "abstract": "© 2022 The Royal Society of Chemistry.Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process—spanning chemical perception to parameter assignment—is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field (“Parsley”) openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.", + "authors": [ + { + "name": "Bruce Macdonald H.E." + }, + { + "name": "Chodera J.D." + }, + { + "name": "Fass J." + }, + { + "name": "Henry M." + }, + { + "name": "Herr J.E." + }, + { + "name": "Kaminow B." + }, + { + "name": "Pulido I." + }, + { + "name": "Rufa D." + }, + { + "name": "Takaba K." + }, + { + "name": "Wang Y." + }, + { + "name": "Zhang I." + } + ], + "citationCount": 3, + "date": "2022-09-08T00:00:00Z", + "journal": "Chemical Science", + "title": "End-to-end differentiable construction of molecular mechanics force fields" + }, + "pmcid": "PMC9600499", + "pmid": "36349096" + } + ], + "toolType": [ + "Library", + "Script" + ], + "topic": [ + { + "term": "Chemistry", + "uri": "http://edamontology.org/topic_3314" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Physics", + "uri": "http://edamontology.org/topic_3318" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/ev-dna/ev-dna.biotools.json b/data/ev-dna/ev-dna.biotools.json new file mode 100644 index 0000000000000..ef0af2d4ad0b5 --- /dev/null +++ b/data/ev-dna/ev-dna.biotools.json @@ -0,0 +1,119 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-08T01:33:16.023808Z", + "biotoolsCURIE": "biotools:ev-dna", + "biotoolsID": "ev-dna", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "julia.burnier@mcgill.ca", + "name": "Julia V. Burnier", + "typeEntity": "Person" + }, + { + "name": "Mingyang Li" + }, + { + "name": "Thupten Tsering" + }, + { + "name": "Yunxi Chen" + } + ], + "description": "Database for EV-associated DNA in human liquid biopsy samples.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + } + ] + } + ], + "homepage": "http://www.evdnadatabase.com", + "lastUpdate": "2023-01-08T01:33:28.189992Z", + "name": "EV-DNA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1002/JEV2.12270", + "metadata": { + "abstract": "© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.Extracellular vesicles (EVs) play a key role in cellular communication both in physiological conditions and in pathologies such as cancer. Emerging evidence has shown that EVs are active carriers of molecular cargo (e.g. protein and nucleic acids) and a powerful source of biomarkers and targets. While recent studies on EV-associated DNA (EV-DNA) in human biofluids have generated a large amount of data, there is currently no database that catalogues information on EV-DNA. To fill this gap, we have manually curated a database of EV-DNA data derived from human biofluids (liquid biopsy) and in-vitro studies, called the Extracellular Vesicle-Associated DNA Database (EV-ADD). This database contains validated experimental details and data extracted from peer-reviewed published literature. It can be easily queried to search for EV isolation methods and characterization, EV-DNA isolation techniques, quality validation, DNA fragment size, volume of starting material, gene names and disease context. Currently, our database contains samples representing 23 diseases, with 13 different types of EV isolation techniques applied on eight different human biofluids (e.g. blood, saliva). In addition, EV-ADD encompasses EV-DNA data both representing the whole genome and specifically including oncogenes, such as KRAS, EGFR, BRAF, MYC, and mitochondrial DNA (mtDNA). An EV-ADD data metric system was also integrated to assign a compliancy score to the MISEV guidelines based on experimental parameters reported in each study. While currently available databases document the presence of proteins, lipids, RNA and metabolites in EVs (e.g. Vesiclepedia, ExoCarta, ExoBCD, EVpedia, and EV-TRACK), to the best of our knowledge, EV-ADD is the first of its kind to compile all available EV-DNA datasets derived from human biofluid samples. We believe that this database provides an important reference resource on EV-DNA-based liquid biopsy research, serving as a learning tool and to showcase the latest developments in the EV-DNA field. EV-ADD will be updated yearly as newly published EV-DNA data becomes available and it is freely available at www.evdnadatabase.com.", + "authors": [ + { + "name": "Abdouh M." + }, + { + "name": "Burnier J.V." + }, + { + "name": "Bustamante P." + }, + { + "name": "Chen Y." + }, + { + "name": "Laskaris A." + }, + { + "name": "Li M." + }, + { + "name": "Nadeau A." + }, + { + "name": "Tsering T." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "Journal of Extracellular Vesicles", + "title": "EV-ADD, a database for EV-associated DNA in human liquid biopsy samples" + }, + "pmcid": "PMC9587709", + "pmid": "36271888" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Lipids", + "uri": "http://edamontology.org/topic_0153" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/evalfq/evalfq.biotools.json b/data/evalfq/evalfq.biotools.json new file mode 100644 index 0000000000000..3d771e45bd5bc --- /dev/null +++ b/data/evalfq/evalfq.biotools.json @@ -0,0 +1,88 @@ +{ + "additionDate": "2023-01-28T12:45:49.778730Z", + "biotoolsCURIE": "biotools:evalfq", + "biotoolsID": "evalfq", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhufeng@zju.edu.cn", + "name": "Feng Zhu", + "typeEntity": "Person" + } + ], + "description": "R Package for Evaluating Label-Free Proteome Quantification", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + }, + { + "term": "Label-free quantification", + "uri": "http://edamontology.org/operation_3634" + }, + { + "term": "Labeled quantification", + "uri": "http://edamontology.org/operation_3635" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + } + ] + } + ], + "homepage": "https://github.com/idrblab/EVALFQ", + "language": [ + "R" + ], + "lastUpdate": "2023-01-28T12:45:49.781353Z", + "license": "GPL-3.0", + "name": "EVALFQ", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC477", + "pmid": "36403090" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Proteogenomics", + "uri": "http://edamontology.org/topic_3922" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ] +} diff --git a/data/evanalyzer/evanalyzer.biotools.json b/data/evanalyzer/evanalyzer.biotools.json new file mode 100644 index 0000000000000..21d2ebcee386a --- /dev/null +++ b/data/evanalyzer/evanalyzer.biotools.json @@ -0,0 +1,167 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T12:51:28.723041Z", + "biotoolsCURIE": "biotools:evanalyzer", + "biotoolsID": "evanalyzer", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "nicole.meisner-kober@plus.ac.at", + "name": "Nicole Meisner‐Kober", + "typeEntity": "Person" + } + ], + "description": "EVAnalyzer is a new open‐source plugin for Fiji, developed for automated single vesicle quantification from fluorescence microscopy images", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Single particle alignment and classification", + "uri": "http://edamontology.org/operation_3458" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/joda01/evanalyzer/releases", + "language": [ + "Java" + ], + "lastUpdate": "2023-01-28T12:51:28.726480Z", + "link": [ + { + "type": [ + "Issue tracker" + ], + "url": "https://github.com/joda01/evanalyzer/issues" + } + ], + "name": "EVAnalyzer", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1002/JEV2.12282", + "metadata": { + "abstract": "© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed ‘EVAnalyzer’, an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno-)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross-reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid-based nanoparticles (LNPs) and protein loading of SiO2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell–single vesicle level, thereby enabling high content EV cell uptake assays and plate-based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open-source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.", + "authors": [ + { + "name": "Benirschke H.M." + }, + { + "name": "Blochl C." + }, + { + "name": "Danmayr J." + }, + { + "name": "Gomes F.G." + }, + { + "name": "Heger Z." + }, + { + "name": "Heuser T." + }, + { + "name": "Himly M." + }, + { + "name": "Hintersteiner M." + }, + { + "name": "Huber C.G." + }, + { + "name": "Jaritsch M." + }, + { + "name": "Johnson L." + }, + { + "name": "Kiefer J." + }, + { + "name": "Klinglmayr E." + }, + { + "name": "Kratochvil Z." + }, + { + "name": "Matea C.-T." + }, + { + "name": "Meisner-Kober N." + }, + { + "name": "Miller A." + }, + { + "name": "Plank T." + }, + { + "name": "Rauter J." + }, + { + "name": "Schurz M." + }, + { + "name": "Stanojlovic V." + }, + { + "name": "Wolf M." + }, + { + "name": "Zimmerebner P." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Extracellular Vesicles", + "title": "EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open-source ImageJ/Fiji plugin" + }, + "pmcid": "PMC9702573", + "pmid": "36437554" + } + ], + "relation": [ + { + "biotoolsID": "fiji", + "type": "uses" + } + ], + "toolType": [ + "Plug-in" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Cytometry", + "uri": "http://edamontology.org/topic_3934" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Lipids", + "uri": "http://edamontology.org/topic_0153" + } + ] +} diff --git a/data/ezcancertarget/ezcancertarget.biotools.json b/data/ezcancertarget/ezcancertarget.biotools.json new file mode 100644 index 0000000000000..10aefa59a730a --- /dev/null +++ b/data/ezcancertarget/ezcancertarget.biotools.json @@ -0,0 +1,146 @@ +{ + "accessibility": "Open access (with restrictions)", + "additionDate": "2023-01-08T01:15:15.041763Z", + "biotoolsCURIE": "biotools:ezcancertarget", + "biotoolsID": "ezcancertarget", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dora.david@med.semmelweis-univ.hu", + "name": "David Dora", + "orcidid": "https://orcid.org/0000-0002-3138-8816", + "typeEntity": "Person" + }, + { + "email": "zoltan.lohinai@koranyi.hu", + "name": "Zoltan Lohinai", + "typeEntity": "Person" + }, + { + "name": "Csongor Gerdán" + }, + { + "name": "Gabor Szegvari" + }, + { + "name": "Timea Dora" + } + ], + "description": "An open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://cycle20.github.io/EZCancerTarget/index.html" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Parsing", + "uri": "http://edamontology.org/operation_1812" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/cycle20/EZCancerTarget", + "language": [ + "R", + "Shell" + ], + "lastUpdate": "2023-01-08T01:17:51.825122Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://cycle20.github.io/EZCancerTarget/" + } + ], + "name": "EZCancerTarget", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13040-022-00307-9", + "metadata": { + "abstract": "© 2022, The Author(s).The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.", + "authors": [ + { + "name": "Dora D." + }, + { + "name": "Dora T." + }, + { + "name": "Gerdan C." + }, + { + "name": "Lohinai Z." + }, + { + "name": "Szegvari G." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BioData Mining", + "title": "EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers" + }, + "pmcid": "PMC9526900", + "pmid": "36183137" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/ezqtl/ezqtl.biotools.json b/data/ezqtl/ezqtl.biotools.json new file mode 100644 index 0000000000000..5216dd474c9e3 --- /dev/null +++ b/data/ezqtl/ezqtl.biotools.json @@ -0,0 +1,137 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T01:51:43.322139Z", + "biotoolsCURIE": "biotools:ezqtl", + "biotoolsID": "ezqtl", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jiyeon.choi2@nih.gov", + "name": "Jiyeon Choi", + "orcidid": "http://orcid.org/0000-0002-0955-2384", + "typeEntity": "Person" + }, + { + "email": "kevin.brown3@nih.gov", + "name": "Kevin M Brown", + "orcidid": "http://orcid.org/0000-0002-8558-6711", + "typeEntity": "Person" + }, + { + "name": "Alyssa Klein", + "orcidid": "http://orcid.org/0000-0003-3763-5731" + }, + { + "name": "Jian Sang", + "orcidid": "http://orcid.org/0000-0003-4953-3417" + }, + { + "name": "Tongwu Zhang", + "orcidid": "http://orcid.org/0000-0003-2124-2706" + } + ], + "description": "A Web Platform for Interactive Visualization and Colocalization of Quantitative Trait Loci and GWAS.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression QTL analysis", + "uri": "http://edamontology.org/operation_3232" + }, + { + "term": "Genetic mapping", + "uri": "http://edamontology.org/operation_0282" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://analysistools.cancer.gov/ezqtl", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-17T01:51:43.324635Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/CBIIT/nci-webtools-dceg-ezQTL" + } + ], + "name": "ezQTL", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.gpb.2022.05.004", + "metadata": { + "abstract": "© 2022Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.", + "authors": [ + { + "name": "Brown K.M." + }, + { + "name": "Choi J." + }, + { + "name": "Klein A." + }, + { + "name": "Sang J." + }, + { + "name": "Zhang T." + } + ], + "citationCount": 1, + "date": "2022-06-01T00:00:00Z", + "journal": "Genomics, Proteomics and Bioinformatics", + "title": "ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci" + }, + "pmcid": "PMC9801033", + "pmid": "35643189" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/farnet/farnet.biotools.json b/data/farnet/farnet.biotools.json new file mode 100644 index 0000000000000..ac885aad27f61 --- /dev/null +++ b/data/farnet/farnet.biotools.json @@ -0,0 +1,77 @@ +{ + "additionDate": "2023-01-28T12:59:46.000759Z", + "biotoolsCURIE": "biotools:farnet", + "biotoolsID": "farnet", + "confidence_flag": "tool", + "credit": [ + { + "email": "aoyueyuan@qq.com", + "name": "Yueyuan Ao", + "typeEntity": "Person" + }, + { + "email": "hwu@uestc.edu.cn", + "name": "Hong Wu", + "typeEntity": "Person" + } + ], + "description": "A novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/JuvenileInWind/FARNet", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T12:59:46.003693Z", + "license": "Not licensed", + "name": "FARNet", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1007/S10278-022-00718-4", + "metadata": { + "abstract": "© 2022, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks. FARNet employs an encoder-decoder structure architecture. To alleviate the problem of limited training data in the medical domain, we adopt a backbone network pre-trained on natural images as the encoder. The decoder includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. We evaluate FARNet on three publicly available anatomical landmark detection datasets, including cephalometric, hand, and spine radiographs. Our network achieves state-of-the-art performances on all three datasets. Code is available at https://github.com/JuvenileInWind/FARNet.", + "authors": [ + { + "name": "Ao Y." + }, + { + "name": "Wu H." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Journal of Digital Imaging", + "title": "Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection" + }, + "pmid": "36401132" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + } + ] +} diff --git a/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json b/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json new file mode 100644 index 0000000000000..414c57f903fb8 --- /dev/null +++ b/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json @@ -0,0 +1,142 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T02:06:22.115769Z", + "biotoolsCURIE": "biotools:fastaptamer_2.0", + "biotoolsID": "fastaptamer_2.0", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "burkedh@missouri.edu", + "name": "Donald H. Burke", + "typeEntity": "Person" + }, + { + "name": "Dong Xu" + }, + { + "name": "Khalid K. Alam" + }, + { + "name": "Paige R. Gruenke" + }, + { + "name": "Skyler T. Kramer", + "orcidid": "http://orcid.org/0000-0001-6539-1792" + } + ], + "description": "A Web Tool for Combinatorial Sequence Selections.", + "download": [ + { + "type": "Container file", + "url": "https://hub.docker.com/repository/docker/skylerkramer/fastaptamer2" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Sequence", + "uri": "http://edamontology.org/data_2044" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + }, + { + "term": "FASTQ", + "uri": "http://edamontology.org/format_1930" + } + ] + } + ], + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Sequence cluster visualisation", + "uri": "http://edamontology.org/operation_0566" + } + ] + } + ], + "homepage": "https://fastaptamer2.missouri.edu/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-22T02:06:22.118273Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/SkylerKramer/FASTAptameR-2.0" + } + ], + "name": "FASTAptameR 2.0", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.omtn.2022.08.030", + "metadata": { + "abstract": "© 2022 The AuthorsCombinatorial selections are powerful strategies for identifying biopolymers with specific biological, biomedical, or chemical characteristics. Unfortunately, most available software tools for high-throughput sequencing analysis have high entrance barriers for many users because they require extensive programming expertise. FASTAptameR 2.0 is an R-based reimplementation of FASTAptamer designed to minimize this barrier while maintaining the ability to answer complex sequence-level and population-level questions. This open-source toolkit features a user-friendly web tool, interactive graphics, up to 100 times faster clustering, an expanded module set, and an extensive user guide. FASTAptameR 2.0 accepts diverse input polymer types and can be applied to any sequence-encoded selection.", + "authors": [ + { + "name": "Alam K.K." + }, + { + "name": "Burke D.H." + }, + { + "name": "Gruenke P.R." + }, + { + "name": "Kramer S.T." + }, + { + "name": "Xu D." + } + ], + "date": "2022-09-13T00:00:00Z", + "journal": "Molecular Therapy - Nucleic Acids", + "title": "FASTAptameR 2.0: A web tool for combinatorial sequence selections" + }, + "pmcid": "PMC9464650", + "pmid": "36159593" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ], + "version": [ + "2.0" + ] +} diff --git a/data/fcclasses3/fcclasses3.biotools.json b/data/fcclasses3/fcclasses3.biotools.json new file mode 100644 index 0000000000000..c90a73b1a0e5b --- /dev/null +++ b/data/fcclasses3/fcclasses3.biotools.json @@ -0,0 +1,68 @@ +{ + "additionDate": "2023-01-28T13:04:20.677259Z", + "biotoolsCURIE": "biotools:fcclasses3", + "biotoolsID": "fcclasses3", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Javier Cerezo" + } + ], + "description": "Vibrationally-resolved spectra simulated at the edge of the harmonic approximation.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Protein secondary structure assignment", + "uri": "http://edamontology.org/operation_0319" + }, + { + "term": "Sorting", + "uri": "http://edamontology.org/operation_3802" + } + ] + } + ], + "homepage": "http://www.iccom.cnr.it/en/fcclasses/", + "lastUpdate": "2023-01-28T13:04:20.679954Z", + "license": "Not licensed", + "name": "FCclasses3", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1002/JCC.27027", + "metadata": { + "abstract": "© 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.We introduce FCclasses3, a code to carry out vibronic simulations of electronic spectra and nonradiative rates, based on the harmonic approximation. Key new features are: implementation of the full family of vertical and adiabatic harmonic models, vibrational analysis in curvilinear coordinates, extension to several electronic spectroscopies and implementation of time-dependent approaches. The use of curvilinear valence internal coordinates allows the adoption of quadratic model potential energy surfaces (PES) of the initial and final states expanded at arbitrary configurations. Moreover, the implementation of suitable projectors provides a robust framework for defining reduced-dimensionality models by sorting flexible coordinates out of the harmonic subset, so that they can then be treated at anharmonic level, or with mixed quantum classical approaches. A set of tools to facilitate input preparation and output analysis is also provided. We show the program at work in the simulation of different spectra (one and two-photon absorption, emission and resonance Raman) and internal conversion rate of a typical rigid molecule, anthracene. Then, we focus on absorption and emission spectra of a series of flexible polyphenyl molecules, highlighting the relevance of some of the newly implemented features. The code is freely available at http://www.iccom.cnr.it/en/fcclasses/.", + "authors": [ + { + "name": "Cerezo J." + }, + { + "name": "Santoro F." + } + ], + "date": "2023-02-05T00:00:00Z", + "journal": "Journal of Computational Chemistry", + "title": "FCclasses3: Vibrationally-resolved spectra simulated at the edge of the harmonic approximation" + }, + "pmid": "36380723" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "NMR", + "uri": "http://edamontology.org/topic_0593" + } + ] +} diff --git a/data/fdalabel/fdalabel.biotools.json b/data/fdalabel/fdalabel.biotools.json new file mode 100644 index 0000000000000..a817ac6f2145d --- /dev/null +++ b/data/fdalabel/fdalabel.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T23:40:38.810926Z", + "biotoolsCURIE": "biotools:fdalabel", + "biotoolsID": "fdalabel", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "p.johann@kitz-heidelberg.de", + "name": "Pascal Johann", + "orcidid": "https://orcid.org/0000-0002-8857-6148", + "typeEntity": "Person" + }, + { + "name": "Dominic Lenz" + }, + { + "name": "Markus Ries" + } + ], + "description": "The FDALabel Database is a web-based application used to perform customizable searches of over 140,000 human prescription, biological, over-the-counter (OTC), and animal drug labeling documents.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://nctr-crs.fda.gov/fdalabel/ui/search", + "lastUpdate": "2023-01-26T23:40:38.814303Z", + "name": "FDALabel", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pone.0252924", + "metadata": { + "abstract": "Copyright: © 2021 Johann et al.Background Glioblastoma (GBM) is the most common malignant brain tumour among adult patients and represents an almost universally fatal disease. Novel therapies for GBM are being developed under the orphan drug legislation and the knowledge on the molecular makeup of this disease has been increasing rapidly. However, the clinical outcomes in GBM patients with currently available therapies are still dismal. An insight into the current drug development pipeline for GBM is therefore of particular interest. Objectives To provide a quantitative clinical-regulatory insight into the status of FDA orphan drug designations for compounds intended to treat GBM. Methods Quantitative cross-sectional analysis of the U.S. Food and Drug Administration Orphan Drug Product database between 1983 and 2020. STROBE criteria were respected. Results Four orphan drugs out of 161 (2,4%) orphan drug designations were approved for the treatment for GBM by the FDA between 1983 and 2020. Fourteen orphan drug designations were subsequently withdrawn for unknown reasons. The number of orphan drug designations per year shows a growing trend. In the last decade, the therapeutic mechanism of action of designated compounds intended to treat glioblastoma shifted from cytotoxic drugs (median year of designation 2008) to immunotherapeutic approaches and small molecules (median year of designation 2014 and 2015 respectively) suggesting an increased focus on precision in the therapeutic mechanism of action for compounds the development pipeline. Conclusion Despite the fact that current pharmacological treatment options in GBM are sparse, the drug development pipeline is steadily growing. In particular, the surge of designated immunotherapies detected in the last years raises the hope that elaborate combination possibilities between classical therapeutic backbones (radiotherapy and chemotherapy) and novel, currently experimental therapeutics may help to provide better therapies for this deadly disease in the future.", + "authors": [ + { + "name": "Johann P." + }, + { + "name": "Lenz D." + }, + { + "name": "Ries M." + } + ], + "citationCount": 2, + "date": "2021-07-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "The drug development pipeline for glioblastoma—A cross sectional assessment of the FDA Orphan Drug Product designation database" + }, + "pmcid": "PMC8263276", + "pmid": "34234357" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Allergy, clinical immunology and immunotherapeutics", + "uri": "http://edamontology.org/topic_3400" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Pharmacology", + "uri": "http://edamontology.org/topic_0202" + }, + { + "term": "Pharmacovigilance", + "uri": "http://edamontology.org/topic_3378" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/fdrdb/fdrdb.biotools.json b/data/fdrdb/fdrdb.biotools.json new file mode 100644 index 0000000000000..b5b54f96cc3a5 --- /dev/null +++ b/data/fdrdb/fdrdb.biotools.json @@ -0,0 +1,125 @@ +{ + "additionDate": "2023-01-28T13:10:21.821035Z", + "biotoolsCURIE": "biotools:fdrdb", + "biotoolsID": "fdrdb", + "confidence_flag": "tool", + "credit": [ + { + "email": "guyunyan@ems.hrbmu.edu.cn", + "name": "Yunyan Gu", + "orcidid": "https://orcid.org/0000-0001-5693-4126", + "typeEntity": "Person" + }, + { + "email": "lianghaihai@ems.hrbmu.edu.cn", + "name": "Haihai Liang", + "typeEntity": "Person" + }, + { + "email": "xxfan@must.edu.mo", + "name": "Xingxing Fan", + "typeEntity": "Person" + } + ], + "description": "Fibrotic Disease-associated RNAome database, is an open and up-to-data online manually curated depository of RNAs alterations and high-throughput datasets about fibrotic diseases across various species.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + } + ] + } + ], + "homepage": "http://www.medsysbio.org/FDRdb", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:10:21.824163Z", + "name": "FDRdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/DATABASE/BAAC095", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Fibrosis is a common and serious disease that exists as a complicated impairment in many organs and triggers a complex cascade of responses. The deregulation of Ribonucleic Acids (RNAs) plays important roles in a variety of organ fibrosis cases. However, for fibrotic diseases, there is still a lack of an integrated platform with up-To-date information on RNA deregulation and high-Throughput data. The Fibrotic Disease-Associated RNAome database (FDRdb) (http://www.medsysbio.org/FDRdb) is a manually curated database of fibrotic disease-Associated RNAome information and high-Throughput datasets. This initial release (i) contains 1947 associations between 912 RNAs and 92 fibrotic diseases in eight species; (ii) collects information on 764 datasets of fibrotic diseases; (iii) provides a user-friendly web interface that allows users to browse, search and download the RNAome information on fibrotic diseases and high-Throughput datasets and (iv) provides tools to analyze the expression profiles of fibrotic diseases, including differential expression analysis and pathway enrichment. The FDRdb is a valuable resource for researchers to explore the mechanisms of RNA dysregulation in organ fibrosis. Database URL: http://www.medsysbio.org/FDRdb", + "authors": [ + { + "name": "Ai L." + }, + { + "name": "Chen T." + }, + { + "name": "Fan X." + }, + { + "name": "Gu Y." + }, + { + "name": "Liang H." + }, + { + "name": "Liang X." + }, + { + "name": "Mu Y." + }, + { + "name": "Wang C." + }, + { + "name": "Xiong K." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Database", + "title": "FDRdb: A manually curated database of fibrotic disease-Associated RNAome and high-Throughput datasets" + }, + "pmcid": "PMC9650723", + "pmid": "36367312" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Cardiology", + "uri": "http://edamontology.org/topic_3335" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Urology and nephrology", + "uri": "http://edamontology.org/topic_3422" + } + ] +} diff --git a/data/febrna/febrna.biotools.json b/data/febrna/febrna.biotools.json new file mode 100644 index 0000000000000..c2757b42a674a --- /dev/null +++ b/data/febrna/febrna.biotools.json @@ -0,0 +1,124 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T02:12:18.372809Z", + "biotoolsCURIE": "biotools:febrna", + "biotoolsID": "febrna", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yltan@wtu.edu.cn", + "name": "Zhi-Jie Tan", + "typeEntity": "Person" + }, + { + "email": "zjtan@whu.edu.cn", + "name": "Ya-Lan Tan", + "typeEntity": "Person" + }, + { + "name": "Li Zhou" + }, + { + "name": "Shixiong Yu" + }, + { + "name": "Xunxun Wang" + } + ], + "description": "An automated fragment-ensemble-based model for building RNA 3D structures.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "RNA inverse folding", + "uri": "http://edamontology.org/operation_0483" + }, + { + "term": "RNA secondary structure alignment", + "uri": "http://edamontology.org/operation_0502" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + }, + { + "term": "RNA structure prediction", + "uri": "http://edamontology.org/operation_2441" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://github.com/Tan-group/FebRNA", + "language": [ + "C", + "Python" + ], + "lastUpdate": "2023-01-22T02:12:18.375302Z", + "license": "GPL-3.0", + "name": "FebRNA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.bpj.2022.08.017", + "metadata": { + "abstract": "© 2022 Biophysical SocietyKnowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.", + "authors": [ + { + "name": "Tan Y.-L." + }, + { + "name": "Tan Z.-J." + }, + { + "name": "Wang X." + }, + { + "name": "Yu S." + }, + { + "name": "Zhou L." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Biophysical Journal", + "title": "FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures" + }, + "pmcid": "PMC9515226", + "pmid": "35978551" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Nucleic acid structure analysis", + "uri": "http://edamontology.org/topic_0097" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + } + ] +} diff --git a/data/fegrow/fegrow.biotools.json b/data/fegrow/fegrow.biotools.json new file mode 100644 index 0000000000000..02682fb0201e9 --- /dev/null +++ b/data/fegrow/fegrow.biotools.json @@ -0,0 +1,123 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T13:13:43.470450Z", + "biotoolsCURIE": "biotools:fegrow", + "biotoolsID": "fegrow", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daniel.cole@ncl.ac.uk", + "name": "Daniel J. Cole", + "orcidid": "https://orcid.org/0000-0003-2933-0719", + "typeEntity": "Person" + } + ], + "description": "An interactive workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://cole-group.github.io/FEgrow/" + }, + { + "type": [ + "Training material" + ], + "url": "https://github.com/cole-group/FEgrow/tree/master/notebooks" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Protein-ligand docking", + "uri": "http://edamontology.org/operation_0482" + }, + { + "term": "Side chain modelling", + "uri": "http://edamontology.org/operation_0480" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://github.com/cole-group/FEgrow", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:13:43.473106Z", + "license": "MIT", + "name": "FEgrow", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S42004-022-00754-9", + "metadata": { + "abstract": "© 2022, The Author(s).Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein–ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein–ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.", + "authors": [ + { + "name": "Bieniek M.K." + }, + { + "name": "Cole D.J." + }, + { + "name": "Cree B." + }, + { + "name": "Horton J.T." + }, + { + "name": "Pirie R." + }, + { + "name": "Tatum N.J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Communications Chemistry", + "title": "An open-source molecular builder and free energy preparation workflow" + }, + "pmcid": "PMC9607723", + "pmid": "36320862" + } + ], + "toolType": [ + "Workflow" + ], + "topic": [ + { + "term": "Computational chemistry", + "uri": "http://edamontology.org/topic_3332" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Physics", + "uri": "http://edamontology.org/topic_3318" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/flowsa/flowsa.biotools.json b/data/flowsa/flowsa.biotools.json new file mode 100644 index 0000000000000..9929c7de875cb --- /dev/null +++ b/data/flowsa/flowsa.biotools.json @@ -0,0 +1,116 @@ +{ + "additionDate": "2023-01-28T13:21:35.087221Z", + "biotoolsCURIE": "biotools:flowsa", + "biotoolsID": "flowsa", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "birney.catherine@epa.gov", + "name": "Catherine Birney", + "orcidid": "https://orcid.org/0000-0003-4467-9927", + "typeEntity": "Person" + } + ], + "description": "A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://github.com/USEPA/flowsa/wiki" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Formatting", + "uri": "http://edamontology.org/operation_0335" + }, + { + "term": "Parsing", + "uri": "http://edamontology.org/operation_1812" + } + ] + } + ], + "homepage": "https://edap-ord-data-commons.s3.amazonaws.com/index.html?prefix=flowsa/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:21:35.089926Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/USEPA/flowsa" + } + ], + "name": "FLOWSA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.3390/APP12115742", + "metadata": { + "abstract": "© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges sur-rounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA’s US Environmentally Extended Input–Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA’s capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA’s Data Commons.", + "authors": [ + { + "name": "Birney C." + }, + { + "name": "Conner M." + }, + { + "name": "Ingwersen W.W." + }, + { + "name": "Li M." + }, + { + "name": "Specht J." + }, + { + "name": "Young B." + } + ], + "date": "2022-06-01T00:00:00Z", + "journal": "Applied Sciences (Switzerland)", + "title": "FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries" + }, + "pmcid": "PMC9628186", + "pmid": "36330151" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Physics", + "uri": "http://edamontology.org/topic_3318" + } + ] +} diff --git a/data/flowuti/flowuti.biotools.json b/data/flowuti/flowuti.biotools.json new file mode 100644 index 0000000000000..90686776c0022 --- /dev/null +++ b/data/flowuti/flowuti.biotools.json @@ -0,0 +1,85 @@ +{ + "additionDate": "2023-01-28T13:24:29.911740Z", + "biotoolsCURIE": "biotools:flowuti", + "biotoolsID": "flowuti", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "gmartin-ibis@us.es", + "name": "Guillermo Martín-Gutiérrez ,", + "typeEntity": "Person" + } + ], + "description": "An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections.", + "download": [ + { + "type": "Source code", + "url": "https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "https://covidiario.shinyapps.io/flowuti/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-28T13:24:29.914384Z", + "license": "Not licensed", + "name": "FlowUTI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1371/JOURNAL.PONE.0277340", + "metadata": { + "abstract": "© 2022 Martín-Gutiérrez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Due to the high prevalence of patients attending with urinary tract infection (UTI) symptoms, the use of flow-cytometry as a rapid screening tool to avoid unnecessary cultures is becoming a widely used system in clinical practice. However, the recommended cut-points applied in flow-cytometry systems differ substantially among authors, making it difficult to obtain reliable conclusions. Here, we present FlowUTI, a shiny web-application created to establish optimal cut-off values in flow-cytometry for different UTI markers, such as bacterial or leukocyte counts, in urine from patients with UTI symptoms. This application provides a user-friendly graphical interface to perform robust statistical analysis without a specific training. Two datasets are analyzed in this manuscript: one composed of 204 urine samples from neonates and infants (≤3 months old) attended in the emergency department with suspected UTI; and the second dataset including 1174 urines samples from an elderly population attended at the primary care level. The source code is available on GitHub (https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI). The web application can be executed locally from the R console. Alternatively, it can be freely accessed at https://covidiario.shinyapps.io/flowuti/. FlowUTI provides an easy-to-use environment for evaluating the efficiency of the urinary screening process with flow-cytometry, reducing the computational burden associated with this kind of analysis.", + "authors": [ + { + "name": "Lepe J.A." + }, + { + "name": "Martin-Gutierrez G." + }, + { + "name": "Martin-Perez C." + }, + { + "name": "Sanchez-Cantalejo E." + }, + { + "name": "Toledo H." + } + ], + "citationCount": 1, + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections" + }, + "pmcid": "PMC9642874", + "pmid": "36346782" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Cytometry", + "uri": "http://edamontology.org/topic_3934" + }, + { + "term": "Geriatric medicine", + "uri": "http://edamontology.org/topic_3399" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/fluspred/fluspred.biotools.json b/data/fluspred/fluspred.biotools.json index 6aee36dd73655..0f1ca09603fb1 100644 --- a/data/fluspred/fluspred.biotools.json +++ b/data/fluspred/fluspred.biotools.json @@ -1,10 +1,30 @@ { + "accessibility": "Open access", "additionDate": "2022-10-03T09:24:57.447429Z", "biotoolsCURIE": "biotools:fluspred", "biotoolsID": "fluspred", + "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ + { + "name": "Anjali Dhall", + "orcidid": "http://orcid.org/0000-0002-0400-2084" + }, + { + "name": "Khushal Sharma", + "orcidid": "http://orcid.org/0000-0002-6993-5408" + }, + { + "name": "Sumeet Patiyal", + "orcidid": "http://orcid.org/0000-0003-1358-292X" + }, + { + "name": "Trinita Roy", + "orcidid": "http://orcid.org/0000-0002-2049-1391" + }, { "name": "Dr Gajendra P.S. Raghava", + "orcidid": "http://orcid.org/0000-0002-8902-2876", "url": "https://webs.iiitd.edu.in/raghava/fluspred/index.html" } ], @@ -24,15 +44,37 @@ { "operation": [ { - "term": "Analysis", - "uri": "http://edamontology.org/operation_2945" + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Protein feature detection", + "uri": "http://edamontology.org/operation_3092" + }, + { + "term": "Protein modelling", + "uri": "http://edamontology.org/operation_0477" + }, + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" } ] } ], "homepage": "https://webs.iiitd.edu.in/raghava/fluspred/index.html", - "lastUpdate": "2022-10-03T09:27:11.430395Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T02:35:05.562232Z", + "license": "GPL-3.0", "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/raghavagps/FluSPred" + }, { "type": [ "Software catalogue" @@ -47,13 +89,59 @@ "Windows" ], "owner": "raghavagps", + "publication": [ + { + "doi": "10.1099/jgv.0.001802", + "metadata": { + "abstract": "Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.", + "authors": [ + { + "name": "Dhall A." + }, + { + "name": "Patiyal S." + }, + { + "name": "Raghava G.P.S." + }, + { + "name": "Roy T." + }, + { + "name": "Sharma K." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "The Journal of general virology", + "title": "In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences" + }, + "pmid": "36318663" + } + ], "toolType": [ + "Command-line tool", "Web application" ], "topic": [ { - "term": "Computational biology", - "uri": "http://edamontology.org/topic_3307" + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" } ] } diff --git a/data/foec2/foec2.biotools.json b/data/foec2/foec2.biotools.json new file mode 100644 index 0000000000000..ab31649c4ce7c --- /dev/null +++ b/data/foec2/foec2.biotools.json @@ -0,0 +1,114 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T13:32:22.359918Z", + "biotoolsCURIE": "biotools:foec2", + "biotoolsID": "foec2", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "p.vandam@genetwister.nl", + "name": "Peter van Dam", + "typeEntity": "Person" + } + ], + "description": "A pipeline that can identify putative effectors in a provided set of Fusarium oxysporum genomes and show their presence/absence variation across all input genomes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Dendrogram visualisation", + "uri": "http://edamontology.org/operation_2938" + }, + { + "term": "Multiple sequence alignment", + "uri": "http://edamontology.org/operation_0492" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + } + ] + } + ], + "homepage": "https://github.com/pvdam3/FoEC2", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-28T13:32:22.362756Z", + "license": "MIT", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://github.com/pvdam3/FoEC" + } + ], + "name": "FoEC2", + "owner": "Chan019", + "publication": [ + { + "doi": "10.3389/FPLS.2022.1012688", + "metadata": { + "abstract": "Copyright © 2022 Brenes Guallar, Fokkens, Rep, Berke and van Dam.The fungus Fusarium oxysporum is infamous for its devastating effects on economically important crops worldwide. F. oxysporum isolates are grouped into formae speciales based on their ability to cause disease on different hosts. Assigning F. oxysporum strains to formae speciales using non-experimental procedures has proven to be challenging due to their genetic heterogeneity and polyphyletic nature. However, genetically diverse isolates of the same forma specialis encode similar repertoires of effectors, proteins that are secreted by the fungus and contribute to the establishment of compatibility with the host. Based on this observation, we previously designed the F. oxysporum Effector Clustering (FoEC) pipeline which is able to classify F. oxysporum strains by forma specialis based on hierarchical clustering of the presence of predicted putative effector sequences, solely using genome assemblies as input. Here we present the updated FoEC2 pipeline which is more user friendly, customizable and, due to multithreading, has improved scalability. It is designed as a Snakemake pipeline and incorporates a new interactive visualization app. We showcase FoEC2 by clustering 537 publicly available F. oxysporum genomes and further analysis of putative effector families as multiple sequence alignments. We confirm classification of isolates into formae speciales and are able to further identify their subtypes. The pipeline is available on github: https://github.com/pvdam3/FoEC2.", + "authors": [ + { + "name": "Berke L." + }, + { + "name": "Brenes Guallar M.A." + }, + { + "name": "Fokkens L." + }, + { + "name": "Rep M." + }, + { + "name": "van Dam P." + } + ], + "date": "2022-10-19T00:00:00Z", + "journal": "Frontiers in Plant Science", + "title": "Fusarium oxysporum effector clustering version 2: An updated pipeline to infer host range" + }, + "pmcid": "PMC9627151", + "pmid": "36340405" + } + ], + "toolType": [ + "Script", + "Workflow" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/fungal_names/fungal_names.biotools.json b/data/fungal_names/fungal_names.biotools.json new file mode 100644 index 0000000000000..74ae1caebd17a --- /dev/null +++ b/data/fungal_names/fungal_names.biotools.json @@ -0,0 +1,97 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-08T00:58:34.960071Z", + "biotoolsCURIE": "biotools:fungal_names", + "biotoolsID": "fungal_names", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "wulh@im.ac.cn", + "name": "Linhuan Wu", + "orcidid": "https://orcid.org/0000-0002-5255-1846", + "typeEntity": "Person" + }, + { + "email": "yaoyj@im.ac.cn", + "name": "Yijian Yao", + "typeEntity": "Person" + }, + { + "name": "Fang Wang" + }, + { + "name": "Ke Wang" + } + ], + "description": "A comprehensive nomenclatural repository and knowledge base for fungal taxonomy.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Taxon", + "uri": "http://edamontology.org/data_1868" + } + } + ], + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://nmdc.cn/fungalnames/", + "lastUpdate": "2023-01-08T00:58:34.963214Z", + "name": "fungal names", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC926", + "pmid": "36271801" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Microbiology", + "uri": "http://edamontology.org/topic_3301" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + }, + { + "term": "Sample collections", + "uri": "http://edamontology.org/topic_3277" + }, + { + "term": "Taxonomy", + "uri": "http://edamontology.org/topic_0637" + } + ] +} diff --git a/data/funpart/funpart.biotools.json b/data/funpart/funpart.biotools.json new file mode 100644 index 0000000000000..a0aec5bbeb696 --- /dev/null +++ b/data/funpart/funpart.biotools.json @@ -0,0 +1,153 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-11T13:36:49.681872Z", + "biotoolsCURIE": "biotools:funpart", + "biotoolsID": "funpart", + "collectionID": [ + "LCSB-CBG" + ], + "credit": [ + { + "email": "antonio.delsol@uni.lu", + "name": "Antonio del Sol", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ], + "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa" + } + ], + "description": "FunPart is a computational tool that partitions heterogeneous cell populations into functionally distinct subpopulations and simultaneously identifies modules of functionally relevant set of genes for each of them.", + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/BarlierC/FunPart" + } + ], + "editPermission": { + "type": "private" + }, + "elixirNode": [ + "Luxembourg" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Gene expression matrix", + "uri": "http://edamontology.org/data_3112" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + } + ], + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Gene regulatory network analysis", + "uri": "http://edamontology.org/operation_1781" + } + ], + "output": [ + { + "data": { + "term": "Annotated text", + "uri": "http://edamontology.org/data_3779" + } + }, + { + "data": { + "term": "Clustered expression profiles", + "uri": "http://edamontology.org/data_3768" + } + } + ] + } + ], + "homepage": "https://github.com/BarlierC/FunPart", + "language": [ + "R" + ], + "lastUpdate": "2023-01-11T13:36:49.684660Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/BarlierC/FunPart" + } + ], + "name": "FunPart", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "KartikeyaS", + "publication": [ + { + "doi": "10.1038/s41419-021-04075-y", + "metadata": { + "abstract": "© 2021, The Author(s).Immunomodulation strategies are crucial for several biomedical applications. However, the immune system is highly heterogeneous and its functional responses to infections remains elusive. Indeed, the characterization of immune response particularities to different pathogens is needed to identify immunomodulatory candidates. To address this issue, we compiled a comprehensive map of functional immune cell states of mouse in response to 12 pathogens. To create this atlas, we developed a single-cell-based computational method that partitions heterogeneous cell types into functionally distinct states and simultaneously identifies modules of functionally relevant genes characterizing them. We identified 295 functional states using 114 datasets of six immune cell types, creating a Catalogus Immune Muris. As a result, we found common as well as pathogen-specific functional states and experimentally characterized the function of an unknown macrophage cell state that modulates the response to Salmonella Typhimurium infection. Thus, we expect our Catalogus Immune Muris to be an important resource for studies aiming at discovering new immunomodulatory candidates.", + "authors": [ + { + "name": "Anguita J." + }, + { + "name": "Barlier C." + }, + { + "name": "Barriales D." + }, + { + "name": "Jung S." + }, + { + "name": "Medvedeva Y.A." + }, + { + "name": "Ravichandran S." + }, + { + "name": "Samosyuk A." + }, + { + "name": "del Sol A." + } + ], + "date": "2021-09-01T00:00:00Z", + "journal": "Cell Death and Disease", + "title": "A Catalogus Immune Muris of the mouse immune responses to diverse pathogens" + }, + "pmcid": "PMC8370971", + "pmid": "34404761", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Function analysis", + "uri": "http://edamontology.org/topic_1775" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/funres/funres.biotools.json b/data/funres/funres.biotools.json new file mode 100644 index 0000000000000..1b24ce309ba82 --- /dev/null +++ b/data/funres/funres.biotools.json @@ -0,0 +1,154 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-11T15:30:42.728886Z", + "biotoolsCURIE": "biotools:funres", + "biotoolsID": "funres", + "collectionID": [ + "LCSB-CBG" + ], + "credit": [ + { + "email": "antonio.delsol@uni.lu", + "name": "Antonio del Sol", + "note": "Group leader, Computational Biology group, Luxembourg Centre for Systems Biomedicine \nFull professor / Chief scientist 1 in Bioinformatics at University of Luxembourg", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ], + "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa" + } + ], + "description": "Resolving tissue-specific functional cell states based on a cell–cell communication network model", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres" + } + ], + "editPermission": { + "type": "private" + }, + "elixirNode": [ + "Luxembourg" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Gene expression matrix", + "uri": "http://edamontology.org/data_3112" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + } + ], + "operation": [ + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + }, + { + "term": "Prediction and recognition", + "uri": "http://edamontology.org/operation_2423" + } + ], + "output": [ + { + "data": { + "term": "Annotated text", + "uri": "http://edamontology.org/data_3779" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + }, + { + "data": { + "term": "Heat map", + "uri": "http://edamontology.org/data_1636" + }, + "format": [ + { + "term": "tiff", + "uri": "http://edamontology.org/format_3591" + } + ] + } + ] + } + ], + "homepage": "https://git-r3lab.uni.lu/kartikeya.singh/funres", + "language": [ + "R" + ], + "lastUpdate": "2023-01-11T15:30:42.731628Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres" + } + ], + "name": "FunRes", + "operatingSystem": [ + "Linux" + ], + "owner": "KartikeyaS", + "publication": [ + { + "doi": "10.1093/bib/bbaa283", + "metadata": { + "abstract": "© 2020 The Author(s) 2020. Published by Oxford University Press.The functional specialization of cell types arises during development and is shaped by cell-cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell-cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.", + "authors": [ + { + "name": "Del Sol A." + }, + { + "name": "Jung S." + }, + { + "name": "Singh K." + } + ], + "citationCount": 3, + "date": "2021-07-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "FunRes: Resolving tissue-specific functional cell states based on a cell-cell communication network model" + }, + "pmcid": "PMC8293827", + "pmid": "33179736", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + } + ] +} diff --git a/data/g3mclass/g3mclass.biotools.json b/data/g3mclass/g3mclass.biotools.json new file mode 100644 index 0000000000000..71c3c06f18a80 --- /dev/null +++ b/data/g3mclass/g3mclass.biotools.json @@ -0,0 +1,105 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T13:37:54.787795Z", + "biotoolsCURIE": "biotools:g3mclass", + "biotoolsID": "g3mclass", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "guvakova@pennmedicine.upenn.edu", + "name": "Marina A. 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It has a complete set of help files that can be viewed after installation.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Parsing", + "uri": "http://edamontology.org/operation_1812" + }, + { + "term": "Statistical calculation", + "uri": "http://edamontology.org/operation_2238" + } + ] + } + ], + "homepage": "https://pypi.org/project/g3mclass", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:37:54.791017Z", + "license": "GPL-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/MathsCell/g3mclass" + } + ], + "name": "g3mclass", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41598-022-23438-9", + "metadata": { + "abstract": "© 2022, The Author(s).The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation–maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output’s accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.", + "authors": [ + { + "name": "Guvakova M.A." + }, + { + "name": "Sokol S." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Reports", + "title": "The g3mclass is a practical software for multiclass classification on biomarkers" + }, + "pmcid": "PMC9637185", + "pmid": "36335194" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/g4atlas/g4atlas.biotools.json b/data/g4atlas/g4atlas.biotools.json new file mode 100644 index 0000000000000..77fc783d930e5 --- /dev/null +++ b/data/g4atlas/g4atlas.biotools.json @@ -0,0 +1,119 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-08T00:53:40.967549Z", + "biotoolsCURIE": "biotools:g4atlas", + "biotoolsID": "g4atlas", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "haopeng.yu@jic.ac.uk", + "name": "Haopeng Yu", + "orcidid": "https://orcid.org/0000-0002-5184-2430", + "typeEntity": "Person" + }, + { + "email": "yiliang.ding@jic.ac.uk", + "name": "Yiliang Ding", + "orcidid": "https://orcid.org/0000-0003-4161-6365", + "typeEntity": "Person" + }, + { + "name": "Bibo Yang" + }, + { + "name": "Xiaofei Yang" + }, + { + "name": "Yiman Qi" + } + ], + "description": "A comprehensive transcriptome-wide G-quadruplex database.", + "download": [ + { + "type": "Downloads page", + "url": "https://www.g4atlas.org/download" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene ID", + "uri": "http://edamontology.org/data_2295" + } + }, + { + "data": { + "term": "Gene name", + "uri": "http://edamontology.org/data_2299" + } + }, + { + "data": { + "term": "Species name", + "uri": "http://edamontology.org/data_1045" + } + } + ], + "operation": [ + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + }, + { + "term": "RNA structure prediction", + "uri": "http://edamontology.org/operation_2441" + }, + { + "term": "Structure visualisation", + "uri": "http://edamontology.org/operation_0570" + } + ] + } + ], + "homepage": "https://www.g4atlas.org/", + "lastUpdate": "2023-01-08T00:55:41.242678Z", + "name": "G4Atlas", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC896", + "pmid": "36243987" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/gaitforemer/gaitforemer.biotools.json b/data/gaitforemer/gaitforemer.biotools.json new file mode 100644 index 0000000000000..a605a08debddb --- /dev/null +++ b/data/gaitforemer/gaitforemer.biotools.json @@ -0,0 +1,79 @@ +{ + "additionDate": "2023-01-28T13:43:29.100681Z", + "biotoolsCURIE": "biotools:gaitforemer", + "biotoolsID": "gaitforemer", + "confidence_flag": "tool", + "credit": [ + { + "email": "eadeli@stanford.edu", + "name": "Ehsan Adeli", + "typeEntity": "Person" + } + ], + "description": "GaitForeMer (Gait Forecasting and impairment estimation transforMer) predicts MDS-UPDRS gait impairment severity scores using learned motion features from the pre-training task of human motion forecasting.", + "editPermission": { + "type": "public" + }, + "homepage": "https://github.com/markendo/GaitForeMer", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:43:29.103394Z", + "license": "GPL-3.0", + "name": "GaitForeMer", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1007/978-3-031-16452-1_13", + "metadata": { + "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Parkinson’s disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical potential. Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity. We introduce GaitForeMer, Gait Forecasting and impairment estimation transforMer, which is first pre-trained on public datasets to forecast gait movements and then applied to clinical data to predict MDS-UPDRS gait impairment severity. Our method outperforms previous approaches that rely solely on clinical data by a large margin, achieving an F$$:1$$ score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we show how public human movement data repositories can assist clinical use cases through learning universal motion representations. The code is available at https://github.com/markendo/GaitForeMer.", + "authors": [ + { + "name": "Adeli E." + }, + { + "name": "Endo M." + }, + { + "name": "Fei-Fei L." + }, + { + "name": "Pohl K.M." + }, + { + "name": "Poston K.L." + }, + { + "name": "Sullivan E.V." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", + "title": "GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation" + }, + "pmcid": "PMC9635991", + "pmid": "36342887" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Neurology", + "uri": "http://edamontology.org/topic_3334" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3794" + } + ] +} diff --git a/data/gen-era_toolbox/gen-era_toolbox.biotools.json b/data/gen-era_toolbox/gen-era_toolbox.biotools.json new file mode 100644 index 0000000000000..96ddd3bd2746a --- /dev/null +++ b/data/gen-era_toolbox/gen-era_toolbox.biotools.json @@ -0,0 +1,29 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-12T15:38:53.335761Z", + "biotoolsCURIE": "biotools:gen-era_toolbox", + "biotoolsID": "gen-era_toolbox", + "cost": "Free of charge", + "description": "The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes.", + "editPermission": { + "type": "private" + }, + "homepage": "https://github.com/Lcornet/GENERA", + "lastUpdate": "2023-01-12T15:40:43.463894Z", + "license": "GPL-3.0", + "maturity": "Mature", + "name": "GEN-ERA toolbox", + "operatingSystem": [ + "Linux" + ], + "owner": "Lcornet", + "toolType": [ + "Workflow" + ], + "topic": [ + { + "term": "Computational biology", + "uri": "http://edamontology.org/topic_3307" + } + ] +} diff --git a/data/genecloudomics/genecloudomics.biotools.json b/data/genecloudomics/genecloudomics.biotools.json new file mode 100644 index 0000000000000..e305a9629ce2e --- /dev/null +++ b/data/genecloudomics/genecloudomics.biotools.json @@ -0,0 +1,136 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:51:53.047663Z", + "biotoolsCURIE": "biotools:genecloudomics", + "biotoolsID": "genecloudomics", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Kumar_Selvarajoo@bii.a-star.edu.sg", + "name": "Kumar Selvarajoo", + "typeEntity": "Person" + }, + { + "email": "mohamed_helmy@bii.a-star.edu.sg", + "name": "Mohamed Helmy", + "typeEntity": "Person" + }, + { + "name": "Rahul Agrawal" + }, + { + "name": "Thuy Tien Bui" + } + ], + "description": "A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://github.com/buithuytien/GeneCloudOmics/blob/master/GeneCloudOmics_Help_1.0.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Expression data visualisation", + "uri": "http://edamontology.org/operation_0571" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + } + ] + } + ], + "homepage": "http://combio-sifbi.org/GeneCloudOmics/", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2022-12-31T15:51:53.050195Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/cbio-astar-tools/GeneCloudOmics" + } + ], + "name": "GeneCloudOmics", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1007/978-1-0716-2617-7_12", + "metadata": { + "abstract": "© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.", + "authors": [ + { + "name": "Helmy M." + }, + { + "name": "Selvarajoo K." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Methods in Molecular Biology", + "title": "Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology" + }, + "pmid": "36227547" + }, + { + "doi": "10.3389/FBINF.2021.693836", + "pmcid": "PMC9581002", + "pmid": "36303746" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + }, + { + "term": "Synthetic biology", + "uri": "http://edamontology.org/topic_3895" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/genenettools/genenettools.biotools.json b/data/genenettools/genenettools.biotools.json new file mode 100644 index 0000000000000..a033ade57af20 --- /dev/null +++ b/data/genenettools/genenettools.biotools.json @@ -0,0 +1,90 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:42:16.791143Z", + "biotoolsCURIE": "biotools:genenettools", + "biotoolsID": "genenettools", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "p.l.horvatovich@rug.nl", + "name": "Peter Horvatovich", + "orcidid": "https://orcid.org/0000-0003-2218-1140", + "typeEntity": "Person" + }, + { + "name": "Marco Grzegorczyk" + }, + { + "name": "Victor Bernal", + "orcidid": "https://orcid.org/0000-0002-9134-7186" + } + ], + "description": "Tests for Gaussian graphical models with shrinkage.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/V-Bernal/GeneNetTools", + "language": [ + "R" + ], + "lastUpdate": "2022-12-31T15:42:16.793672Z", + "license": "Not licensed", + "name": "GeneNetTools", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC657", + "pmcid": "PMC9665865", + "pmid": "36179082" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/geneselectml/geneselectml.biotools.json b/data/geneselectml/geneselectml.biotools.json new file mode 100644 index 0000000000000..c203295abb34a --- /dev/null +++ b/data/geneselectml/geneselectml.biotools.json @@ -0,0 +1,98 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T13:49:02.460703Z", + "biotoolsCURIE": "biotools:geneselectml", + "biotoolsID": "geneselectml", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "osman.dag@hacettepe.edu.tr", + "name": "Osman Dag", + "typeEntity": "Person" + } + ], + "description": "This web-tool enables the researchers to find differentially expressed genes using different machine learning algorithms.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene prediction", + "uri": "http://edamontology.org/operation_2454" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://www.softmed.hacettepe.edu.tr/GeneSelectML", + "lastUpdate": "2023-01-28T13:49:02.463681Z", + "license": "Not licensed", + "name": "GeneSelectML", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1007/S11517-022-02695-W", + "metadata": { + "abstract": "© 2022, International Federation for Medical and Biological Engineering.Abstract: Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared to univariate analysis. However, there exist serious differences among most studies analyzing the same dataset for the reasons arising from the methods. Therefore, there is a strong need for easily accessible, user-friendly, and interactive tool to perform gene selection for RNA-seq data via machine learning algorithms simultaneously not to miss DEGs. We develop an open-source and freely available web-based tool for gene selection via machine learning algorithms that can deal with high performance computation. This tool includes six machine learning algorithms having different aspects. Moreover, the tool involves classical pre-processing steps; filtering, normalization, transformation, and univariate analysis. It also offers well-arranged graphical approaches; network plot, heatmap, venn diagram, and box-and-whisker plot. Gene ontology analysis is provided for both mRNA and miRNA DEGs. The implementation is carried out on Alzheimer RNA-seq data to demonstrate the use of this web-based tool. Eleven genes are suggested by at least two out of six methods. One of these genes, hsa-miR-148a-3p, might be considered as a new biomarker for Alzheimer’s disease diagnosis. Kidney Chromophobe dataset is also analyzed to demonstrate the validity of GeneSelectML web tool on a different dataset. GeneSelectML is distinguished in that it simultaneously uses different machine learning algorithms for gene selection and can perform pre-processing, graphical representation, and gene ontology analyses on the same tool. This tool is freely available at www.softmed.hacettepe.edu.tr/GeneSelectML. Graphical abstract: [Figure not available: see fulltext.].", + "authors": [ + { + "name": "Dag O." + }, + { + "name": "Ilk O." + }, + { + "name": "Kasikci M." + }, + { + "name": "Yesiltepe M." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Medical and Biological Engineering and Computing", + "title": "GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms" + }, + "pmid": "36355333" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/genie_web/genie_web.biotools.json b/data/genie_web/genie_web.biotools.json new file mode 100644 index 0000000000000..05c1164c7c399 --- /dev/null +++ b/data/genie_web/genie_web.biotools.json @@ -0,0 +1,120 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:33:10.352003Z", + "biotoolsCURIE": "biotools:genie_web", + "biotoolsID": "genie_web", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mwilsons@asu.edu", + "name": "Melissa A. Wilson", + "orcidid": "https://orcid.org/0000-0002-2614-0285", + "typeEntity": "Person" + }, + { + "email": "cartwright@asu.edu", + "name": "Reed A. Cartwright", + "typeEntity": "Person" + }, + { + "name": "Andreina I. Castillo" + }, + { + "name": "Ben H. Roos" + }, + { + "name": "Michael S. Rosenberg" + } + ], + "description": "An interactive real-time simulation for teaching genetic drift.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Allele frequency distribution analysis", + "uri": "http://edamontology.org/operation_0554" + } + ] + } + ], + "homepage": "https://cartwrig.ht/apps/genie/", + "language": [ + "R" + ], + "lastUpdate": "2022-12-31T15:33:10.354595Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/AndreinaCastillo/Genie_manuscript_data_analysis" + } + ], + "name": "Genie", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12052-022-00161-7", + "metadata": { + "abstract": "© 2022, The Author(s).Neutral evolution is a fundamental concept in evolutionary biology but teaching this and other non-adaptive concepts is especially challenging. Here we present Genie, a browser-based educational tool that demonstrates population-genetic concepts such as genetic drift, population isolation, gene flow, and genetic mutation. Because it does not need to be downloaded and installed, Genie can scale to large groups of students and is useful for both in-person and online instruction. Genie was used to teach genetic drift to Evolution students at Arizona State University during Spring 2016 and Spring 2017. The effectiveness of Genie to teach key genetic drift concepts and misconceptions was assessed with the Genetic Drift Inventory developed by Price et al. (CBE Life Sci Educ 13(1):65–75, 2014). Overall, Genie performed comparably to that of traditional static methods across all evaluated classes. We have empirically demonstrated that Genie can be successfully integrated with traditional instruction to reduce misconceptions about genetic drift.", + "authors": [ + { + "name": "Cartwright R.A." + }, + { + "name": "Castillo A.I." + }, + { + "name": "Roos B.H." + }, + { + "name": "Rosenberg M.S." + }, + { + "name": "Wilson M.A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Evolution: Education and Outreach", + "title": "Genie: an interactive real-time simulation for teaching genetic drift" + }, + "pmcid": "PMC9555832", + "pmid": "36237301" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Physics", + "uri": "http://edamontology.org/topic_3318" + }, + { + "term": "Population genetics", + "uri": "http://edamontology.org/topic_3056" + } + ] +} diff --git a/data/genomepaint/genomepaint.biotools.json b/data/genomepaint/genomepaint.biotools.json new file mode 100644 index 0000000000000..4a558d49daf77 --- /dev/null +++ b/data/genomepaint/genomepaint.biotools.json @@ -0,0 +1,157 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T23:46:42.535571Z", + "biotoolsCURIE": "biotools:genomepaint", + "biotoolsID": "genomepaint", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jinghui.zhang@stjude.org", + "name": "Jinghui Zhang", + "typeEntity": "Person" + }, + { + "email": "xin.zhou@stjude.org", + "name": "Xin Zhou", + "typeEntity": "Person" + }, + { + "name": "Jian Wang" + }, + { + "name": "John Easton" + } + ], + "description": "Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://genomepaint.stjude.cloud/", + "lastUpdate": "2023-01-26T23:46:42.538157Z", + "name": "GenomePaint", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.CCELL.2020.12.011", + "metadata": { + "abstract": "© 2020 Elsevier Inc.GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome data from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and supports custom data upload. We used GenomePaint to unveil aberrant splicing that disrupts the RING domain of CREBBP, discover cis activation of the MYC oncogene by duplication of the NOTCH1-MYC enhancer in B-lineage acute lymphoblastic leukemia, and explore the inter- and intra-tumor heterogeneity at EGFR in adult glioblastomas. These examples demonstrate that deep multi-omics exploration of individual cancer genomes enabled by GenomePaint can lead to biological insights for follow-up validation. © 2020 Elsevier Inc.Zhou et al. develop GenomePaint for visualizing coding and non-coding variants in cancer. With a primary focus on pediatric cancer, GenomePaint enables detection of common and rare driver variants within cancer subtypes and discovery of novel oncogenic events by integrating DNA, RNA, and epigenetic data from individual cancer genomes.", + "authors": [ + { + "name": "Baker S.J." + }, + { + "name": "Brady S.W." + }, + { + "name": "Easton J." + }, + { + "name": "Edmonson M.N." + }, + { + "name": "Flasch D." + }, + { + "name": "Li C." + }, + { + "name": "Liu Y." + }, + { + "name": "Liu Y." + }, + { + "name": "Ma X." + }, + { + "name": "Newman S." + }, + { + "name": "Patel J." + }, + { + "name": "Paul R." + }, + { + "name": "Rusch M.C." + }, + { + "name": "Shao Y." + }, + { + "name": "Sioson E." + }, + { + "name": "Tian L." + }, + { + "name": "Valentine M." + }, + { + "name": "Wang J." + }, + { + "name": "Zhang J." + }, + { + "name": "Zhou X." + } + ], + "citationCount": 10, + "date": "2021-01-11T00:00:00Z", + "journal": "Cancer Cell", + "title": "Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint" + }, + "pmcid": "PMC7884056", + "pmid": "33434514" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Exome sequencing", + "uri": "http://edamontology.org/topic_3676" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/genomesidekick/genomesidekick.biotools.json b/data/genomesidekick/genomesidekick.biotools.json new file mode 100644 index 0000000000000..2645eaf015c4b --- /dev/null +++ b/data/genomesidekick/genomesidekick.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-19T10:31:01.688390Z", + "biotoolsCURIE": "biotools:genomesidekick", + "biotoolsID": "genomesidekick", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dchapski@ucla.edu", + "name": "Douglas J. Chapski", + "orcidid": "http://orcid.org/0000-0002-6730-7627", + "typeEntity": "Person" + }, + { + "name": "Ashley J. Zhu" + }, + { + "name": "Junjie Chen" + }, + { + "name": "René R. Sevag Packard" + }, + { + "name": "Thomas M. Vondriska" + } + ], + "description": "The genomeSidekick data analysis tool is a simple and efficient application that allows users to analyze and visualize RNA-seq and ATAC-seq data without having to learn the nitty gritty bioinformatics. This document will provide a comprehensive overview of the functions and capabilities of each tab within the application. For your convenience, the app can be used both online as a website or locally run in your RStudio. If you run into any problems while using the app in RStudio, refer to the Troubleshooting section to see some common errors and solutions that may occur.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "RNA-Seq analysis", + "uri": "http://edamontology.org/operation_3680" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://genomesidekick.shinyapps.io/genomesidekick/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-19T10:31:01.690754Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://www.github.com/dchapski/genomeSidekick" + } + ], + "name": "genomeSidekick", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/fbinf.2022.831025", + "pmcid": "PMC9580848", + "pmid": "36304311" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/genomickb/genomickb.biotools.json b/data/genomickb/genomickb.biotools.json new file mode 100644 index 0000000000000..50962c885c101 --- /dev/null +++ b/data/genomickb/genomickb.biotools.json @@ -0,0 +1,117 @@ +{ + "additionDate": "2023-01-28T13:54:40.841859Z", + "biotoolsCURIE": "biotools:genomickb", + "biotoolsID": "genomickb", + "confidence_flag": "tool", + "credit": [ + { + "email": "drjieliu@umich.edu", + "name": "Jie Liu", + "orcidid": "https://orcid.org/0000-0002-9504-0587", + "typeEntity": "Person" + } + ], + "description": "Genomic Knowledgebase (GenomicKB) is a graph database which use a knowledge graph to consolidates genomic datasets and annotations from over 30 consortia and portals.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + }, + { + "term": "Relation extraction", + "uri": "http://edamontology.org/operation_3625" + } + ] + } + ], + "homepage": "https://gkb.dcmb.med.umich.edu/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T13:54:40.845230Z", + "license": "Not licensed", + "name": "GenomicKB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC957", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.", + "authors": [ + { + "name": "Feng F." + }, + { + "name": "Gao Y." + }, + { + "name": "Huang Y." + }, + { + "name": "Li T." + }, + { + "name": "Liu J." + }, + { + "name": "Tang F." + }, + { + "name": "Yang S." + }, + { + "name": "Yao Y." + }, + { + "name": "Zhu D." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "GenomicKB: a knowledge graph for the human genome" + }, + "pmcid": "PMC9825430", + "pmid": "36318240" + } + ], + "toolType": [ + "Desktop application", + "Web application" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Human biology", + "uri": "http://edamontology.org/topic_2815" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/ggmotif/ggmotif.biotools.json b/data/ggmotif/ggmotif.biotools.json new file mode 100644 index 0000000000000..430766445e549 --- /dev/null +++ b/data/ggmotif/ggmotif.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T13:56:44.338082Z", + "biotoolsCURIE": "biotools:ggmotif", + "biotoolsID": "ggmotif", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "absklhhc@gmail.com", + "name": "Huichuan Huang", + "orcidid": "https://orcid.org/0000-0002-8400-7116", + "typeEntity": "Person" + } + ], + "description": "An R Package for the extraction and visualization of motifs from MEME software.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Phylogenetic inference", + "uri": "http://edamontology.org/operation_0323" + }, + { + "term": "Phylogenetic tree editing", + "uri": "http://edamontology.org/operation_0326" + }, + { + "term": "Phylogenetic tree visualisation", + "uri": "http://edamontology.org/operation_0567" + }, + { + "term": "Sequence motif recognition", + "uri": "http://edamontology.org/operation_0239" + } + ] + } + ], + "homepage": "https://github.com/lixiang117423/ggmotif", + "language": [ + "R" + ], + "lastUpdate": "2023-01-28T13:56:44.340770Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://CRAN.R-project.org/package=ggmotif" + } + ], + "name": "ggmotif", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1371/JOURNAL.PONE.0276979", + "metadata": { + "abstract": "© 2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.MEME (Multiple Em for Motif Elicitation) is the most commonly used tool to identify motifs within deoxyribonucleic acid (DNA) or protein sequences. However, the results generated by the MEMEare saved using file formats .xml and .txt, which are difficult to read, visualize, or integrate with other widely used phylogenetic tree packages, such as ggtree. To overcome this problem, we developed the ggmotif R package, which provides two easy-to-use functions that can facilitate the extraction and visualization of motifs from the results files generated by the MEME. ggmotif can extract the information of the location of motif(s) on the corresponding sequence(s) from the .xml format file and visualize it. Additionally, the data extracted by ggmotif can be easily integrated with the phylogenetic data. On the other hand, ggmotif can obtain the sequence of each motif from the .txt format file and draw the sequence logo with the function ggseqlogo from the ggseqlogo R package. The ggmotif R package is freely available (including examples and vignettes) from GitHub at https://github. com/lixiang117423/ggmotif or from CRAN at https://CRAN.R-project.org/package=ggmotif.", + "authors": [ + { + "name": "Huang H." + }, + { + "name": "Li X." + }, + { + "name": "Liu Y." + }, + { + "name": "Ma L." + }, + { + "name": "Mei X." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "ggmotif: An R Package for the extraction and visualization of motifs from MEME software" + }, + "pmcid": "PMC9632824", + "pmid": "36327240" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/ggplot2/ggplot2.biotools.json b/data/ggplot2/ggplot2.biotools.json index dfd22de811e75..49cae2573939e 100644 --- a/data/ggplot2/ggplot2.biotools.json +++ b/data/ggplot2/ggplot2.biotools.json @@ -37,11 +37,10 @@ } ], "homepage": "http://ggplot2.org/", - "homepage_status": 1, "language": [ "R" ], - "lastUpdate": "2018-12-10T12:58:55Z", + "lastUpdate": "2023-01-17T02:43:35.495397Z", "name": "ggplot2", "operatingSystem": [ "Linux", @@ -54,6 +53,12 @@ "doi": "10.1007/978-3-319-24277-4" } ], + "relation": [ + { + "biotoolsID": "ggtranscript", + "type": "usedBy" + } + ], "toolType": [ "Library" ], diff --git a/data/ggtranscript/ggtranscript.biotools.json b/data/ggtranscript/ggtranscript.biotools.json new file mode 100644 index 0000000000000..f2411f4736d05 --- /dev/null +++ b/data/ggtranscript/ggtranscript.biotools.json @@ -0,0 +1,132 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T02:41:54.108279Z", + "biotoolsCURIE": "biotools:ggtranscript", + "biotoolsID": "ggtranscript", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "David Zhang", + "orcidid": "http://orcid.org/0000-0003-2382-8460" + }, + { + "name": "Emil K. Gustavsson", + "orcidid": "http://orcid.org/0000-0003-0541-7537" + }, + { + "name": "Mina Ryten", + "orcidid": "http://orcid.org/0000-0001-9520-6957" + }, + { + "name": "Regina H. Reynolds", + "orcidid": "http://orcid.org/0000-0001-6470-7919" + }, + { + "name": "Sonia Garcia-Ruiz", + "orcidid": "http://orcid.org/0000-0003-4913-5312" + } + ], + "description": "An R package for the visualization and interpretation of transcript isoforms using ggplot2.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://dzhang32.github.io/ggtranscript/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome annotation", + "uri": "http://edamontology.org/operation_0362" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/dzhang32/ggtranscript/tree/v0.99.3", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T02:43:14.074635Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://doi.org/10.5281/zenodo.6374061" + } + ], + "name": "ggtranscript", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac409", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Motivation: The advent of long-read sequencing technologies has increased demand for the visualization and interpretation of transcripts. However, tools that perform such visualizations remain inflexible and lack the ability to easily identify differences between transcript structures. Here, we introduce ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts. As a ggplot2 extension, ggtranscript inherits the functionality and familiarity of ggplot2 making it easy to use.", + "authors": [ + { + "name": "Garcia-Ruiz S." + }, + { + "name": "Gustavsson E.K." + }, + { + "name": "Reynolds R.H." + }, + { + "name": "Ryten M." + }, + { + "name": "Zhang D." + } + ], + "citationCount": 4, + "date": "2022-08-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "ggtranscript: An R package for the visualization and interpretation of transcript isoforms using ggplot2" + }, + "pmcid": "PMC9344834", + "pmid": "35751589" + } + ], + "relation": [ + { + "biotoolsID": "ggplot2", + "type": "uses" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + } + ] +} diff --git a/data/gifdti/gifdti.biotools.json b/data/gifdti/gifdti.biotools.json new file mode 100644 index 0000000000000..1c22dc9efe158 --- /dev/null +++ b/data/gifdti/gifdti.biotools.json @@ -0,0 +1,73 @@ +{ + "additionDate": "2023-01-28T14:04:18.419955Z", + "biotoolsCURIE": "biotools:gifdti", + "biotoolsID": "gifdti", + "confidence_flag": "tool", + "credit": [ + { + "name": "Jianxin Wang", + "orcidid": "https://orcid.org/0000-0003-1516-0480" + } + ], + "description": "Prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning.", + "editPermission": { + "type": "public" + }, + "homepage": "https://github.com/zhaoqichang/GIFDTI", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:04:18.422455Z", + "license": "Not licensed", + "name": "GIFDTI", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1109/TCBB.2022.3225423", + "metadata": { + "abstract": "IEEEDrug discovery and drug repurposing often rely on the successful prediction of drug-target interactions (DTIs). Recent advances have shown great promise in applying deep learning to drug-target interaction prediction. One challenge in building deep learning-based models is to adequately represent drugs and proteins that encompass the fundamental local chemical environments and long-distance information among amino acids of proteins (or atoms of drugs). Another challenge is to efficiently model the intermolecular interactions between drugs and proteins, which plays vital roles in the DTIs. To this end, we propose a novel model, GIFDTI, which consists of three key components: the sequence feature extractor (CNNFormer), the global molecular feature extractor (GF), and the intermolecular interaction modeling module (IIF). Specifically, CNNFormer incorporates CNN and Transformer to capture the local patterns and encode the long-distance relationship among tokens (atoms or amino acids) in a sequence. Then, GF and IIF extract the global molecular features and the intermolecular interaction features, respectively. We evaluate GIFDTI on six realistic evaluation strategies and the results show it improves DTI prediction performance compared to state-of-the-art methods. Moreover, case studies confirm that our model can be a useful tool to accurately yield low-cost DTIs. The codes of GIFDTI are available at https://github.com/zhaoqichang/GIFDTI.", + "authors": [ + { + "name": "Duan G." + }, + { + "name": "Li Y." + }, + { + "name": "Wang J." + }, + { + "name": "Zhao H." + }, + { + "name": "Zhao Q." + }, + { + "name": "Zheng K." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics", + "title": "GIFDTI: Prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning" + }, + "pmid": "36445997" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/gift_imagej/gift_imagej.biotools.json b/data/gift_imagej/gift_imagej.biotools.json new file mode 100644 index 0000000000000..ca23c07aa3f15 --- /dev/null +++ b/data/gift_imagej/gift_imagej.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:26:33.500444Z", + "biotoolsCURIE": "biotools:gift_imagej", + "biotoolsID": "gift_imagej", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Jennifer.huling@uni-rostock.de", + "name": "Jennifer Huling", + "orcidid": "https://orcid.org/0000-0001-5588-8922", + "typeEntity": "Person" + }, + { + "name": "Niels Grabow" + }, + { + "name": "Andreas Götz", + "orcidid": "https://orcid.org/0000-0003-0463-8741" + }, + { + "name": "Sabine Illner", + "orcidid": "https://orcid.org/0000-0002-2033-2964" + } + ], + "description": "General Image Fiber Tool (GIFT) is an ImageJ macro tool which allows the users to measure the average diameter of electrospun fibers in scanning electron microscopy (SEM) images.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://github.com/IBMTRostock/GIFT", + "lastUpdate": "2022-12-31T15:27:15.676018Z", + "license": "MIT", + "name": "GIFT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PONE.0275528", + "metadata": { + "abstract": "© 2022 Huling et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This paper details the development and testing of the GIFT macro, which is a freely available program for ImageJ for the automated measurement of fiber diameters in SEM images of electrospun materials. The GIFT macro applies a validated method which distinguishes fiber diameters based on distance frequencies within an image. In this work, we introduce an applied version of the GIFT method which has been designed to be user-friendly while still allowing complete control over the various parameters involved in the image processing steps. The macro quickly processes large data sets and creates results that are reproducible and accurate. The program outputs both raw data and fiber diameter averages, so that the user can quickly assess the results and has the opportunity for further analysis if desired. The GIFT macro was compared directly to other software designed for fiber diameter measurements and was found to have comparable or lower average error, especially when measuring very small fibers, and reduced processing times per image. The macro, detailed instructions for use, and sample images are freely available online (https://github.com/IBMTRostock/GIFT). We believe that the GIFT macro is a valuable new tool for researchers looking to quickly, easily and reliably assess fiber diameters in electrospun materials.", + "authors": [ + { + "name": "Gotz A." + }, + { + "name": "Grabow N." + }, + { + "name": "Huling J." + }, + { + "name": "Illner S." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "GIFT: An ImageJ macro for automated fiber diameter quantification" + }, + "pmcid": "PMC9529089", + "pmid": "36191031" + } + ], + "relation": [ + { + "biotoolsID": "imagej", + "type": "uses" + } + ], + "toolType": [ + "Plug-in" + ], + "topic": [ + { + "term": "Electron microscopy", + "uri": "http://edamontology.org/topic_0611" + } + ] +} diff --git a/data/gigasom.jl/gigasom.jl.biotools.json b/data/gigasom.jl/gigasom.jl.biotools.json index fc36d63081b61..12335375c2237 100644 --- a/data/gigasom.jl/gigasom.jl.biotools.json +++ b/data/gigasom.jl/gigasom.jl.biotools.json @@ -90,7 +90,7 @@ "language": [ "Julia" ], - "lastUpdate": "2022-08-23T14:09:50.569709Z", + "lastUpdate": "2023-01-05T08:45:18.955295Z", "license": "Apache-2.0", "link": [ { @@ -148,7 +148,7 @@ "name": "Vondrasek J." } ], - "citationCount": 3, + "citationCount": 4, "date": "2020-11-01T00:00:00Z", "journal": "GigaScience", "title": "GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets" diff --git a/data/giloop/giloop.biotools.json b/data/giloop/giloop.biotools.json new file mode 100644 index 0000000000000..311451db48d77 --- /dev/null +++ b/data/giloop/giloop.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T14:07:42.668199Z", + "biotoolsCURIE": "biotools:giloop", + "biotoolsID": "giloop", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kc.w@cityu.edu.hk", + "name": "Ka-Chun Wong", + "typeEntity": "Person" + }, + { + "email": "lixt314@jlu.edu.cn", + "name": "Xiangtao Li", + "typeEntity": "Person" + } + ], + "description": "GILoop is a deep learning model for detecting CTCF-mediated loops on Hi-C contact maps.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Loop modelling", + "uri": "http://edamontology.org/operation_0481" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/fzbio/GILoop", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:07:42.670757Z", + "license": "MIT", + "name": "GILoop", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.ISCI.2022.105535", + "metadata": { + "abstract": "© 2022Graph and image are two common representations of Hi-C cis-contact maps. Existing computational tools have only adopted Hi-C data modeled as unitary data structures but neglected the potential advantages of synergizing the information of different views. Here we propose GILoop, a dual-branch neural network that learns from both representations to identify genome-wide CTCF-mediated loops. With GILoop, we explore the combined strength of integrating the two view representations of Hi-C data and corroborate the complementary relationship between the views. In particular, the model outperforms the state-of-the-art loop calling framework and is also more robust against low-quality Hi-C libraries. We also uncover distinct preferences for matrix density by graph-based and image-based models, revealing interesting insights into Hi-C data elucidation. Finally, along with multiple transfer-learning case studies, we demonstrate that GILoop can accurately model the organizational and functional patterns of CTCF-mediated looping across different cell lines.", + "authors": [ + { + "name": "Gao T." + }, + { + "name": "Huang L." + }, + { + "name": "Li X." + }, + { + "name": "Lin J." + }, + { + "name": "Toseef M." + }, + { + "name": "Wang F." + }, + { + "name": "Wong K.-C." + }, + { + "name": "Zheng Z." + } + ], + "date": "2022-12-22T00:00:00Z", + "journal": "iScience", + "title": "GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data" + }, + "pmcid": "PMC9700007", + "pmid": "36444296" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/glaucoma-trel/glaucoma-trel.biotools.json b/data/glaucoma-trel/glaucoma-trel.biotools.json new file mode 100644 index 0000000000000..ad83a30331b5c --- /dev/null +++ b/data/glaucoma-trel/glaucoma-trel.biotools.json @@ -0,0 +1,100 @@ +{ + "additionDate": "2023-01-28T14:10:55.050207Z", + "biotoolsCURIE": "biotools:glaucoma-trel", + "biotoolsID": "glaucoma-trel", + "confidence_flag": "tool", + "credit": [ + { + "email": "julio.vera-gonzalez@uk-erlangen.de", + "name": "Julio Vera", + "orcidid": "https://orcid.org/0000-0002-3076-5122", + "typeEntity": "Person" + }, + { + "name": "Bettina Hohberger", + "typeEntity": "Person" + } + ], + "description": "A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Enrichment analysis", + "uri": "http://edamontology.org/operation_3501" + } + ] + } + ], + "homepage": "http://www.jveralab.net/glaucoma-trel/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-28T14:10:55.052952Z", + "license": "CC-BY-4.0", + "name": "Glaucoma-TrEl", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1186/S13104-022-06210-0", + "metadata": { + "abstract": "© 2022, The Author(s).Objective: Glaucoma is a chronic neurological disease that is associated with high intraocular pressure (IOP), causes gradual damage to retinal ganglion cells, and often culminates in vision loss. Recent research suggests that glaucoma is a complex multifactorial disease in which multiple interlinked genes and pathways play a role during onset and development. Also, differential availability of trace elements seems to play a role in glaucoma pathophysiology, although their mechanism of action is unknown. The aim of this work is to disseminate a web-based repository on interactions between trace elements and protein-coding genes linked to glaucoma pathophysiology. Results: In this study, we present Glaucoma-TrEl, a web database containing information about interactions between trace elements and protein-coding genes that are linked to glaucoma. In the database, we include interactions between 437 unique genes and eight trace elements. Our analysis found a large number of interactions between trace elements and protein-coding genes mutated or linked to the pathophysiology of glaucoma. We associated genes interacting with multiple trace elements to pathways known to play a role in glaucoma. The web-based platform provides an easy-to-use and interactive tool, which serves as an information hub facilitating future research work on trace elements in glaucoma.", + "authors": [ + { + "name": "Chatterjee T." + }, + { + "name": "Choudhari J.K." + }, + { + "name": "Eberhardt M." + }, + { + "name": "Hohberger B." + }, + { + "name": "Vera J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Research Notes", + "title": "Glaucoma-TrEl: A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma" + }, + "pmcid": "PMC9673420", + "pmid": "36401306" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Genetics", + "uri": "http://edamontology.org/topic_3053" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Neurology", + "uri": "http://edamontology.org/topic_3334" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/glmsingle/glmsingle.biotools.json b/data/glmsingle/glmsingle.biotools.json new file mode 100644 index 0000000000000..9bac40ee95e15 --- /dev/null +++ b/data/glmsingle/glmsingle.biotools.json @@ -0,0 +1,109 @@ +{ + "additionDate": "2023-01-28T14:14:42.045529Z", + "biotoolsCURIE": "biotools:glmsingle", + "biotoolsID": "glmsingle", + "confidence_flag": "tool", + "credit": [ + { + "email": "jacob.samuel.prince@gmail.com", + "name": "Jacob S Prince", + "orcidid": "https://orcid.org/0000-0001-6169-9503", + "typeEntity": "Person" + } + ], + "description": "GLMsingle is a toolbox for obtaining accurate single-trial estimates in fMRI time-series data. We provide both MATLAB and Python implementations.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/cvnlab/GLMsingle", + "language": [ + "MATLAB", + "Python" + ], + "lastUpdate": "2023-01-28T14:14:42.048090Z", + "license": "BSD-3-Clause", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/jacob-prince/GLMsingle_paper" + } + ], + "name": "GLMsingle", + "owner": "Chan019", + "publication": [ + { + "doi": "10.7554/ELIFE.77599", + "metadata": { + "abstract": "© Prince et al.Advances in artificial intelligence have inspired a paradigm shift in human neurosci-ence, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.", + "authors": [ + { + "name": "Charest I." + }, + { + "name": "Kay K.N." + }, + { + "name": "Kurzawski J.W." + }, + { + "name": "Prince J.S." + }, + { + "name": "Pyles J.A." + }, + { + "name": "Tarr M.J." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "eLife", + "title": "Improving the accuracy of single-trial fMRI response estimates using GLMsingle" + }, + "pmcid": "PMC9708069", + "pmid": "36444984" + } + ], + "toolType": [ + "Library", + "Script", + "Suite" + ], + "topic": [ + { + "term": "MRI", + "uri": "http://edamontology.org/topic_3444" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Neurobiology", + "uri": "http://edamontology.org/topic_3304" + } + ] +} diff --git a/data/gmembeddings/gmembeddings.biotools.json b/data/gmembeddings/gmembeddings.biotools.json new file mode 100644 index 0000000000000..b02a2dfc246bb --- /dev/null +++ b/data/gmembeddings/gmembeddings.biotools.json @@ -0,0 +1,90 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:20:00.203144Z", + "biotoolsCURIE": "biotools:gmembeddings", + "biotoolsID": "gmembeddings", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tataruc@oregonstate.edu", + "name": "Christine Tataru", + "typeEntity": "Person" + }, + { + "name": "Austin Eaton" + }, + { + "name": "Maude M David" + } + ], + "description": "An R Package to Apply Embedding Techniques to Microbiome Data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Essential dynamics", + 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interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/gold_db/gold_db.biotools.json b/data/gold_db/gold_db.biotools.json new file mode 100644 index 0000000000000..a329358fedfa2 --- /dev/null +++ b/data/gold_db/gold_db.biotools.json @@ -0,0 +1,145 @@ +{ + "additionDate": "2023-01-28T14:21:00.864475Z", + "biotoolsCURIE": "biotools:gold_db", + "biotoolsID": "gold_db", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tbreddy@lbl.gov", + "name": "T B K Reddy", + "orcidid": "https://orcid.org/0000-0002-0871-5567", + "typeEntity": "Person" + } + ], + "description": "GOLD: Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": 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its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.", + "authors": [ + { + "name": "Bertsch J." + }, + { + "name": "Chen I.-M.A." + }, + { + "name": "Favognano A." + }, + { + "name": "Kandimalla M." + }, + { + "name": "Kyrpides N.C." + }, + { + "name": "Li C.T." + }, + { + "name": "Mukherjee S." + }, + { + "name": "Nicolopoulos P.A." + }, + { + "name": "Ovchinnikova G." + }, + { + "name": "Reddy T.B.K." + }, + { + "name": "Stamatis D." + }, + { + "name": "Sundaramurthi J.C." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9" + }, + "pmcid": "PMC9825498", + "pmid": "36318257" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Metagenomic sequencing", + "uri": "http://edamontology.org/topic_3837" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Metatranscriptomics", + "uri": "http://edamontology.org/topic_3941" + }, + { + "term": "Sample collections", + "uri": "http://edamontology.org/topic_3277" + }, + { + "term": "Whole genome sequencing", + "uri": "http://edamontology.org/topic_3673" + } + ], + "version": [ + "9.0" + ] +} diff --git a/data/goldvariants/goldvariants.biotools.json b/data/goldvariants/goldvariants.biotools.json new file mode 100644 index 0000000000000..0cf61d5e54a7b --- /dev/null +++ b/data/goldvariants/goldvariants.biotools.json @@ -0,0 +1,164 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T23:55:19.382691Z", + "biotoolsCURIE": "biotools:goldvariants", + "biotoolsID": "goldvariants", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Kathleen.freson@kuleuven.be", + "name": "Kathleen Freson", + "orcidid": "https://orcid.org/0000-0002-4381-2442", + "typeEntity": "Person" + }, + { + "name": "David-Alexandre Trégouët" + }, + { + "name": "Karyn Megy" + }, + { + "name": "Kate Downes" + } + ], + "description": "Resource for sharing rare genetic variants detected in bleeding, thrombotic, and platelet disorders.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Variant classification", + "uri": "http://edamontology.org/operation_3225" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + } + ] + } + ], + "homepage": "https://redcap.isth.org/surveys/index.php?s=MK94LDCXTW", + "lastUpdate": "2023-01-26T23:55:19.385465Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://www.isth.org/page/GinTh_GeneLists" + } + ], + "name": "GoldVariants", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1111/JTH.15459", + "metadata": { + "abstract": "© 2021 The Authors. Journal of Thrombosis and Haemostasis published by Wiley Periodicals LLC on behalf of International Society on Thrombosis and Haemostasis.The implementation of high-throughput sequencing (HTS) technologies in research and diagnostic laboratories has linked many new genes to rare bleeding, thrombotic, and platelet disorders (BTPD), and revealed multiple genetic variants linked to those disorders, many of them being of uncertain pathogenicity when considering the accepted evidence (variant consequence, frequency in control datasets, number of reported patients, prediction models, and functional assays). The sequencing effort has also resulted in resources for gathering disease-causing variants associated with specific genes, but for BTPD, such well-curated databases exist only for a few genes. On the other hand, submissions by individuals or diagnostic laboratories to the variant database ClinVar are hampered by the lack of a submission process tailored to capture the specific features of hemostatic diseases. As we move toward the implementation of HTS in the diagnosis of BTPD, the Scientific and Standardization Committee for Genetics in Thrombosis and Haemostasis has developed and tested a REDCap-based interface, aimed at the community, to submit curated genetic variants for diagnostic-grade BTPD genes. Here, we describe the use of the interface and the initial submission of 821 variants from 30 different centers covering 14 countries. This open-access variant resource will be shared with the community to improve variant classification and regular bulk data transfer to ClinVar.", + "authors": [ + { + "name": "Bastida J.M." + }, + { + "name": "Brooks S." + }, + { + "name": "Bury L." + }, + { + "name": "Downes K." + }, + { + "name": "Freson K." + }, + { + "name": "Gomez K." + }, + { + "name": "Leinoe E." + }, + { + "name": "Megy K." + }, + { + "name": "Morel-Kopp M.-C." + }, + { + "name": "Morgan N.V." + }, + { + "name": "Othman M." + }, + { + "name": "Ouwehand W.H." + }, + { + "name": "Perez Botero J." + }, + { + "name": "Rivera J." + }, + { + "name": "Schulze H." + }, + { + "name": "Tregouet D.-A." + } + ], + "citationCount": 7, + "date": "2021-10-01T00:00:00Z", + "journal": "Journal of Thrombosis and Haemostasis", + "title": "GoldVariants, a resource for sharing rare genetic variants detected in bleeding, thrombotic, and platelet disorders: Communication from the ISTH SSC Subcommittee on Genomics in Thrombosis and Hemostasis" + }, + "pmcid": "PMC9291976", + "pmid": "34355501" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Exome sequencing", + "uri": "http://edamontology.org/topic_3676" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Haematology", + "uri": "http://edamontology.org/topic_3408" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/gpsadb/gpsadb.biotools.json b/data/gpsadb/gpsadb.biotools.json new file mode 100644 index 0000000000000..c2b54bba70184 --- /dev/null +++ b/data/gpsadb/gpsadb.biotools.json @@ -0,0 +1,138 @@ +{ + "additionDate": "2023-01-28T14:25:56.036571Z", + "biotoolsCURIE": "biotools:gpsadb", + "biotoolsID": "gpsadb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "guoshipeng2008@126.com", + "name": "Shipeng Guo", + "orcidid": "https://orcid.org/0000-0002-9286-7132", + "typeEntity": "Person" + }, + { + "email": "liushengchun1968@163.com", + "name": "Shengchun Liu", + "typeEntity": "Person" + }, + { + "email": "weihong@wmu.edu.cn", + "name": "Weihong Song", + "typeEntity": "Person" + } + ], + "description": "A comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://www.gpsadb.com/", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-28T14:25:56.039197Z", + "license": "CC-BY-4.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://guotosky.vip:13838/GPSA/" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/xuzhougeng/auto_sra_rnaseq_pipeline" + } + ], + "name": "GPSAdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1066", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Gene knock-out/down methods are commonly used to explore the functions of genes of interest, but a database that systematically collects perturbed data is not available currently. Manual curation of all the available human cell line perturbed RNA-seq datasets enabled us to develop a comprehensive human perturbation database (GPSAdb, https://www.gpsadb.com/). The current version of GPSAdb collected 3048 RNA-seq datasets associated with 1458 genes, which were knocked out/down by siRNA, shRNA, CRISPR/Cas9, or CRISPRi. The database provides full exploration of these datasets and generated 6096 new perturbed gene sets (up and down separately). GPSAdb integrated the gene sets and developed an online tool, genetic perturbation similarity analysis (GPSA), to identify candidate causal perturbations from differential gene expression data. In summary, GPSAdb is a powerful platform that aims to assist life science researchers to easily access and analyze public perturbed data and explore differential gene expression data in depth.", + "authors": [ + { + "name": "Dong X." + }, + { + "name": "Guo S." + }, + { + "name": "Hu D." + }, + { + "name": "Jiang Y." + }, + { + "name": "Liu S." + }, + { + "name": "Song W." + }, + { + "name": "Wang Q." + }, + { + "name": "Xu Z." + }, + { + "name": "Zhang J." + }, + { + "name": "Zhou Q." + } + ], + "citationCount": 1, + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "GPSAdb: a comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets" + }, + "pmcid": "PMC9825484", + "pmid": "36416261" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/gr2d2/gr2d2.biotools.json b/data/gr2d2/gr2d2.biotools.json new file mode 100644 index 0000000000000..e888e6b4d3295 --- /dev/null +++ b/data/gr2d2/gr2d2.biotools.json @@ -0,0 +1,104 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T15:13:38.604006Z", + "biotoolsCURIE": "biotools:gr2d2", + "biotoolsID": "gr2d2", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "doraz@hku.hk", + "name": "Yan Dora Zhang", + "typeEntity": "Person" + }, + { + "name": "Dailin Gan" + }, + { + "name": "Guosheng Yin" + } + ], + "description": "The GR2D2 estimator for the precision matrices.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/RavenGan/GR2D2", + "language": [ + "R" + ], + "lastUpdate": "2022-12-31T15:13:38.606604Z", + "license": "Not licensed", + "name": "GR2D2", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC426", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.", + "authors": [ + { + "name": "Gan D." + }, + { + "name": "Yin G." + }, + { + "name": "Zhang Y.D." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "The GR2D2 estimator for the precision matrices" + }, + "pmid": "36184191" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/gr_predictor/gr_predictor.biotools.json b/data/gr_predictor/gr_predictor.biotools.json new file mode 100644 index 0000000000000..13065e0e248c0 --- /dev/null +++ b/data/gr_predictor/gr_predictor.biotools.json @@ -0,0 +1,105 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-19T10:42:07.264361Z", + "biotoolsCURIE": "biotools:gr_predictor", + "biotoolsID": "gr_predictor", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Kosuke Kawama" + }, + { + "name": "Yusaku Fukushima" + }, + { + "name": "Masateru Ohta", + "orcidid": "http://orcid.org/0000-0002-6580-7185" + }, + { + "name": "Mitsunori Ikeguchi", + "orcidid": "http://orcid.org/0000-0003-3199-6931" + }, + { + "name": "Takashi Yoshidome", + "orcidid": "http://orcid.org/0000-0001-7407-1942" + } + ], + "description": "A Deep-Learning Model for Predicting the Hydration Structures around Proteins.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Protein modelling", + "uri": "http://edamontology.org/operation_0477" + } + ] + } + ], + "homepage": "https://github.com/YoshidomeGroup-Hydration/gr-predictor", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-19T10:42:07.266784Z", + "license": "GPL-3.0", + "name": "gr Predictor", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/acs.jcim.2c00987", + "metadata": { + "abstract": "© 2022 American Chemical Society. All rights reserved.Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water site distribution function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time ranging from hours to tens of hours. Here, we propose a deep learning (DL) model that rapidly estimates the distribution functions around proteins obtained using the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our DL model are in good agreement with those obtained using the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the DL model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit, the prediction by the DL model is completed in less than 1 min, more than 2 orders of magnitude faster than the calculation time of the 3D-RISM theory. The position of water molecules around the protein was estimated based on the distribution function obtained by our DL model, and the position of waters estimated by our DL model was in good agreement with that of water molecules estimated using the 3D-RISM theory and of crystallographic waters. Therefore, our DL model provides a practical and efficient way to calculate the three-dimensional water site distribution functions and to estimate the position of water molecules around the protein. The program called \"gr Predictor\" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor.", + "authors": [ + { + "name": "Fukushima Y." + }, + { + "name": "Ikeguchi M." + }, + { + "name": "Kawama K." + }, + { + "name": "Ohta M." + }, + { + "name": "Yoshidome T." + } + ], + "date": "2022-09-26T00:00:00Z", + "journal": "Journal of Chemical Information and Modeling", + "title": "Gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins" + }, + "pmid": "36068974" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/topic_0176" + }, + { + "term": "Protein folding, stability and design", + "uri": "http://edamontology.org/topic_0130" + }, + { + "term": "Protein folds and structural domains", + "uri": "http://edamontology.org/topic_0736" + } + ] +} diff --git a/data/gra-gcn/gra-gcn.biotools.json b/data/gra-gcn/gra-gcn.biotools.json new file mode 100644 index 0000000000000..09ba4d08d8d0b --- /dev/null +++ b/data/gra-gcn/gra-gcn.biotools.json @@ -0,0 +1,89 @@ +{ + "additionDate": "2023-01-28T14:29:30.363249Z", + "biotoolsCURIE": "biotools:gra-gcn", + "biotoolsID": "gra-gcn", + "confidence_flag": "tool", + "credit": [ + { + "name": "Zhenyu Yue", + "orcidid": "https://orcid.org/0000-0002-9370-2540" + } + ], + "description": "Dense granule protein prediction in Apicomplexa protozoa through graph convolutional network.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + } + ] + } + ], + "homepage": "http://dgpd.tlds.cc/GRAGCN/index/", + "lastUpdate": "2023-01-28T14:29:30.365986Z", + "license": "Other", + "name": "GRA-GCN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1109/TCBB.2022.3224836", + "metadata": { + "abstract": "IEEEDense granule proteins (GRAs) are secreted by Apicomplexa protozoa, which are closely related to an extensive variety of farm animal diseases. Predicting GRAs is an integral part in prevention and treatment of parasitic diseases. Considering that biological experiment approach is time-consuming and labor-intensive, computational method is a superior choice. Hence, developing an effective computational method for GRAs prediction is of urgency. In this paper, we present a novel computational method named GRA-GCN through graph convolutional network. In terms of the graph theory, the GRAs prediction can be regarded as a node classification task. GRA-GCN leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation. To our knowledge, this is the first attempt to utilize computational approach for GRAs prediction. Evaluated by 5-fold cross-validations, the GRA-GCN method achieves satisfactory performance, and is superior to four classic machine learning-based methods and three state-of-the-art models. The analysis of the comprehensive experiment results and a case study could offer valuable information for understanding complex mechanisms, and would contribute to accurate prediction of GRAs. Moreover, we also implement a web server at http://dgpd.tlds.cc/GRAGCN/index/, for facilitating the process of using our model.", + "authors": [ + { + "name": "Feng H." + }, + { + "name": "Lu Z." + }, + { + "name": "Shi H." + }, + { + "name": "Xue W." + }, + { + "name": "Yang C." + }, + { + "name": "Yue Z." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics", + "title": "GRA-GCN: dense granule protein prediction in Apicomplexa protozoa through graph convolutional network" + }, + "pmid": "36441896" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Laboratory techniques", + "uri": "http://edamontology.org/topic_3361" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/grace-ako/grace-ako.biotools.json b/data/grace-ako/grace-ako.biotools.json new file mode 100644 index 0000000000000..cc42dfb5d2ac5 --- /dev/null +++ b/data/grace-ako/grace-ako.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-19T10:49:41.365863Z", + "biotoolsCURIE": "biotools:grace-ako", + "biotoolsID": "grace-ako", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "doraz@hku.hk", + "name": "Yan Dora Zhang", + "orcidid": "http://orcid.org/0000-0002-5302-3690", + "typeEntity": "Person" + }, + { + "name": "Peixin Tian" + }, + { + "name": "Yiqian Hu" + }, + { + "name": "Zhonghua Liu" + } + ], + "description": "A Novel and Stable Knockoff Filter for Variable Selection Incorporating Gene Network Structures.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Feature selection", + "uri": "http://edamontology.org/operation_3936" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/mxxptian/GraceAKO", + "language": [ + "R" + ], + "lastUpdate": "2023-01-19T10:49:41.368434Z", + "license": "Not licensed", + "name": "Grace-AKO", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/s12859-022-05016-y", + "metadata": { + "abstract": "© 2022, The Author(s).Motivation: Variable selection is a common statistical approach to identifying genes associated with clinical outcomes of scientific interest. There are thousands of genes in genomic studies, while only a limited number of individual samples are available. Therefore, it is important to develop a method to identify genes associated with outcomes of interest that can control finite-sample false discovery rate (FDR) in high-dimensional data settings. Results: This article proposes a novel method named Grace-AKO for graph-constrained estimation (Grace), which incorporates aggregation of multiple knockoffs (AKO) with the network-constrained penalty. Grace-AKO can control FDR in finite-sample settings and improve model stability simultaneously. Simulation studies show that Grace-AKO has better performance in finite-sample FDR control than the original Grace model. We apply Grace-AKO to the prostate cancer data in The Cancer Genome Atlas program by incorporating prostate-specific antigen (PSA) pathways in the Kyoto Encyclopedia of Genes and Genomes as the prior information. Grace-AKO finally identifies 47 candidate genes associated with PSA level, and more than 75% of the detected genes can be validated.", + "authors": [ + { + "name": "Hu Y." + }, + { + "name": "Liu Z." + }, + { + "name": "Tian P." + }, + { + "name": "Zhang Y.D." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Grace-AKO: a novel and stable knockoff filter for variable selection incorporating gene network structures" + }, + "pmcid": "PMC9664829", + "pmid": "36376815" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/grape_pipeline/grape_pipeline.biotools.json b/data/grape_pipeline/grape_pipeline.biotools.json new file mode 100644 index 0000000000000..30396d70cf766 --- /dev/null +++ b/data/grape_pipeline/grape_pipeline.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:01:21.188605Z", + "biotoolsCURIE": "biotools:grape_pipeline", + "biotoolsID": "grape_pipeline", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "gloriouslair@gmail.com", + "name": "Mikhail Lebedev", + "typeEntity": "Person" + }, + { + "name": "Aleksandr Medvedev", + "orcidid": "http://orcid.org/0000-0002-6871-4240" + }, + { + "name": "Dmitry Kolobkov", + "orcidid": "http://orcid.org/0000-0003-4225-2057" + }, + { + "name": "Pavel Nikonorov", + "orcidid": "http://orcid.org/0000-0002-8471-2069" + } + ], + "description": "Genomic Relatedness Detection Pipeline.", + "download": [ + { + "type": "Container file", + "url": "https://hub.docker.com/r/genxnetwork/grape" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + }, + { + "term": "Relation extraction", + "uri": "http://edamontology.org/operation_3625" + } + ] + } + ], + "homepage": "https://github.com/genxnetwork/grape", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T21:01:21.191427Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://dockstore.org/organizations/GenX/collections/GRAPE" + } + ], + "name": "GRAPE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1101/2022.03.11.483988" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/graphbio/graphbio.biotools.json b/data/graphbio/graphbio.biotools.json new file mode 100644 index 0000000000000..00140d0102dc3 --- /dev/null +++ b/data/graphbio/graphbio.biotools.json @@ -0,0 +1,111 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:07:35.933839Z", + "biotoolsCURIE": "biotools:graphbio", + "biotoolsID": "graphbio", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Tian-Xin Zhao" + }, + { + "name": "Ze-Lin Wang" + } + ], + "description": "A shiny web app to easily perform popular visualization analysis for omics data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene expression profile", + "uri": "http://edamontology.org/data_0928" + }, + "format": [ + { + "term": "CSV", + "uri": "http://edamontology.org/format_3752" + }, + { + "term": "xls", + "uri": "http://edamontology.org/format_3468" + } + ] + } + ], + "operation": [ + { + "term": "Dot plot plotting", + "uri": "http://edamontology.org/operation_0490" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Principal component visualisation", + "uri": "http://edamontology.org/operation_2939" + }, + { + "term": "Scatter plot plotting", + "uri": "http://edamontology.org/operation_2940" + } + ] + } + ], + "homepage": "http://www.graphbio1.com/en/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T21:07:35.936381Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://www.graphbio1.com/" + } + ], + "name": "GraphBio", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/fgene.2022.957317", + "metadata": { + "abstract": "Copyright © 2022 Zhao and Wang.Background: Massive amounts of omics data are produced and usually require sophisticated visualization analysis. These analyses often require programming skills, which are difficult for experimental biologists. Thus, more user-friendly tools are urgently needed. Methods and Results: Herein, we present GraphBio, a shiny web app to easily perform visualization analysis for omics data. GraphBio provides 15 popular visualization analysis methods, including heatmap, volcano plots, MA plots, network plots, dot plots, chord plots, pie plots, four quadrant diagrams, Venn diagrams, cumulative distribution curves, principal component analysis (PCA), survival analysis, receiver operating characteristic (ROC) analysis, correlation analysis, and text cluster analysis. It enables experimental biologists without programming skills to easily perform popular visualization analysis and get publication-ready figures. Conclusion: GraphBio, as an online web application, is freely available at http://www.graphbio1.com/en/ (English version) and http://www.graphbio1.com/ (Chinese version). The source code of GraphBio is available at https://github.com/databio2022/GraphBio.", + "authors": [ + { + "name": "Wang Z." + }, + { + "name": "Zhao T." + } + ], + "citationCount": 1, + "date": "2022-09-07T00:00:00Z", + "journal": "Frontiers in Genetics", + "title": "GraphBio: A shiny web app to easily perform popular visualization analysis for omics data" + }, + "pmcid": "PMC9490469", + "pmid": "36159985" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + } + ] +} diff --git a/data/grasp_web/grasp_web.biotools.json b/data/grasp_web/grasp_web.biotools.json new file mode 100644 index 0000000000000..68f8c93642aa6 --- /dev/null +++ b/data/grasp_web/grasp_web.biotools.json @@ -0,0 +1,208 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:57:12.480428Z", + "biotoolsCURIE": "biotools:grasp_web", + "biotoolsID": "grasp_web", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "m.boden@uq.edu.au", + "name": "Mikael Bodén", + "orcidid": "https://orcid.org/0000-0003-3548-268X", + "typeEntity": "Person" + }, + { + "email": "e.gillam@uq.edu.au", + "name": "Elizabeth M. J. Gillam", + "typeEntity": "Person" + }, + { + "name": "Ariane Mora", + "orcidid": "https://orcid.org/0000-0003-1331-8192" + }, + { + "name": "Gabriel Foley", + "orcidid": "https://orcid.org/0000-0002-0487-2629" + } + ], + "description": "Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP).", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Phylogenetic tree", + "uri": "http://edamontology.org/data_0872" + } + }, + { + "data": { + "term": "Sequence alignment", + "uri": "http://edamontology.org/data_0863" + } + } + ], + "operation": [ + { + "term": "Ancestral reconstruction", + "uri": "http://edamontology.org/operation_3745" + }, + { + "term": "Phylogenetic inference", + "uri": "http://edamontology.org/operation_0323" + }, + { + "term": "Protein identification", + "uri": "http://edamontology.org/operation_3767" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://grasp.scmb.uq.edu.au", + "language": [ + "Java", + "JavaScript" + ], + "lastUpdate": "2022-12-31T01:57:12.483780Z", + "license": "AGPL-3.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://bodenlab.github.io/GRASP-suite" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/bodenlab/GRASP" + } + ], + "name": "GRASP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1010633", + "metadata": { + "abstract": "Copyright: © 2022 Foley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.", + "authors": [ + { + "name": "Balderson B." + }, + { + "name": "Barnard R.T." + }, + { + "name": "Boden M." + }, + { + "name": "Bottoms S." + }, + { + "name": "Carsten J." + }, + { + "name": "Essebier A." + }, + { + "name": "Foley G." + }, + { + "name": "Gillam E.M.J." + }, + { + "name": "Guddat L." + }, + { + "name": "Gumulya Y." + }, + { + "name": "Haltrich D." + }, + { + "name": "Kobe B." + }, + { + "name": "Lamprecht M.L." + }, + { + "name": "Mora A." + }, + { + "name": "Newell R." + }, + { + "name": "Ross C.M." + }, + { + "name": "Rost B." + }, + { + "name": "Schenk G." + }, + { + "name": "Sieber V." + }, + { + "name": "Sutzl L." + }, + { + "name": "Thomson R.E.S." + }, + { + "name": "Zaugg J." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)" + }, + "pmcid": "PMC9632902", + "pmid": "36279274" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Enzymes", + "uri": "http://edamontology.org/topic_0821" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Molecular evolution", + "uri": "http://edamontology.org/topic_3945" + }, + { + "term": "Phylogeny", + "uri": "http://edamontology.org/topic_0084" + } + ] +} diff --git a/data/gravis/gravis.biotools.json b/data/gravis/gravis.biotools.json new file mode 100644 index 0000000000000..1393bda16a1ac --- /dev/null +++ b/data/gravis/gravis.biotools.json @@ -0,0 +1,100 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:48:54.201819Z", + "biotoolsCURIE": "biotools:gravis", + "biotoolsID": "gravis", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Chixiang Lu", + "orcidid": "https://orcid.org/0000-0003-0665-2627" + }, + { + "name": "Hong-Yu Zhou", + "orcidid": "https://orcid.org/0000-0002-1256-7050" + }, + { + "name": "Liansheng Wang", + "orcidid": "https://orcid.org/0000-0002-2096-454X" + }, + { + "name": "Yizhou Yu", + "orcidid": "https://orcid.org/0000-0002-0470-5548" + } + ], + "description": "Grouping Augmented Views from Independent Sources for Dermatology Analysis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + } + ] + } + ], + "homepage": "https://bit.ly/3xiFyjx", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-31T01:48:54.204465Z", + "license": "Not licensed", + "name": "GraVIS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/TMI.2022.3216005", + "metadata": { + "abstract": "© 2022 IEEE.Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are dominated by noise-contrastive estimation (NCE, also known as contrastive learning), which aims to learn invariant visual representations by contrasting one homogeneous image pair with a large number of heterogeneous image pairs in each training step. Nonetheless, NCE-based approaches still suffer from one major problem that is one homogeneous pair is not enough to extract robust and invariant semantic information. Inspired by the archetypical triplet loss, we propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images, to group homogeneous dermatology images while separating heterogeneous ones. In addition, a hardness-aware attention is introduced and incorporated to address the importance of homogeneous image views with similar appearance instead of those dissimilar homogeneous ones. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks, sometimes by 5 percents under extremely limited supervision. More importantly, when equipped with the pre-trained weights provided by GraVIS, a single model could achieve better results than winners that heavily rely on ensemble strategies in the well-known ISIC 2017 challenge.", + "authors": [ + { + "name": "Lu C." + }, + { + "name": "Wang L." + }, + { + "name": "Yu Y." + }, + { + "name": "Zhou H.-Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "IEEE Transactions on Medical Imaging", + "title": "GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis" + }, + "pmid": "36260573" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Dermatology", + "uri": "http://edamontology.org/topic_3404" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/grop/grop.biotools.json b/data/grop/grop.biotools.json new file mode 100644 index 0000000000000..8a0f68204cab3 --- /dev/null +++ b/data/grop/grop.biotools.json @@ -0,0 +1,137 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:41:26.301394Z", + "biotoolsCURIE": "biotools:grop", + "biotoolsID": "grop", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "keanjin.lim@zafu.edu.cn", + "name": "Kean-Jin Lim", + "typeEntity": "Person" + }, + { + "email": "wzhj21@163.com", + "name": "Zhengjia Wang", + "typeEntity": "Person" + }, + { + "name": "Hongmiao Jin" + }, + { + "name": "Wenlei Guo" + } + ], + "description": "A genomic information repository for oilplants.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Genome assembly", + "uri": "http://edamontology.org/operation_0525" + }, + { + "term": "Genome visualisation", + "uri": "http://edamontology.org/operation_3208" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + } + ] + } + ], + "homepage": "http://www.grop.site", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-31T01:41:26.304394Z", + "name": "GROP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FPLS.2022.1023938", + "metadata": { + "abstract": "Copyright © 2022 Guo, Jin, Chen, Huang, Zheng, Cheng, Liu, Yang, Chen, Lim and Wang.Biomass energy is an essential component of the agriculture economy and represents an important and particularly significant renewable energy source in the fight against fossil fuel depletion and global warming. The recognition that many plants naturally synthesize hydrocarbons makes these oil plants indispensable resources for biomass energy, and the advancement of next-generation sequencing technology in recent years has now made available mountains of data on plants that synthesize oil. We have utilized a combination of bioinformatic protocols to acquire key information from this massive amount of genomic data and to assemble it into an oil plant genomic information repository, built through website technology, including Django, Bootstrap, and echarts, to create the Genomic Information Repository for Oil Plants (GROP) portal (http://grop.site/) for genomics research on oil plants. The current version of GROP integrates the coding sequences, protein sequences, genome structure, functional annotation information, and other information from 18 species, 22 genome assemblies, and 46 transcriptomes. GROP also provides BLAST, genome browser, functional enrichment, and search tools. The integration of the massive amounts of oil plant genomic data with key bioinformatics tools in a database with a user-friendly interface allows GROP to serve as a central information repository to facilitate studies on oil plants by researchers worldwide.", + "authors": [ + { + "name": "Chen F." + }, + { + "name": "Chen J." + }, + { + "name": "Cheng Z." + }, + { + "name": "Guo W." + }, + { + "name": "Huang J." + }, + { + "name": "Jin H." + }, + { + "name": "Lim K.-J." + }, + { + "name": "Liu X." + }, + { + "name": "Wang Z." + }, + { + "name": "Yang Z." + }, + { + "name": "Zheng D." + } + ], + "date": "2022-10-06T00:00:00Z", + "journal": "Frontiers in Plant Science", + "title": "GROP: A genomic information repository for oilplants" + }, + "pmcid": "PMC9583018", + "pmid": "36275551" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Whole genome sequencing", + "uri": "http://edamontology.org/topic_3673" + } + ] +} diff --git a/data/gseapy/gseapy.biotools.json b/data/gseapy/gseapy.biotools.json new file mode 100644 index 0000000000000..57d782ae326ed --- /dev/null +++ b/data/gseapy/gseapy.biotools.json @@ -0,0 +1,99 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T14:32:01.587640Z", + "biotoolsCURIE": "biotools:gseapy", + "biotoolsID": "gseapy", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "gpeltz@stanford.edu", + "name": "Gary Peltz", + "orcidid": "https://orcid.org/0000-0001-6191-7697", + "typeEntity": "Person" + } + ], + "description": "A comprehensive package for performing gene set enrichment analysis in Python.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + } + ] + } + ], + "homepage": "https://pypi.org/project/gseapy/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:32:01.591567Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/zqfang/GSEApy" + } + ], + "name": "GSEApy", + "operatingSystem": [ + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC757", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets. RESULTS: We present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis. AVAILABILITY AND IMPLEMENTATION: The new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Fang Z." + }, + { + "name": "Liu X." + }, + { + "name": "Peltz G." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python" + }, + "pmcid": "PMC9805564", + "pmid": "36426870" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/gspa/gspa.biotools.json b/data/gspa/gspa.biotools.json new file mode 100644 index 0000000000000..a57dbb689945a --- /dev/null +++ b/data/gspa/gspa.biotools.json @@ -0,0 +1,106 @@ +{ + "additionDate": "2023-01-28T14:35:06.543196Z", + "biotoolsCURIE": "biotools:gspa", + "biotoolsID": "gspa", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "credit": [ + { + "email": "cousinsh@stanford.edu", + "name": "Henry Cousins", + "orcidid": "https://orcid.org/0000-0002-8694-0604", + "typeEntity": "Person" + }, + { + "email": "russ.altman@stanford.edu", + "name": "Russ B Altman", + "orcidid": "https://orcid.org/0000-0003-3859-2905", + "typeEntity": "Person" + } + ], + "description": "Gene set proximity analysis (GSPA) is a method for identifying critical gene sets in functional genetic datasets using low-dimensional gene embeddings.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Gene-set enrichment analysis", + "uri": "http://edamontology.org/operation_2436" + }, + { + "term": "Protein-protein interaction analysis", + "uri": "http://edamontology.org/operation_2949" + } + ] + } + ], + "homepage": "https://github.com/henrycousins/gspa", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:35:06.545928Z", + "license": "BSD-3-Clause", + "name": "GSPA", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC735", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY AND IMPLEMENTATION: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Altman R.B." + }, + { + "name": "Cong L." + }, + { + "name": "Cousins H." + }, + { + "name": "Guo Y." + }, + { + "name": "Hall T." + }, + { + "name": "Tso L." + }, + { + "name": "Tzeng K.T.H." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19" + }, + "pmcid": "PMC9805577", + "pmid": "36394254" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Genetics", + "uri": "http://edamontology.org/topic_3053" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Pharmacology", + "uri": "http://edamontology.org/topic_0202" + } + ] +} diff --git a/data/h4scfates/h4scfates.biotools.json b/data/h4scfates/h4scfates.biotools.json new file mode 100644 index 0000000000000..f42d493a6840c --- /dev/null +++ b/data/h4scfates/h4scfates.biotools.json @@ -0,0 +1,98 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T14:38:10.266589Z", + "biotoolsCURIE": "biotools:h4scfates", + "biotoolsID": "h4scfates", + "confidence_flag": "tool", + "credit": [ + { + "email": "igor.adameyko@meduniwien.ac.at", + "name": "Igor Adameyko", + "orcidid": "https://orcid.org/0000-0001-5471-0356", + "typeEntity": "Person" + } + ], + "description": "A scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Simulation analysis", + "uri": "http://edamontology.org/operation_0244" + } + ] + } + ], + "homepage": "https://pypi.org/project/scFates/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:38:10.269327Z", + "license": "BSD-3-Clause", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/LouisFaure/scFates/" + } + ], + "name": "h4scFates", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC746", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). AVAILABILITY AND IMPLEMENTATION: scFates is released as open-source software under the BSD 3-Clause 'New' License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Adameyko I." + }, + { + "name": "Faure L." + }, + { + "name": "Kharchenko P.V." + }, + { + "name": "Soldatov R." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data" + }, + "pmcid": "PMC9805561", + "pmid": "36394263" + } + ], + "toolType": [ + "Library", + "Suite" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/haplodmf/haplodmf.biotools.json b/data/haplodmf/haplodmf.biotools.json new file mode 100644 index 0000000000000..469d7eb62c596 --- /dev/null +++ b/data/haplodmf/haplodmf.biotools.json @@ -0,0 +1,87 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:30:53.802498Z", + "biotoolsCURIE": "biotools:haplodmf", + "biotoolsID": "haplodmf", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yannisun@cityu.edu.hk", + "name": "Yanni Sun", + "orcidid": "https://orcid.org/0000-0003-1373-8023", + "typeEntity": "Person" + }, + { + "name": "Dehan Cai", + "orcidid": "https://orcid.org/0000-0002-8148-4574" + }, + { + "name": "Jiayu Shang", + "orcidid": "https://orcid.org/0000-0001-5974-4985" + } + ], + "description": "Viral Haplotype reconstruction from long reads via Deep Matrix Factorization.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Haplotype mapping", + "uri": "http://edamontology.org/operation_0487" + }, + { + "term": "Read mapping", + "uri": "http://edamontology.org/operation_3198" + } + ] + } + ], + "homepage": "https://github.com/dhcai21/HaploDMF", + "language": [ + "Python", + "Shell" + ], + "lastUpdate": "2022-12-31T01:30:53.805018Z", + "license": "Not licensed", + "name": "HaploDMF", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC708", + "pmid": "36308467" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/hariboss/hariboss.biotools.json b/data/hariboss/hariboss.biotools.json new file mode 100644 index 0000000000000..091d888783ff6 --- /dev/null +++ b/data/hariboss/hariboss.biotools.json @@ -0,0 +1,120 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T02:33:07.168655Z", + "biotoolsCURIE": "biotools:hariboss", + "biotoolsID": "hariboss", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Paraskevi.Gkeka@sanofi.com", + "name": "P. Gkeka", + "orcidid": "http://orcid.org/0000-0002-0752-3539", + "typeEntity": "Person" + }, + { + "email": "mbonomi@pasteur.fr", + "name": "M. Bonomi", + "orcidid": "http://orcid.org/0000-0002-7321-0004", + "typeEntity": "Person" + }, + { + "name": "F. P. Panei", + "orcidid": "http://orcid.org/0000-0002-6272-9126" + }, + { + "name": "H. Menager", + "orcidid": "http://orcid.org/0000-0002-7552-1009" + }, + { + "name": "R. Torchet", + "orcidid": "http://orcid.org/0000-0002-2306-5566" + } + ], + "description": "A curated database of RNA-small molecules structures to aid rational drug design.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein-ligand docking", + "uri": "http://edamontology.org/operation_0482" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://hariboss.pasteur.cloud", + "lastUpdate": "2023-01-22T02:33:07.171327Z", + "name": "HARIBOSS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac483", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 'undruggable' protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that have hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule (RNA-SM) recognition. RESULTS: Here, we present Harnessing RIBOnucleic acid-Small molecule Structures (HARIBOSS), a curated collection of RNA-SM structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and ultimately the development of in silico strategies to identify RNA-targeting small molecules. AVAILABILITY AND IMPLEMENTATION: HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Bonomi M." + }, + { + "name": "Gkeka P." + }, + { + "name": "Menager H." + }, + { + "name": "Panei F.P." + }, + { + "name": "Torchet R." + } + ], + "citationCount": 1, + "date": "2022-09-02T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design" + }, + "pmid": "35799352" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "NMR", + "uri": "http://edamontology.org/topic_0593" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "X-ray diffraction", + "uri": "http://edamontology.org/topic_2828" + } + ] +} diff --git a/data/hcdt/hcdt.biotools.json b/data/hcdt/hcdt.biotools.json new file mode 100644 index 0000000000000..b66e824c15031 --- /dev/null +++ b/data/hcdt/hcdt.biotools.json @@ -0,0 +1,136 @@ +{ + "additionDate": "2023-01-28T14:42:00.611396Z", + "biotoolsCURIE": "biotools:hcdt", + "biotoolsID": "hcdt", + "confidence_flag": "tool", + "credit": [ + { + "email": "lijin@hainmc.edu.cn", + "name": "Jin Li", + "orcidid": "https://orcid.org/0000-0002-6131-456X", + "typeEntity": "Person" + }, + { + "email": "lixia@hrbmu.edu.cn", + "name": "Xia Li", + "orcidid": "https://orcid.org/0000-0002-9794-2648", + "typeEntity": "Person" + }, + { + "email": "wanglm@hainmc.edu.cn", + "name": "Limei Wang", + "typeEntity": "Person" + } + ], + "description": "HCDT (Highly Confident Drug-Target Database) is a combined database that provides validated associations between drugs and target genes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "http://hainmu-biobigdata.com/hcdt", + "lastUpdate": "2023-01-28T14:42:00.614272Z", + "license": "Other", + "name": "HCDT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/DATABASE/BAAC101", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Drug-Target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-Target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-Target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt", + "authors": [ + { + "name": "Bi X." + }, + { + "name": "Chen J." + }, + { + "name": "Chen R." + }, + { + "name": "Chen Z." + }, + { + "name": "Feng D." + }, + { + "name": "Han H." + }, + { + "name": "Li J." + }, + { + "name": "Li K." + }, + { + "name": "Li T." + }, + { + "name": "Li X." + }, + { + "name": "Li Y." + }, + { + "name": "Wang L." + }, + { + "name": "Wang Z." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Database", + "title": "HCDT: An integrated highly confident drug-Target resource" + }, + "pmcid": "PMC9684616", + "pmid": "36420558" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Safety sciences", + "uri": "http://edamontology.org/topic_3377" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/hclc-fc/hclc-fc.biotools.json b/data/hclc-fc/hclc-fc.biotools.json new file mode 100644 index 0000000000000..86f6666279df0 --- /dev/null +++ b/data/hclc-fc/hclc-fc.biotools.json @@ -0,0 +1,111 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:14:36.676546Z", + "biotoolsCURIE": "biotools:hclc-fc", + "biotoolsID": "hclc-fc", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "shuzhang@mtu.edu", + "name": "Shuanglin Zhang", + "orcidid": "http://orcid.org/0000-0002-9478-1199", + "typeEntity": "Person" + }, + { + "name": "Qiuying Sha", + "orcidid": "http://orcid.org/0000-0002-9342-3269" + }, + { + "name": "Xiaoyu Liang", + "orcidid": "http://orcid.org/0000-0001-7796-2441" + }, + { + "name": "Xuewei Cao", + "orcidid": "http://orcid.org/0000-0003-2136-0964" + } + ], + "description": "A novel statistical method for phenome-wide association studies.\n\nWe derived a novel and powerful multivariate method, which we referred to as HCLC-FC (Hierarchical Clustering Linear Combination with False discovery rate Control), to test the association between a genetic variant with multiple phenotypes for each phenotypic category in phenome-wide association studies (PheWAS). The R package HCLCFC is a novel tool that allows users to partition a large number of phenotypes into disjoint clusters; applicable to electronic medical records (EMR)-based PheWAS.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/XiaoyuLiang/HCLCFC", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T21:14:36.679151Z", + "license": "Not licensed", + "name": "HCLC-FC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pone.0276646", + "metadata": { + "abstract": "© 2022 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.", + "authors": [ + { + "name": "Cao X." + }, + { + "name": "Liang X." + }, + { + "name": "Sha Q." + }, + { + "name": "Zhang S." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "HCLC-FC: A novel statistical method for phenome-wide association studies" + }, + "pmcid": "PMC9645610", + "pmid": "36350801" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Phenomics", + "uri": "http://edamontology.org/topic_3298" + } + ] +} diff --git a/data/health_gym/health_gym.biotools.json b/data/health_gym/health_gym.biotools.json new file mode 100644 index 0000000000000..fa5a4b9594dba --- /dev/null +++ b/data/health_gym/health_gym.biotools.json @@ -0,0 +1,98 @@ +{ + "additionDate": "2023-01-28T14:49:32.845722Z", + "biotoolsCURIE": "biotools:health_gym", + "biotoolsID": "health_gym", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "n.kuo@unsw.edu.au", + "name": "Nicholas I-Hsien Kuo", + "typeEntity": "Person" + } + ], + "description": "A growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/Nic5472K/ScientificData2021_HealthGym", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:49:32.848249Z", + "license": "MIT", + "name": "Health Gym", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1038/S41597-022-01784-7", + "metadata": { + "abstract": "© 2022, The Author(s).In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the development of reproducible and generalisable machine learning applications in health care. Here we introduce the Health Gym - a growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning. The three synthetic datasets described in this paper present patient cohorts with acute hypotension and sepsis in the intensive care unit, and people with human immunodeficiency virus (HIV) receiving antiretroviral therapy. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends in variables over time in the synthetic datasets mirror those in the real datasets. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low.", + "authors": [ + { + "name": "Barbieri S." + }, + { + "name": "Bohm M." + }, + { + "name": "Finfer S." + }, + { + "name": "Garcia F." + }, + { + "name": "Jorm L." + }, + { + "name": "Kaiser R." + }, + { + "name": "Kuo N.I.-H." + }, + { + "name": "Polizzotto M.N." + }, + { + "name": "Sonnerborg A." + }, + { + "name": "Zazzi M." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms" + }, + "pmcid": "PMC9652426", + "pmid": "36369205" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Critical care medicine", + "uri": "http://edamontology.org/topic_3403" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + } + ] +} diff --git a/data/hgd_db/hgd_db.biotools.json b/data/hgd_db/hgd_db.biotools.json new file mode 100644 index 0000000000000..190295eccf936 --- /dev/null +++ b/data/hgd_db/hgd_db.biotools.json @@ -0,0 +1,148 @@ +{ + "additionDate": "2023-01-28T14:55:18.577908Z", + "biotoolsCURIE": "biotools:hgd_db", + "biotoolsID": "hgd_db", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tangbx@big.ac.cn", + "name": "Bixia Tang", + "orcidid": "https://orcid.org/0000-0002-9357-4411", + "typeEntity": "Person" + }, + { + "email": "zhaowm@big.ac.cn", + "name": "Wenming Zhao", + "typeEntity": "Person" + } + ], + "description": "An integrated homologous gene database across multiple species.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Gene functional annotation", + "uri": "http://edamontology.org/operation_3672" + }, + { + "term": "Homology-based gene prediction", + "uri": "http://edamontology.org/operation_3663" + }, + { + "term": "Phylogenetic tree reconciliation", + "uri": "http://edamontology.org/operation_3947" + }, + { + "term": "Relation extraction", + "uri": "http://edamontology.org/operation_3625" + } + ] + } + ], + "homepage": "https://ngdc.cncb.ac.cn/hgd", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T14:55:18.580556Z", + "license": "CC-BY-3.0", + "name": "HGD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC970", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Homology is fundamental to infer genes' evolutionary processes and relationships with shared ancestry. Existing homolog gene resources vary in terms of inferring methods, homologous relationship and identifiers, posing inevitable difficulties for choosing and mapping homology results from one to another. Here, we present HGD (Homologous Gene Database, https://ngdc.cncb.ac.cn/hgd), a comprehensive homologs resource integrating multi-species, multi-resources and multi-omics, as a complement to existing resources providing public and one-stop data service. Currently, HGD houses a total of 112 383 644 homologous pairs for 37 species, including 19 animals, 16 plants and 2 microorganisms. Meanwhile, HGD integrates various annotations from public resources, including 16 909 homologs with traits, 276 670 homologs with variants, 398 573 homologs with expression and 536 852 homologs with gene ontology (GO) annotations. HGD provides a wide range of omics gene function annotations to help users gain a deeper understanding of gene function.", + "authors": [ + { + "name": "Bao Y." + }, + { + "name": "Chen X." + }, + { + "name": "Du Z." + }, + { + "name": "Duan G." + }, + { + "name": "Gao Y." + }, + { + "name": "Hao L." + }, + { + "name": "Li Z." + }, + { + "name": "Song S." + }, + { + "name": "Sun Y." + }, + { + "name": "Tang B." + }, + { + "name": "Tian D." + }, + { + "name": "Wu G." + }, + { + "name": "Xiao J." + }, + { + "name": "Zhang Z." + }, + { + "name": "Zhao W." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "HGD: an integrated homologous gene database across multiple species" + }, + "pmcid": "PMC9825607", + "pmid": "36318261" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/hgtree/hgtree.biotools.json b/data/hgtree/hgtree.biotools.json new file mode 100644 index 0000000000000..adaaa33ca0afc --- /dev/null +++ b/data/hgtree/hgtree.biotools.json @@ -0,0 +1,108 @@ +{ + "additionDate": "2023-01-28T14:58:17.905317Z", + "biotoolsCURIE": "biotools:hgtree", + "biotoolsID": "hgtree", + "confidence_flag": "tool", + "credit": [ + { + "email": "heebal@snu.ac.kr", + "name": "Heebal Kim", + "orcidid": "https://orcid.org/0000-0003-3064-1303", + "typeEntity": "Person" + } + ], + "description": "A comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Gene tree construction", + "uri": "http://edamontology.org/operation_0553" + }, + { + "term": "Phylogenetic tree reconciliation", + "uri": "http://edamontology.org/operation_3947" + }, + { + "term": "Species tree construction", + "uri": "http://edamontology.org/operation_0544" + } + ] + } + ], + "homepage": "http://hgtree2.snu.ac.kr", + "lastUpdate": "2023-01-28T14:58:17.907923Z", + "license": "Other", + "name": "HGTree", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/NAR/GKAC929", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.HGTree is a database that provides horizontal gene transfer (HGT) event information on 2472 prokaryote genomes using the tree-reconciliation method. HGTree was constructed in 2015, and a large number of prokaryotic genomes have been additionally published since then. To cope with the rapid rise of prokaryotic genome data, we present HGTree v2.0 (http://hgtree2.snu.ac.kr), a newly updated version of our HGT database with much more extensive data, including a total of 20 536 completely sequenced non-redundant prokaryotic genomes, and more reliable HGT information results curated with various steps. As a result, HGTree v2.0 has a set of expanded data results of 6 361 199 putative horizontally transferred genes integrated with additional functional information such as the KEGG pathway, virulence factors and antimicrobial resistance. Furthermore, various visualization tools in the HGTree v2.0 database website provide intuitive biological insights, allowing the users to investigate their genomes of interest.", + "authors": [ + { + "name": "Ahn S." + }, + { + "name": "Cho S." + }, + { + "name": "Choi Y." + }, + { + "name": "Kim H." + }, + { + "name": "Lee S." + }, + { + "name": "Park M." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "HGTree v2.0: a comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method" + }, + "pmcid": "PMC9825516", + "pmid": "36350646" + } + ], + "toolType": [ + "Database portal", + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Phylogeny", + "uri": "http://edamontology.org/topic_0084" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ], + "version": [ + "2.0" + ] +} diff --git a/data/hi-lasso/hi-lasso.biotools.json b/data/hi-lasso/hi-lasso.biotools.json new file mode 100644 index 0000000000000..732345f8873e4 --- /dev/null +++ b/data/hi-lasso/hi-lasso.biotools.json @@ -0,0 +1,135 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T02:43:08.674587Z", + "biotoolsCURIE": "biotools:hi-lasso", + "biotoolsID": "hi-lasso", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mingon.kang@unlv.edu", + "name": "Mingon Kang", + "orcidid": "http://orcid.org/0000-0002-9565-9523", + "typeEntity": "Person" + }, + { + "email": "youngsoonkim@gnu.ac.kr", + "name": "Youngsoon Kim", + "typeEntity": "Person" + }, + { + "name": "Jongkwon Jo" + }, + { + "name": "Joongyang Park" + }, + { + "name": "Seungha Jung" + } + ], + "description": "High-performance python and apache spark packages for feature selection with high-dimensional data.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://hi-lasso.readthedocs.io/en/latest/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature selection", + "uri": "http://edamontology.org/operation_3936" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/datax-lab/Hi-LASSO", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-22T02:43:08.677084Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://pypi.org/project/Hi-LASSO-spark" + }, + { + "type": [ + "Repository" + ], + "url": "https://pypi.org/project/hi-lasso" + } + ], + "name": "Hi-LASSO", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pone.0278570", + "metadata": { + "abstract": "Copyright: © 2022 Jo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO's outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/dataxlab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark.", + "authors": [ + { + "name": "Jo J." + }, + { + "name": "Jung S." + }, + { + "name": "Kang M." + }, + { + "name": "Kim Y." + }, + { + "name": "Park J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data" + }, + "pmcid": "PMC9714948", + "pmid": "36455001" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Simulation experiment", + "uri": "http://edamontology.org/topic_3524" + } + ] +} diff --git a/data/hichipdb/hichipdb.biotools.json b/data/hichipdb/hichipdb.biotools.json new file mode 100644 index 0000000000000..a2d1553a9c6f0 --- /dev/null +++ b/data/hichipdb/hichipdb.biotools.json @@ -0,0 +1,90 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:16:53.682048Z", + "biotoolsCURIE": "biotools:hichipdb", + "biotoolsID": "hichipdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ruijiang@tsinghua.edu.cn", + "name": "Rui Jiang", + "orcidid": "https://orcid.org/0000-0002-7533-3753", + "typeEntity": "Person" + }, + { + "email": "whwong@stanford.edu", + "name": "Wing Hung Wong", + "orcidid": "https://orcid.org/0000-0001-7466-2339", + "typeEntity": "Person" + }, + { + "name": "Qiao Liu" + }, + { + "name": "Wanwen Zeng" + } + ], + "description": "A comprehensive database of HiChIP regulatory interactions.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "SNP annotation", + "uri": "http://edamontology.org/operation_3661" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "http://health.tsinghua.edu.cn/hichipdb/", + "lastUpdate": "2022-12-31T01:16:53.684605Z", + "name": "HiChIPdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC859", + "pmid": "36215037" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "ChIP-on-chip", + "uri": "http://edamontology.org/topic_3179" + }, + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/hifens/hifens.biotools.json b/data/hifens/hifens.biotools.json new file mode 100644 index 0000000000000..94b8c8d0241ff --- /dev/null +++ b/data/hifens/hifens.biotools.json @@ -0,0 +1,91 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:11:51.277187Z", + "biotoolsCURIE": "biotools:hifens", + "biotoolsID": "hifens", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mistelit@mail.nih.gov", + "name": "Tom Misteli", + "typeEntity": "Person" + }, + { + "name": "Asaf Shilo" + }, + { + "name": "Gianluca Pegoraro" + } + ], + "description": "High-throughput FISH detection of endogenous pre-mRNA splicing isoforms.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Alternative splicing prediction", + "uri": "http://edamontology.org/operation_0264" + }, + { + "term": "Exonic splicing enhancer prediction", + "uri": "http://edamontology.org/operation_0446" + }, + { + "term": "Splice site prediction", + "uri": "http://edamontology.org/operation_0433" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/CBIIT/mistelilab-hifens", + "language": [ + "R" + ], + "lastUpdate": "2022-12-31T01:11:51.280202Z", + "license": "GPL-3.0", + "name": "HiFENS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC869", + "pmid": "36243969" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Probes and primers", + "uri": "http://edamontology.org/topic_0632" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + } + ] +} diff --git a/data/histofl/histofl.biotools.json b/data/histofl/histofl.biotools.json new file mode 100644 index 0000000000000..c18bd0b60a1c5 --- /dev/null +++ b/data/histofl/histofl.biotools.json @@ -0,0 +1,127 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T00:28:09.591578Z", + "biotoolsCURIE": "biotools:histofl", + "biotoolsID": "histofl", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "faisalmahmood@bwh.harvard.edu", + "name": "Faisal Mahmood", + "typeEntity": "Person" + }, + { + "name": "Ming Y. Lu" + }, + { + "name": "Richard J. Chen" + }, + { + "name": "Tiffany Y. Chen" + } + ], + "description": "Federated learning for computational pathology on gigapixel whole slide images.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "http://github.com/mahmoodlab/HistoFL", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-27T00:28:09.594205Z", + "license": "GPL-3.0", + "name": "HistoFL", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.MEDIA.2021.102298", + "metadata": { + "abstract": "© 2021 The Author(s)Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such datasets. Multi-centric and collaborative integration of medical data across multiple institutions can naturally help overcome this challenge and boost the model performance but is limited by privacy concerns among other difficulties that may arise in the complex data sharing process as models scale towards using hundreds of thousands of gigapixel whole slide images. In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy. We evaluated our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels. Additionally, we present a weakly-supervised learning framework for survival prediction and patient stratification from whole slide images and demonstrate its effectiveness in a federated setting. Our results show that using federated learning, we can effectively develop accurate weakly-supervised deep learning models from distributed data silos without direct data sharing and its associated complexities, while also preserving differential privacy using randomized noise generation. We also make available an easy-to-use federated learning for computational pathology software package: http://github.com/mahmoodlab/HistoFL.", + "authors": [ + { + "name": "Chen R.J." + }, + { + "name": "Chen T.Y." + }, + { + "name": "Kong D." + }, + { + "name": "Lipkova J." + }, + { + "name": "Lu M.Y." + }, + { + "name": "Mahmood F." + }, + { + "name": "Singh R." + }, + { + "name": "Williamson D.F.K." + } + ], + "citationCount": 26, + "date": "2022-02-01T00:00:00Z", + "journal": "Medical Image Analysis", + "title": "Federated learning for computational pathology on gigapixel whole slide images" + }, + "pmcid": "PMC9340569", + "pmid": "34911013" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Data security", + "uri": "http://edamontology.org/topic_3263" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/hn-ppisp/hn-ppisp.biotools.json b/data/hn-ppisp/hn-ppisp.biotools.json new file mode 100644 index 0000000000000..bfde6a9ee8a32 --- /dev/null +++ b/data/hn-ppisp/hn-ppisp.biotools.json @@ -0,0 +1,75 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T15:00:48.743735Z", + "biotoolsCURIE": "biotools:hn-ppisp", + "biotoolsID": "hn-ppisp", + "confidence_flag": "tool", + "credit": [ + { + "email": "pubin@hnu.edu.cn", + "name": "Bin Pu", + "typeEntity": "Person" + } + ], + "description": "A hybrid network based on MLP-Mixer for protein-protein interaction site prediction.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Protein feature detection", + "uri": "http://edamontology.org/operation_3092" + }, + { + "term": "Protein interaction network prediction", + "uri": "http://edamontology.org/operation_3094" + }, + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + } + ] + } + ], + "homepage": "https://github.com/ylxu05/HN-PPISP", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T15:00:48.746180Z", + "license": "Not licensed", + "name": "HN-PPISP", + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIB/BBAC480", + "pmid": "36403092" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein interaction experiment", + "uri": "http://edamontology.org/topic_3957" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/hnc-predictor/hnc-predictor.biotools.json b/data/hnc-predictor/hnc-predictor.biotools.json new file mode 100644 index 0000000000000..9f560fd0c6360 --- /dev/null +++ b/data/hnc-predictor/hnc-predictor.biotools.json @@ -0,0 +1,105 @@ +{ + "additionDate": "2023-01-28T15:04:30.257808Z", + "biotoolsCURIE": "biotools:hnc-predictor", + "biotoolsID": "hnc-predictor", + "confidence_flag": "tool", + "credit": [ + { + "name": "Clifton D. Fuller" + } + ], + "description": "Development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer', 'clinic-ready'", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://uic-evl.github.io/hnc-predictor/", + "lastUpdate": "2023-01-28T15:04:30.260262Z", + "license": "Not licensed", + "name": "HNC-PREDICTOR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1016/J.EJCA.2022.10.011", + "metadata": { + "abstract": "© 2022 The AuthorsBackground: Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a ‘one-dose-fits-all’ approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international ‘big-data’ to facilitate risk-based stratification of patients with HNC. Methods: The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts: a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups. Results: Performance score, AJCC8th stage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66–0.77]) and in all three validation cohorts (c-indices: 0.76 [0.69–0.83], 0.73 [0.68–0.77], and 0.75 [0.68–0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17–46% for the high-risk group compared to 92–98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64–0.81]). These models are integrated in a clinic-ready interactive web interface: https://uic-evl.github.io/hnc-predictor/ Conclusions: Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.", + "authors": [ + { + "name": "Ahmed S." + }, + { + "name": "Fuller C.D." + }, + { + "name": "Garden A.S." + }, + { + "name": "Gunn B." + }, + { + "name": "Hope A.J." + }, + { + "name": "Langendijk J.A." + }, + { + "name": "Marai G.E." + }, + { + "name": "Mohamed A.S." + }, + { + "name": "Moreno A." + }, + { + "name": "Nipu N." + }, + { + "name": "Sijtsema N.M." + }, + { + "name": "Wahid K." + }, + { + "name": "van Dijk L.V." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "European Journal of Cancer", + "title": "Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer" + }, + "pmid": "36442460" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/honto/honto.biotools.json b/data/honto/honto.biotools.json new file mode 100644 index 0000000000000..fa6b0bd2e0d90 --- /dev/null +++ b/data/honto/honto.biotools.json @@ -0,0 +1,91 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T15:07:20.645438Z", + "biotoolsCURIE": "biotools:honto", + "biotoolsID": "honto", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "paolo.franciosa@uniroma1.it", + "name": "Paolo Giulio Franciosa", + "orcidid": "https://orcid.org/0000-0002-5464-4069", + "typeEntity": "Person" + } + ], + "description": "A tool designed for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/cumbof/honto", + "language": [ + "C", + "Python" + ], + "lastUpdate": "2023-01-28T15:07:20.648215Z", + "license": "MIT", + "name": "honto", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC763", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. AVAILABILITY AND IMPLEMENTATION: Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.", + "authors": [ + { + "name": "Apollonio N." + }, + { + "name": "Blankenberg D." + }, + { + "name": "Cumbo F." + }, + { + "name": "Franciosa P.G." + }, + { + "name": "Santoni D." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks" + }, + "pmcid": "PMC9805585", + "pmid": "36440918" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/hproteome-bsite/hproteome-bsite.biotools.json b/data/hproteome-bsite/hproteome-bsite.biotools.json new file mode 100644 index 0000000000000..6ad80f5e9ffb2 --- /dev/null +++ b/data/hproteome-bsite/hproteome-bsite.biotools.json @@ -0,0 +1,91 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:06:21.382484Z", + "biotoolsCURIE": "biotools:hproteome-bsite", + "biotoolsID": "hproteome-bsite", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chaok@snu.ac.kr", + "name": "Chaok Seok", + "typeEntity": "Person" + }, + { + "name": "Jiho Sim" + }, + { + "name": "Sohee Kwon" + } + ], + "description": "An online database for ligand binding sites in human proteome.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Binding site prediction", + "uri": "http://edamontology.org/operation_2575" + }, + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + }, + { + "term": "Protein-ligand docking", + "uri": "http://edamontology.org/operation_0482" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + } + ] + } + ], + "homepage": "https://galaxy.seoklab.org/hproteome-bsite/database/domains/39056", + "lastUpdate": "2022-12-31T01:06:21.386008Z", + "name": "HProteome-BSite", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC873", + "pmid": "36243970" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ] +} diff --git a/data/hsdatabase/hsdatabase.biotools.json b/data/hsdatabase/hsdatabase.biotools.json new file mode 100644 index 0000000000000..02c4107db9efb --- /dev/null +++ b/data/hsdatabase/hsdatabase.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T01:01:49.080128Z", + "biotoolsCURIE": "biotools:hsdatabase", + "biotoolsID": "hsdatabase", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dsmit242@uwo.ca", + "name": "David Roy Smith", + "orcidid": "https://orcid.org/0000-0001-9560-5210", + "typeEntity": "Person" + }, + { + "email": "xi.zhang@dal.ca", + "name": "Xi Zhang", + "orcidid": "https://orcid.org/0000-0003-2821-9066", + "typeEntity": "Person" + }, + { + "name": "Yining Hu" + } + ], + "description": "Database of highly similar duplicate genes from plants, animals, and algae.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Genome visualisation", + "uri": "http://edamontology.org/operation_3208" + } + ] + } + ], + "homepage": "http://hsdfinder.com/database/", + "lastUpdate": "2022-12-31T01:01:49.082939Z", + "name": "HSDatabase", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/DATABASE/BAAC086", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press.Gene duplication is an important evolutionary mechanism capable of providing new genetic material, which in some instances can help organisms adapt to various environmental conditions. Recent studies, for example, have indicated that highly similar duplicate genes (HSDs) are aiding adaptation to extreme conditions via gene dosage. However, for most eukaryotic genomes HSDs remain uncharacterized, partly because they can be hard to identify and categorize efficiently and effectively. Here, we collected and curated HSDs in nuclear genomes from various model animals, land plants and algae and indexed them in an online, open-access sequence repository called HSDatabase. Currently, this database contains 117 864 curated HSDs from 40 distinct genomes; it includes statistics on the total number of HSDs per genome as well as individual HSD copy numbers/lengths and provides sequence alignments of the duplicate gene copies. HSDatabase also allows users to download sequences of gene copies, access genome browsers, and link out to other databases, such as Pfam and Kyoto Encyclopedia of Genes and Genomes. What is more, a built-in Basic Local Alignment Search Tool option is available to conveniently explore potential homologous sequences of interest within and across species. HSDatabase has a user-friendly interface and provides easy access to the source data. It can be used on its own for comparative analyses of gene duplicates or in conjunction with HSDFinder, a newly developed bioinformatics tool for identifying, annotating, categorizing and visualizing HSDs. Database URL: http://hsdfinder.com/database/", + "authors": [ + { + "name": "Hu Y." + }, + { + "name": "Smith D.R." + }, + { + "name": "Zhang X." + } + ], + "citationCount": 1, + "date": "2022-01-01T00:00:00Z", + "journal": "Database", + "title": "HSDatabase-A database of highly similar duplicate genes from plants, animals, and algae" + }, + "pmcid": "PMC9547538", + "pmid": "36208223" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Carbohydrates", + "uri": "http://edamontology.org/topic_0152" + }, + { + "term": "Data submission, annotation and curation", + "uri": "http://edamontology.org/topic_0219" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/hsdfinder/hsdfinder.biotools.json b/data/hsdfinder/hsdfinder.biotools.json new file mode 100644 index 0000000000000..82365d74dd42c --- /dev/null +++ b/data/hsdfinder/hsdfinder.biotools.json @@ -0,0 +1,86 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:55:58.680941Z", + "biotoolsCURIE": "biotools:hsdfinder", + "biotoolsID": "hsdfinder", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "dsmit242@uwo.ca", + "name": "David Roy Smith", + "typeEntity": "Person" + }, + { + "email": "xzha25@uwo.ca", + "name": "Xi Zhang", + "typeEntity": "Person" + }, + { + "name": "Yining Hu" + } + ], + "description": "An integrated tool for predicting highly similar duplicates (HSDs) in eukaryotic genomes.\nHSDFinder aims to become a useful platform for the identification and analysis of HSDs in the eukaryotic genomes, which deepen our insights into the gene duplication mechanisms driving the genome adaptation.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Duplication detection", + "uri": "http://edamontology.org/operation_3963" + }, + { + "term": "Genome visualisation", + "uri": "http://edamontology.org/operation_3208" + }, + { + "term": "Heat map generation", + "uri": "http://edamontology.org/operation_0531" + } + ] + } + ], + "homepage": "http://hsdfinder.com", + "lastUpdate": "2022-12-31T00:56:45.595468Z", + "name": "HSDFinder", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2021.803176", + "pmcid": "PMC9580922", + "pmid": "36303740" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/hsnet/hsnet.biotools.json b/data/hsnet/hsnet.biotools.json new file mode 100644 index 0000000000000..72043bb3f7e46 --- /dev/null +++ b/data/hsnet/hsnet.biotools.json @@ -0,0 +1,87 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:51:45.800347Z", + "biotoolsCURIE": "biotools:hsnet", + "biotoolsID": "hsnet", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "b.sham@auckland.ac.nz", + "name": "Chiu-Wing Sham", + "orcidid": "https://orcid.org/0000-0001-7007-6746" + }, + { + "name": "Chong Fu" + }, + { + "name": "Wenchao Zhang" + }, + { + "name": "Yu Zheng", + "orcidid": "https://orcid.org/0000-0002-5816-4126" + } + ], + "description": "A hybrid semantic network for polyp segmentation.", + "editPermission": { + "type": "private" + }, + "homepage": "https://github.com/baiboat/HSNet", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-31T00:51:45.804041Z", + "license": "Not licensed", + "name": "HSNet", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.COMPBIOMED.2022.106173", + "metadata": { + "abstract": "© 2022 Elsevier LtdAutomatic polyp segmentation can help physicians to effectively locate polyps (a.k.a. region of interests) in clinical practice, in the way of screening colonoscopy images assisted by neural networks (NN). However, two significant bottlenecks hinder its effectiveness, disappointing physicians’ expectations. (1) Changeable polyps in different scaling, orientation, and illumination, bring difficulty in accurate segmentation. (2) Current works building on a dominant decoder–encoder network tend to overlook appearance details (e.g., textures) for a tiny polyp, degrading the accuracy to differentiate polyps. For alleviating the bottlenecks, we investigate a hybrid semantic network (HSNet) that adopts both advantages of Transformer and convolutional neural networks (CNN), aiming at improving polyp segmentation. Our HSNet contains a cross-semantic attention module (CSA), a hybrid semantic complementary module (HSC), and a multi-scale prediction module (MSP). Unlike previous works on segmenting polyps, we newly insert the CSA module, which can fill the gap between low-level and high-level features via an interactive mechanism that exchanges two types of semantics from different NN attentions. By a dual-branch structure of Transformer and CNN, we newly design an HSC module, for capturing both long-range dependencies and local details of appearance. Besides, the MSP module can learn weights for fusing stage-level prediction masks of a decoder. Experimentally, we compared our work with 10 state-of-the-art works, including both recent and classical works, showing improved accuracy (via 7 evaluative metrics) over 5 benchmark datasets, e.g., it achieves 0.926/0.877 mDic/mIoU on Kvasir-SEG, 0.948/0.905 mDic/mIoU on ClinicDB, 0.810/0.735 mDic/mIoU on ColonDB, 0.808/0.74 mDic/mIoU on ETIS, and 0.903/0.839 mDic/mIoU on Endoscene. The proposed model is available at (https://github.com/baiboat/HSNet).", + "authors": [ + { + "name": "Fu C." + }, + { + "name": "Sham C.-W." + }, + { + "name": "Zhang F." + }, + { + "name": "Zhang W." + }, + { + "name": "Zhao Y." + }, + { + "name": "Zheng Y." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Computers in Biology and Medicine", + "title": "HSNet: A hybrid semantic network for polyp segmentation" + }, + "pmid": "36257278" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/htaadvar/htaadvar.biotools.json b/data/htaadvar/htaadvar.biotools.json new file mode 100644 index 0000000000000..4fd92be5f2283 --- /dev/null +++ b/data/htaadvar/htaadvar.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:42:15.233812Z", + "biotoolsCURIE": "biotools:htaadvar", + "biotoolsID": "htaadvar", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Guoyan Zhu" + }, + { + "name": "Wei-Zhen Zhou" + }, + { + "name": "Yujing Zhang" + } + ], + "description": "Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene symbol", + "uri": "http://edamontology.org/data_1026" + } + } + ], + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + } + ] + } + ], + "homepage": "http://htaadvar.fwgenetics.org", + "lastUpdate": "2022-12-31T00:42:15.236585Z", + "name": "HTAADVar", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.GIM.2022.08.024", + "metadata": { + "abstract": "© 2022 The AuthorsPurpose: Early detection and pathogenicity interpretation of disease-associated variants are crucial but challenging in molecular diagnosis, especially for insidious and life-threatening diseases, such as heritable thoracic aortic aneurysm and dissection (HTAAD). In this study, we developed HTAADVar, an unbiased and fully automated system for the molecular diagnosis of HTAAD. Methods: We developed HTAADVar (http://htaadvar.fwgenetics.org) under the American College of Medical Genetics and Genomics/Association for Molecular Pathology framework, with optimizations based on disease- and gene-specific knowledge, expert panel recommendations, and variant observations. HTAADVar provides variant interpretation with a self-built database through the web server and the stand-alone programs. Results: We constructed an expert-reviewed database by integrating 4373 variants in HTAAD genes, with comprehensive metadata curated from 697 publications and an in-house study of 790 patients. We further developed an interpretation system to assess variants automatically. Notably, HTAADVar showed a multifold increase in performance compared with public tools, reaching a sensitivity of 92.64% and specificity of 70.83%. The molecular diagnostic yield of HTAADVar among 790 patients (42.03%) also matched the clinical data, independently demonstrating its good performance in clinical application. Conclusion: HTAADVar represents the first fully automated system for accurate variant interpretation for HTAAD. The framework of HTAADVar could also be generalized for the molecular diagnosis of other genetic diseases.", + "authors": [ + { + "name": "Chen Q." + }, + { + "name": "Li W." + }, + { + "name": "Luo M." + }, + { + "name": "Shen H." + }, + { + "name": "Shu C." + }, + { + "name": "Yang H." + }, + { + "name": "Zeng Q." + }, + { + "name": "Zhang Y." + }, + { + "name": "Zhou W.-Z." + }, + { + "name": "Zhou Z." + }, + { + "name": "Zhu G." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Genetics in Medicine", + "title": "HTAADVar: Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection" + }, + "pmid": "36194209" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/htseq-clip/htseq-clip.biotools.json b/data/htseq-clip/htseq-clip.biotools.json new file mode 100644 index 0000000000000..9b2b48a6465c6 --- /dev/null +++ b/data/htseq-clip/htseq-clip.biotools.json @@ -0,0 +1,116 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-28T15:11:28.535534Z", + "biotoolsCURIE": "biotools:htseq-clip", + "biotoolsID": "htseq-clip", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "schwarzl@embl.de", + "name": "Thomas Schwarzl", + "orcidid": "https://orcid.org/0000-0001-7697-7000", + "typeEntity": "Person" + } + ], + "description": "htseq-clip, a python package developed for preprocessing, extracting and summarizing crosslink site counts from i/eCLIP experimental data.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://htseq-clip.readthedocs.io/en/latest" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + }, + { + "term": "RNA binding site prediction", + "uri": "http://edamontology.org/operation_3902" + }, + { + "term": "RNA-binding protein prediction", + "uri": "http://edamontology.org/operation_3901" + } + ] + } + ], + "homepage": "https://github.com/EMBL-Hentze-group/htseq-clip", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-28T15:11:28.537995Z", + "license": "MIT", + "name": "htseq-clip", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Chan019", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC747", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Transcriptome-wide detection of binding sites of RNA-binding proteins is achieved using Individual-nucleotide crosslinking and immunoprecipitation (iCLIP) and its derivative enhanced CLIP (eCLIP) sequencing methods. Here, we introduce htseq-clip, a python package developed for preprocessing, extracting and summarizing crosslink site counts from i/eCLIP experimental data. The package delivers crosslink site count matrices along with other metrics, which can be directly used for filtering and downstream analyses such as the identification of differential binding sites. AVAILABILITY AND IMPLEMENTATION: The Python package htseq-clip is available via pypi (python package index), bioconda and the Galaxy Tool Shed under the open source MIT License. The code is hosted at https://github.com/EMBL-Hentze-group/htseq-clip and documentation is available under https://htseq-clip.readthedocs.io/en/latest.", + "authors": [ + { + "name": "Ashaf N." + }, + { + "name": "Fritz M." + }, + { + "name": "Hentze M.W." + }, + { + "name": "Huber W." + }, + { + "name": "Sahadevan S." + }, + { + "name": "Schwarzl T." + }, + { + "name": "Sekaran T." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets" + }, + "pmcid": "PMC9825771", + "pmid": "36394253" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Protein binding sites", + "uri": "http://edamontology.org/topic_3534" + }, + { + "term": "RNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3794" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/ianimal/ianimal.biotools.json b/data/ianimal/ianimal.biotools.json new file mode 100644 index 0000000000000..64cb6fd83328e --- /dev/null +++ b/data/ianimal/ianimal.biotools.json @@ -0,0 +1,102 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:35:03.606366Z", + "biotoolsCURIE": "biotools:ianimal", + "biotoolsID": "ianimal", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "shzhao@mail.hzau.edu.cn", + "name": "Shuhong Zhao", + "typeEntity": "Person" + }, + { + "email": "xiaoleiliu@mail.hzau.edu.cn", + "name": "Xiaolei Liu", + "typeEntity": "Person" + }, + { + "name": "Hong Liu" + }, + { + "name": "Yuhua Fu" + } + ], + "description": "A cross-species omics knowledgebase for animals.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "GO concept ID", + "uri": "http://edamontology.org/data_1176" + } + }, + { + "data": { + "term": "Identifier", + "uri": "http://edamontology.org/data_0842" + } + } + ], + "operation": [ + { + "term": "Gene regulatory network analysis", + "uri": "http://edamontology.org/operation_1781" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://ianimal.pro/", + "lastUpdate": "2022-12-31T00:35:03.609061Z", + "name": "IAnimal", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC936", + "pmid": "36300629" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Phenomics", + "uri": "http://edamontology.org/topic_3298" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Whole genome sequencing", + "uri": "http://edamontology.org/topic_3673" + } + ] +} diff --git a/data/iantisplodge/iantisplodge.biotools.json b/data/iantisplodge/iantisplodge.biotools.json new file mode 100644 index 0000000000000..a45a2978a73b4 --- /dev/null +++ b/data/iantisplodge/iantisplodge.biotools.json @@ -0,0 +1,100 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:25:06.067069Z", + "biotoolsCURIE": "biotools:iantisplodge", + "biotoolsID": "iantisplodge", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "christoph.Lippert@hpi.de", + "name": "Christoph Lippert", + "typeEntity": "Person" + }, + { + "name": "Eric L Lindberg" + }, + { + "name": "Norbert Hübner" + }, + { + "name": "Jesper B Lund", + "orcidid": "https://orcid.org/0000-0001-9483-1603" + } + ], + "description": "A neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deisotoping", + "uri": "http://edamontology.org/operation_3629" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "https://github.com/HealthML/AntiSplodge/", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-31T00:28:49.301672Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://pypi.org/project/antisplodge/" + } + ], + "name": "iAntiSplodge", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NARGAB/LQAC073", + "pmcid": "PMC9549785", + "pmid": "36225530" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/ican/ican.biotools.json b/data/ican/ican.biotools.json new file mode 100644 index 0000000000000..4ff83c66b6a1a --- /dev/null +++ b/data/ican/ican.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:20:04.996941Z", + "biotoolsCURIE": "biotools:ican", + "biotoolsID": "ican", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kurata@bio.kyutech.ac.jp", + "name": "Hiroyuki Kurata", + "orcidid": "https://orcid.org/0000-0003-4254-2214", + "typeEntity": "Person" + }, + { + "name": "Sho Tsukiyama" + } + ], + "description": "Interpretable cross-attention network for identifying drug and target protein interactions.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein interaction network analysis", + "uri": "http://edamontology.org/operation_0276" + }, + { + "term": "Protein interaction network prediction", + "uri": "http://edamontology.org/operation_3094" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://github.com/kuratahiroyuki/ICAN", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-31T00:20:04.999830Z", + "license": "MIT", + "name": "ICAN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PONE.0276609", + "metadata": { + "abstract": "© 2022 Kurata, Tsukiyama. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.", + "authors": [ + { + "name": "Kurata H." + }, + { + "name": "Tsukiyama S." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "ICAN: Interpretable cross-attention network for identifying drug and target protein interactions" + }, + "pmcid": "PMC9591068", + "pmid": "36279284" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/icardiotoxcsm/icardiotoxcsm.biotools.json b/data/icardiotoxcsm/icardiotoxcsm.biotools.json new file mode 100644 index 0000000000000..4963e044b782a --- /dev/null +++ b/data/icardiotoxcsm/icardiotoxcsm.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:15:05.074311Z", + "biotoolsCURIE": "biotools:icardiotoxcsm", + "biotoolsID": "icardiotoxcsm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Alex G. C. de Sá" + }, + { + "name": "Saba Iftkhar" + }, + { + "name": "David B. Ascher", + "orcidid": "https://orcid.org/0000-0003-2948-2413" + }, + { + "name": "Douglas E. V. Pires", + "orcidid": "https://orcid.org/0000-0002-3004-2119" + } + ], + "description": "A Web Server for Predicting Cardiotoxicity of Small Molecules.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "SMILES string", + "uri": "http://edamontology.org/data_2301" + } + } + ], + "operation": [ + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://biosig.lab.uq.edu.au/cardiotoxcsm", + "lastUpdate": "2022-12-31T00:15:05.077046Z", + "name": "icardioToxCSM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/ACS.JCIM.2C00822", + "metadata": { + "abstract": "© 2022 American Chemical Society.The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.", + "authors": [ + { + "name": "Aljarf R." + }, + { + "name": "Ascher D.B." + }, + { + "name": "De Sa A.G.C." + }, + { + "name": "Iftkhar S." + }, + { + "name": "Pires D.E.V." + }, + { + "name": "Velloso J.P.L." + } + ], + "date": "2022-10-24T00:00:00Z", + "journal": "Journal of Chemical Information and Modeling", + "title": "CardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules" + }, + "pmid": "36219164" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/icescreen/icescreen.biotools.json b/data/icescreen/icescreen.biotools.json new file mode 100644 index 0000000000000..5e6893e5a1529 --- /dev/null +++ b/data/icescreen/icescreen.biotools.json @@ -0,0 +1,97 @@ +{ + "additionDate": "2022-12-31T00:10:13.950087Z", + "biotoolsCURIE": "biotools:icescreen", + "biotoolsID": "icescreen", + "confidence_flag": "high", + "credit": [ + { + "email": "helene.chiapello@inrae.fr", + "name": "Hélène Chiapello", + "orcidid": "https://orcid.org/0000-0001-5102-0632", + "typeEntity": "Person" + }, + { + "email": "nathalie.leblond@univ-lorraine.fr", + "name": "Nathalie Leblond-Bourget", + "typeEntity": "Person" + }, + { + "name": "Thomas Lacroix" + }, + { + "name": "Julie Lao", + "orcidid": "https://orcid.org/0000-0002-5290-3098" + } + ], + "description": "A tool to detect Firmicute ICEs and IMEs, isolated or enclosed in composite structures.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Protein feature detection", + "uri": "http://edamontology.org/operation_3092" + } + ] + } + ], + "homepage": "https://icescreen.migale.inrae.fr", + "lastUpdate": "2022-12-31T00:10:13.952610Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://anaconda.org/search?q=icescreen" + }, + { + "type": [ + "Repository" + ], + "url": "https://forgemia.inra.fr/ices_imes_analysis/icescreen" + } + ], + "name": "ICEscreen", + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NARGAB/LQAC079", + "pmcid": "PMC9585547", + "pmid": "36285285" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Mobile genetic elements", + "uri": "http://edamontology.org/topic_0798" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Structural variation", + "uri": "http://edamontology.org/topic_3175" + } + ], + "version": [ + "1.1.0" + ] +} diff --git a/data/idjexpress/idjexpress.biotools.json b/data/idjexpress/idjexpress.biotools.json new file mode 100644 index 0000000000000..9d2ffb0c02ac2 --- /dev/null +++ b/data/idjexpress/idjexpress.biotools.json @@ -0,0 +1,95 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-31T00:00:08.724956Z", + "biotoolsCURIE": "biotools:idjexpress", + "biotoolsID": "idjexpress", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jan.mauer@gmail.com", + "name": "Jan Mauer", + "typeEntity": "Person" + }, + { + "email": "linhiel@gmail.com", + "name": "Lina Marcela Gallego-Paez", + "typeEntity": "Person" + } + ], + "description": "An Integrated Application for Differential Splicing Analysis and Visualization.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Alternative splicing prediction", + "uri": "http://edamontology.org/operation_0264" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Weighted correlation network analysis", + "uri": "http://edamontology.org/operation_3766" + } + ] + } + ], + "homepage": "https://github.com/MauerLab/DJExpress", + "language": [ + "R" + ], + "lastUpdate": "2022-12-31T00:00:08.728069Z", + "license": "MIT", + "name": "iDJExpress", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2022.786898", + "pmcid": "PMC9580925", + "pmid": "36304260" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/idna-abf/idna-abf.biotools.json b/data/idna-abf/idna-abf.biotools.json new file mode 100644 index 0000000000000..f7206096be234 --- /dev/null +++ b/data/idna-abf/idna-abf.biotools.json @@ -0,0 +1,161 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-30T23:56:23.080805Z", + "biotoolsCURIE": "biotools:idna-abf", + "biotoolsID": "idna-abf", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "knakai@ims.u-tokyo.ac.jp", + "name": "Kenta Nakai", + "typeEntity": "Person" + }, + { + "email": "weileyi@sdu.edu.cn", + "name": "Leyi Wei", + "typeEntity": "Person" + }, + { + "name": "Junru Jin" + }, + { + "name": "Yingying Yu" + } + ], + "description": "Multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "DNA sequence", + "uri": "http://edamontology.org/data_3494" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + }, + { + "term": "Whole genome methylation analysis", + "uri": "http://edamontology.org/operation_3206" + } + ] + } + ], + "homepage": "https://inner.wei-group.net/idnaabf/#/", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-30T23:57:22.473995Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/FakeEnd/iDNA_ABF" + } + ], + "name": "iDNA-ABF", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13059-022-02780-1", + "metadata": { + "abstract": "© 2022, The Author(s).In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.", + "authors": [ + { + "name": "Dai Y." + }, + { + "name": "Jiang Y." + }, + { + "name": "Jin J." + }, + { + "name": "Li Z." + }, + { + "name": "Nakai K." + }, + { + "name": "Pang C." + }, + { + "name": "Su R." + }, + { + "name": "Wang R." + }, + { + "name": "Wei L." + }, + { + "name": "Yu Y." + }, + { + "name": "Zeng X." + }, + { + "name": "Zou Q." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Genome Biology", + "title": "iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations" + }, + "pmcid": "PMC9575223", + "pmid": "36253864" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Methylated DNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3674" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + } + ] +} diff --git a/data/idpconformergenerator/idpconformergenerator.biotools.json b/data/idpconformergenerator/idpconformergenerator.biotools.json new file mode 100644 index 0000000000000..9f6100eaf2cab --- /dev/null +++ b/data/idpconformergenerator/idpconformergenerator.biotools.json @@ -0,0 +1,150 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-22T02:54:07.414857Z", + "biotoolsCURIE": "biotools:idpconformergenerator", + "biotoolsID": "idpconformergenerator", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Zi Hao Liu" + }, + { + "name": "João M.C. Teixeira", + "orcidid": "http://orcid.org/0000-0002-9113-0622" + }, + { + "name": "Julie D. Forman-Kay", + "orcidid": "http://orcid.org/0000-0001-8265-972X" + }, + { + "name": "Teresa Head-Gordon", + "orcidid": "http://orcid.org/0000-0003-0025-8987" + } + ], + "description": "A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://idpconformergenerator.readthedocs.io/en/latest/index.html" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Protein disorder prediction", + "uri": "http://edamontology.org/operation_3904" + }, + { + "term": "Protein secondary structure comparison", + "uri": "http://edamontology.org/operation_2488" + }, + { + "term": "Protein secondary structure prediction (coils)", + "uri": "http://edamontology.org/operation_0470" + } + ] + } + ], + "homepage": "https://github.com/julie-forman-kay-lab/IDPConformerGenerator", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-22T02:54:07.417386Z", + "license": "Apache-2.0", + "name": "IDPConformerGenerator", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/acs.jpca.2c03726", + "metadata": { + "abstract": "© 2022 The Authors. Published by American Chemical Society.The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.", + "authors": [ + { + "name": "Forman-Kay J.D." + }, + { + "name": "Haghighatlari M." + }, + { + "name": "Head-Gordon T." + }, + { + "name": "Krzeminski M." + }, + { + "name": "Li J." + }, + { + "name": "Liu Z.H." + }, + { + "name": "Namini A." + }, + { + "name": "Shamandy A.A." + }, + { + "name": "Teixeira J.M.C." + }, + { + "name": "Vernon R.M." + }, + { + "name": "Yu L." + }, + { + "name": "Zhang O." + } + ], + "citationCount": 2, + "date": "2022-09-08T00:00:00Z", + "journal": "Journal of Physical Chemistry A", + "title": "IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States" + }, + "pmcid": "PMC9465686", + "pmid": "36030416" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/topic_0176" + }, + { + "term": "Protein disordered structure", + "uri": "http://edamontology.org/topic_3538" + }, + { + "term": "Protein folds and structural domains", + "uri": "http://edamontology.org/topic_0736" + }, + { + "term": "Protein secondary structure", + "uri": "http://edamontology.org/topic_3542" + } + ] +} diff --git a/data/idvip/idvip.biotools.json b/data/idvip/idvip.biotools.json new file mode 100644 index 0000000000000..de8684f5ebe06 --- /dev/null +++ b/data/idvip/idvip.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-30T23:52:02.823170Z", + "biotoolsCURIE": "biotools:idvip", + "biotoolsID": "idvip", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Hui-Ju Kao" + }, + { + "name": "Shun-Long Weng" + }, + { + "name": "Kai-Yao Huang", + "orcidid": "https://orcid.org/0000-0001-9855-1035" + } + ], + "description": "iDVIP is a web server for identifying Viral integrase inhibitory peptides (VINIPs).", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], + "operation": [ + { + "term": "Peptide identification", + "uri": "http://edamontology.org/operation_3631" + } + ] + } + ], + "homepage": "http://mer.hc.mmh.org.tw/iDVIP/", + "lastUpdate": "2022-12-30T23:52:02.825676Z", + "name": "iDVIP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC406", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.", + "authors": [ + { + "name": "Chen C.-H." + }, + { + "name": "Huang K.-Y." + }, + { + "name": "Kao H.-J." + }, + { + "name": "Weng S.-L." + }, + { + "name": "Weng T.-H." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "iDVIP: identification and characterization of viral integrase inhibitory peptides" + }, + "pmid": "36215051" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/iflnc/iflnc.biotools.json b/data/iflnc/iflnc.biotools.json new file mode 100644 index 0000000000000..946acb1543e5d --- /dev/null +++ b/data/iflnc/iflnc.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-30T23:48:09.265568Z", + "biotoolsCURIE": "biotools:iflnc", + "biotoolsID": "iflnc", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chan.zhou@umassmed.edu", + "name": "Chan Zhou", + "typeEntity": "Person" + }, + { + "name": "Peng Zhou" + }, + { + "name": "Zixiu Li" + }, + { + "name": "Zhiping Weng", + "orcidid": "https://orcid.org/0000-0002-3032-7966" + } + ], + "description": "Flnc is software that can accurately identify full-length long noncoding RNAs (lncRNAs) from human RNA-seq data. lncRNAs are linear transcripts of more than 200 nucleotides that do not encode proteins.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Peak calling", + "uri": "http://edamontology.org/operation_3222" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + } + ] + } + ], + "homepage": "https://github.com/CZhouLab/Flnc", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-30T23:48:09.269599Z", + "license": "Not licensed", + "name": "iFlnc", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3390/NCRNA8050070", + "metadata": { + "abstract": "© 2022 by the authors.Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30–75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.", + "authors": [ + { + "name": "Fitzgerald K.A." + }, + { + "name": "Kwon E." + }, + { + "name": "Li Z." + }, + { + "name": "Weng Z." + }, + { + "name": "Zhou C." + }, + { + "name": "Zhou P." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "Non-coding RNA", + "title": "Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data" + }, + "pmcid": "PMC9607125", + "pmid": "36287122" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/iguana/iguana.biotools.json b/data/iguana/iguana.biotools.json new file mode 100644 index 0000000000000..aa595c77d2e90 --- /dev/null +++ b/data/iguana/iguana.biotools.json @@ -0,0 +1,102 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:39:59.409644Z", + "biotoolsCURIE": "biotools:iguana", + "biotoolsID": "iguana", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "n.m.rajpoot@warwick.ac.uk", + "name": "Nasir M. Rajpoot", + "orcidid": "http://orcid.org/0000-0002-4706-1308", + "typeEntity": "Person" + }, + { + "name": "David Snead", + "orcidid": "http://orcid.org/0000-0002-0766-9650" + }, + { + "name": "Fayyaz Minhas", + "orcidid": "http://orcid.org/0000-0001-9129-1189" + }, + { + "name": "Simon Graham", + "orcidid": "http://orcid.org/0000-0002-2214-8212" + } + ], + "description": "IGUANA is a graph neural network built for colon biopsy screening. IGUANA represents a whole-slide image (WSI) as a graph built with nodes on top of glands in the tissue, each node associated with a set of interpretable features. The output of the pipeline is explainable, indicating glands and features that contribute to a WSI being predicted as abnormal.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://iguana.dcs.warwick.ac.uk/", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T19:39:59.413770Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/TissueImageAnalytics/iguana" + } + ], + "name": "IGUANA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1101/2022.10.17.22279804" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Gastroenterology", + "uri": "http://edamontology.org/topic_3409" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/imagej/imagej.biotools.json b/data/imagej/imagej.biotools.json index 228d7c8a6f662..fcb70b4ba2147 100644 --- a/data/imagej/imagej.biotools.json +++ b/data/imagej/imagej.biotools.json @@ -117,7 +117,7 @@ "language": [ "Java" ], - "lastUpdate": "2022-09-17T12:17:33.939600Z", + "lastUpdate": "2022-12-31T15:27:53.188668Z", "link": [ { "type": [ @@ -148,7 +148,7 @@ "name": "Schneider C.A." } ], - "citationCount": 32651, + "citationCount": 34223, "date": "2012-07-01T00:00:00Z", "journal": "Nature Methods", "title": "NIH Image to ImageJ: 25 years of image analysis" @@ -159,6 +159,10 @@ } ], "relation": [ + { + "biotoolsID": "gift_imagej", + "type": "usedBy" + }, { "biotoolsID": "irimage", "type": "usedBy" diff --git a/data/improve-dd/improve-dd.biotools.json b/data/improve-dd/improve-dd.biotools.json new file mode 100644 index 0000000000000..72ab80dd4cb35 --- /dev/null +++ b/data/improve-dd/improve-dd.biotools.json @@ -0,0 +1,125 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T08:52:08.315104Z", + "biotoolsCURIE": "biotools:improve-dd", + "biotoolsID": "improve-dd", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "s.aitken@ed.ac.uk", + "name": "Stuart Aitken", + "orcidid": "http://orcid.org/0000-0003-4867-4568", + "typeEntity": "Person" + }, + { + "name": "Caroline F Wright", + "orcidid": "http://orcid.org/0000-0003-2958-5076" + }, + { + "name": "Colin A. Semple", + "orcidid": "http://orcid.org/0000-0003-1765-4118" + }, + { + "name": "David R FitzPatrick", + "orcidid": "http://orcid.org/0000-0003-4861-969X" + }, + { + "name": "Helen V Firth", + "orcidid": "http://orcid.org/0000-0002-6410-0882" + }, + { + "name": "Matthew E Hurles", + "orcidid": "http://orcid.org/0000-0002-2333-7015" + } + ], + "description": "Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Variant prioritisation", + "uri": "http://edamontology.org/operation_3226" + } + ] + } + ], + "homepage": "https://github.com/Stuart-Aitken/IMPROVE-DD", + "language": [ + "R" + ], + "lastUpdate": "2023-01-23T08:52:08.318739Z", + "license": "GPL-3.0", + "name": "IMPROVE-DD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.xhgg.2022.100162", + "metadata": { + "abstract": "© 2022 The AuthorsDiagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.", + "authors": [ + { + "name": "Aitken S." + }, + { + "name": "Firth H.V." + }, + { + "name": "FitzPatrick D.R." + }, + { + "name": "Hurles M.E." + }, + { + "name": "Semple C.A." + }, + { + "name": "Wright C.F." + } + ], + "date": "2023-01-12T00:00:00Z", + "journal": "Human Genetics and Genomics Advances", + "title": "IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders" + }, + "pmcid": "PMC9763511", + "pmid": "36561149" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Exome sequencing", + "uri": "http://edamontology.org/topic_3676" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Neurology", + "uri": "http://edamontology.org/topic_3334" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + } + ] +} diff --git a/data/indelsrnamute/indelsrnamute.biotools.json b/data/indelsrnamute/indelsrnamute.biotools.json new file mode 100644 index 0000000000000..1af30d9fd5cab --- /dev/null +++ b/data/indelsrnamute/indelsrnamute.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:28:10.472421Z", + "biotoolsCURIE": "biotools:indelsrnamute", + "biotoolsID": "indelsrnamute", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "alexach3@sce.ac.il", + "name": "Alexander Churkin", + "orcidid": "https://orcid.org/0000-0003-4275-257X", + "typeEntity": "Person" + }, + { + "name": "Danny Barash" + }, + { + "name": "Yann Ponty" + } + ], + "description": "Predicting deleterious multiple point substitutions and indels mutations.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + }, + { + "term": "RNA structure prediction", + "uri": "http://edamontology.org/operation_2441" + } + ] + } + ], + "homepage": "https://www.cs.bgu.ac.il/~dbarash/Churkin/SCE/IndelsRNAmute/", + "lastUpdate": "2022-12-29T19:28:10.476914Z", + "name": "IndelsRNAmute", + "operatingSystem": [ + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-04943-0", + "metadata": { + "abstract": "© 2022, The Author(s).Background: RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations. Results: An inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user’s choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs. Conclusions: We developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.", + "authors": [ + { + "name": "Barash D." + }, + { + "name": "Churkin A." + }, + { + "name": "Ponty Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations" + }, + "pmcid": "PMC9569039", + "pmid": "36241988" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Nucleic acid structure analysis", + "uri": "http://edamontology.org/topic_0097" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/insistc/insistc.biotools.json b/data/insistc/insistc.biotools.json new file mode 100644 index 0000000000000..599119a5a40cf --- /dev/null +++ b/data/insistc/insistc.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T08:59:25.624524Z", + "biotoolsCURIE": "biotools:insistc", + "biotoolsID": "insistc", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Haiyan Hu" + }, + { + "name": "Hansi Zheng" + }, + { + "name": "Saidi Wang" + }, + { + "name": "Xiaoman Li" + } + ], + "description": "Incorporating Network Structure Information for Single-Cell Type Classification.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "http://hulab.ucf.edu/research/projects/INSISTC/INSISTC_Manual.txt" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene regulatory network analysis", + "uri": "http://edamontology.org/operation_1781" + }, + { + "term": "Gene regulatory network prediction", + "uri": "http://edamontology.org/operation_2437" + }, + { + "term": "Structure classification", + "uri": "http://edamontology.org/operation_2996" + } + ] + } + ], + "homepage": "https://hulab.ucf.edu/research/projects/INSISTC/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-23T08:59:25.627189Z", + "license": "Not licensed", + "name": "INSISTC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.ygeno.2022.110480", + "metadata": { + "abstract": "© 2022Uncovering gene regulatory mechanisms in individual cells can provide insight into cell heterogeneity and function. Recent accumulated Single-Cell RNA-Seq data have made it possible to analyze gene regulation at single-cell resolution. Understanding cell-type-specific gene regulation can assist in more accurate cell type and state identification. Computational approaches utilizing such relationships are under development. Methods pioneering in integrating gene regulatory mechanism discovery with cell-type classification encounter challenges such as determine gene regulatory relationships and incorporate gene regulatory network structure. To fill this gap, we developed INSISTC, a computational method to incorporate gene regulatory network structure information for single-cell type classification. INSISTC is capable of identifying cell-type-specific gene regulatory mechanisms while performing single-cell type classification. INSISTC demonstrated its accuracy in cell type classification and its potential for providing insight into molecular mechanisms specific to individual cells. In comparison with the alternative methods, INSISTC demonstrated its complementary performance for gene regulation interpretation.", + "authors": [ + { + "name": "Hu H." + }, + { + "name": "Li X." + }, + { + "name": "Wang S." + }, + { + "name": "Zheng H." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "Genomics", + "title": "INSISTC: Incorporating network structure information for single-cell type classification" + }, + "pmid": "36075505" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Gene regulation", + "uri": "http://edamontology.org/topic_0204" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} diff --git a/data/iofs-sa/iofs-sa.biotools.json b/data/iofs-sa/iofs-sa.biotools.json new file mode 100644 index 0000000000000..02db726d5e84b --- /dev/null +++ b/data/iofs-sa/iofs-sa.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:22:56.204141Z", + "biotoolsCURIE": "biotools:iofs-sa", + "biotoolsID": "iofs-sa", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Guohua Wang" + }, + { + "name": "Tong Liu" + }, + { + "name": "Youlin Wu" + }, + { + "name": "Xudong Zhao", + "orcidid": "https://orcid.org/0000-0003-2272-6278" + }, + { + "name": "Yuanyuan He", + "orcidid": "https://orcid.org/0000-0002-7305-5120" + } + ], + "description": "An interactive online feature selection tool for survival analysis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature selection", + "uri": "http://edamontology.org/operation_3936" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "https://bioinfor.nefu.edu.cn/IOFS-SA/", + "language": [ + "JavaScript", + "Python" + ], + "lastUpdate": "2022-12-29T19:22:56.208147Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/Yuan-23/IOFS-SA-ecp-data-main" + } + ], + "name": "IOFS-SA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.COMPBIOMED.2022.106121", + "metadata": { + "abstract": "© 2022 Elsevier LtdBackground: Survival analysis is a primary problem before clinical treatments to cancer patients after their operations. In order to make this kind of analysis simple, many corresponding tools have been proposed. Though these tools are easy to use, there exist still two fatal flaws. One is that sample grouping is commonly empirical and wrongly based on original gene expressions or survival time. The other is that their feature selection methods mostly depend univariate semi-supervised regression or the multivariate one without considering the small sample size compared with the high dimension. Objective: In order to solve the two problems, we design an automatic feature selection web tool which can also satisfy interactive sample grouping. Methods: An automatic feature selection is performed on user-defined data or TCGA data. users can also perform manual feature selection. Then, hierarchical clustering is used and an automatic re-clustering strategy is proposed after interactive risk score split. Kaplan–Meier survival curve and log-rank test are utilized as the measurement. Results: Experimental results on 53 datasets from TCGA demonstrate the effectiveness of our method. The tree view, heat map and scatter map can intuitively display the result of the selected genes to the doctors for further research. Conclusions: This method is suitable for survival analysis of high-dimensional small sample data sets. At the same time, it also provides a platform for researchers to analyze custom data. It solves the problems of the existing web tools and provides an effective feature selection method for survival analysis. Availability: The full code package is freely available and can be downloaded at https://github.com/Yuan-23/IOFS-SA-ecp-data-main, and the online version at https://bioinfor.nefu.edu.cn/IOFS-SA/ is ready for use freely.", + "authors": [ + { + "name": "He Y." + }, + { + "name": "Liu T." + }, + { + "name": "Wang G." + }, + { + "name": "Wu Y." + }, + { + "name": "Zhao X." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Computers in Biology and Medicine", + "title": "IOFS-SA: An interactive online feature selection tool for survival analysis" + }, + "pmid": "36201885" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/ipida-gcn/ipida-gcn.biotools.json b/data/ipida-gcn/ipida-gcn.biotools.json new file mode 100644 index 0000000000000..81baa76a87c0b --- /dev/null +++ b/data/ipida-gcn/ipida-gcn.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:16:01.854806Z", + "biotoolsCURIE": "biotools:ipida-gcn", + "biotoolsID": "ipida-gcn", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "bliu@bliulab.net", + "name": "Bin Liu", + "orcidid": "https://orcid.org/0000-0002-8520-8374", + "typeEntity": "Person" + }, + { + "name": "Hang Wei" + }, + { + "name": "Jialu Hou" + } + ], + "description": "Identification of piRNA-disease associations based on Graph Convolutional Network.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + } + ] + } + ], + "homepage": "http://bliulab.net/iPiDA-GCN/", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T19:16:01.857590Z", + "license": "Not licensed", + "name": "iPiDA-GCN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1010671", + "metadata": { + "abstract": "Copyright: © 2022 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Motivation Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. Results With the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.", + "authors": [ + { + "name": "Hou J." + }, + { + "name": "Liu B." + }, + { + "name": "Wei H." + } + ], + "date": "2022-10-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network" + }, + "pmcid": "PMC9662734", + "pmid": "36301998" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Cardiology", + "uri": "http://edamontology.org/topic_3335" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + } + ] +} diff --git a/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json b/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json new file mode 100644 index 0000000000000..af1c73fef87e1 --- /dev/null +++ b/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json @@ -0,0 +1,142 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:09:12.090902Z", + "biotoolsCURIE": "biotools:ipromoter-seqvec", + "biotoolsID": "ipromoter-seqvec", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "binh.p.nguyen@vuw.ac.nz", + "name": "Binh P. Nguyen", + "orcidid": "https://orcid.org/0000-0001-6203-6664", + "typeEntity": "Person" + }, + { + "email": "susantorahardja@ieee.org", + "name": "Susanto Rahardja", + "typeEntity": "Person" + }, + { + "name": "Quang H. Trinh" + }, + { + "name": "Thanh-Hoang Nguyen-Vo" + } + ], + "description": "Identifying promoters using bidirectional long short-term memory and sequence-embedded features.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "DNA sequence", + "uri": "http://edamontology.org/data_3494" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + }, + { + "term": "cis-regulatory element prediction", + "uri": "http://edamontology.org/operation_0441" + } + ] + } + ], + "homepage": "http://124.197.54.240:5001", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T19:09:12.094633Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/mldlproject/2022-iPromoter-Seqvec" + } + ], + "name": "iPromoter-Seqvec", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12864-022-08829-6", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec – an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. Results: The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. Conclusions: iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec.", + "authors": [ + { + "name": "Nguyen B.P." + }, + { + "name": "Nguyen L." + }, + { + "name": "Nguyen-Hoang P.-U." + }, + { + "name": "Nguyen-Vo T.-H." + }, + { + "name": "Rahardja S." + }, + { + "name": "Trinh Q.H." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Genomics", + "title": "iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features" + }, + "pmcid": "PMC9531353", + "pmid": "36192696" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/isnodi-lsgt/isnodi-lsgt.biotools.json b/data/isnodi-lsgt/isnodi-lsgt.biotools.json new file mode 100644 index 0000000000000..0a58f471ef502 --- /dev/null +++ b/data/isnodi-lsgt/isnodi-lsgt.biotools.json @@ -0,0 +1,66 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T19:01:42.367195Z", + "biotoolsCURIE": "biotools:isnodi-lsgt", + "biotoolsID": "isnodi-lsgt", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Bin Liu" + }, + { + "name": "Wenxiang Zhang" + } + ], + "description": "identifying snoRNA-disease associations based on local similarity constraint and global topological constraint.", + "editPermission": { + "type": "private" + }, + "homepage": "http://bliulab.net/iSnoDi-LSGT/", + "lastUpdate": "2022-12-29T19:01:42.369928Z", + "name": "iSnoDi-LSGT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1261/RNA.079325.122", + "metadata": { + "abstract": "© 2022 Zhang and Liu; Published by Cold Spring Harbor Laboratory Press for the RNA Society.Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.", + "authors": [ + { + "name": "Liu B." + }, + { + "name": "Zhang W." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "RNA (New York, N.Y.)", + "title": "iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints" + }, + "pmid": "36192132" + } + ], + "toolType": [ + "Desktop application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Laboratory techniques", + "uri": "http://edamontology.org/topic_3361" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/isomirdb/isomirdb.biotools.json b/data/isomirdb/isomirdb.biotools.json new file mode 100644 index 0000000000000..b875b3fdf46ad --- /dev/null +++ b/data/isomirdb/isomirdb.biotools.json @@ -0,0 +1,91 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:55:24.036795Z", + "biotoolsCURIE": "biotools:isomirdb", + "biotoolsID": "isomirdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "andreas.keller@ccb.uni-saarland.de", + "name": "Andreas Keller", + "orcidid": "https://orcid.org/0000-0002-5361-0895", + "typeEntity": "Person" + }, + { + "email": "ernesto.aparicio@ccb.uni-saarland.de", + "name": "Ernesto Aparicio-Puerta", + "orcidid": "https://orcid.org/0000-0002-3470-1425", + "typeEntity": "Person" + }, + { + "name": "Pascal Hirsch" + }, + { + "name": "Georges P Schmartz", + "orcidid": "https://orcid.org/0000-0002-9627-9223" + } + ], + "description": "A miRNA expression database with isoform resolution.\nisomiRdb stores miRNA and isomiR expression values for 42499 miRNA-seq samples collected from miRMaster, The Cancer Genome Atlas and Sequence Read Archive and uniformly processed from raw reads using sRNAbench .", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + }, + { + "term": "miRNA expression analysis", + "uri": "http://edamontology.org/operation_3792" + }, + { + "term": "miRNA target prediction", + "uri": "http://edamontology.org/operation_0463" + } + ] + } + ], + "homepage": "https://www.ccb.uni-saarland.de/isomirdb", + "lastUpdate": "2022-12-29T18:55:24.039381Z", + "name": "isomiRdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC884", + "pmid": "36243964" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/isomirtar/isomirtar.biotools.json b/data/isomirtar/isomirtar.biotools.json new file mode 100644 index 0000000000000..fd7320b0fddd1 --- /dev/null +++ b/data/isomirtar/isomirtar.biotools.json @@ -0,0 +1,141 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:51:11.528332Z", + "biotoolsCURIE": "biotools:isomirtar", + "biotoolsID": "isomirtar", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "snersisyan@hse.ru", + "name": "Stepan Nersisyan", + "typeEntity": "Person" + }, + { + "name": "Aleksandra Gorbonos" + }, + { + "name": "Alexander Tonevitsky" + }, + { + "name": "Maxim Shkurnikov" + } + ], + "description": "A comprehensive portal of pan-cancer 5'-isomiR targeting.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Disease name", + "uri": "http://edamontology.org/data_3668" + } + }, + { + "data": { + "term": "Gene name", + "uri": "http://edamontology.org/data_2299" + } + } + ], + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "miRNA target prediction", + "uri": "http://edamontology.org/operation_0463" + } + ] + } + ], + "homepage": "https://isomirtar.hse.ru", + "language": [ + "JavaScript" + ], + "lastUpdate": "2022-12-29T18:51:11.531183Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/s-a-nersisyan/isomiRTar" + } + ], + "name": "isomiRTar", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.7717/PEERJ.14205", + "metadata": { + "abstract": "Copyright 2022 Nersisyan et al.Inaccurate cleavage of pri- and pre-miRNA hairpins by Drosha and Dicer results in the generation of miRNA isoforms known as isomiRs. isomiRs with 50-end variations (50-isomiRs) create a new dimension in miRNA research since they have different seed regions and distinct targetomes. We developed isomiRTar (https://isomirtar.hse.ru)—a comprehensive portal that allows one to analyze expression profiles and targeting activity of 50-isomiRs in cancer. Using the Cancer Genome Atlas sequencing data, we compiled the list of 1022 50-isomiRs expressed in 9282 tumor samples across 31 cancer types. Sequences of these isomiRs were used to predict target genes with miRDB and TargetScan. The putative interactions were then subjected to the co-expression analysis in each cancer type to identify isomiR-target pairs supported by significant negative correlations. Downstream analysis of the data deposited in isomiRTar revealed both cancer-specific and cancer-conserved 50-isomiR expression landscapes. Pairs of isomiRs differing in one nucleotide shift from 50-end had poorly overlapping targetomes with the median Jaccard index of 0.06. The analysis of colorectal cancer 50-isomiR-mediated regulatory networks revealed promising candidate tumor suppressor isomiRs: hsamiR-203a-3p|+1, hsa-miR-192-5p|+1 and hsa-miR-148a-3p|0. In summary, we believe that isomiRTar will help researchers find novel mechanisms of isomiR-mediated gene silencing in different types of cancer.", + "authors": [ + { + "name": "Gorbonos A." + }, + { + "name": "Makhonin A." + }, + { + "name": "Nersisyan S." + }, + { + "name": "Shkurnikov M." + }, + { + "name": "Tonevitsky A." + }, + { + "name": "Zhiyanov A." + } + ], + "citationCount": 1, + "date": "2022-10-17T00:00:00Z", + "journal": "PeerJ", + "title": "isomiRTar: a comprehensive portal of pan-cancer 50-isomiR targeting" + }, + "pmcid": "PMC9583861", + "pmid": "36275459" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/jalview/jalview.biotools.json b/data/jalview/jalview.biotools.json index a7c64e9044875..c0c4b45423db5 100644 --- a/data/jalview/jalview.biotools.json +++ b/data/jalview/jalview.biotools.json @@ -23,11 +23,11 @@ "description": "Jalview is a free program for multiple sequence alignment editing, visualisation and analysis. Use it to view and edit sequence alignments, analyse them with phylogenetic trees and principal components analysis (PCA) plots and explore molecular structures and annotation.", "documentation": [ { - "note": "Jalview training videos", + "note": "Hands-on exercises, Training courses and Training videos", "type": [ "Training material" ], - "url": "https://www.jalview.org/Help/Getting-Started" + "url": "https://www.jalview.org/training/" }, { "type": [ @@ -39,28 +39,37 @@ "type": [ "FAQ" ], - "url": "https://www.jalview.org/faq" + "url": "https://www.jalview.org/help/faq" }, { "type": [ "User manual" ], - "url": "https://www.jalview.org/about/documentation" + "url": "https://www.jalview.org/help/documentation/" } ], "download": [ { "note": "Binaries for all platforms", "type": "Binaries", - "url": "https://www.jalview.org/getdown/release/?osChoice=all" + "url": "https://www.jalview.org/download/?os=all" + }, + { + "note": "Executable JAR file", + "type": "Binaries", + "url": "https://www.jalview.org/download/other/jar/" }, { "type": "Downloads page", "url": "https://www.jalview.org/download" }, + { + "type": "Icon", + "url": "https://www.jalview.org/favicon.svg" + }, { "type": "Source code", - "url": "https://www.jalview.org/source/" + "url": "https://www.jalview.org/download/source/" } ], "editPermission": { @@ -259,7 +268,7 @@ } ], "homepage": "https://www.jalview.org/", - "lastUpdate": "2022-07-11T17:26:57.927086Z", + "lastUpdate": "2023-01-25T12:01:14.360386Z", "license": "GPL-3.0", "link": [ { @@ -267,19 +276,33 @@ "type": [ "Other" ], - "url": "https://builds.jalview.org/browse/JB-GPC/latest/artifact" + "url": "https://www.jalview.org/development/jalview_develop/" }, { + "note": "Twitter feed", "type": [ - "Issue tracker" + "Social media" ], - "url": "https://issues.jalview.org/" + "url": "https://twitter.com/Jalview" }, { + "note": "YouTube training videos", "type": [ - "Mailing list" + "Social media" ], - "url": "https://www.jalview.org/mailman/listinfo/jalview-discuss" + "url": "https://www.youtube.com/channel/UCIjpnvZB770yz7ftbrJ0tfw" + }, + { + "type": [ + "Discussion forum" + ], + "url": "https://discourse.jalview.org/" + }, + { + "type": [ + "Issue tracker" + ], + "url": "https://issues.jalview.org/" }, { "type": [ @@ -318,7 +341,7 @@ "name": "Waterhouse A.M." } ], - "citationCount": 5258, + "citationCount": 5654, "date": "2009-05-07T00:00:00Z", "journal": "Bioinformatics", "title": "Jalview Version 2-A multiple sequence alignment editor and analysis workbench" @@ -326,6 +349,14 @@ } ], "relation": [ + { + "biotoolsID": "3d-beacons", + "type": "uses" + }, + { + "biotoolsID": "bioconda", + "type": "includedIn" + }, { "biotoolsID": "chimera", "type": "uses" @@ -334,13 +365,33 @@ "biotoolsID": "chimerax", "type": "uses" }, + { + "biotoolsID": "ensembl", + "type": "uses" + }, { "biotoolsID": "jabaws", "type": "uses" }, + { + "biotoolsID": "pdb", + "type": "uses" + }, + { + "biotoolsID": "pfam", + "type": "uses" + }, { "biotoolsID": "pymol", "type": "uses" + }, + { + "biotoolsID": "rfam", + "type": "uses" + }, + { + "biotoolsID": "uniprot", + "type": "uses" } ], "toolType": [ @@ -358,6 +409,6 @@ ], "validated": 1, "version": [ - "2.11.2.3" + "2.11.2.6" ] } diff --git a/data/jlcrb/jlcrb.biotools.json b/data/jlcrb/jlcrb.biotools.json new file mode 100644 index 0000000000000..23eb83024fef0 --- /dev/null +++ b/data/jlcrb/jlcrb.biotools.json @@ -0,0 +1,97 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:41:08.330275Z", + "biotoolsCURIE": "biotools:jlcrb", + "biotoolsID": "jlcrb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Xiuquan Du" + }, + { + "name": "Zhigang Xue" + } + ], + "description": "A unified multi-view-based joint representation learning for CircRNA binding sites prediction.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Sequence", + "uri": "http://edamontology.org/data_2044" + } + } + ], + "operation": [ + { + "term": "Binding site prediction", + "uri": "http://edamontology.org/operation_2575" + } + ] + } + ], + "homepage": "http://82.157.188.204/JLCRB/", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T18:41:08.332899Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/Xuezg/JLCRB" + } + ], + "name": "JLCRB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.JBI.2022.104231", + "metadata": { + "abstract": "© 2022 Elsevier Inc.CircRNAs usually bind to the corresponding RBPs(RNA Binding proteins) and play a key role in gene regulation. Therefore, it is important to identify the binding sites of RBPs on CircRNAs for the regulation of certain diseases. Due to the information provided by the single view feature is limited, the current mainstream methods are mainly to detect the RBP binding sites by constructing multi-view models. However, with the number of view features increases, the invalid information also increases, and the existing methods only simply concatenate together various features from different views, while ignoring the intrinsic connection between multi-view data. To solve this problem, we propose a new multi-view joint representation learning network by improving the consistency of multi-view feature information. First, the network uses different feature encoding methods to fully extract the feature information of RNA, respectively. Then we construct the intrinsic connection between the views by generating a global joint representation of multiple views, and this is used for feature calibration of each view to highlight important features and suppress unimportant ones. Finally, the depth features obtained from the fusion of multiple views are used to detect the binding sites of RNAs. The average AUC of our method is 93.68% in 37 CircRNA-RBP datasets. The experimental results show that the prediction performance of the method is better than existing methods. The code and datasets are obtained at https://github.com/Xuezg/JLCRB. In addition, we also provide a free web server that is freely available at http://82.157.188.204/JLCRB/.", + "authors": [ + { + "name": "Du X." + }, + { + "name": "Xue Z." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Biomedical Informatics", + "title": "JLCRB: A unified multi-view-based joint representation learning for CircRNA binding sites prediction" + }, + "pmid": "36309196" + } + ], + "toolType": [ + "Script", + "Web application" + ], + "topic": [ + { + "term": "Gene regulation", + "uri": "http://edamontology.org/topic_0204" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/jupytope/jupytope.biotools.json b/data/jupytope/jupytope.biotools.json new file mode 100644 index 0000000000000..3cd0c4c6f1415 --- /dev/null +++ b/data/jupytope/jupytope.biotools.json @@ -0,0 +1,100 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:25:33.219115Z", + "biotoolsCURIE": "biotools:jupytope", + "biotoolsID": "jupytope", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "asckkwoh@ntu.edu.sg", + "name": "Kwoh Chee Keong", + "typeEntity": "Person" + }, + { + "name": "Ng Teng Ann" + }, + { + "name": "Shamima Rashid" + } + ], + "description": "Computational extraction of structural properties of viral epitopes.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Epitope mapping", + "uri": "http://edamontology.org/operation_0416" + }, + { + "term": "Side chain modelling", + "uri": "http://edamontology.org/operation_0480" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://github.com/shamimarashid/Jupytope", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T21:25:33.221668Z", + "license": "GPL-3.0", + "name": "Jupytope", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac362", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Epitope residues located on viral surface proteins are of immense interest in immunology and related applications such as vaccine development, disease diagnosis and drug design. Most tools rely on sequence-based statistical comparisons, such as information entropy of residue positions in aligned columns to infer location and properties of epitope sites. To facilitate cross-structural comparisons of epitopes on viral surface proteins, a python-based extraction tool implemented with Jupyter notebook is presented (Jupytope). Given a viral antigen structure of interest, a list of known epitope sites and a reference structure, the corresponding epitope structural properties can quickly be obtained. The tool integrates biopython modules for commonly used software such as NACCESS, DSSP as well as residue depth and outputs a list of structure-derived properties such as dihedral angles, solvent accessibility, residue depth and secondary structure that can be saved in several convenient data formats. To ensure correct spatial alignment, Jupytope takes a list of given epitope sites and their corresponding reference structure and aligns them before extracting the desired properties. Examples are demonstrated for epitopes of Influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) viral strains. The extracted properties assist detection of two Influenza subtypes and show potential in distinguishing between four major clades of SARS-CoV2, as compared with randomized labels. The tool will facilitate analytical and predictive works on viral epitopes through the extracted structural information. Jupytope and extracted datasets are available at https://github.com/shamimarashid/Jupytope.", + "authors": [ + { + "name": "Kwoh C.K." + }, + { + "name": "Ng T.A." + }, + { + "name": "Rashid S." + } + ], + "date": "2022-11-19T00:00:00Z", + "journal": "Briefings in bioinformatics", + "title": "Jupytope: computational extraction of structural properties of viral epitopes" + }, + "pmid": "36094101" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Vaccinology", + "uri": "http://edamontology.org/topic_3966" + } + ] +} diff --git a/data/justdeepit/justdeepit.biotools.json b/data/justdeepit/justdeepit.biotools.json new file mode 100644 index 0000000000000..0491612a31de6 --- /dev/null +++ b/data/justdeepit/justdeepit.biotools.json @@ -0,0 +1,106 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:36:13.406814Z", + "biotoolsCURIE": "biotools:justdeepit", + "biotoolsID": "justdeepit", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "sun@biunit.dev", + "name": "Jianqiang Sun", + "typeEntity": "Person" + }, + { + "name": "Takehiko Yamanaka" + }, + { + "name": "Wei Cao" + } + ], + "description": "Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis.\n\nDeep learning has been applied to solve various problems, especially in image recognition, across many fields including the life sciences and agriculture.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + } + ] + } + ], + "homepage": "https://github.com/biunit/JustDeepIt", + "language": [ + "JavaScript", + "Python" + ], + "lastUpdate": "2022-12-29T18:36:13.409218Z", + "license": "MIT", + "name": "JustDeepIt", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FPLS.2022.964058", + "metadata": { + "abstract": "Copyright © 2022 Sun, Cao and Yamanaka.Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U2-Net; and (4) leaf segmentation with U2-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.", + "authors": [ + { + "name": "Cao W." + }, + { + "name": "Sun J." + }, + { + "name": "Yamanaka T." + } + ], + "date": "2022-10-06T00:00:00Z", + "journal": "Frontiers in Plant Science", + "title": "JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis" + }, + "pmcid": "PMC9583140", + "pmid": "36275541" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Informatics", + "uri": "http://edamontology.org/topic_0605" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + } + ] +} diff --git a/data/kage/kage.biotools.json b/data/kage/kage.biotools.json new file mode 100644 index 0000000000000..358391731b2be --- /dev/null +++ b/data/kage/kage.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:31:05.193188Z", + "biotoolsCURIE": "biotools:kage", + "biotoolsID": "kage", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ivargry@ifi.uio.no", + "name": "Ivar Grytten", + "orcidid": "https://orcid.org/0000-0001-8941-942X", + "typeEntity": "Person" + }, + { + "name": "Geir Kjetil Sandve" + }, + { + "name": "Knut Dagestad Rand" + } + ], + "description": "KAGE is a tool for efficiently genotyping short SNPs and indels from short genomic reads.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Haplotype mapping", + "uri": "http://edamontology.org/operation_0487" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "k-mer counting", + "uri": "http://edamontology.org/operation_3472" + } + ] + } + ], + "homepage": "https://github.com/ivargr/kage", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T18:31:05.196220Z", + "license": "GPL-3.0", + "name": "KAGE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13059-022-02771-2", + "metadata": { + "abstract": "© 2022, The Author(s).Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.", + "authors": [ + { + "name": "Dagestad Rand K." + }, + { + "name": "Grytten I." + }, + { + "name": "Sandve G.K." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Genome Biology", + "title": "KAGE: fast alignment-free graph-based genotyping of SNPs and short indels" + }, + "pmcid": "PMC9531401", + "pmid": "36195962" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Genotyping experiment", + "uri": "http://edamontology.org/topic_3516" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/kmdiff/kmdiff.biotools.json b/data/kmdiff/kmdiff.biotools.json new file mode 100644 index 0000000000000..0c8da3145b7bb --- /dev/null +++ b/data/kmdiff/kmdiff.biotools.json @@ -0,0 +1,86 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:17:05.271259Z", + "biotoolsCURIE": "biotools:kmdiff", + "biotoolsID": "kmdiff", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pierre.peterlongo@inria.fr", + "name": "Pierre Peterlongo", + "orcidid": "https://orcid.org/0000-0003-0776-6407", + "typeEntity": "Person" + }, + { + "name": "Rayan Chikhi" + }, + { + "name": "Téo Lemane", + "orcidid": "https://orcid.org/0000-0002-7210-3178" + } + ], + "description": "kmdiff provides differential k-mers analysis between two populations (control and case). Each population is represented by a set of short-read sequencing. Outputs are differentially represented k-mers between controls and cases.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "k-mer counting", + "uri": "http://edamontology.org/operation_3472" + } + ] + } + ], + "homepage": "https://github.com/tlemane/kmdiff", + "language": [ + "C++", + "Shell" + ], + "lastUpdate": "2022-12-29T18:17:05.273839Z", + "license": "AGPL-3.0", + "name": "kmdiff", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC689", + "pmid": "36315078" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + } + ] +} diff --git a/data/knowledge4covid-19/knowledge4covid-19.biotools.json b/data/knowledge4covid-19/knowledge4covid-19.biotools.json new file mode 100644 index 0000000000000..781dcb2c9c94d --- /dev/null +++ b/data/knowledge4covid-19/knowledge4covid-19.biotools.json @@ -0,0 +1,146 @@ +{ + "accessibility": "Open access", + "additionDate": "2022-12-29T18:13:27.284679Z", + "biotoolsCURIE": "biotools:knowledge4covid-19", + "biotoolsID": "knowledge4covid-19", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Ahmad Sakor" + }, + { + "name": "Samaneh Jozashoori" + }, + { + "name": "Fotis Aisopos", + "typeEntity": "Person" + }, + { + "name": "Maria-Esther Vidal", + "typeEntity": "Person" + } + ], + "description": "A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + }, + { + "term": "Named-entity and concept recognition", + "uri": "http://edamontology.org/operation_3280" + } + ] + } + ], + "homepage": "https://github.com/SDM-TIB/Knowledge4COVID-19", + "language": [ + "Python" + ], + "lastUpdate": "2022-12-29T18:13:27.287582Z", + "license": "Apache-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://zenodo.org/record/4701817#.YH336-8zbol" + } + ], + "name": "Knowledge4COVID-19", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.WEBSEM.2022.100760", + "metadata": { + "abstract": "© 2022 Elsevier B.V.In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug–drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug–drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.", + "authors": [ + { + "name": "Aisopos F." + }, + { + "name": "Bougiatiotis K." + }, + { + "name": "Iglesias E." + }, + { + "name": "Jozashoori S." + }, + { + "name": "Krithara A." + }, + { + "name": "Niazmand E." + }, + { + "name": "Padiya T." + }, + { + "name": "Paliouras G." + }, + { + "name": "Rivas A." + }, + { + "name": "Rohde P.D." + }, + { + "name": "Sakor A." + }, + { + "name": "Vidal M.-E." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Journal of Web Semantics", + "title": "Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities" + }, + "pmcid": "PMC9558693", + "pmid": "36268112" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Drug metabolism", + "uri": "http://edamontology.org/topic_3375" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Pharmacology", + "uri": "http://edamontology.org/topic_0202" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/ltm/ltm.biotools.json b/data/ltm/ltm.biotools.json new file mode 100644 index 0000000000000..f79cd46755b5c --- /dev/null +++ b/data/ltm/ltm.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:05:06.453195Z", + "biotoolsCURIE": "biotools:ltm", + "biotoolsID": "ltm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "john.hogenesch@cchmc.org", + "name": "John B. Hogenesch", + "typeEntity": "Person" + }, + { + "name": "Gang Wu" + }, + { + "name": "Ron C. Anafi" + }, + { + "name": "Marc D. Ruben", + "orcidid": "http://orcid.org/0000-0002-7893-0238" + } + ], + "description": "LTM is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression data and directly links gene expression variation to clock strength independent of longitudinal data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + } + ] + } + ], + "homepage": "https://github.com/gangwug/LTMR", + "language": [ + "R" + ], + "lastUpdate": "2023-01-23T09:05:06.456750Z", + "license": "Not licensed", + "name": "LTM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac686", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function. RESULTS: We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues. AVAILABILITY AND IMPLEMENTATION: LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Anafi R.C." + }, + { + "name": "Francey L.J." + }, + { + "name": "Hogenesch J.B." + }, + { + "name": "Lee Y.Y." + }, + { + "name": "Ruben M.D." + }, + { + "name": "Wu G." + } + ], + "date": "2022-12-13T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "An in silico genome-wide screen for circadian clock strength in human samples" + }, + "pmcid": "PMC9750125", + "pmid": "36321857" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/mag-sd/mag-sd.biotools.json b/data/mag-sd/mag-sd.biotools.json new file mode 100644 index 0000000000000..49b6c114f0bc0 --- /dev/null +++ b/data/mag-sd/mag-sd.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T00:34:34.183689Z", + "biotoolsCURIE": "biotools:mag-sd", + "biotoolsID": "mag-sd", + "collectionID": [ + "COVID-19", + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "wangyaqi@hdu.edu.cn", + "name": "Yaqi Wang", + "orcidid": "https://orcid.org/0000-0002-4627-3392", + "typeEntity": "Person" + }, + { + "name": "Jingxiong Li", + "orcidid": "https://orcid.org/0000-0002-6519-5043" + }, + { + "name": "Lingling Sun", + "orcidid": "https://orcid.org/0000-0002-6410-1471" + }, + { + "name": "Qun Jin", + "orcidid": "https://orcid.org/0000-0002-1325-4275" + } + ], + "description": "MAG-SD is an image classification model focusing on pneumonia (including COVID-19) using CXR images.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + } + ] + } + ], + "homepage": "https://github.com/JasonLeeGHub/MAG-SD", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-27T00:34:34.186171Z", + "license": "Not licensed", + "name": "MAG-SD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/JBHI.2021.3058293", + "metadata": { + "abstract": "© 2013 IEEE.Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.", + "authors": [ + { + "name": "Jin Q." + }, + { + "name": "Li J." + }, + { + "name": "Liu J." + }, + { + "name": "Sun L." + }, + { + "name": "Wang J." + }, + { + "name": "Wang S." + }, + { + "name": "Wang Y." + } + ], + "citationCount": 18, + "date": "2021-05-01T00:00:00Z", + "journal": "IEEE Journal of Biomedical and Health Informatics", + "title": "Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images" + }, + "pmcid": "PMC8545167", + "pmid": "33560995" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + }, + { + "term": "Tomography", + "uri": "http://edamontology.org/topic_3452" + } + ] +} diff --git a/data/matplotlib/matplotlib.biotools.json b/data/matplotlib/matplotlib.biotools.json new file mode 100644 index 0000000000000..2e1baf201c648 --- /dev/null +++ b/data/matplotlib/matplotlib.biotools.json @@ -0,0 +1,47 @@ +{ + "additionDate": "2023-01-27T12:38:00.914570Z", + "biotoolsCURIE": "biotools:matplotlib", + "biotoolsID": "matplotlib", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "John Hunter", + "url": "https://matplotlib.org/stable/users/project/citing.html" + } + ], + "description": "Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.", + "documentation": [ + { + "type": [ + "Installation instructions" + ], + "url": "https://matplotlib.org/stable/index.html" + } + ], + "download": [ + { + "type": "Container file", + "url": "https://matplotlib.org/stable/users/getting_started/" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "https://matplotlib.org/", + "lastUpdate": "2023-01-27T13:11:55.624826Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/ucsd-ccbb/visJS2jupyter" + } + ], + "name": "Matplotlib", + "owner": "iacs-biocomputacion", + "version": [ + "3.6.0 Released" + ] +} diff --git a/data/membrain_pipeline/membrain_pipeline.biotools.json b/data/membrain_pipeline/membrain_pipeline.biotools.json new file mode 100644 index 0000000000000..7c25cd797a2c8 --- /dev/null +++ b/data/membrain_pipeline/membrain_pipeline.biotools.json @@ -0,0 +1,122 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:35:03.050563Z", + "biotoolsCURIE": "biotools:membrain_pipeline", + "biotoolsID": "membrain_pipeline", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ben.engel@unibas.ch", + "name": "Benjamin D. Engel", + "orcidid": "http://orcid.org/0000-0002-0941-4387", + "typeEntity": "Person" + }, + { + "email": "tingying.peng@helmholtz-muenchen.de", + "name": "Tingying Peng", + "orcidid": "http://orcid.org/0000-0002-7881-1749", + "typeEntity": "Person" + }, + { + "name": "Lorenz Lamm", + "orcidid": "http://orcid.org/0000-0003-0698-7769" + }, + { + "name": "Ricardo D. Righetto", + "orcidid": "http://orcid.org/0000-0003-4247-4303" + } + ], + "description": "A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Single particle alignment and classification", + "uri": "http://edamontology.org/operation_3458" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/CellArchLab/MemBrain", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T21:35:03.053149Z", + "license": "MPL-2.0", + "name": "MemBrain", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.cmpb.2022.106990", + "metadata": { + "abstract": "© 2022Background and Objective: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. Methods: We present MemBrain – a new deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. Results: MemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. Conclusions: MemBrain is a powerful and annotation-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain.", + "authors": [ + { + "name": "Engel B.D." + }, + { + "name": "Lamm L." + }, + { + "name": "Martinez-Sanchez A." + }, + { + "name": "Peng T." + }, + { + "name": "Poge M." + }, + { + "name": "Righetto R.D." + }, + { + "name": "Wietrzynski W." + } + ], + "citationCount": 3, + "date": "2022-09-01T00:00:00Z", + "journal": "Computer Methods and Programs in Biomedicine", + "title": "MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms" + }, + "pmid": "35858496" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Membrane and lipoproteins", + "uri": "http://edamontology.org/topic_0820" + }, + { + "term": "Protein folds and structural domains", + "uri": "http://edamontology.org/topic_0736" + }, + { + "term": "Tomography", + "uri": "http://edamontology.org/topic_3452" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/meta-boa/meta-boa.biotools.json b/data/meta-boa/meta-boa.biotools.json new file mode 100644 index 0000000000000..ccea04a686b5a --- /dev/null +++ b/data/meta-boa/meta-boa.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:14:36.383238Z", + "biotoolsCURIE": "biotools:meta-boa", + "biotoolsID": "meta-boa", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "cuperlovim@nrc.ca", + "name": "Miroslava Čuperlović-Culf", + "orcidid": "http://orcid.org/0000-0002-9483-8159", + "typeEntity": "Person" + }, + { + "name": "Emily Hashimoto-Roth" + }, + { + "name": "Mathieu Lavallée-Adam" + }, + { + "name": "Anuradha Surendra", + "orcidid": "http://orcid.org/0000-0002-4736-3592" + }, + { + "name": "Steffany A. L. Bennett", + "orcidid": "http://orcid.org/0000-0001-7944-5800" + } + ], + "description": "META-BOA (METAbolomics data Balancing with Over-sampling Algorithms) is a software solution for handling sample imbalance primarily for metabolomics and lipidomics datasets.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Principal component visualisation", + "uri": "http://edamontology.org/operation_2939" + } + ] + } + ], + "homepage": "https://complimet.ca/meta-boa", + "language": [ + "R" + ], + "lastUpdate": "2023-01-23T09:14:36.386097Z", + "name": "META-BOA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac649", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. RESULTS: METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses. AVAILABILITY AND IMPLEMENTATION: META-BOA is available at https://complimet.ca/meta-boa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Bennett S.A.L." + }, + { + "name": "Cuperlovic-Culf M." + }, + { + "name": "Hashimoto-Roth E." + }, + { + "name": "Lavallee-Adam M." + }, + { + "name": "Surendra A." + } + ], + "date": "2022-11-30T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance" + }, + "pmid": "36222566" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Lipids", + "uri": "http://edamontology.org/topic_0153" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + } + ] +} diff --git a/data/metaphage/metaphage.biotools.json b/data/metaphage/metaphage.biotools.json new file mode 100644 index 0000000000000..896acf4619338 --- /dev/null +++ b/data/metaphage/metaphage.biotools.json @@ -0,0 +1,120 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T00:11:57.272744Z", + "biotoolsCURIE": "biotools:metaphage", + "biotoolsID": "metaphage", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Evelien M. Adriaenssens" + }, + { + "name": "Gioele Lazzari" + }, + { + "name": "Mattia Pandolfo" + }, + { + "name": "Andrea Telatin", + "orcidid": "http://orcid.org/0000-0001-7619-281X" + }, + { + "name": "Nicola Vitulo", + "orcidid": "http://orcid.org/0000-0002-9571-0747" + } + ], + "description": "An automated pipeline for analyzing, annotating, and classifying bacteriophages in metagenomics sequencing data.", + "documentation": [ + { + "type": [ + "General", + "Installation instructions", + "User manual" + ], + "url": "https://mattiapandolfovr.github.io/MetaPhage/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome annotation", + "uri": "http://edamontology.org/operation_0362" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/MattiaPandolfoVR/MetaPhage", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-20T00:11:57.275978Z", + "license": "GPL-3.0", + "name": "MetaPhage", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1128/msystems.00741-22", + "metadata": { + "abstract": "© 2022 Pandolfo et al.Phages are the most abundant biological entities on the planet, and they play an important role in controlling density, diversity, and network interactions among bacterial communities through predation and gene transfer. To date, a variety of bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. However, new users attempting bacteriophage analysis can struggle to select the best methods and interpret the variety of results produced. Here, we present MetaPhage, a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The report both summarizes and visualizes the key findings and enables further exploration of key results via interactive filterable tables. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks in different locations, from local server to the cloud; this ensures reproducible results from containerized packages. MetaPhage is designed to enable scalability and reproducibility; also, it can be easily expanded to include new miners and methods as they are developed in this continuously growing field. MetaPhage is freely available under a GPL-3.0 license at https://github.com/ MattiaPandolfoVR/MetaPhage.", + "authors": [ + { + "name": "Adriaenssens E.M." + }, + { + "name": "Lazzari G." + }, + { + "name": "Pandolfo M." + }, + { + "name": "Telatin A." + }, + { + "name": "Vitulo N." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "mSystems", + "title": "MetaPhage: an Automated Pipeline for Analyzing, Annotating, and Classifying Bacteriophages in Metagenomics Sequencing Data" + }, + "pmcid": "PMC9599279", + "pmid": "36069454" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Metagenomic sequencing", + "uri": "http://edamontology.org/topic_3837" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/microbeseg/microbeseg.biotools.json b/data/microbeseg/microbeseg.biotools.json new file mode 100644 index 0000000000000..ca9ab50fcee61 --- /dev/null +++ b/data/microbeseg/microbeseg.biotools.json @@ -0,0 +1,126 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:23:45.902940Z", + "biotoolsCURIE": "biotools:microbeseg", + "biotoolsID": "microbeseg", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ralf.mikut@kit.edu", + "name": "Ralf Mikut", + "orcidid": "http://orcid.org/0000-0001-9100-5496", + "typeEntity": "Person" + }, + { + "name": "Johannes Seiffarth", + "orcidid": "http://orcid.org/0000-0002-2087-9847" + }, + { + "name": "Katharina Nöh", + "orcidid": "http://orcid.org/0000-0002-5407-2275" + }, + { + "name": "Tim Scherr", + "orcidid": "http://orcid.org/0000-0001-8755-2825" + } + ], + "description": "Accurate Cell Segmentation with OMERO Data Management.Wan-Microbi is used.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + } + ] + } + ], + "homepage": "https://github.com/hip-satomi/microbeSEG", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-23T09:23:45.905435Z", + "license": "MIT", + "name": "microbeSEG", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pone.0277601", + "metadata": { + "abstract": "Copyright: © 2022 Scherr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.", + "authors": [ + { + "name": "Kohlheyer D." + }, + { + "name": "Mikut R." + }, + { + "name": "Neumann O." + }, + { + "name": "Noh K." + }, + { + "name": "Scharr H." + }, + { + "name": "Scherr T." + }, + { + "name": "Schilling M.P." + }, + { + "name": "Seiffarth J." + }, + { + "name": "Wollenhaupt B." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation" + }, + "pmcid": "PMC9707790", + "pmid": "36445903" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Biotechnology", + "uri": "http://edamontology.org/topic_3297" + }, + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/mineprot/mineprot.biotools.json b/data/mineprot/mineprot.biotools.json index e24f950ed8573..9863a5a09b198 100644 --- a/data/mineprot/mineprot.biotools.json +++ b/data/mineprot/mineprot.biotools.json @@ -15,10 +15,15 @@ "type": "private" }, "homepage": "https://github.com/huiwenke/MineProt", - "lastUpdate": "2022-11-29T00:20:37.613034Z", + "lastUpdate": "2023-01-02T02:02:59.046480Z", "name": "MineProt", "owner": "huiwenke", + "publication": [ + { + "doi": "10.48550/arXiv.2212.07809" + } + ], "version": [ - "0.2.2" + "0.2.3" ] } diff --git a/data/mopower/mopower.biotools.json b/data/mopower/mopower.biotools.json new file mode 100644 index 0000000000000..509b47e812d19 --- /dev/null +++ b/data/mopower/mopower.biotools.json @@ -0,0 +1,104 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T00:52:21.692941Z", + "biotoolsCURIE": "biotools:mopower", + "biotoolsID": "mopower", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "hsyed@ku.edu.tr", + "name": "Hamzah Syed", + "orcidid": "http://orcid.org/0000-0001-6981-6962", + "typeEntity": "Person" + }, + { + "name": "Chiara Bacchelli" + }, + { + "name": "Daniel Kelberman" + }, + { + "name": "Georg W Otto" + }, + { + "name": "Philip L Beales" + } + ], + "description": "R-shiny application for the simulation and power calculation of multi-omics studies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://hsyed.shinyapps.io/MOPower/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T00:52:21.695409Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/HSyed91/MOPower" + } + ], + "name": "MOPower", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1101/2021.12.19.473339" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + } + ] +} diff --git a/data/mouse-embeddings/mouse-embeddings.biotools.json b/data/mouse-embeddings/mouse-embeddings.biotools.json new file mode 100644 index 0000000000000..b45e709e4b5b0 --- /dev/null +++ b/data/mouse-embeddings/mouse-embeddings.biotools.json @@ -0,0 +1,94 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T00:23:27.159303Z", + "biotoolsCURIE": "biotools:mouse-embeddings", + "biotoolsID": "mouse-embeddings", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "t.konopka@qmul.ac.uk", + "name": "Tomasz Konopka", + "orcidid": "https://orcid.org/0000-0003-3042-4712", + "typeEntity": "Person" + }, + { + "name": "Damian Smedley" + }, + { + "name": "Letizia Vestito" + } + ], + "description": "Dimensional reduction of phenotypes from 53 000 mouse models reveals a diverse landscape of gene function.", + "download": [ + { + "type": "Container file", + "url": "https://zenodo.org/record/5493439" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Ontology visualisation", + "uri": "http://edamontology.org/operation_3559" + } + ] + } + ], + "homepage": "https://github.com/tkonopka/mouse-embeddings", + "language": [ + "R" + ], + "lastUpdate": "2023-01-27T00:23:27.161663Z", + "license": "MIT", + "name": "mouse-embeddings", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOADV/VBAB026", + "pmcid": "PMC8633315", + "pmid": "34870209" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/nanopore_py/nanopore_py.biotools.json b/data/nanopore_py/nanopore_py.biotools.json new file mode 100644 index 0000000000000..7d0f6ff1e6fe9 --- /dev/null +++ b/data/nanopore_py/nanopore_py.biotools.json @@ -0,0 +1,137 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T01:07:12.421744Z", + "biotoolsCURIE": "biotools:nanopore_py", + "biotoolsID": "nanopore_py", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "rbluo@cs.hku.hk", + "name": "Ruibang Luo", + "orcidid": "https://orcid.org/0000-0001-9711-6533", + "typeEntity": "Person" + }, + { + "email": "shoudongzhang@cuhk.edu.hk", + "name": "Shoudong Zhang", + "orcidid": "https://orcid.org/0000-0001-7332-7627", + "typeEntity": "Person" + }, + { + "email": "xiaochuanle@126.com", + "name": "Chuanle Xiao", + "orcidid": "https://orcid.org/0000-0002-4680-0682", + "typeEntity": "Person" + }, + { + "email": "luoming@scbg.ac.cn", + "name": "Ming Luo", + "typeEntity": "Person" + } + ], + "description": "Applications and potentials of nanopore sequencing in the (epi)genome and (epi)transcriptome era.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Base-calling", + "uri": "http://edamontology.org/operation_3185" + }, + { + "term": "Indel detection", + "uri": "http://edamontology.org/operation_0452" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + } + ] + } + ], + "homepage": "https://github.com/jts/nanopore-paper-analysis", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-01-27T01:07:12.424208Z", + "license": "Not licensed", + "name": "Nanopore", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.XINN.2021.100153", + "metadata": { + "abstract": "© 2021 The Author(s)The Human Genome Project opened an era of (epi)genomic research, and also provided a platform for the development of new sequencing technologies. During and after the project, several sequencing technologies continue to dominate nucleic acid sequencing markets. Currently, Illumina (short-read), PacBio (long-read), and Oxford Nanopore (long-read) are the most popular sequencing technologies. Unlike PacBio or the popular short-read sequencers before it, which, as examples of the second or so-called Next-Generation Sequencing platforms, need to synthesize when sequencing, nanopore technology directly sequences native DNA and RNA molecules. Nanopore sequencing, therefore, avoids converting mRNA into cDNA molecules, which not only allows for the sequencing of extremely long native DNA and full-length RNA molecules but also document modifications that have been made to those native DNA or RNA bases. In this review on direct DNA sequencing and direct RNA sequencing using Oxford Nanopore technology, we focus on their development and application achievements, discussing their challenges and future perspective. We also address the problems researchers may encounter applying these approaches in their research topics, and how to resolve them.", + "authors": [ + { + "name": "Leung A.W.-S." + }, + { + "name": "Luo M." + }, + { + "name": "Luo R." + }, + { + "name": "Xiao C." + }, + { + "name": "Xie S." + }, + { + "name": "Zhang D." + }, + { + "name": "Zhang S." + }, + { + "name": "Zheng Z." + } + ], + "citationCount": 11, + "date": "2021-11-28T00:00:00Z", + "journal": "The Innovation", + "title": "Applications and potentials of nanopore sequencing in the (epi)genome and (epi)transcriptome era" + }, + "pmcid": "PMC8640597", + "pmid": "34901902" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Structural variation", + "uri": "http://edamontology.org/topic_3175" + } + ] +} diff --git a/data/norfs/norfs.biotools.json b/data/norfs/norfs.biotools.json new file mode 100644 index 0000000000000..ae6cf278c44f8 --- /dev/null +++ b/data/norfs/norfs.biotools.json @@ -0,0 +1,131 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T01:16:29.490166Z", + "biotoolsCURIE": "biotools:norfs", + "biotoolsID": "norfs", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Matthew Mort" + }, + { + "name": "Sudhakaran Prabakaran" + }, + { + "name": "Matthew D.C. Neville", + "typeEntity": "Person" + }, + { + "name": "Robin Kohze", + "typeEntity": "Person" + } + ], + "description": "A platform for curated products from novel open reading frames prompts reinterpretation of disease variants.", + "documentation": [ + { + "type": [ + "API documentation" + ], + "url": "https://norfs.org/api" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Coding region prediction", + "uri": "http://edamontology.org/operation_0436" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Variant filtering", + "uri": "http://edamontology.org/operation_3675" + } + ] + } + ], + "homepage": "http://nORFs.org", + "lastUpdate": "2023-01-27T01:16:29.492820Z", + "name": "nORFs", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1101/GR.263202.120", + "metadata": { + "abstract": "© 2021 Neville et al.Recent evidence from proteomics and deep massively parallel sequencing studies have revealed that eukaryotic genomes contain substantial numbers of as-yet-uncharacterized open reading frames (ORFs). We define these uncharacterized ORFs as novel ORFs (nORFs). nORFs in humans are mostly under 100 codons and are found in diverse regions of the genome, including in long noncoding RNAs, pseudogenes, 3′ UTRs, 5′ UTRs, and alternative reading frames of canonical protein coding exons. There is therefore a pressing need to evaluate the potential functional importance of these unannotated transcripts and proteins in biological pathways and human disease on a larger scale, rather than one at a time. In this study, we outline the creation of a valuable nORFs data set with experimental evidence of translation for the community, use measures of heritability and selection that reveal signals for functional importance, and show the potential implications for functional interpretation of genetic variants in nORFs. Our results indicate that some variants that were previously classified as being benign or of uncertain significance may have to be reinterpreted.", + "authors": [ + { + "name": "Cooper D.N." + }, + { + "name": "Erady C." + }, + { + "name": "Hayden M." + }, + { + "name": "Kohze R." + }, + { + "name": "Matthew D.C. Neville" + }, + { + "name": "Meena N." + }, + { + "name": "Mort M." + }, + { + "name": "Prabakaran S." + } + ], + "citationCount": 8, + "date": "2021-01-01T00:00:00Z", + "journal": "Genome Research", + "title": "A platform for curated products from novel open reading frames prompts reinterpretation of disease variants" + }, + "pmcid": "PMC7849405", + "pmid": "33468550" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + } + ] +} diff --git a/data/oncopubminer/oncopubminer.biotools.json b/data/oncopubminer/oncopubminer.biotools.json new file mode 100644 index 0000000000000..357e067b2a390 --- /dev/null +++ b/data/oncopubminer/oncopubminer.biotools.json @@ -0,0 +1,133 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:44:01.378518Z", + "biotoolsCURIE": "biotools:oncopubminer", + "biotoolsID": "oncopubminer", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "niubf@cnic.cn", + "name": "Qiming Zhou", + "typeEntity": "Person" + }, + { + "email": "qimingzhou@chosenmedtech.com", + "name": "Beifang Niu", + "typeEntity": "Person" + }, + { + "name": "Quan Xu" + }, + { + "name": "Yueyue Liu" + } + ], + "description": "A platform for oncology publication mining.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Literature search", + "uri": "http://edamontology.org/operation_0305" + }, + { + "term": "Text annotation", + "uri": "http://edamontology.org/operation_3778" + }, + { + "term": "Text mining", + "uri": "http://edamontology.org/operation_0306" + } + ] + } + ], + "homepage": "https://oncopubminer.chosenmedinfo.com", + "lastUpdate": "2023-01-17T21:44:01.380985Z", + "name": "OncoPubMiner", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bib/bbac383", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.", + "authors": [ + { + "name": "Chen F." + }, + { + "name": "Duan X." + }, + { + "name": "Guo Z." + }, + { + "name": "Hu J." + }, + { + "name": "Li H." + }, + { + "name": "Liu S." + }, + { + "name": "Liu Y." + }, + { + "name": "Niu B." + }, + { + "name": "Song N." + }, + { + "name": "Su J." + }, + { + "name": "Xu Q." + }, + { + "name": "Zhai J." + }, + { + "name": "Zheng W." + }, + { + "name": "Zhou J." + }, + { + "name": "Zhou Q." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "OncoPubMiner: a platform for mining oncology publications" + }, + "pmid": "36058206" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/ontoparon/ontoparon.biotools.json b/data/ontoparon/ontoparon.biotools.json new file mode 100644 index 0000000000000..487f5c1cd8f1d --- /dev/null +++ b/data/ontoparon/ontoparon.biotools.json @@ -0,0 +1,111 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T01:28:13.765448Z", + "biotoolsCURIE": "biotools:ontoparon", + "biotoolsID": "ontoparon", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jean.charlet@sorbonne-universite.fr", + "name": "Jean Charlet", + "typeEntity": "Person" + } + ], + "description": "Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Named-entity and concept recognition", + "uri": "http://edamontology.org/operation_3280" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Text annotation", + "uri": "http://edamontology.org/operation_3778" + } + ] + } + ], + "homepage": "https://bioportal.bioontology.org/ontologies/ONTOPARON", + "lastUpdate": "2023-01-27T01:28:13.767929Z", + "name": "OntoPaRON", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PONE.0244604", + "metadata": { + "abstract": "Copyright: © 2021 Cardoso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient’s motor and cognitive state.", + "authors": [ + { + "name": "Aime X." + }, + { + "name": "Cardoso S." + }, + { + "name": "Charlet J." + }, + { + "name": "Grabli D." + }, + { + "name": "Guezennec G." + }, + { + "name": "Meininger V." + }, + { + "name": "Meneton P." + } + ], + "citationCount": 1, + "date": "2021-01-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network" + }, + "pmcid": "PMC7787442", + "pmid": "33406098" + } + ], + "toolType": [ + "Ontology" + ], + "topic": [ + { + "term": "Medical informatics", + "uri": "http://edamontology.org/topic_3063" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/openedc/openedc.biotools.json b/data/openedc/openedc.biotools.json new file mode 100644 index 0000000000000..496faa51ed8f5 --- /dev/null +++ b/data/openedc/openedc.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-27T01:39:52.853100Z", + "biotoolsCURIE": "biotools:openedc", + "biotoolsID": "openedc", + "collectionID": [ + "RD-Candidate" + ], + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "leonard.greulich@uni-muenster.de", + "name": "Leonard Greulich", + "orcidid": "https://orcid.org/0000-0003-3148-2105", + "typeEntity": "Person" + }, + { + "name": "Martin Dugas", + "orcidid": "https://orcid.org/0000-0001-9740-0788" + }, + { + "name": "Stefan Hegselmann", + "orcidid": "https://orcid.org/0000-0002-2145-3258" + } + ], + "description": "An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC).", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://openedc.org", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-27T01:39:52.855875Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/imi-muenster/OpenEDC" + } + ], + "name": "OpenEDC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.2196/29176", + "metadata": { + "abstract": "© 2021 Eesti Rakenduslingvistika Uhingu Aastaraamat. All rights reserved.Background: Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective: The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods: We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results: We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions: Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.", + "authors": [ + { + "name": "Dugas M." + }, + { + "name": "Greulich L." + }, + { + "name": "Hegselmann S." + } + ], + "date": "2021-11-01T00:00:00Z", + "journal": "JMIR Medical Informatics", + "title": "An open-source, standard-compliant, and mobile electronic data capture system for medical research (openedc): Design and evaluation study" + }, + "pmcid": "PMC8663450", + "pmid": "34806987" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicines research and development", + "uri": "http://edamontology.org/topic_3376" + }, + { + "term": "Protein expression", + "uri": "http://edamontology.org/topic_0108" + } + ] +} diff --git a/data/palo/palo.biotools.json b/data/palo/palo.biotools.json new file mode 100644 index 0000000000000..56f3f36316b30 --- /dev/null +++ b/data/palo/palo.biotools.json @@ -0,0 +1,79 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T21:55:43.617068Z", + "biotoolsCURIE": "biotools:palo", + "biotoolsID": "palo", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Wenpin Hou" + }, + { + "name": "Zhicheng Ji" + } + ], + "description": "Spatially-aware color palette optimization for single-cell and spatial data.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://winnie09.github.io/Wenpin_Hou/pages/Palo.html" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/Winnie09/Palo", + "language": [ + "R" + ], + "lastUpdate": "2023-01-17T21:55:43.619658Z", + "license": "MIT", + "name": "Palo", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac368", + "metadata": { + "abstract": "© 2022 The Author(s).In the exploratory data analysis of single-cell or spatial genomic data, single-cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assign visually similar colors to spatially neighboring clusters, making it hard to identify the distinction between clusters. To address this issue, we developed Palo that optimizes the color palette assignment for single-cell and spatial data in a spatially aware manner. Palo identifies pairs of clusters that are spatially neighboring to each other and assigns visually distinct colors to those neighboring pairs. We demonstrate that Palo leads to improved visualization in real single-cell and spatial genomic datasets.", + "authors": [ + { + "name": "Hou W." + }, + { + "name": "Ji Z." + } + ], + "date": "2022-07-15T00:00:00Z", + "journal": "Bioinformatics", + "title": "Palo: spatially aware color palette optimization for single-cell and spatial data" + }, + "pmcid": "PMC9272793", + "pmid": "35642896" + } + ], + "toolType": [ + "Library" + ] +} diff --git a/data/pdxs/pdxs.biotools.json b/data/pdxs/pdxs.biotools.json new file mode 100644 index 0000000000000..0d64c65de6793 --- /dev/null +++ b/data/pdxs/pdxs.biotools.json @@ -0,0 +1,208 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T22:03:15.931324Z", + "biotoolsCURIE": "biotools:pdxs", + "biotoolsID": "pdxs", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Carol.Bult@jax.org", + "name": "Carol J. Bult", + "orcidid": "http://orcid.org/0000-0001-9433-210X", + "typeEntity": "Person" + }, + { + "name": "Anuj Srivastava" + }, + { + "name": "Xing Yi Woo" + }, + { + "name": "Michael W. Lloyd", + "orcidid": "http://orcid.org/0000-0003-1021-8129" + } + ], + "description": "A Genomically and Clinically Annotated Patient Derived Xenograft (PDX) Resource for Preclinical Research in Non-Small Cell Lung Cancer.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Nucleic acid design", + "uri": "http://edamontology.org/operation_3095" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "http://tumor.informatics.jax.org/mtbwi/pdxSearch.do", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-17T22:03:15.933841Z", + "license": "Apache-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/TheJacksonLaboratory/PDX-SOC" + } + ], + "name": "PDXs", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1158/0008-5472.CAN-22-0948", + "metadata": { + "abstract": "© 2022 The Authors.Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.", + "authors": [ + { + "name": "Airhart S.D." + }, + { + "name": "Bult C.J." + }, + { + "name": "Bundy M." + }, + { + "name": "Chavaree M." + }, + { + "name": "Chen M." + }, + { + "name": "Cheng M." + }, + { + "name": "Domanskyi S." + }, + { + "name": "Gandara D.R." + }, + { + "name": "Gandour-Edwards R." + }, + { + "name": "George J." + }, + { + "name": "Goodwin N." + }, + { + "name": "Graber J.H." + }, + { + "name": "Grubb S.C." + }, + { + "name": "Holland W." + }, + { + "name": "Jocoy E.L." + }, + { + "name": "Karuturi R.K.M." + }, + { + "name": "Keck J." + }, + { + "name": "Lara Jr P.N." + }, + { + "name": "Liu E.T." + }, + { + "name": "Lloyd M.W." + }, + { + "name": "Mack P.C." + }, + { + "name": "Neuhauser S.B." + }, + { + "name": "Openshaw T.H." + }, + { + "name": "Paisie C." + }, + { + "name": "Peterson J.G." + }, + { + "name": "Riess J.W." + }, + { + "name": "Sanderson B.J." + }, + { + "name": "Simons A.K." + }, + { + "name": "Srivastava A." + }, + { + "name": "Stafford G.A." + }, + { + "name": "Tepper C.G." + }, + { + "name": "Tsai R.A." + }, + { + "name": "Woo X.Y." + } + ], + "date": "2022-11-15T00:00:00Z", + "journal": "Cancer Research", + "title": "A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer" + }, + "pmcid": "PMC9664138", + "pmid": "36069866" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene structure", + "uri": "http://edamontology.org/topic_0114" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/pentaho/pentaho.biotools.json b/data/pentaho/pentaho.biotools.json new file mode 100644 index 0000000000000..d5e07ead68325 --- /dev/null +++ b/data/pentaho/pentaho.biotools.json @@ -0,0 +1,50 @@ +{ + "additionDate": "2023-01-26T10:15:11.979844Z", + "biotoolsCURIE": "biotools:pentaho", + "biotoolsID": "pentaho", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Hitachi Vantara ; Pentaho Corporation", + "typeEntity": "Consortium", + "url": "https://www.hitachivantara.com/en-us/products/dataops-software/data-integration-analytics.html?source=pentaho-redirect" + } + ], + "description": "Pentaho is business intelligence (BI) software that provides data integration, OLAP services, reporting, information dashboards, data mining and extract, transform, load (ETL) capabilities.", + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/pentaho" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://github.com/pentaho", + "lastUpdate": "2023-01-26T10:20:00.178345Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/pentaho/pentaho-kettle" + } + ], + "name": "Pentaho", + "owner": "iacs-biocomputacion", + "version": [ + "Stable release 9.2.0.0-290 June 2, 2021;" + ] +} diff --git a/data/phenocomb/phenocomb.biotools.json b/data/phenocomb/phenocomb.biotools.json new file mode 100644 index 0000000000000..abe1ecfa60de2 --- /dev/null +++ b/data/phenocomb/phenocomb.biotools.json @@ -0,0 +1,92 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T00:26:06.912410Z", + "biotoolsCURIE": "biotools:phenocomb", + "biotoolsID": "phenocomb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "david.m.woods@cuanschutz.edu", + "name": "David M. Woods", + "orcidid": "http://orcid.org/0000-0002-6328-8107", + "typeEntity": "Person" + }, + { + "name": "Ann Strange" + }, + { + "name": "Brian Thompson", + "orcidid": "http://orcid.org/0000-0003-0983-2762" + }, + { + "name": "Carol Amato", + "orcidid": "http://orcid.org/0000-0002-4945-8819" + }, + { + "name": "Emily Monk", + "orcidid": "http://orcid.org/0000-0003-1037-4172" + }, + { + "name": "Paulo E. P. Burke", + "orcidid": "http://orcid.org/0000-0002-9640-299X" + } + ], + "description": "A discovery tool to assess complex phenotypes in high-dimension, single-cell datasets.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/SciOmicsLab/PhenoComb", + "language": [ + "R" + ], + "lastUpdate": "2023-01-20T00:26:06.915047Z", + "license": "Not licensed", + "name": "PhenoComb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioadv/vbac052" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cytometry", + "uri": "http://edamontology.org/topic_3934" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/photizo/photizo.biotools.json b/data/photizo/photizo.biotools.json new file mode 100644 index 0000000000000..923588ec935d7 --- /dev/null +++ b/data/photizo/photizo.biotools.json @@ -0,0 +1,131 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T21:29:00.795555Z", + "biotoolsCURIE": "biotools:photizo", + "biotoolsID": "photizo", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "cdendrou@well.ox.ac.uk", + "name": "Calliope A. Dendrou", + "orcidid": "http://orcid.org/0000-0003-1179-4021", + "typeEntity": "Person" + }, + { + "name": "Melissa Grant-Peters" + }, + { + "name": "Charlotte Rich-Griffin", + "orcidid": "http://orcid.org/0000-0001-8212-9542" + }, + { + "name": "Gianfelice Cinque", + "orcidid": "http://orcid.org/0000-0001-6801-8010" + }, + { + "name": "Jonathan E. Grant-Peters", + "orcidid": "http://orcid.org/0000-0002-6383-1253" + } + ], + "description": "An open-source library for cross-sample analysis of FTIR spectroscopy data.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://github.com/DendrouLab/Photizo/blob/master/Photizo_documentation.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Principal component analysis", + "uri": "http://edamontology.org/operation_3960" + }, + { + "term": "Principal component visualisation", + "uri": "http://edamontology.org/operation_2939" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Spectral analysis", + "uri": "http://edamontology.org/operation_3214" + } + ] + } + ], + "homepage": "https://github.com/DendrouLab/Photizo", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T21:29:00.798138Z", + "license": "Not licensed", + "name": "Photizo", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac346", + "metadata": { + "abstract": "© 2022 The Author(s) 2022. Published by Oxford University Press.Motivation: With continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterization of a sample. The spatially resolved nature of this method lends itself well to histological profiling of complex biological specimens. However, current software can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible. Results: To overcome these limitations, we have developed Photizo-an open-source Python library enabling high-Throughput spectral data pre-processing, visualization and downstream analysis, including principal component analysis, clustering, macromolecular quantification and mapping. Photizo can be used for analysis of data without a spatial component, as well as spatially resolved data, obtained e.g. by scanning mode IR microspectroscopy and IR imaging by focal plane array detector.", + "authors": [ + { + "name": "Cinque G." + }, + { + "name": "Dendrou C.A." + }, + { + "name": "Grant-Peters J.E." + }, + { + "name": "Grant-Peters M." + }, + { + "name": "Rich-Griffin C." + } + ], + "citationCount": 1, + "date": "2022-07-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "Photizo: An open-source library for cross-sample analysis of FTIR spectroscopy data" + }, + "pmcid": "PMC9237726", + "pmid": "35608303" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "NMR", + "uri": "http://edamontology.org/topic_0593" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Sample collections", + "uri": "http://edamontology.org/topic_3277" + } + ] +} diff --git a/data/pleistodist/pleistodist.biotools.json b/data/pleistodist/pleistodist.biotools.json new file mode 100644 index 0000000000000..2622842a7f6b9 --- /dev/null +++ b/data/pleistodist/pleistodist.biotools.json @@ -0,0 +1,88 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:36:49.074948Z", + "biotoolsCURIE": "biotools:pleistodist", + "biotoolsID": "pleistodist", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "David J.X. Tan", + "orcidid": "http://orcid.org/0000-0001-7019-7871" + }, + { + "name": "Michael J. Andersen", + "orcidid": "http://orcid.org/0000-0002-7220-5588" + } + ], + "description": "A toolbox for visualising and quantifying the effects of Pleistocene sea-level change on island archipelagos.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + }, + { + "term": "Sequence assembly visualisation", + "uri": "http://edamontology.org/operation_3184" + } + ] + } + ], + "homepage": "https://github.com/g33k5p34k/PleistoDistR", + "language": [ + "R" + ], + "lastUpdate": "2023-01-23T09:36:49.077566Z", + "license": "MIT", + "name": "PleistoDist", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1111/2041-210X.14024", + "metadata": { + "abstract": "© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.Pleistocene sea-level change played a significant role in the evolution and assembly of island biotas. The formation of land bridges between islands during Quaternary glacial maxima, when sea levels were more than 120 metres below present-day sea levels, often facilitated historical dispersal and gene flow between islands that are today geographically disconnected. Despite the importance of Pleistocene sea-level change, few software packages exist that model the effects of this phenomenon in a standardised and generalised manner. Here, we present PleistoDist, an R package that allows users to visualise and quantify the effects of Pleistocene sea-level change on islands over time, and test multiple temporally explicit hypotheses of inter-island dispersal and community assembly. Re-analysing published datasets, we demonstrate how using PleistoDist to account for historical sea-level change can provide greater explanatory power when analysing extant island communities, and show how population genetic simulations can be used to generate spatiotemporally explicit neutral expectations of population genetic structure across island archipelagos.", + "authors": [ + { + "name": "Andersen M.J." + }, + { + "name": "Gyllenhaal E.F." + }, + { + "name": "Tan D.J.X." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Methods in Ecology and Evolution", + "title": "PleistoDist: A toolbox for visualising and quantifying the effects of Pleistocene sea-level change on island archipelagos" + } + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Evolutionary biology", + "uri": "http://edamontology.org/topic_3299" + }, + { + "term": "Population genetics", + "uri": "http://edamontology.org/topic_3056" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + } + ] +} diff --git a/data/polynote/polynote.biotools.json b/data/polynote/polynote.biotools.json new file mode 100644 index 0000000000000..40466eeb0e365 --- /dev/null +++ b/data/polynote/polynote.biotools.json @@ -0,0 +1,39 @@ +{ + "additionDate": "2023-01-27T12:17:23.020048Z", + "biotoolsCURIE": "biotools:polynote", + "biotoolsID": "polynote", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "jeremyrsmith, jonathanindig,omidmogasemi", + "url": "https://polynote.org/0.5.0/about/" + } + ], + "description": "Features\n\nPolynote is a different kind of notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly. It encourages reproducible notebooks with its immutable data model. (Learn more)\n\nHere are some of its unique features:", + "download": [ + { + "type": "Software package", + "url": "https://github.com/polynote/polynote" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "https://polynote.org/latest/", + "lastUpdate": "2023-01-27T12:22:29.792695Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/polynote/polynote" + } + ], + "name": "Polynote", + "owner": "iacs-biocomputacion", + "version": [ + "0.5.0" + ] +} diff --git a/data/prot-on/prot-on.biotools.json b/data/prot-on/prot-on.biotools.json new file mode 100644 index 0000000000000..49d1aef0723f6 --- /dev/null +++ b/data/prot-on/prot-on.biotools.json @@ -0,0 +1,107 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T20:01:56.562029Z", + "biotoolsCURIE": "biotools:prot-on", + "biotoolsID": "prot-on", + "cost": "Free of charge", + "description": "The identification of critical amino acids for redesigning the structurally characterized protein-protein interfaces paves the way for developing protein-based therapeutics to dealing with diverse range of diseases. To find such amino acids positions, the residues across the protein interaction surfaces are either randomly or strategically mutated. Scanning mutations in this manner is experimentally costly. Therefore, computational methods have been developed to estimate the impact of an interfacial mutation on protein-protein interactions. In this work, we present the PROT-ON tool that scans all possible interfacial mutations by using EvoEF1 (active in both on the web server and stand-alone versions) or FoldX (active only in the stand-alone version) with the aim of finding the most mutable positions.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Protein complex", + "uri": "http://edamontology.org/data_2877" + }, + "format": [ + { + "term": "PDB", + "uri": "http://edamontology.org/format_1476" + } + ] + } + ], + "operation": [ + { + "term": "Protein-protein interaction analysis", + "uri": "http://edamontology.org/operation_2949" + } + ], + "output": [ + { + "data": { + "term": "Protein interaction data", + "uri": "http://edamontology.org/data_0906" + }, + "format": [ + { + "term": "Format (by type of data)", + "uri": "http://edamontology.org/format_2350" + } + ] + } + ] + } + ], + "homepage": "http://proton.tools.ibg.edu.tr:8001", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-27T05:27:32.953630Z", + "license": "Apache-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/CSB-KaracaLab/prot-on" + }, + { + "type": [ + "Service" + ], + "url": "http://proton.tools.ibg.edu.tr:8001" + } + ], + "name": "PROT-ON", + "operatingSystem": [ + "Linux", + "Mac" + ], + "owner": "mehdikosaca", + "toolType": [ + "Bioinformatics portal", + "Command-line tool", + "Web application", + "Web service" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Biophysics", + "uri": "http://edamontology.org/topic_3306" + }, + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Protein binding sites", + "uri": "http://edamontology.org/topic_3534" + }, + { + "term": "Protein folding, stability and design", + "uri": "http://edamontology.org/topic_0130" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + } + ] +} diff --git a/data/psg-bar/psg-bar.biotools.json b/data/psg-bar/psg-bar.biotools.json new file mode 100644 index 0000000000000..f3fcef6b169e6 --- /dev/null +++ b/data/psg-bar/psg-bar.biotools.json @@ -0,0 +1,125 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:49:37.778987Z", + "biotoolsCURIE": "biotools:psg-bar", + "biotoolsID": "psg-bar", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "acherkasov@prostatecentre.com", + "name": "Artem Cherkasov", + "typeEntity": "Person" + }, + { + "name": "Mariia Radaeva" + }, + { + "name": "Martin Ester" + }, + { + "name": "Mohit Pandey", + "orcidid": "http://orcid.org/0000-0002-2562-7155" + } + ], + "description": "Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein structure prediction", + "uri": "http://edamontology.org/operation_0474" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://github.com/diamondspark/PSG-BAR", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-23T09:49:37.781552Z", + "license": "Not licensed", + "name": "PSG-BAR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3390/molecules27165114", + "metadata": { + "abstract": "© 2022 by the authors.Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.", + "authors": [ + { + "name": "Cherkasov A." + }, + { + "name": "Ester M." + }, + { + "name": "Fernandez M." + }, + { + "name": "Garland O." + }, + { + "name": "Mslati H." + }, + { + "name": "Pandey M." + }, + { + "name": "Radaeva M." + } + ], + "date": "2022-08-01T00:00:00Z", + "journal": "Molecules", + "title": "Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks" + }, + "pmcid": "PMC9416537", + "pmid": "36014351" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Protein structural motifs and surfaces", + "uri": "http://edamontology.org/topic_0166" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/pyascore/pyascore.biotools.json b/data/pyascore/pyascore.biotools.json new file mode 100644 index 0000000000000..676b6d064f7f8 --- /dev/null +++ b/data/pyascore/pyascore.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T00:33:07.219299Z", + "biotoolsCURIE": "biotools:pyascore", + "biotoolsID": "pyascore", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jvillen@uw.edu", + "name": "Judit Villén", + "orcidid": "http://orcid.org/0000-0002-1005-1739", + "typeEntity": "Person" + }, + { + "name": "Anthony S. Barente", + "orcidid": "http://orcid.org/0000-0002-8641-3775" + } + ], + "description": "A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "PTM localisation", + "uri": "http://edamontology.org/operation_3755" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://github.com/AnthonyOfSeattle/pyAscoreValidation", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-20T00:33:07.222595Z", + "license": "MIT", + "name": "pyAscore", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1021/acs.jproteome.2c00194", + "metadata": { + "abstract": "© 2022 American Chemical Society.Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.", + "authors": [ + { + "name": "Barente A.S." + }, + { + "name": "Villen J." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Journal of Proteome Research", + "title": "A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data" + }, + "pmid": "36315500" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Protein modifications", + "uri": "http://edamontology.org/topic_0601" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json b/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json new file mode 100644 index 0000000000000..c0cdafb7fea03 --- /dev/null +++ b/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json @@ -0,0 +1,49 @@ +{ + "additionDate": "2023-01-26T08:45:11.328282Z", + "biotoolsCURIE": "biotools:rabbit_in_a_hat", + "biotoolsID": "rabbit_in_a_hat", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Observational Health Data Sciences and Informatics OHDSI", + "typeEntity": "Consortium", + "typeRole": [ + "Documentor" + ], + "url": "https://www.ohdsi.org/" + } + ], + "description": "Rabbit-In-a-Hat comes with WhiteRabbit and is designed to read and display a WhiteRabbit scan document. WhiteRabbit generates information about the source data while Rabbit-In-a-Hat uses that information and through a graphical user interface to allow a user to connect source data to tables and columns within the CDM. Rabbit-In-a-Hat generates documentation for the ETL process it does not generate code to create an ETL", + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/OHDSI/WhiteRabbit/issues" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "http://ohdsi.github.io/WhiteRabbit/RabbitInAHat.html", + "lastUpdate": "2023-01-26T09:14:17.671863Z", + "link": [ + { + "type": [ + "Software catalogue" + ], + "url": "http://ohdsi.github.io/WhiteRabbit/RabbitInAHat.html" + } + ], + "name": "Rabbit in a Hat", + "owner": "iacs-biocomputacion", + "relation": [ + { + "biotoolsID": "white_rabbit", + "type": "uses" + } + ], + "version": [ + "CDM version (v4, v5 or v6)" + ] +} diff --git a/data/razers3/razers3.biotools.json b/data/razers3/razers3.biotools.json index 0222827bf904e..474e437e2f814 100644 --- a/data/razers3/razers3.biotools.json +++ b/data/razers3/razers3.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2021-04-22T01:09:26Z", "biotoolsCURIE": "biotools:razers3", "biotoolsID": "razers3", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "RazerS 3 is a tool for mapping millions of short genomic reads onto a\nreference genome. It was designed with focus on mapping next-generation\nsequencing reads onto whole DNA genomes. RazerS 3 searches for matches of\nreads with a percent identity above a given threshold (-i X), whereby it\ndetects alignments with mismatches as well as gaps.", "editPermission": { "type": "private" @@ -25,9 +28,18 @@ } ], "homepage": "https://github.com/seqan/seqan/tree/master/apps/razers3", - "lastUpdate": "2022-12-09T21:27:29.443267Z", + "lastUpdate": "2023-01-13T02:29:11.821835Z", + "license": "GPL-3.0", "name": "razers3", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "leipzig", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Genomics", diff --git a/data/redash/redash.biotools.json b/data/redash/redash.biotools.json new file mode 100644 index 0000000000000..2f924cd783570 --- /dev/null +++ b/data/redash/redash.biotools.json @@ -0,0 +1,24 @@ +{ + "additionDate": "2023-01-27T12:45:51.303827Z", + "biotoolsCURIE": "biotools:redash", + "biotoolsID": "redash", + "collectionID": [ + "IMPaCT-Data" + ], + "description": "Redash helps you make sense of your data\nConnect and query your data sources, build dashboards to visualize data and share them with your company.", + "editPermission": { + "type": "public" + }, + "homepage": "https://redash.io/", + "lastUpdate": "2023-01-27T12:46:02.009529Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://redash.io/help/open-source/setup" + } + ], + "name": "Redash", + "owner": "iacs-biocomputacion" +} diff --git a/data/refinem/refinem.biotools.json b/data/refinem/refinem.biotools.json index 586ca94990f90..629d75b4a248d 100644 --- a/data/refinem/refinem.biotools.json +++ b/data/refinem/refinem.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2021-05-27T09:07:28Z", "biotoolsCURIE": "biotools:refinem", "biotoolsID": "refinem", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "RefineM is a set of tools for improving population genomes. It provides methods designed to improve the completeness of a genome along with methods for identifying and removing contamination.", "editPermission": { "authors": [ @@ -13,10 +16,15 @@ "language": [ "Python" ], - "lastUpdate": "2022-12-09T21:29:48.668121Z", + "lastUpdate": "2023-01-13T02:24:47.330904Z", "license": "GPL-3.0", "maturity": "Legacy", "name": "RefineM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "Kigaard", "toolType": [ "Command-line tool" diff --git a/data/reframed/reframed.biotools.json b/data/reframed/reframed.biotools.json index 5c231b2536825..28b2e7029f7a4 100644 --- a/data/reframed/reframed.biotools.json +++ b/data/reframed/reframed.biotools.json @@ -6,20 +6,16 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", - "description": "ReFramed implements many constraint-based simulation methods (see list below), and contains interfaces to other libraries of the COBRA ecosystem including escher, cobrapy, and optlang.", + "description": "ReFramed is a Python 3 library for metabolic model simulation.\n\nIt currently supports 15 different constraint-based simulation methods (FBA variants), including knockout simulation, thermodynamic analysis, protein allocation, transcriptomics integration, and community simulation.", "documentation": [ { "type": [ - "General" + "General", + "Installation instructions" ], - "url": "https://github.com/cdanielmachado/reframed" - } - ], - "download": [ - { - "type": "Source code", - "url": "https://github.com/cdanielmachado/reframed" + "url": "https://reframed.readthedocs.io/en/latest/" } ], "editPermission": { @@ -29,8 +25,16 @@ "language": [ "Python" ], - "lastUpdate": "2022-04-28T15:39:08.442209Z", + "lastUpdate": "2023-01-13T02:20:38.809559Z", "license": "Apache-2.0", "name": "reFramed", - "owner": "tntiniak" + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "tntiniak", + "toolType": [ + "Library" + ] } diff --git a/data/regtools/regtools.biotools.json b/data/regtools/regtools.biotools.json new file mode 100644 index 0000000000000..49a3cb965e9cb --- /dev/null +++ b/data/regtools/regtools.biotools.json @@ -0,0 +1,46 @@ +{ + "additionDate": "2023-01-24T13:41:32.753177Z", + "biotoolsCURIE": "biotools:regtools", + "biotoolsID": "regtools", + "description": "Integrate DNA-seq and RNA-seq data to identify mutations that are associated with regulatory effects on gene expression.", + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + }, + { + "term": "Splicing analysis", + "uri": "http://edamontology.org/operation_2499" + } + ] + } + ], + "homepage": "https://regtools.readthedocs.io/en/latest/", + "language": [ + "C", + "C++" + ], + "lastUpdate": "2023-01-24T13:41:38.999372Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/griffithlab/regtools" + } + ], + "name": "regtools", + "owner": "alexcorm", + "topic": [ + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/remm_score/remm_score.biotools.json b/data/remm_score/remm_score.biotools.json new file mode 100644 index 0000000000000..873351d418dbe --- /dev/null +++ b/data/remm_score/remm_score.biotools.json @@ -0,0 +1,159 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-03T14:28:36.818779Z", + "biotoolsCURIE": "biotools:remm_score", + "biotoolsID": "remm_score", + "collectionID": [ + "Rare Disease" + ], + "credit": [ + { + "email": "lusine.nazaretyan@bih-charite.de", + "name": "Lusiné Nazaretyan", + "orcidid": "https://orcid.org/0000-0001-5820-4143" + }, + { + "email": "martin.kircher@bih-charite.de", + "name": "Martin Kircher", + "orcidid": "https://orcid.org/0000-0001-9278-5471" + }, + { + "email": "max.schubach@bih-charite.de", + "name": "Max Schubach", + "orcidid": "https://orcid.org/0000-0002-2032-6679" + } + ], + "description": "ReMM score is a tool that scores the positions in the human genome in terms of their regulatory probability.\n\nWe use curated regulatory variants involved in Mendelian disease and contrast them with proxy-neutral variants that survived natural selection in a machine learning framework. We use an algorithm for highly imbalanced data, called hyperSMURF, to differentiate deleterious from neutral variants.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://remm.bihealth.org/" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://doi.org/10.5281/zenodo.6576087", + "version": "v0.4" + } + ], + "editPermission": { + "type": "private" + }, + "homepage": "https://remm.bihealth.org/", + "lastUpdate": "2023-01-03T14:28:36.822941Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/kircherlab/ReMM" + } + ], + "name": "ReMM score", + "operatingSystem": [ + "Linux" + ], + "owner": "visze", + "publication": [ + { + "doi": "10.1101/2022.03.14.484240" + }, + { + "doi": "10.1016/j.ajhg.2016.07.005", + "metadata": { + "abstract": "© 2016 American Society of Human GeneticsThe interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.", + "authors": [ + { + "name": "Groza T." + }, + { + "name": "Haendel M.A." + }, + { + "name": "Hochheiser H." + }, + { + "name": "Jacobsen J.O.B." + }, + { + "name": "Jager M." + }, + { + "name": "Kohler S." + }, + { + "name": "Lewis S.E." + }, + { + "name": "McMurry J.A." + }, + { + "name": "Mungall C.J." + }, + { + "name": "Robinson P.N." + }, + { + "name": "Schubach M." + }, + { + "name": "Smedley D." + }, + { + "name": "Spielmann M." + }, + { + "name": "Valentini G." + }, + { + "name": "Washington N.L." + }, + { + "name": "Zemojtel T." + } + ], + "citationCount": 133, + "date": "2016-09-01T00:00:00Z", + "journal": "American Journal of Human Genetics", + "title": "A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease" + }, + "pmcid": "PMC5011059", + "pmid": "27569544", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Bioinformatics portal", + "Web API", + "Web application", + "Workflow" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Functional genomics", + "uri": "http://edamontology.org/topic_0085" + }, + { + "term": "Human genetics", + "uri": "http://edamontology.org/topic_3574" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + } + ], + "version": [ + "v0.3.1.post1", + "v0.4" + ] +} diff --git a/data/repair/repair.biotools.json b/data/repair/repair.biotools.json index 2c96a1a6f3f27..d202c5c256ac1 100644 --- a/data/repair/repair.biotools.json +++ b/data/repair/repair.biotools.json @@ -3,6 +3,7 @@ "additionDate": "2021-03-19T13:56:30Z", "biotoolsCURIE": "biotools:repair", "biotoolsID": "repair", + "confidence_flag": "tool", "cost": "Free of charge", "credit": [ { @@ -37,7 +38,7 @@ "type": "private" }, "homepage": "https://utrecht-university.shinyapps.io/repair/", - "lastUpdate": "2022-12-09T21:36:08.890945Z", + "lastUpdate": "2023-01-13T02:13:28.728709Z", "license": "CC-BY-4.0", "link": [ { @@ -49,6 +50,11 @@ ], "maturity": "Emerging", "name": "RePAIR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "valeriabonapersona", "publication": [ { @@ -117,7 +123,7 @@ "name": "Yam K.Y." } ], - "citationCount": 14, + "citationCount": 15, "date": "2021-04-01T00:00:00Z", "journal": "Nature Neuroscience", "title": "Increasing the statistical power of animal experiments with historical control data" diff --git a/data/rexdb/rexdb.biotools.json b/data/rexdb/rexdb.biotools.json index 597a05800ba2a..fb131cb4ddcfa 100644 --- a/data/rexdb/rexdb.biotools.json +++ b/data/rexdb/rexdb.biotools.json @@ -11,7 +11,17 @@ "cost": "Free of charge", "credit": [ { - "name": "ELIXIR-CZ", + "name": "Nina Hoštáková" + }, + { + "name": "Petr Novák" + }, + { + "name": "Pavel Neumann", + "orcidid": "http://orcid.org/0000-0001-6711-6639" + }, + { + "name": "Jiří Macas", "typeEntity": "Institute" } ], @@ -42,7 +52,7 @@ "Data" ], "homepage": "http://repeatexplorer.org/?page_id=918", - "lastUpdate": "2022-12-09T21:42:13.872439Z", + "lastUpdate": "2023-01-13T02:09:25.460825Z", "license": "Not licensed", "link": [ { @@ -54,6 +64,11 @@ ], "maturity": "Emerging", "name": "REXdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "kavonrtep", "publication": [ { @@ -74,7 +89,7 @@ "name": "Novak P." } ], - "citationCount": 108, + "citationCount": 114, "date": "2019-01-03T00:00:00Z", "journal": "Mobile DNA", "title": "Systematic survey of plant LTR-retrotransposons elucidates phylogenetic relationships of their polyprotein domains and provides a reference for element classification" diff --git a/data/rexposome/rexposome.biotools.json b/data/rexposome/rexposome.biotools.json index 205931b319699..59c4029777f14 100644 --- a/data/rexposome/rexposome.biotools.json +++ b/data/rexposome/rexposome.biotools.json @@ -10,56 +10,87 @@ { "email": "carles.hernandez@isglobal.org", "name": "Carles Hernandez-Ferrer", + "orcidid": "https://orcid.org/0000-0002-8029-7160", + "typeEntity": "Person", + "typeRole": [ + "Developer" + ] + }, + { + "email": "juanr.gonzalez@isglobal.org", + "name": "Juan R Gonzalez", "typeEntity": "Person", "typeRole": [ "Primary contact" ] + }, + { + "email": "xavier.escriba@isglobal.org", + "name": "Xavier Escribà Montagut", + "typeEntity": "Person", + "typeRole": [ + "Maintainer" + ] } ], "description": "Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.", "documentation": [ { "type": [ - "User manual" + "API documentation" ], "url": "http://bioconductor.org/packages/release/bioc/manuals/rexposome/man/rexposome.pdf" + }, + { + "type": [ + "General" + ], + "url": "https://isglobal-brge.github.io/rexposome" } ], "download": [ { "type": "Software package", - "url": "http://bioconductor.org/packages/release/bioc/src/contrib/rexposome_1.2.0.tar.gz" + "url": "https://bioconductor.org/packages/release/bioc/src/contrib/rexposome_1.20.0.tar.gz", + "version": "1.20.0" } ], "editPermission": { "authors": [ "brgelab", + "chernan3", "sergitobara" ], "type": "group" }, "function": [ { + "cmd": "imputation, pca, correlation, clustering, exwas, mexwas", + "note": "Impute missing data, perform PCA on the multidimensional space, compute correlations across the multiples variables, group samples according the variables, perform GWAS-like association studies against a significant phenotype.", "operation": [ { - "term": "Classification", - "uri": "http://edamontology.org/operation_2990" + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" } ] }, { + "cmd": "plotHistogram, plotFamily, plotPCA, plotCorrelation, plotClassification, plotPHE, plotEXP, plotExwas, plotEffect", + "note": "Plots histograms of single or family of variables, plot results of applying a PCA to the set, plot the correlations computed across the variables, plot samples classification after performing a clustering based on the variables, plot the score of correlation between PCs and phenotypes of interest, plot the score of correlation between the PCs and the variables, plot the p-values of the GWAS-like analysis, plot the effects of association.", "operation": [ { - "term": "Clustering", - "uri": "http://edamontology.org/operation_3432" + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" } ] }, { + "cmd": "readExposome, loadExposome", + "note": "Load both tsv-like files and data.frame-like R objects..", "operation": [ { - "term": "Comparison", - "uri": "http://edamontology.org/operation_2424" + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" } ] } @@ -68,14 +99,14 @@ "language": [ "R" ], - "lastUpdate": "2022-10-19T07:22:44.373806Z", + "lastUpdate": "2023-01-24T16:51:31.600757Z", "license": "MIT", "link": [ { "type": [ "Mirror" ], - "url": "http://bioconductor.org/packages/rexposome/" + "url": "https://bioconductor.org/packages/rexposome/" } ], "name": "rexposome", @@ -86,6 +117,133 @@ ], "owner": "shadi.m", "publication": [ + { + "doi": "10.1038/s41467-022-34422-2", + "metadata": { + "abstract": "© 2022, The Author(s).Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations (https://helixomics.isglobal.org/) will serve to guide future investigation into the biological imprints of the early life exposome.", + "authors": [ + { + "name": "Andrusaityte S." + }, + { + "name": "Borras E." + }, + { + "name": "Bustamante M." + }, + { + "name": "Cadiou S." + }, + { + "name": "Carracedo A." + }, + { + "name": "Casas M." + }, + { + "name": "Chatzi L." + }, + { + "name": "Coen M." + }, + { + "name": "Estivill X." + }, + { + "name": "Gonzalez J.R." + }, + { + "name": "Grazuleviciene R." + }, + { + "name": "Gutzkow K.B." + }, + { + "name": "Hernandez-Ferrer C." + }, + { + "name": "Heude B." + }, + { + "name": "Keun H.C." + }, + { + "name": "Lau C.-H.E." + }, + { + "name": "Maitre L." + }, + { + "name": "Mason D." + }, + { + "name": "Nieuwenhuijsen M." + }, + { + "name": "Papadopoulou E.Z." + }, + { + "name": "Pelegri-Siso D." + }, + { + "name": "Quintela I." + }, + { + "name": "Robinson O." + }, + { + "name": "Ruiz-Arenas C." + }, + { + "name": "Sabido E." + }, + { + "name": "Sakhi A.K." + }, + { + "name": "Siskos A.P." + }, + { + "name": "Slama R." + }, + { + "name": "Sunyer J." + }, + { + "name": "Tamayo I." + }, + { + "name": "Thiel D." + }, + { + "name": "Thomsen C." + }, + { + "name": "Urquiza J." + }, + { + "name": "Vafeiadi M." + }, + { + "name": "Vives-Usano M." + }, + { + "name": "Vrijheid M." + }, + { + "name": "Wright J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Nature Communications", + "title": "Multi-omics signatures of the human early life exposome" + }, + "pmcid": "PMC9678903", + "pmid": "36411288", + "type": [ + "Usage" + ] + }, { "doi": "10.1093/bioinformatics/btz526", "metadata": { @@ -113,7 +271,7 @@ "name": "Wellenius G.A." } ], - "citationCount": 9, + "citationCount": 10, "date": "2019-12-15T00:00:00Z", "journal": "Bioinformatics", "title": "Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages" @@ -124,13 +282,23 @@ ] } ], + "relation": [ + { + "biotoolsID": "ctdquerier", + "type": "usedBy" + }, + { + "biotoolsID": "omicrexposome", + "type": "usedBy" + } + ], "toolType": [ "Library" ], "topic": [ { - "term": "Medical informatics", - "uri": "http://edamontology.org/topic_3063" + "term": "Biomedical science", + "uri": "http://edamontology.org/topic_3344" } ], "validated": 1, diff --git a/data/rfsc/rfsc.biotools.json b/data/rfsc/rfsc.biotools.json index 72da6d92a985a..27189d77bf09f 100644 --- a/data/rfsc/rfsc.biotools.json +++ b/data/rfsc/rfsc.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2021-09-23T18:41:28.258166Z", "biotoolsCURIE": "biotools:rfsc", "biotoolsID": "rfsc", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Reference-Free Sequence Classification Tool for DNA sequences in metagenomic samples.", "editPermission": { "type": "private" @@ -31,9 +34,22 @@ } ], "homepage": "https://github.com/cobilab/RFSC", - "lastUpdate": "2022-12-09T21:47:51.381518Z", + "language": [ + "Python", + "Shell" + ], + "lastUpdate": "2023-01-13T02:01:51.664527Z", + "license": "GPL-3.0", "name": "RFSC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "Alexandre", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "DNA", diff --git a/data/s2d/s2d.biotools.json b/data/s2d/s2d.biotools.json index cf4b0dc4e2bed..2a6b03590ee7a 100644 --- a/data/s2d/s2d.biotools.json +++ b/data/s2d/s2d.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access (with restrictions)", "additionDate": "2022-08-30T08:04:38.115656Z", "biotoolsCURIE": "biotools:s2d", "biotoolsID": "s2d", + "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ { "email": "mv245@cam.ac.uk", @@ -26,8 +29,22 @@ ], "type": "group" }, + "function": [ + { + "operation": [ + { + "term": "Prediction and recognition", + "uri": "http://edamontology.org/operation_2423" + }, + { + "term": "Structure analysis", + "uri": "http://edamontology.org/operation_2480" + } + ] + } + ], "homepage": "http://www-mvsoftware.ch.cam.ac.uk/index.php/s2D", - "lastUpdate": "2022-12-09T21:49:24.815015Z", + "lastUpdate": "2023-01-13T01:59:03.396548Z", "license": "GPL-3.0", "name": "s2D", "operatingSystem": [ diff --git a/data/salmobase2/salmobase2.biotools.json b/data/salmobase2/salmobase2.biotools.json index 906cacccd8d88..a835784354195 100644 --- a/data/salmobase2/salmobase2.biotools.json +++ b/data/salmobase2/salmobase2.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2019-10-02T11:59:28Z", "biotoolsCURIE": "biotools:salmobase2", "biotoolsID": "salmobase2", + "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ { "email": "amine.namouchi@nmbu.no", @@ -17,17 +20,30 @@ "editPermission": { "type": "private" }, + "elixirNode": [ + "Norway" + ], "homepage": "https://salmobase.org/", - "lastUpdate": "2019-10-02T12:00:47Z", + "lastUpdate": "2023-01-13T15:46:45.931725Z", "name": "salmobase2", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "aminenamouchi", "publication": [ { - "doi": "10.7490/f1000research.1117096.1", - "type": [ - "Other" - ], - "version": "2.0" + "doi": "10.7490/f1000research.1117096.1" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" } ], "validated": 1, diff --git a/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json b/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json index 7553c00588850..7d7f41db34136 100644 --- a/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json +++ b/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json @@ -1,7 +1,13 @@ { + "accessibility": "Open access", "additionDate": "2021-11-11T00:21:23.837989Z", "biotoolsCURIE": "biotools:sars-cov-2-network-analysis", "biotoolsID": "sars-cov-2-network-analysis", + "collectionID": [ + "COVID-19" + ], + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Predict human proteins that interact with SARS-CoV-2 and trace provenance of predictions", "editPermission": { "type": "private" @@ -39,9 +45,21 @@ } ], "homepage": "https://github.com/Murali-group/SARS-CoV-2-network-analysis", - "lastUpdate": "2022-12-09T21:54:45.911486Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-13T01:36:26.743528Z", + "license": "GPL-3.0", "name": "SARS-CoV-2 Protein Interaction Network Analysis", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "tmmurali", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Human biology", diff --git a/data/sbmldiagrams/sbmldiagrams.biotools.json b/data/sbmldiagrams/sbmldiagrams.biotools.json new file mode 100644 index 0000000000000..5997d23333163 --- /dev/null +++ b/data/sbmldiagrams/sbmldiagrams.biotools.json @@ -0,0 +1,125 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T03:02:21.657696Z", + "biotoolsCURIE": "biotools:sbmldiagrams", + "biotoolsID": "sbmldiagrams", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "hsauro@uw.edu", + "name": "Herbert M Sauro", + "orcidid": "https://orcid.org/0000-0002-3659-6817", + "typeEntity": "Person" + }, + { + "name": "Jessie Jiang" + }, + { + "name": "Jin Xu", + "orcidid": "https://orcid.org/0000-0001-6738-9979" + } + ], + "description": "A python package to process and visualize SBML layout and render.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://sys-bio.github.io/SBMLDiagrams/index.html" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://github.com/sys-bio/SBMLDiagrams", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-26T03:02:21.660316Z", + "license": "MIT", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://www.youtube.com/watch?v=zF3_fkDp2Xk&ab_channel=SunnyXu" + }, + { + "type": [ + "Repository" + ], + "url": "http://pypi.org/project/SBMLDiagrams/" + } + ], + "name": "SBMLDiagrams", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC730", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: The systems biology markup language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now, there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add a layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF. AVAILABILITY AND IMPLEMENTATION: SBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Jiang J." + }, + { + "name": "Sauro H.M." + }, + { + "name": "Xu J." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "SBMLDiagrams: a python package to process and visualize SBML layout and render" + }, + "pmcid": "PMC9805581", + "pmid": "36370074" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Software engineering", + "uri": "http://edamontology.org/topic_3372" + }, + { + "term": "Systems biology", + "uri": "http://edamontology.org/topic_2259" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/scab/scab.biotools.json b/data/scab/scab.biotools.json new file mode 100644 index 0000000000000..c0a8c49a47050 --- /dev/null +++ b/data/scab/scab.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:47:01.521581Z", + "biotoolsCURIE": "biotools:scab", + "biotoolsID": "scab", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "sqjin@whu.edu.cn", + "name": "Suoqin Jin", + "orcidid": "https://orcid.org/0000-0002-5131-0215", + "typeEntity": "Person" + }, + { + "email": "xfzou@whu.edu.cn", + "name": "Xiufen Zou", + "orcidid": "https://orcid.org/0000-0001-5294-0764", + "typeEntity": "Person" + }, + { + "name": "Qinran Zhang", + "orcidid": "https://orcid.org/0000-0001-8920-586X" + } + ], + "description": "scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Cell migration analysis", + "uri": "http://edamontology.org/operation_3446" + }, + { + "term": "Deisotoping", + "uri": "http://edamontology.org/operation_3629" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + } + ] + } + ], + "homepage": "https://github.com/jinworks/scAB", + "language": [ + "R" + ], + "lastUpdate": "2023-01-26T02:47:01.525682Z", + "license": "GPL-3.0", + "name": "scAB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1109", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.", + "authors": [ + { + "name": "Jin S." + }, + { + "name": "Zhang Q." + }, + { + "name": "Zou X." + } + ], + "date": "2022-11-28T00:00:00Z", + "journal": "Nucleic acids research", + "title": "scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data" + }, + "pmcid": "PMC9757078", + "pmid": "36440766" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Biomarkers", + "uri": "http://edamontology.org/topic_3360" + }, + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/scaffold_generator/scaffold_generator.biotools.json b/data/scaffold_generator/scaffold_generator.biotools.json new file mode 100644 index 0000000000000..9062cbad1127c --- /dev/null +++ b/data/scaffold_generator/scaffold_generator.biotools.json @@ -0,0 +1,124 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:41:20.826962Z", + "biotoolsCURIE": "biotools:scaffold_generator", + "biotoolsID": "scaffold_generator", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "christoph.steinbeck@uni-jena.de", + "name": "Christoph Steinbeck", + "orcidid": "https://orcid.org/0000-0001-6966-0814", + "typeEntity": "Person" + }, + { + "name": "Achim Zielesny", + "orcidid": "http://orcid.org/0000-0003-0722-4229" + }, + { + "name": "Jonas Schaub", + "orcidid": "http://orcid.org/0000-0003-1554-6666" + }, + { + "name": "Julian Zander", + "orcidid": "http://orcid.org/0000-0001-8197-076X" + } + ], + "description": "A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK).", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "S/MAR prediction", + "uri": "http://edamontology.org/operation_0444" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://github.com/Julian-Z98/ScaffoldGenerator", + "language": [ + "Java" + ], + "lastUpdate": "2023-01-26T02:41:20.829388Z", + "license": "LGPL-2.1", + "name": "Scaffold Generator", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13321-022-00656-X", + "metadata": { + "abstract": "© 2022, The Author(s).The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.", + "authors": [ + { + "name": "Schaub J." + }, + { + "name": "Steinbeck C." + }, + { + "name": "Zander J." + }, + { + "name": "Zielesny A." + } + ], + "citationCount": 1, + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Cheminformatics", + "title": "Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)" + }, + "pmcid": "PMC9650898", + "pmid": "36357931" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Anatomy", + "uri": "http://edamontology.org/topic_3067" + }, + { + "term": "Cheminformatics", + "uri": "http://edamontology.org/topic_2258" + }, + { + "term": "Chemistry", + "uri": "http://edamontology.org/topic_3314" + }, + { + "term": "DNA binding sites", + "uri": "http://edamontology.org/topic_3125" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/scawmv/scawmv.biotools.json b/data/scawmv/scawmv.biotools.json new file mode 100644 index 0000000000000..99909774ef408 --- /dev/null +++ b/data/scawmv/scawmv.biotools.json @@ -0,0 +1,107 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:35:57.444194Z", + "biotoolsCURIE": "biotools:scawmv", + "biotoolsID": "scawmv", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhixianglin@cuhk.edu.hk", + "name": "Zhixiang Lin", + "typeEntity": "Person" + }, + { + "name": "Pengcheng Zeng", + "orcidid": "https://orcid.org/0000-0002-0724-5313" + }, + { + "name": "Yuanyuan Ma", + "orcidid": "https://orcid.org/0000-0001-9103-5513" + } + ], + "description": "An adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Expression profile clustering", + "uri": "http://edamontology.org/operation_0313" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + } + ] + } + ], + "homepage": "https://github.com/pengchengzeng/scAWMV", + "language": [ + "MATLAB" + ], + "lastUpdate": "2023-01-26T02:35:57.447126Z", + "license": "Not licensed", + "name": "scAWMV", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC739", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the cellular heterogeneity. RESULTS: We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data. AVAILABILITY AND IMPLEMENTATION: The software and datasets are available at https://github.com/pengchengzeng/scAWMV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Lin Z." + }, + { + "name": "Ma Y." + }, + { + "name": "Zeng P." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data" + }, + "pmcid": "PMC9805575", + "pmid": "36383176" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/scehr/scehr.biotools.json b/data/scehr/scehr.biotools.json new file mode 100644 index 0000000000000..3e3b8c4db04a7 --- /dev/null +++ b/data/scehr/scehr.biotools.json @@ -0,0 +1,81 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:30:32.166112Z", + "biotoolsCURIE": "biotools:scehr", + "biotoolsID": "scehr", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Chengxi Zang" + }, + { + "name": "Fei Wang" + } + ], + "description": "Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + } + ] + } + ], + "homepage": "https://github.com/calvin-zcx/SCEHR", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-26T02:30:32.169216Z", + "license": "Not licensed", + "name": "SCEHR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/ICDM51629.2021.00097", + "metadata": { + "abstract": "© 2021 IEEE.Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical risk prediction problems based on longitudinal electronic health records (EHR). We propose a general supervised contrastive loss {L}Contrastive Cross Entropy+\\lambda {L}Supervised Contrastive Regularizer for learning both binary classification (e.g. in-hospital mortality prediction) and multi-label classification (e.g. phenotyping) in a unified framework. Our supervised contrastive loss practices the key idea of contrastive learning, namely, pulling similar samples closer and pushing dissimilar ones apart from each other, simultaneously by its two components: {L}Contrastive Cross Entropy tries to contrast samples with learned anchors which represent positive and negative clusters, and {L}Supervised Contrastive Regularizer tries to contrast samples with each other according to their supervised labels. We propose two versions of the above supervised contrastive loss and our experiments on real-world EHR data demonstrate that our proposed loss functions show benefits in improving the performance of strong baselines and even state-of-the-art models on benchmarking tasks for clinical risk predictions. Our loss functions work well with extremely imbalanced data which are common for clinical risk prediction problems. Our loss functions can be easily used to replace (binary or multi-label) cross-entropy loss adopted in existing clinical predictive models. The Pytorch code is released at https://github.com/calvin-zcx/SCEHR.", + "authors": [ + { + "name": "Wang F." + }, + { + "name": "Zang C." + } + ], + "date": "2021-01-01T00:00:00Z", + "journal": "Proceedings - IEEE International Conference on Data Mining, ICDM", + "title": "SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records" + }, + "pmcid": "PMC9692209", + "pmid": "36438203" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/sciga/sciga.biotools.json b/data/sciga/sciga.biotools.json index 4bc5195b11d04..167280bb29245 100644 --- a/data/sciga/sciga.biotools.json +++ b/data/sciga/sciga.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2021-03-15T01:47:37Z", "biotoolsCURIE": "biotools:sciga", "biotoolsID": "sciga", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "SCIGA is a software for 10X single cell immunoglobulin repertoires analysis. It uses raw reads or output of Cellranger as input, and performs reads quality control, immunoglobulin sequence assembly, sequence annotation, heavy- and light- chain pairing, computing statistics and visualizing.", "editPermission": { "type": "private" @@ -35,9 +38,21 @@ } ], "homepage": "https://github.com/sciensic/SCIGA", - "lastUpdate": "2022-12-09T22:01:39.982143Z", + "language": [ + "Perl" + ], + "lastUpdate": "2023-01-13T01:32:58.110399Z", + "license": "GPL-3.0", "name": "SCIGA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "yehaocheng", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Cell biology", diff --git a/data/sclc/sclc.biotools.json b/data/sclc/sclc.biotools.json new file mode 100644 index 0000000000000..55bbe3c06fa81 --- /dev/null +++ b/data/sclc/sclc.biotools.json @@ -0,0 +1,165 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:25:51.126383Z", + "biotoolsCURIE": "biotools:sclc", + "biotoolsID": "sclc", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pommier@nih.gov", + "name": "Yves Pommier", + "typeEntity": "Person" + }, + { + "name": "Camille Tlemsani" + }, + { + "name": "Jane E. Johnson" + }, + { + "name": "Lorinc S. Pongor" + } + ], + "description": "SCLC CellMiner Cross Database (SclcCellMinerCDB) allows translational researchers to conduct analyses across all major SCLC cancer cell line pharmacogenomic data sources from NCI, Sanger GDSC, and Broad CCLE/CTRP.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Gene methylation analysis", + "uri": "http://edamontology.org/operation_3207" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + }, + { + "term": "Promoter prediction", + "uri": "http://edamontology.org/operation_0440" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://discover.nci.nih.gov/SclcCellMinerCDB/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-26T02:25:51.128967Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/pongorlorinc/SCLC_methylation" + } + ], + "name": "SCLC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.ISCI.2022.105338", + "metadata": { + "abstract": "© 2022DNA methylation is a key regulator of gene expression and a clinical therapeutic predictor. We examined global DNA methylation beyond the generally used promoter areas in human small cell lung cancer (SCLC) and find that gene body methylation is a robust positive predictor of gene expression. Combining promoter and gene body methylation better predicts gene expression than promoter methylation alone including genes involved in the neuroendocrine classification of SCLC and the expression of therapeutically relevant genes including MGMT, SLFN11, and DLL3. Importantly, for super-enhancer (SE) covered genes such as NEUROD1 or MYC, using H3K27ac and NEUROD1, ASCL1, and POU2F3 ChIP-seq data, we show that genic methylation is inversely proportional to expression, thus providing a new approach to identify potential SE regulated genes involved in SCLC pathogenesis. To advance SCLC transitional research, these data are integrated into our web portal (https://discover.nci.nih.gov/SclcCellMinerCDB/) for open and easy access to basic and clinical investigators.", + "authors": [ + { + "name": "Aladjem M.I." + }, + { + "name": "Arakawa Y." + }, + { + "name": "Elloumi F." + }, + { + "name": "Gross J.M." + }, + { + "name": "Jo U." + }, + { + "name": "Johnson J.E." + }, + { + "name": "Kollipara R.K." + }, + { + "name": "Minna J.D." + }, + { + "name": "Mosavarpour S." + }, + { + "name": "Pommier Y." + }, + { + "name": "Pongor L.S." + }, + { + "name": "Reinhold W." + }, + { + "name": "Roper N." + }, + { + "name": "Teicher B.A." + }, + { + "name": "Thomas A." + }, + { + "name": "Tlemsani C." + }, + { + "name": "Varma S." + } + ], + "date": "2022-11-18T00:00:00Z", + "journal": "iScience", + "title": "Integrative epigenomic analyses of small cell lung cancer cells demonstrates the clinical translational relevance of gene body methylation" + }, + "pmcid": "PMC9619308", + "pmid": "36325065" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pharmacogenomics", + "uri": "http://edamontology.org/topic_0208" + }, + { + "term": "Protein expression", + "uri": "http://edamontology.org/topic_0108" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/scomap/scomap.biotools.json b/data/scomap/scomap.biotools.json index c148f92755ca8..3aa34b6a986e5 100644 --- a/data/scomap/scomap.biotools.json +++ b/data/scomap/scomap.biotools.json @@ -7,7 +7,21 @@ "KU Leuven", "VIB" ], + "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ + { + "name": "Carmen Bravo González-Blas", + "orcidid": "https://orcid.org/0000-0003-0973-9410" + }, + { + "name": "Sara Aibar", + "orcidid": "https://orcid.org/0000-0001-6104-7134" + }, + { + "name": "Xiao-Jiang Quan", + "orcidid": "https://orcid.org/0000-0001-7359-0564" + }, { "name": "Stein Aerts", "orcidid": "https://orcid.org/0000-0002-8006-0315", @@ -25,20 +39,29 @@ "url": "https://rawcdn.githack.com/aertslab/ScoMAP/f6cd6724682d4b6c2a8f44b2a18824a56cff2146/vignettes/Vignette.html" } ], - "download": [ - { - "type": "Source code", - "url": "https://github.com/aertslab/ScoMAP", - "version": "0.1.0" - } - ], "editPermission": { "type": "private" }, "homepage": "https://github.com/aertslab/ScoMAP", - "lastUpdate": "2022-12-09T22:08:49.039437Z", + "language": [ + "R" + ], + "lastUpdate": "2023-01-13T01:27:28.247425Z", "license": "GPL-3.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://drive.google.com/drive/folders/1wH-2VHbKaDEKANByS_jW78Gq4mi8wifb" + } + ], "name": "ScoMAP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "bits@vib.be", "publication": [ { @@ -89,7 +112,7 @@ "name": "de Waegeneer M." } ], - "citationCount": 26, + "citationCount": 27, "date": "2020-05-01T00:00:00Z", "journal": "Molecular Systems Biology", "title": "Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics" @@ -102,6 +125,9 @@ "version": "0.1.0" } ], + "toolType": [ + "Library" + ], "topic": [ { "term": "Mapping", diff --git a/data/scriptella/scriptella.biotools.json b/data/scriptella/scriptella.biotools.json new file mode 100644 index 0000000000000..e77f61a1e038d --- /dev/null +++ b/data/scriptella/scriptella.biotools.json @@ -0,0 +1,69 @@ +{ + "additionDate": "2023-01-26T09:36:09.265276Z", + "biotoolsCURIE": "biotools:scriptella", + "biotoolsID": "scriptella", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Fyodor Kupolov", + "url": "https://www.linkedin.com/in/kupolov?original_referer=https%3A%2F%2Fscriptella.org%2F" + } + ], + "description": "Scriptella is an open source ETL (Extract-Transform-Load) and script execution tool written in Java.\n\nOur primary focus is simplicity. You don't have to study yet another complex XML-based language - use SQL (or other scripting language suitable for the data source) to perform required transformations.", + "documentation": [ + { + "type": [ + "API documentation" + ], + "url": "https://scriptella.org/docs/api/index.html" + }, + { + "type": [ + "Installation instructions" + ], + "url": "https://scriptella.org/tutorial.html" + }, + { + "type": [ + "Release notes" + ], + "url": "https://scriptella.org/reference/index.html" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://scriptella.org/download.html" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://scriptella.org/index.html", + "lastUpdate": "2023-01-26T09:38:09.489924Z", + "link": [ + { + "type": [ + "Technical monitoring" + ], + "url": "https://scriptella.org/reference/index.html" + } + ], + "name": "Scriptella", + "owner": "iacs-biocomputacion", + "version": [ + "1.2" + ] +} diff --git a/data/scrnax/scrnax.biotools.json b/data/scrnax/scrnax.biotools.json index b5376cb060830..ce9b736cd548b 100644 --- a/data/scrnax/scrnax.biotools.json +++ b/data/scrnax/scrnax.biotools.json @@ -1,15 +1,30 @@ { + "accessibility": "Open access", "additionDate": "2022-03-18T17:09:46.764239Z", "biotoolsCURIE": "biotools:scrnax", "biotoolsID": "scrnax", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "ScRNAX: cross-species transfer of high quality 3’UTR annotation for single cell RNA-Seq", "editPermission": { "type": "private" }, "homepage": "https://github.com/bi-compbio/scrnax/", - "lastUpdate": "2022-12-09T22:11:56.323735Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-13T01:19:41.649532Z", + "license": "MIT", "name": "scRNAx", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "holgerklein", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Cell biology", diff --git a/data/scshapes/scshapes.biotools.json b/data/scshapes/scshapes.biotools.json index a8114a8974535..780b112d7a2e2 100644 --- a/data/scshapes/scshapes.biotools.json +++ b/data/scshapes/scshapes.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2022-10-07T06:57:09.401426Z", "biotoolsCURIE": "biotools:scshapes", "biotoolsID": "scshapes", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "A novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). Each gene is modelled independently under each treatment condition using the error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion and likelihood ratio test statistic.", "editPermission": { "type": "private" @@ -17,9 +20,21 @@ } ], "homepage": "https://github.com/Malindrie/scShapes", - "lastUpdate": "2022-12-09T22:33:59.089443Z", + "language": [ + "R" + ], + "lastUpdate": "2023-01-13T01:16:55.081986Z", + "license": "GPL-3.0", "name": "scShapes", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "Malindrie", + "toolType": [ + "Library" + ], "topic": [ { "term": "Gene expression", diff --git a/data/scthi/scthi.biotools.json b/data/scthi/scthi.biotools.json index 5e346f22a00af..07ef436cf95c4 100644 --- a/data/scthi/scthi.biotools.json +++ b/data/scthi/scthi.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2020-08-07T12:40:18Z", "biotoolsCURIE": "biotools:scthi", "biotoolsID": "scthi", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "scTHI is an R/Bioconductor package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment.", "documentation": [ { @@ -10,15 +13,41 @@ "General" ], "url": "https://bioconductor.org/packages/release/bioc/vignettes/scTHI/inst/doc/vignette.html" + }, + { + "type": [ + "User manual" + ], + "url": "https://bioconductor.org/packages/release/bioc/manuals/scTHI/man/scTHI.pdf" } ], "editPermission": { "type": "private" }, "homepage": "https://bioconductor.org/packages/release/bioc/html/scTHI.html", - "lastUpdate": "2022-12-09T22:39:13.010917Z", + "language": [ + "R" + ], + "lastUpdate": "2023-01-13T01:14:19.381642Z", + "license": "GPL-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/miccec/scTHI" + } + ], "name": "scTHI", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "michele.ceccarelli", + "toolType": [ + "Library" + ], "topic": [ { "term": "Cell biology", diff --git a/data/sdypools/sdypools.biotools.json b/data/sdypools/sdypools.biotools.json deleted file mode 100644 index 9a84b4eabd23e..0000000000000 --- a/data/sdypools/sdypools.biotools.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "additionDate": "2021-08-29T04:44:20Z", - "biotoolsCURIE": "biotools:sdypools", - "biotoolsID": "sdypools", - "description": "Sdy Pools adalah situs yang menampilkan hasil togel sdy 4D tercepat dan paling akurat. Keluaran angka 4D Sydney diambil langsung dari website resminya, jadi angkanya tidak berbeda dengan website resminya. Wla TOP SDY ini dibuat untuk mempermudah para pemain togel Sydney Indonesia dengan menampilkan Live Draw Sydney dari berbagai sumber terpercaya, cepat dan tepat waktu.", - "editPermission": { - "type": "private" - }, - "homepage": "https://rebrand.ly/daftar-papua4d", - "lastUpdate": "2022-08-04T04:50:59.687185Z", - "name": "Sdypools - Sdy Pools - Data Sdy - Keluaran Sdy - Togel Sdy", - "owner": "admin" -} diff --git a/data/secret6/secret6.biotools.json b/data/secret6/secret6.biotools.json new file mode 100644 index 0000000000000..b1c6b9409858a --- /dev/null +++ b/data/secret6/secret6.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:19:06.848679Z", + "biotoolsCURIE": "biotools:secret6", + "biotoolsID": "secret6", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "hyou@sjtu.edu.cn", + "name": "Hong-Yu Ou", + "typeEntity": "Person" + }, + { + "name": "Jiahao Guan" + }, + { + "name": "Jianfeng Zhang" + }, + { + "name": "Zhaoyan Chen", + "typeEntity": "Person" + } + ], + "description": "A comprehensive resource of bacterial Type VI Secretion Systems.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/operation_0310" + } + ] + } + ], + "homepage": "https://bioinfo-mml.sjtu.edu.cn/SecReT6/", + "lastUpdate": "2023-01-26T02:19:06.851582Z", + "name": "SecReT6", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1007/S11427-022-2172-X", + "metadata": { + "abstract": "© 2022, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.Type VI Secretion System (T6SS) plays significant roles in microbial activities via injecting effectors into adjacent cells or environments. T6SS increasingly gained attention due to its important influence on pathogenesis, microbial competition, etc. T6SS-associated research is explosively expanding on numerous grounds that call for an efficient resource. The SecReT6 version 3 provides comprehensive information on T6SS and the interactions between T6SS and T6SS-related proteins such as T6SS regulators and T6SS effectors. To assist T6SS researches like microbial competition and regulatory mechanisms, SecReT6 v3 developed online tools for detection and analysis of T6SS and T6SS-related proteins and estimation of T6SS-dependent killing risk. We have identified a novel T6SS regulator and T6SS-dependent killing capacity in Acinetobacter baumannii clinical isolates with the aid of SecReT6 v3. 17,212 T6SSs and plentiful T6SS-related proteins in 26,573 bacterial complete genomes were also detected, analyzed and incorporated into the database. The database is freely available at https://bioinfo-mml.sjtu.edu.cn/SecReT6/.", + "authors": [ + { + "name": "Chen Z." + }, + { + "name": "Deng Z." + }, + { + "name": "Djordjevic M." + }, + { + "name": "Guan J." + }, + { + "name": "Li G." + }, + { + "name": "Ou H.-Y." + }, + { + "name": "Tai C." + }, + { + "name": "Wang H." + }, + { + "name": "Wang M." + }, + { + "name": "Zhang J." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Science China Life Sciences", + "title": "SecReT6 update: a comprehensive resource of bacterial Type VI Secretion Systems" + }, + "pmid": "36346548" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Proteins", + "uri": "http://edamontology.org/topic_0078" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + } + ] +} diff --git a/data/sedb_2.0/sedb_2.0.biotools.json b/data/sedb_2.0/sedb_2.0.biotools.json new file mode 100644 index 0000000000000..14f4b5f4d477f --- /dev/null +++ b/data/sedb_2.0/sedb_2.0.biotools.json @@ -0,0 +1,147 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:13:59.893429Z", + "biotoolsCURIE": "biotools:sedb_2.0", + "biotoolsID": "sedb_2.0", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "lcqbio@163.com", + "name": "Chunquan Li", + "orcidid": "https://orcid.org/0000-0002-4700-5496", + "typeEntity": "Person" + }, + { + "name": "Chao Song" + }, + { + "name": "Qiuyu Wang" + }, + { + "name": "Yuezhu Wang" + } + ], + "description": "A comprehensive super-enhancer database of human and mouse.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Peak calling", + "uri": "http://edamontology.org/operation_3222" + } + ] + } + ], + "homepage": "http://www.licpathway.net/sedb", + "lastUpdate": "2023-01-26T02:13:59.896121Z", + "name": "SEdb 2.0", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC968", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Super-enhancers (SEs) are cell-specific DNA cis-regulatory elements that can supervise the transcriptional regulation processes of downstream genes. SEdb 2.0 (http://www.licpathway.net/sedb) aims to provide a comprehensive SE resource and annotate their potential roles in gene transcriptions. Compared with SEdb 1.0, we have made the following improvements: (i) Newly added the mouse SEs and expanded the scale of human SEs. SEdb 2.0 contained 1 167 518 SEs from 1739 human H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) samples and 550 226 SEs from 931 mouse H3K27ac ChIP-seq samples, which was five times that of SEdb 1.0. (ii) Newly added transcription factor binding sites (TFBSs) in SEs identified by TF motifs and TF ChIP-seq data. (iii) Added comprehensive (epi)genetic annotations of SEs, including chromatin accessibility regions, methylation sites, chromatin interaction regions and topologically associating domains (TADs). (iv) Newly embedded and updated search and analysis tools, including 'Search SE by TF-based', 'Differential-Overlapping-SE analysis' and 'SE-based TF-Gene analysis'. (v) Newly provided quality control (QC) metrics for ChIP-seq processing. In summary, SEdb 2.0 is a comprehensive update of SEdb 1.0, which curates more SEs and annotation information than SEdb 1.0. SEdb 2.0 provides a friendly platform for researchers to more comprehensively clarify the important role of SEs in the biological process.", + "authors": [ + { + "name": "Ai B." + }, + { + "name": "Bai X." + }, + { + "name": "Feng C." + }, + { + "name": "Li C." + }, + { + "name": "Liu X." + }, + { + "name": "Qian F." + }, + { + "name": "Song C." + }, + { + "name": "Wang F." + }, + { + "name": "Wang Q." + }, + { + "name": "Wang Y." + }, + { + "name": "Zhang G." + }, + { + "name": "Zhang J." + }, + { + "name": "Zhang Y." + }, + { + "name": "Zhao J." + }, + { + "name": "Zhao X." + }, + { + "name": "Zhou L." + }, + { + "name": "Zhu J." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "SEdb 2.0: a comprehensive super-enhancer database of human and mouse" + }, + "pmcid": "PMC9825585", + "pmid": "36318264" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Epigenetics", + "uri": "http://edamontology.org/topic_3295" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Methylated DNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3674" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/seesawpred/seesawpred.biotools.json b/data/seesawpred/seesawpred.biotools.json new file mode 100644 index 0000000000000..32c154fb81f41 --- /dev/null +++ b/data/seesawpred/seesawpred.biotools.json @@ -0,0 +1,149 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-11T11:17:03.217970Z", + "biotoolsCURIE": "biotools:seesawpred", + "biotoolsID": "seesawpred", + "collectionID": [ + "LCSB-CBG" + ], + "credit": [ + { + "email": "antonio.delsol@uni.lu", + "name": "Antonio del Sol", + "note": "Group leader, Computational Biology group, Luxembourg Centre for Systems Biomedicine \nFull professor / Chief scientist 1 in Bioinformatics at University of Luxembourg", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ], + "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa" + } + ], + "description": "A Web Application for Predicting Cell-fate Determinants in Cell Differentiation", + "documentation": [ + { + "note": "Help section on the webpage contains the documentation and T&C..", + "type": [ + "User manual" + ], + "url": "http://seesaw.lcsb.uni.lu" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://git-r3lab.uni.lu/andras.hartmann/seesaw" + } + ], + "editPermission": { + "type": "group" + }, + "elixirNode": [ + "Luxembourg" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Expression data", + "uri": "http://edamontology.org/data_2603" + }, + "format": [ + { + "term": "CSV", + "uri": "http://edamontology.org/format_3752" + } + ] + } + ], + "operation": [ + { + "term": "Gene regulatory network prediction", + "uri": "http://edamontology.org/operation_2437" + } + ], + "output": [ + { + "data": { + "term": "Gene symbol", + "uri": "http://edamontology.org/data_1026" + }, + "format": [ + { + "term": "scores format", + "uri": "http://edamontology.org/format_1999" + } + ] + } + ] + } + ], + "homepage": "http://seesaw.lcsb.uni.lu", + "language": [ + "R" + ], + "lastUpdate": "2023-01-11T11:17:03.220473Z", + "license": "AGPL-3.0", + "link": [ + { + "type": [ + "Service" + ], + "url": "http://seesaw.lcsb.uni.lu" + } + ], + "name": "SeesawPred", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "KartikeyaS", + "publication": [ + { + "doi": "10.1038/s41598-018-31688-9", + "metadata": { + "abstract": "© 2018, The Author(s).Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.", + "authors": [ + { + "name": "Hartmann A." + }, + { + "name": "Okawa S." + }, + { + "name": "Zaffaroni G." + }, + { + "name": "del Sol A." + } + ], + "citationCount": 5, + "date": "2018-12-01T00:00:00Z", + "journal": "Scientific Reports", + "title": "SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation" + }, + "pmcid": "PMC6127256", + "pmid": "30190516", + "type": [ + "Primary" + ], + "version": "0.5.1" + } + ], + "toolType": [ + "Bioinformatics portal" + ], + "topic": [ + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ], + "version": [ + "0.5.1" + ] +} diff --git a/data/segcond/segcond.biotools.json b/data/segcond/segcond.biotools.json new file mode 100644 index 0000000000000..21db8fd6c31c4 --- /dev/null +++ b/data/segcond/segcond.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:09:53.623238Z", + "biotoolsCURIE": "biotools:segcond", + "biotoolsID": "segcond", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "cnikolaou@fleming.gr", + "name": "Christoforos Nikolaou", + "orcidid": "https://orcid.org/0000-0003-3001-6786", + "typeEntity": "Person" + }, + { + "email": "thomas.graf@crg.eu", + "name": "Thomas Graf", + "typeEntity": "Person" + }, + { + "name": "Antonios Klonizakis", + "orcidid": "https://orcid.org/0000-0001-9850-6971" + } + ], + "description": "SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Peak calling", + "uri": "http://edamontology.org/operation_3222" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/AntonisK95/SEGCOND", + "language": [ + "R" + ], + "lastUpdate": "2023-01-26T02:09:53.626113Z", + "license": "Not licensed", + "name": "SEGCOND", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC742", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The compartmentalization of biochemical reactions, involved in the activation of gene expression in the eukaryotic nucleus, leads to the formation of membraneless bodies through liquid-liquid phase separation. These formations, called transcriptional condensates, appear to play important roles in gene regulation as they are assembled through the association of multiple enhancer regions in 3D genomic space. To date, we are still lacking efficient computational methodologies to identify the regions responsible for the formation of such condensates, based on genomic and conformational data. RESULTS: In this work, we present SEGCOND, a computational framework aiming to highlight genomic regions involved in the formation of transcriptional condensates. SEGCOND is flexible in combining multiple genomic datasets related to enhancer activity and chromatin accessibility, to perform a genome segmentation. It then uses this segmentation for the detection of highly transcriptionally active regions of the genome. At a final step, and through the integration of Hi-C data, it identifies regions of putative transcriptional condensates (PTCs) as genomic domains where multiple enhancer elements coalesce in 3D space. SEGCOND identifies a subset of enhancer segments with increased transcriptional activity. PTCs are also found to significantly overlap highly interconnected enhancer elements and super enhancers obtained through two independent approaches. Application of SEGCOND on data from a well-defined system of B-cell to macrophage transdifferentiation leads to the identification of previously unreported genes with a likely role in the process. AVAILABILITY AND IMPLEMENTATION: Source code and details for the implementation of SEGCOND is available at https://github.com/AntonisK95/SEGCOND. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Graf T." + }, + { + "name": "Klonizakis A." + }, + { + "name": "Nikolaou C." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data" + }, + "pmcid": "PMC9805567", + "pmid": "36394233" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "ChIP-seq", + "uri": "http://edamontology.org/topic_3169" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + } + ] +} diff --git a/data/sensdeep/sensdeep.biotools.json b/data/sensdeep/sensdeep.biotools.json new file mode 100644 index 0000000000000..def4494a0148f --- /dev/null +++ b/data/sensdeep/sensdeep.biotools.json @@ -0,0 +1,93 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-26T02:05:31.475404Z", + "biotoolsCURIE": "biotools:sensdeep", + "biotoolsID": "sensdeep", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "enginaybey@gmail.com", + "name": "Engin Aybey", + "orcidid": "http://orcid.org/0000-0002-1780-262X", + "typeEntity": "Person" + }, + { + "email": "ozgur.gumus@ege.edu.tr", + "name": "Özgür Gümüş", + "typeEntity": "Person" + } + ], + "description": "An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein interaction prediction", + "uri": "http://edamontology.org/operation_2492" + }, + { + "term": "Protein secondary structure prediction", + "uri": "http://edamontology.org/operation_0267" + }, + { + "term": "Protein-protein binding site prediction", + "uri": "http://edamontology.org/operation_2464" + } + ] + } + ], + "homepage": "https://github.com/enginaybey/SENSDeep", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-26T02:05:31.479552Z", + "license": "Not licensed", + "name": "SENSDeep", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1007/S12539-022-00543-X", + "metadata": { + "abstract": "© 2022, International Association of Scientists in the Interdisciplinary Areas.Purpose: The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein–protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field. Methods: In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method. Results: SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown. Availability and implementation: https://github.com/enginaybey/SENSDeep.", + "authors": [ + { + "name": "Aybey E." + }, + { + "name": "Gumus O." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Interdisciplinary Sciences – Computational Life Sciences", + "title": "SENSDeep: An Ensemble Deep Learning Method for Protein–Protein Interaction Sites Prediction" + }, + "pmid": "36346583" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Protein binding sites", + "uri": "http://edamontology.org/topic_3534" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + } + ] +} diff --git a/data/sesam/sesam.biotools.json b/data/sesam/sesam.biotools.json new file mode 100644 index 0000000000000..5ebc18de3ea24 --- /dev/null +++ b/data/sesam/sesam.biotools.json @@ -0,0 +1,138 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T23:25:30.835941Z", + "biotoolsCURIE": "biotools:sesam", + "biotoolsID": "sesam", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "matthieu.falque@inrae.fr", + "name": "Matthieu Falque", + "orcidid": "https://orcid.org/0000-0002-6444-858X", + "typeEntity": "Person" + }, + { + "name": "Adrien Vidal", + "orcidid": "http://orcid.org/0000-0001-5139-6445" + }, + { + "name": "Franck Gauthier", + "orcidid": "http://orcid.org/0000-0003-0574-065X" + }, + { + "name": "Olivier C. Martin", + "orcidid": "http://orcid.org/0000-0002-5295-5963" + } + ], + "description": "The SeSAM R package allows fully automatic construction of two successive genetic maps.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genetic mapping", + "uri": "http://edamontology.org/operation_0282" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Heat map generation", + "uri": "http://edamontology.org/operation_0531" + }, + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + }, + { + "term": "Physical mapping", + "uri": "http://edamontology.org/operation_2944" + } + ] + } + ], + "homepage": "https://forgemia.inra.fr/gqe-acep/sesam", + "language": [ + "C++", + "R" + ], + "lastUpdate": "2023-01-25T23:25:30.839002Z", + "license": "GPL-3.0", + "name": "SeSAM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05045-7", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Genotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs. Results: In this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++. Conclusions: SeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases.", + "authors": [ + { + "name": "Chevrolier N." + }, + { + "name": "Falque M." + }, + { + "name": "Gauthier F." + }, + { + "name": "Guiglielmoni N." + }, + { + "name": "Jasson S." + }, + { + "name": "Leroux D." + }, + { + "name": "Martin O.C." + }, + { + "name": "Rodrigez W." + }, + { + "name": "Tourrette E." + }, + { + "name": "Vidal A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "SeSAM: software for automatic construction of order-robust linkage maps" + }, + "pmcid": "PMC9675223", + "pmid": "36402957" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/seth_1/seth_1.biotools.json b/data/seth_1/seth_1.biotools.json index 03f463d5a2726..0290c272a2c27 100644 --- a/data/seth_1/seth_1.biotools.json +++ b/data/seth_1/seth_1.biotools.json @@ -3,11 +3,20 @@ "additionDate": "2022-08-31T12:21:37.655267Z", "biotoolsCURIE": "biotools:seth_1", "biotoolsID": "seth_1", + "confidence_flag": "tool", "cost": "Free of charge", "credit": [ { "email": "mheinzinger@rostlab.org", - "name": "Michael Heinzinger" + "name": "Michael Heinzinger", + "orcidid": "http://orcid.org/0000-0002-9601-3580" + }, + { + "name": "Dagmar Ilzhoefer" + }, + { + "name": "Burkhard Rost", + "orcidid": "http://orcid.org/0000-0003-0179-8424" } ], "description": "SETH is a novel method that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input.", @@ -40,7 +49,7 @@ "language": [ "Python" ], - "lastUpdate": "2022-12-09T22:41:16.630201Z", + "lastUpdate": "2023-01-13T01:07:49.788164Z", "license": "GPL-3.0", "link": [ { @@ -58,6 +67,8 @@ "publication": [ { "doi": "10.1101/2022.06.23.497276", + "pmcid": "PMC9580958", + "pmid": "36304335", "type": [ "Primary" ] diff --git a/data/seurat/seurat.biotools.json b/data/seurat/seurat.biotools.json index b789a870ab2bf..dc2ce7723a8c2 100644 --- a/data/seurat/seurat.biotools.json +++ b/data/seurat/seurat.biotools.json @@ -6,27 +6,39 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", + "credit": [ + { + "name": "Andrew Butler", + "orcidid": "https://orcid.org/0000-0003-3608-0463" + }, + { + "name": "Charlotte Darby", + "orcidid": "https://orcid.org/0000-0003-2195-5300" + }, + { + "name": "Saket Choudhary", + "orcidid": "https://orcid.org/0000-0001-5202-7633" + }, + { + "name": "Shiwei Zheng", + "orcidid": "https://orcid.org/0000-0001-6682-6743" + } + ], "description": "Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.", "documentation": [ { "type": [ - "General" + "Installation instructions" ], - "url": "https://satijalab.org/seurat/" + "url": "https://satijalab.org/seurat/articles/install.html" }, { "type": [ - "Installation instructions" + "User manual" ], - "url": "https://satijalab.org/seurat/articles/install.html" - } - ], - "download": [ - { - "type": "Source code", - "url": "https://github.com/satijalab/seurat/", - "version": "4.0" + "url": "https://cloud.r-project.org/web/packages/Seurat/Seurat.pdf" } ], "editPermission": { @@ -36,8 +48,28 @@ "language": [ "R" ], - "lastUpdate": "2022-12-09T22:42:32.070365Z", + "lastUpdate": "2023-01-13T01:01:50.285126Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://cloud.r-project.org/web/packages/Seurat/index.html" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/satijalab/seurat" + } + ], "name": "Seurat", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "tntiniak", "publication": [ { @@ -121,13 +153,18 @@ "name": "Zheng S." } ], - "citationCount": 1006, + "citationCount": 1135, "date": "2021-06-24T00:00:00Z", "journal": "Cell", "title": "Integrated analysis of multimodal single-cell data" - } + }, + "pmcid": "PMC8238499", + "pmid": "34062119" } ], + "toolType": [ + "Library" + ], "topic": [ { "term": "RNA-Seq", diff --git a/data/sgppools/sgppools.biotools.json b/data/sgppools/sgppools.biotools.json deleted file mode 100644 index 1d243b3a50e13..0000000000000 --- a/data/sgppools/sgppools.biotools.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "additionDate": "2021-08-17T15:29:00Z", - "biotoolsCURIE": "biotools:sgppools", - "biotoolsID": "sgppools", - "description": "Sgppools adalah agen judi togel online singapore 4D terbaik Indonesia dengan data keluaran prize 4d sgp pools online resmi melaui wla singapura. Link daftar bandar toto singapore pools serta agen judi togel sgp pool keluaran hari ini tercepat. Agen sgp pools menyediakan data pengeluaran singapore paling lengkap dan mudah di dapatkan. Situs toto sgp pools dengan diskon bettingan singapura 4d terbesar di Indonesia. Pengeluaran sgp hari ini tercepat, data pengeluaran sgp pools online hari ini, prediksi jitu sgp pools online, data keluaran togel sgp pools 4d, situs togel online sgp pools terpercaya.", - "editPermission": { - "type": "private" - }, - "homepage": "https://ipabindia.org/sgp-pools/", - "lastUpdate": "2021-08-17T15:43:31Z", - "link": [ - { - "note": "Agen Togel Sgp Pools Online", - "type": [ - "Other" - ], - "url": "https://yk2daily.net/togel/sgp-pools.html" - } - ], - "name": "Sgppools - Sgp Pools 4D - Data Sgp Prize - Togel Sgp Online", - "owner": "sgppools" -} diff --git a/data/shimming_toolbox/shimming_toolbox.biotools.json b/data/shimming_toolbox/shimming_toolbox.biotools.json new file mode 100644 index 0000000000000..e87b295dfafcf --- /dev/null +++ b/data/shimming_toolbox/shimming_toolbox.biotools.json @@ -0,0 +1,109 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T23:18:45.865485Z", + "biotoolsCURIE": "biotools:shimming_toolbox", + "biotoolsID": "shimming_toolbox", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jcohen@polymtl.ca", + "name": "Julien Cohen-Adad", + "orcidid": "https://orcid.org/0000-0003-3662-9532" + }, + { + "name": "Gaspard Cereza" + }, + { + "name": "Alexandre D'Astous", + "orcidid": "https://orcid.org/0000-0003-0381-7334" + }, + { + "name": "Eva Alonso-Ortiz", + "orcidid": "https://orcid.org/0000-0001-6590-7234" + } + ], + "description": "An open-source software toolbox for B0 and B1 shimming in MRI.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://shimming-toolbox.org", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T23:18:45.868336Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/shimming-toolbox/shimming-toolbox" + } + ], + "name": "Shimming Toolbox", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1002/MRM.29528", + "metadata": { + "abstract": "© 2022 International Society for Magnetic Resonance in Medicine.Purpose: Introduce Shimming Toolbox (https://shimming-toolbox.org), an open-source software package for prototyping new methods and performing static, dynamic, and real-time B0 shimming as well as B1 shimming experiments. Methods: Shimming Toolbox features various field mapping techniques, manual and automatic masking for the brain and spinal cord, B0 and B1 shimming capabilities accessible through a user-friendly graphical user interface. Validation of Shimming Toolbox was demonstrated in three scenarios: (i) B0 dynamic shimming in the brain at 7T using custom AC/DC coils, (ii) B0 real-time shimming in the spinal cord at 3T, and (iii) B1 static shimming in the spinal cord at 7T. Results: The B0 dynamic shimming of the brain at 7T took about 10 min to perform. It showed a 47% reduction in the standard deviation of the B0 field, associated with noticeable improvements in geometric distortions in EPI images. Real-time dynamic xyz-shimming in the spinal cord took about 5 min and showed a 30% reduction in the standard deviation of the signal distribution. B1 static shimming experiments in the spinal cord took about 10 min to perform and showed a 40% reduction in the coefficient of variation of the B1 field. Conclusion: Shimming Toolbox provides an open-source platform where researchers can collaborate, prototype and conveniently test B0 and B1 shimming experiments. Future versions will include additional field map preprocessing techniques, optimization algorithms, and compatibility across multiple MRI manufacturers.", + "authors": [ + { + "name": "Alonso-Ortiz E." + }, + { + "name": "Cereza G." + }, + { + "name": "Cohen-Adad J." + }, + { + "name": "D'Astous A." + }, + { + "name": "Gilbert K.M." + }, + { + "name": "Papp D." + }, + { + "name": "Stockmann J.P." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Magnetic Resonance in Medicine", + "title": "Shimming toolbox: An open-source software toolbox for B0 and B1 shimming in MRI" + }, + "pmid": "36441743" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "MRI", + "uri": "http://edamontology.org/topic_3444" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + } + ] +} diff --git a/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json b/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json deleted file mode 100644 index c208b53117f80..0000000000000 --- a/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "accessibility": "Open access (with restrictions)", - "additionDate": "2021-05-05T11:10:08Z", - "biotoolsCURIE": "biotools:sierpinski-triangle-recursion", - "biotoolsID": "sierpinski-triangle-recursion", - "collectionID": [ - "File Exchange", - "MATLAB" - ], - "cost": "Free of charge (with restrictions)", - "credit": [ - { - "name": "Trong Hoang Vo", - "typeEntity": "Person", - "typeRole": [ - "Primary contact" - ], - "url": "https://www.mathworks.com/matlabcentral/profile/7944589-trong-hoang-vo" - } - ], - "description": "This function draws Sierpinski triangle by using recursion", - "download": [ - { - "type": "Screenshot", - "url": "https://www.mathworks.com//matlabcentral/images/default_screenshot.jpg" - } - ], - "editPermission": { - "type": "private" - }, - "homepage": "https://www.mathworks.com/matlabcentral/fileexchange/56551-recursion-for-sierpinski-triangle", - "language": [ - "MATLAB" - ], - "lastUpdate": "2021-05-23T10:23:32Z", - "name": "Recursion for Sierpinski Triangle", - "operatingSystem": [ - "Linux", - "Mac", - "Windows" - ], - "owner": "zsmag19", - "toolType": [ - "Library" - ], - "version": [ - "1.0" - ] -} diff --git a/data/sighotspotter/sighotspotter.biotools.json b/data/sighotspotter/sighotspotter.biotools.json index c577bebc36cf9..38846a8111b46 100644 --- a/data/sighotspotter/sighotspotter.biotools.json +++ b/data/sighotspotter/sighotspotter.biotools.json @@ -2,6 +2,9 @@ "additionDate": "2020-01-14T09:00:54Z", "biotoolsCURIE": "biotools:SigHotSpotter", "biotoolsID": "SigHotSpotter", + "collectionID": [ + "LCSB-CBG" + ], "confidence_flag": "tool", "description": "scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.", "editPermission": { @@ -26,7 +29,7 @@ } ], "homepage": "https://SigHotSpotter.lcsb.uni.lu", - "lastUpdate": "2021-01-16T13:39:45Z", + "lastUpdate": "2023-01-13T14:22:07.793452Z", "link": [ { "type": [ @@ -53,7 +56,7 @@ "name": "del Sol A." } ], - "citationCount": 5, + "citationCount": 8, "date": "2020-01-01T00:00:00Z", "journal": "Bioinformatics", "title": "SigHotSpotter: Scrna-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies" diff --git a/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json b/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json index fe85226b78d44..65d36c7c008aa 100644 --- a/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json +++ b/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json @@ -7,6 +7,7 @@ "File Exchange", "MATLAB" ], + "confidence_flag": "tool", "cost": "Free of charge (with restrictions)", "credit": [ { @@ -32,7 +33,8 @@ "language": [ "MATLAB" ], - "lastUpdate": "2021-05-13T17:23:58Z", + "lastUpdate": "2023-01-13T00:48:40.745396Z", + "license": "Other", "name": "SimBiology model for Target-Mediated Drug Disposition (TMDD)", "operatingSystem": [ "Linux", @@ -53,11 +55,12 @@ "name": "Mager D.E." } ], - "citationCount": 392, + "citationCount": 440, "date": "2001-12-01T00:00:00Z", "journal": "Journal of Pharmacokinetics and Pharmacodynamics", "title": "General pharmacokinetic model for drugs exhibiting target-mediated drug disposition" - } + }, + "pmid": "11999290" } ], "toolType": [ diff --git a/data/simbu/simbu.biotools.json b/data/simbu/simbu.biotools.json new file mode 100644 index 0000000000000..2fb9c894bb13b --- /dev/null +++ b/data/simbu/simbu.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T09:59:40.630909Z", + "biotoolsCURIE": "biotools:simbu", + "biotoolsID": "simbu", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "markus.list@tum.de", + "name": "Markus List", + "orcidid": "http://orcid.org/0000-0002-0941-4168", + "typeEntity": "Person" + }, + { + "name": "Alexander Dietrich", + "orcidid": "http://orcid.org/0000-0002-8661-0453" + }, + { + "name": "Francesca Finotello", + "orcidid": "http://orcid.org/0000-0003-0712-4658" + }, + { + "name": "Gregor Sturm", + "orcidid": "http://orcid.org/0000-0001-9584-7842" + } + ], + "description": "The goal of SimBu is to simulate pseudo-bulk RNAseq datasets with variable cell-type fractions baed on public or private single-cell RNAseq datasets.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "http://omnideconv.org/SimBu/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Deisotoping", + "uri": "http://edamontology.org/operation_3629" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + } + ] + } + ], + "homepage": "https://github.com/omnideconv/SimBu", + "language": [ + "R" + ], + "lastUpdate": "2023-01-23T09:59:40.633629Z", + "license": "GPL-3.0", + "name": "SimBu", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac499", + "pmid": "36124800" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Cytometry", + "uri": "http://edamontology.org/topic_3934" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/simple-blast/simple-blast.biotools.json b/data/simple-blast/simple-blast.biotools.json index 9be2afc458c25..149f5c8884a41 100644 --- a/data/simple-blast/simple-blast.biotools.json +++ b/data/simple-blast/simple-blast.biotools.json @@ -7,6 +7,7 @@ "File Exchange", "MATLAB" ], + "confidence_flag": "tool", "cost": "Free of charge (with restrictions)", "credit": [ { @@ -32,7 +33,8 @@ "language": [ "MATLAB" ], - "lastUpdate": "2021-05-24T08:17:03Z", + "lastUpdate": "2023-01-13T00:44:48.077913Z", + "license": "Other", "name": "A simple BLAST algorithm", "operatingSystem": [ "Linux", diff --git a/data/simpledsfviewer1/simpledsfviewer1.biotools.json b/data/simpledsfviewer1/simpledsfviewer1.biotools.json index d6f9a24cb1323..1129b95daaaa4 100644 --- a/data/simpledsfviewer1/simpledsfviewer1.biotools.json +++ b/data/simpledsfviewer1/simpledsfviewer1.biotools.json @@ -1,15 +1,86 @@ { + "accessibility": "Open access", "additionDate": "2022-03-19T02:23:01.202501Z", "biotoolsCURIE": "biotools:simpledsfviewer1", "biotoolsID": "simpledsfviewer1", - "description": "for characterizing protein thermal stability in different temperatures\n\nhttps://github.com/hscsun/SimpleDSFviewer-5.0.git\n\n\nhttps://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.3703&file=pro3703-sup-0001-Supinfo.docx", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "David G. Fernig" + }, + { + "name": "Edwin A. Yates" + }, + { + "name": "Yong Li" + }, + { + "name": "Changye Sun", + "orcidid": "https://orcid.org/0000-0001-8602-9629" + } + ], + "description": "For characterizing protein thermal stability in different temperature", "editPermission": { "type": "private" }, "homepage": "https://doi.org/10.1002/pro.3703", - "lastUpdate": "2022-12-09T22:45:25.625518Z", + "language": [ + "MATLAB" + ], + "lastUpdate": "2023-01-13T15:52:12.945453Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.3703&file=pro3703-sup-0001-Supinfo.docx" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/hscsun/SimpleDSFviewer-5.0" + } + ], "name": "SimpleDSFviewer", + "operatingSystem": [ + "Linux", + "Mac" + ], "owner": "MD", + "publication": [ + { + "doi": "10.1002/pro.3703", + "metadata": { + "abstract": "© 2019 The Protein SocietyDifferential scanning fluorimetry (DSF) is a widely used thermal shift assay for measuring protein stability and protein–ligand interactions that are simple, cheap, and amenable to high throughput. However, data analysis remains a challenge, requiring improved methods. Here, the program SimpleDSFviewer, a user-friendly interface, is described to help the researchers who apply DSF technique in their studies. SimpleDSFviewer integrates melting curve (MC) normalization, smoothing, and melting temperature (Tm) analysis and directly previews analyzed data, providing an efficient analysis tool for DSF. SimpleDSFviewer is developed in Matlab, and it is freely available for all users to use in Matlab workspace or with Matlab Runtime. It is easy to use and an efficient tool for researchers to preview and analyze their data in a very short time.", + "authors": [ + { + "name": "Fernig D.G." + }, + { + "name": "Li Y." + }, + { + "name": "Sun C." + }, + { + "name": "Yates E.A." + } + ], + "citationCount": 10, + "date": "2020-01-01T00:00:00Z", + "journal": "Protein Science", + "title": "SimpleDSFviewer: A tool to analyze and view differential scanning fluorimetry data for characterizing protein thermal stability and interactions" + }, + "pmcid": "PMC6933846", + "pmid": "31394001" + } + ], + "toolType": [ + "Library" + ], "topic": [ { "term": "Protein folding, stability and design", diff --git a/data/siper/siper.biotools.json b/data/siper/siper.biotools.json new file mode 100644 index 0000000000000..d7af956c176c6 --- /dev/null +++ b/data/siper/siper.biotools.json @@ -0,0 +1,161 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-13T14:40:14.164348Z", + "biotoolsCURIE": "biotools:siper", + "biotoolsID": "siper", + "collectionID": [ + "LCSB-CBG" + ], + "credit": [ + { + "email": "antonio.delsol@uni.lu", + "name": "Antonio del Sol", + "note": "Group leader, Computational Biology group, Luxembourg Centre for Systems Biomedicine \t\nFull professor / Chief scientist 1 in Bioinformatics at University of Luxembourg", + "typeEntity": "Person", + "typeRole": [ + "Primary contact" + ], + "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa" + } + ], + "description": "A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion", + "documentation": [ + { + "type": [ + "Quick start guide" + ], + "url": "https://siper.uni.lu/pages/information" + } + ], + "download": [ + { + "type": "Source code", + "url": "https://git-r3lab.uni.lu/menglin.zheng/SiPer" + } + ], + "editPermission": { + "type": "private" + }, + "elixirNode": [ + "Luxembourg" + ], + "elixirPlatform": [ + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Gene expression matrix", + "uri": "http://edamontology.org/data_3112" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + }, + { + "data": { + "term": "Transcription factor name", + "uri": "http://edamontology.org/data_2755" + }, + "format": [ + { + "term": "TSV", + "uri": "http://edamontology.org/format_3475" + } + ] + } + ], + "operation": [ + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + }, + { + "term": "Prediction and recognition", + "uri": "http://edamontology.org/operation_2423" + } + ], + "output": [ + { + "data": { + "term": "Compound name", + "uri": "http://edamontology.org/data_0990" + }, + "format": [ + { + "term": "Annotated text format", + "uri": "http://edamontology.org/format_3780" + } + ] + }, + { + "data": { + "term": "Protein name", + "uri": "http://edamontology.org/data_1009" + }, + "format": [ + { + "term": "Annotated text format", + "uri": "http://edamontology.org/format_3780" + } + ] + } + ] + } + ], + "homepage": "https://siper.uni.lu/", + "language": [ + "R" + ], + "lastUpdate": "2023-01-13T14:40:14.167404Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://git-r3lab.uni.lu/menglin.zheng/SiPer" + } + ], + "name": "SiPer", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "KartikeyaS", + "publication": [ + { + "doi": "10.1016/j.stemcr.2022.10.013", + "pmid": "36400030", + "type": [ + "Primary" + ] + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Computational biology", + "uri": "http://edamontology.org/topic_3307" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/siq/siq.biotools.json b/data/siq/siq.biotools.json new file mode 100644 index 0000000000000..e056ad02efd4f --- /dev/null +++ b/data/siq/siq.biotools.json @@ -0,0 +1,89 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T00:41:15.996734Z", + "biotoolsCURIE": "biotools:siq", + "biotoolsID": "siq", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "M.Tijsterman@lumc.nl", + "name": "Marcel Tijsterman", + "orcidid": "http://orcid.org/0000-0001-8465-9002", + "typeEntity": "Person" + }, + { + "email": "R.van_Schendel@lumc.nl", + "name": "Robin van Schendel", + "orcidid": "http://orcid.org/0000-0001-7068-0679", + "typeEntity": "Person" + }, + { + "name": "Joost Schimmel", + "orcidid": "http://orcid.org/0000-0002-2620-4349" + } + ], + "description": "Easy quantitative measurement of mutation profiles in sequencing data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "PCR primer design", + "uri": "http://edamontology.org/operation_0308" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://siq.researchlumc.nl/SIQPlotteR/", + "lastUpdate": "2023-01-20T00:41:15.999475Z", + "name": "SIQ", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/nargab/lqac063", + "pmcid": "PMC9442499", + "pmid": "36071722" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Nucleic acid sites, features and motifs", + "uri": "http://edamontology.org/topic_3511" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/sitepath/sitepath.biotools.json b/data/sitepath/sitepath.biotools.json new file mode 100644 index 0000000000000..33a10e9608211 --- /dev/null +++ b/data/sitepath/sitepath.biotools.json @@ -0,0 +1,155 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T23:02:54.727743Z", + "biotoolsCURIE": "biotools:sitepath", + "biotoolsID": "sitepath", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "wap@ism.cams.cn", + "name": "Aiping Wu", + "orcidid": "https://orcid.org/0000-0002-5869-651X", + "typeEntity": "Person" + }, + { + "email": "zhy@ism.cams.cn", + "name": "Hang-Yu Zhou", + "typeEntity": "Person" + }, + { + "name": "Chengyang Ji" + }, + { + "name": "Na Han" + } + ], + "description": "A visual tool to identify polymorphism clades and help find fixed and parallel mutations.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://wuaipinglab.github.io/sitePath/" + }, + { + "type": [ + "User manual" + ], + "url": "https://bioconductor.org/packages/release/bioc/manuals/sitePath/man/sitePath.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Phylogenetic inference", + "uri": "http://edamontology.org/operation_0323" + }, + { + "term": "Phylogenetic tree editing", + "uri": "http://edamontology.org/operation_0326" + }, + { + "term": "Phylogenetic tree visualisation", + "uri": "http://edamontology.org/operation_0567" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "https://github.com/wuaipinglab/sitePath", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T23:02:54.731826Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://bioconductor.org/packages/release/bioc/html/sitePath.html" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/wuaipinglab/sitePath_assessment" + } + ], + "name": "sitePath", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05064-4", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed. Results: Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software. Conclusions: The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.", + "authors": [ + { + "name": "Cheng Y." + }, + { + "name": "Han N." + }, + { + "name": "Ji C." + }, + { + "name": "Shang J." + }, + { + "name": "Weng S." + }, + { + "name": "Wu A." + }, + { + "name": "Yang R." + }, + { + "name": "Zhou H.-Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "sitePath: a visual tool to identify polymorphism clades and help find fixed and parallel mutations" + }, + "pmcid": "PMC9701067", + "pmid": "36434502" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/skempi/skempi.biotools.json b/data/skempi/skempi.biotools.json index a9bbf4c706c99..4a506468640c2 100644 --- a/data/skempi/skempi.biotools.json +++ b/data/skempi/skempi.biotools.json @@ -3,6 +3,8 @@ "additionDate": "2021-08-27T15:39:27Z", "biotoolsCURIE": "biotools:skempi", "biotoolsID": "skempi", + "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ { "email": "b.jimenezgarcia@uu.nl", @@ -71,7 +73,8 @@ "type": "private" }, "homepage": "https://life.bsc.es/pid/skempi2", - "lastUpdate": "2022-12-09T22:47:21.402108Z", + "lastUpdate": "2023-01-11T01:35:37.359315Z", + "license": "CC-BY-4.0", "maturity": "Mature", "name": "SKEMPI", "operatingSystem": [ @@ -102,11 +105,13 @@ "name": "Moal I.H." } ], - "citationCount": 94, + "citationCount": 96, "date": "2019-02-01T00:00:00Z", "journal": "Bioinformatics", "title": "SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation" - } + }, + "pmcid": "PMC6361233", + "pmid": "30020414" } ], "toolType": [ diff --git a/data/smallareamapp/smallareamapp.biotools.json b/data/smallareamapp/smallareamapp.biotools.json new file mode 100644 index 0000000000000..842e5ccd585bd --- /dev/null +++ b/data/smallareamapp/smallareamapp.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T22:53:28.429731Z", + "biotoolsCURIE": "biotools:smallareamapp", + "biotoolsID": "smallareamapp", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jonathan.simkin@bccancer.bc.ca", + "name": "Jonathan Simkin", + "typeEntity": "Person" + }, + { + "name": "Gina Ogilvie" + }, + { + "name": "Ryan R Woods" + }, + { + "name": "Trevor J B Dummer" + } + ], + "description": "Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ] + } + ], + "homepage": "https://github.com/jdsimkin04/smallareamapp", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T22:53:28.457635Z", + "license": "MIT", + "name": "smallareamapp", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FONC.2022.833265", + "metadata": { + "abstract": "Copyright © 2022 Simkin, Dummer, Erickson, Otterstatter, Woods and Ogilvie.Introduction: There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package. Materials and methods: Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran’s I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values. Results: The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06–2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14–2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0). Discussion: We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.", + "authors": [ + { + "name": "Dummer T.J.B." + }, + { + "name": "Erickson A.C." + }, + { + "name": "Ogilvie G." + }, + { + "name": "Otterstatter M.C." + }, + { + "name": "Simkin J." + }, + { + "name": "Woods R.R." + } + ], + "date": "2022-10-19T00:00:00Z", + "journal": "Frontiers in Oncology", + "title": "Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package" + }, + "pmcid": "PMC9627310", + "pmid": "36338766" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + } + ] +} diff --git a/data/smashpp/smashpp.biotools.json b/data/smashpp/smashpp.biotools.json index 0858afa3eee5e..178d8505ee924 100644 --- a/data/smashpp/smashpp.biotools.json +++ b/data/smashpp/smashpp.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2020-04-17T15:39:16Z", "biotoolsCURIE": "biotools:smashpp", "biotoolsID": "smashpp", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Find and visualize rearrangements in DNA sequences", "editPermission": { "type": "private" @@ -31,9 +34,21 @@ } ], "homepage": "https://github.com/smortezah/smashpp", - "lastUpdate": "2022-12-09T22:50:47.912724Z", + "language": [ + "C++" + ], + "lastUpdate": "2023-01-11T01:30:41.960585Z", + "license": "GPL-3.0", "name": "Smash++", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "admin", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "DNA", diff --git a/data/snpkit/snpkit.biotools.json b/data/snpkit/snpkit.biotools.json index c1f195b107f1f..6be6302835dac 100644 --- a/data/snpkit/snpkit.biotools.json +++ b/data/snpkit/snpkit.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2022-02-22T16:09:21.482950Z", "biotoolsCURIE": "biotools:snpkit", "biotoolsID": "snpkit", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "A Modular/Scalable workflow for Microbial Variant Calling, Recombination detection and Phylogenetic tree reconstruction.", "editPermission": { "type": "private" @@ -67,9 +70,29 @@ } ], "homepage": "https://alipirani88.github.io/snpkit/", - "lastUpdate": "2022-12-09T22:56:42.079419Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-11T01:28:44.951149Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/alipirani88/snpkit" + } + ], "name": "snpkit", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "alipirani88", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Phylogenetics", diff --git a/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json b/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json new file mode 100644 index 0000000000000..169358f347f61 --- /dev/null +++ b/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T22:49:31.072923Z", + "biotoolsCURIE": "biotools:soluprotmutsupdb", + "biotoolsID": "soluprotmutsupdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "222755@mail.muni.cz", + "name": "David Bednar", + "typeEntity": "Person" + }, + { + "email": "mazurenko@mail.muni.cz", + "name": "Stanislav Mazurenko", + "typeEntity": "Person" + }, + { + "name": "Jan Velecký" + }, + { + "name": "Marie Hamsikova" + } + ], + "description": "A manually curated database of protein solubility changes upon mutations.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Protein solubility prediction", + "uri": "http://edamontology.org/operation_0409" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + } + ] + } + ], + "homepage": "http://loschmidt.chemi.muni.cz/soluprotmutdb", + "lastUpdate": "2023-01-25T22:49:31.075847Z", + "name": "SoluProtMutsupDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.CSBJ.2022.11.009", + "metadata": { + "abstract": "© 2022 The Author(s)Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB – the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.", + "authors": [ + { + "name": "Bednar D." + }, + { + "name": "Damborsky J." + }, + { + "name": "Hamsikova M." + }, + { + "name": "Mazurenko S." + }, + { + "name": "Musil M." + }, + { + "name": "Stourac J." + }, + { + "name": "Velecky J." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Computational and Structural Biotechnology Journal", + "title": "SoluProtMutDB: A manually curated database of protein solubility changes upon mutations" + }, + "pmcid": "PMC9678803", + "pmid": "36420168" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Data submission, annotation and curation", + "uri": "http://edamontology.org/topic_0219" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Proteins", + "uri": "http://edamontology.org/topic_0078" + } + ] +} diff --git a/data/sophie/sophie.biotools.json b/data/sophie/sophie.biotools.json new file mode 100644 index 0000000000000..c482e66a792cd --- /dev/null +++ b/data/sophie/sophie.biotools.json @@ -0,0 +1,127 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T10:05:34.670743Z", + "biotoolsCURIE": "biotools:sophie", + "biotoolsID": "sophie", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pskums@gsu.edu", + "name": "Pavel Skums", + "typeEntity": "Person" + }, + { + "name": "Fatemeh Mohebbi" + }, + { + "name": "Vyacheslav Tsyvina" + }, + { + "name": "Yury Khudyakov" + } + ], + "description": "Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.", + "download": [ + { + "type": "Container file", + "url": "https://zenodo.org/record/6792964" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Ancestral reconstruction", + "uri": "http://edamontology.org/operation_3745" + }, + { + "term": "Phylogenetic inference (maximum likelihood and Bayesian methods)", + "uri": "http://edamontology.org/operation_0547" + }, + { + "term": "Phylogenetic inference (parsimony methods)", + "uri": "http://edamontology.org/operation_0545" + }, + { + "term": "Phylogenetic reconstruction", + "uri": "http://edamontology.org/operation_3478" + } + ] + } + ], + "homepage": "https://github.com/compbel/SOPHIE/", + "language": [ + "MATLAB", + "Python" + ], + "lastUpdate": "2023-01-23T10:05:34.673303Z", + "license": "MIT", + "name": "SOPHIE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.cels.2022.07.005", + "metadata": { + "abstract": "© 2022 Elsevier Inc.Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.", + "authors": [ + { + "name": "Baykal P.I." + }, + { + "name": "Khudyakov Y." + }, + { + "name": "Mohebbi F." + }, + { + "name": "Nemira A." + }, + { + "name": "Ramachandran S." + }, + { + "name": "Skums P." + }, + { + "name": "Tsyvina V." + } + ], + "date": "2022-10-19T00:00:00Z", + "journal": "Cell Systems", + "title": "SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework" + }, + "pmcid": "PMC9590096", + "pmid": "36265470" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Public health and epidemiology", + "uri": "http://edamontology.org/topic_3305" + }, + { + "term": "Virology", + "uri": "http://edamontology.org/topic_0781" + } + ] +} diff --git a/data/sourcefinder/sourcefinder.biotools.json b/data/sourcefinder/sourcefinder.biotools.json new file mode 100644 index 0000000000000..3305d0df003b9 --- /dev/null +++ b/data/sourcefinder/sourcefinder.biotools.json @@ -0,0 +1,129 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T22:39:40.498567Z", + "biotoolsCURIE": "biotools:sourcefinder", + "biotoolsID": "sourcefinder", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daytan@food.dtu.dk", + "name": "Derya Aytan-Aktug", + "orcidid": "https://orcid.org/0000-0002-7086-8791", + "typeEntity": "Person" + }, + { + "name": "Vladislav Grigorjev" + }, + { + "name": "Frank M. Aarestrup", + "orcidid": "https://orcid.org/0000-0002-7116-2723" + }, + { + "name": "Ole Lund", + "orcidid": "https://orcid.org/0000-0003-1108-0491" + } + ], + "description": "A Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Antimicrobial resistance prediction", + "uri": "http://edamontology.org/operation_3482" + }, + { + "term": "Genome assembly", + "uri": "http://edamontology.org/operation_0525" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + }, + { + "term": "k-mer counting", + "uri": "http://edamontology.org/operation_3472" + } + ] + } + ], + "homepage": "https://cge.food.dtu.dk/services/SourceFinder/", + "lastUpdate": "2023-01-25T22:39:40.501155Z", + "name": "SourceFinder", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1128/SPECTRUM.02641-22", + "metadata": { + "abstract": "© 2022 American Society for Microbiology. All rights reserved.High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/ services/SourceFinder/).", + "authors": [ + { + "name": "Aarestrup F.M." + }, + { + "name": "Aytan-Aktug D." + }, + { + "name": "Clausen P.T.L.C." + }, + { + "name": "Davis J.J." + }, + { + "name": "Grigorjev V." + }, + { + "name": "Lund O." + }, + { + "name": "Munk P." + }, + { + "name": "Nguyen M." + }, + { + "name": "Szarvas J." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "Microbiology Spectrum", + "title": "SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies" + }, + "pmcid": "PMC9769690", + "pmid": "36377945" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Mobile genetic elements", + "uri": "http://edamontology.org/topic_0798" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + } + ] +} diff --git a/data/spacelid/spacelid.biotools.json b/data/spacelid/spacelid.biotools.json new file mode 100644 index 0000000000000..bbe94cf45a69e --- /dev/null +++ b/data/spacelid/spacelid.biotools.json @@ -0,0 +1,135 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T22:15:55.942421Z", + "biotoolsCURIE": "biotools:spacelid", + "biotoolsID": "spacelid", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "chenyuzong@sz.tsinghua.edu.cn", + "name": "Yu Zong Chen", + "typeEntity": "Person" + }, + { + "email": "chuxiyi@nbu.edu.cn", + "name": "Xin-Yi Chu", + "typeEntity": "Person" + }, + { + "name": "Junyong Wang" + }, + { + "name": "Yufen Zhao" + } + ], + "description": "Database of space life investigations and bioinformatics of microbiology in extreme environments.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + } + ] + } + ], + "homepage": "http://bidd.group/spacelid/", + "lastUpdate": "2023-01-25T22:15:55.945189Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://bidd.group/spacelid/browse.php" + } + ], + "name": "SpaceLID", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FMICB.2022.1017773", + "metadata": { + "abstract": "Copyright © 2022 Wang, Wang, Zeng, Wang, Yu, Wei, Cai, Chu, Chen and Zhao.Biological experiments performed in space crafts like space stations, space shuttles, and recoverable satellites has enabled extensive spaceflight life investigations (SLIs). In particular, SLIs have revealed distinguished space effects on microbial growth, survival, metabolite production, biofilm formation, virulence development and drug resistant mutations. These provide unique perspectives to ground-based microbiology and new opportunities for industrial pharmaceutical and metabolite productions. SLIs are with specialized experimental setups, analysis methods and research outcomes, which can be accessed by established databases National Aeronautics and Space Administration (NASA) Life Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing research across diverse fields may be better facilitated by databases of convenient search facilities and categorized presentation of comprehensive contents. We therefore developed the Space Life Investigation Database (SpaceLID) http://bidd.group/spacelid/, which collected SLIs from published academic papers. Currently, this database provides detailed menu search facilities and categorized contents about the studied phenomena, materials, experimental procedures, analysis methods, and research outcomes of 448 SLIs of 90 species (microbial, plant, animal, human), 81 foods and 106 pharmaceuticals, including 232 SLIs not covered by the established databases. The potential applications of SpaceLID are illustrated by the examples of published experimental design and bioinformatic analysis of spaceflight microbial phenomena.", + "authors": [ + { + "name": "Cai M." + }, + { + "name": "Chen Y.Z." + }, + { + "name": "Chu X.-Y." + }, + { + "name": "Wang J." + }, + { + "name": "Wang S." + }, + { + "name": "Wang T." + }, + { + "name": "Wei Y." + }, + { + "name": "Yu Z." + }, + { + "name": "Zeng X." + }, + { + "name": "Zhao Y." + } + ], + "date": "2022-11-03T00:00:00Z", + "journal": "Frontiers in Microbiology", + "title": "Database of space life investigations and bioinformatics of microbiology in extreme environments" + }, + "pmcid": "PMC9668873", + "pmid": "36406421" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Microbiology", + "uri": "http://edamontology.org/topic_3301" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/spark/spark.biotools.json b/data/spark/spark.biotools.json index ced60f3ea3dbd..b0d4821154573 100644 --- a/data/spark/spark.biotools.json +++ b/data/spark/spark.biotools.json @@ -2,6 +2,9 @@ "additionDate": "2020-01-09T18:32:41Z", "biotoolsCURIE": "biotools:Spark", "biotoolsID": "Spark", + "collectionID": [ + "IMPaCT-Data" + ], "confidence_flag": "tool", "description": "Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. Melanoma expression analysis with Big Data technologies.", "editPermission": { @@ -26,7 +29,7 @@ } ], "homepage": "http://spark.apache.org/", - "lastUpdate": "2021-01-16T14:09:48Z", + "lastUpdate": "2023-01-26T13:37:07.250455Z", "name": "Spark", "owner": "Pub2Tools", "publication": [ diff --git a/data/sparkec/sparkec.biotools.json b/data/sparkec/sparkec.biotools.json new file mode 100644 index 0000000000000..c38fc82fce6e2 --- /dev/null +++ b/data/sparkec/sparkec.biotools.json @@ -0,0 +1,98 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T22:08:40.634085Z", + "biotoolsCURIE": "biotools:sparkec", + "biotoolsID": "sparkec", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "roberto.rey.exposito@udc.es", + "name": "Roberto R. Expósito", + "orcidid": "https://orcid.org/0000-0002-2077-1473", + "typeEntity": "Person" + }, + { + "name": "Juan Touriño" + }, + { + "name": "Marco Martínez-Sánchez" + } + ], + "description": "Speeding up alignment-based DNA error correction tools.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Phasing", + "uri": "http://edamontology.org/operation_3454" + }, + { + "term": "k-mer counting", + "uri": "http://edamontology.org/operation_3472" + } + ] + } + ], + "homepage": "https://github.com/UDC-GAC/SparkEC", + "language": [ + "Java" + ], + "lastUpdate": "2023-01-25T22:08:40.637878Z", + "license": "GPL-3.0", + "name": "SparkEC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05013-1", + "metadata": { + "abstract": "© 2022, The Author(s).Background: In recent years, huge improvements have been made in the context of sequencing genomic data under what is called Next Generation Sequencing (NGS). However, the DNA reads generated by current NGS platforms are not free of errors, which can affect the quality of downstream analysis. Although error correction can be performed as a preprocessing step to overcome this issue, it usually requires long computational times to analyze those large datasets generated nowadays through NGS. Therefore, new software capable of scaling out on a cluster of nodes with high performance is of great importance. Results: In this paper, we present SparkEC, a parallel tool capable of fixing those errors produced during the sequencing process. For this purpose, the algorithms proposed by the CloudEC tool, which is already proved to perform accurate corrections, have been analyzed and optimized to improve their performance by relying on the Apache Spark framework together with the introduction of other enhancements such as the usage of memory-efficient data structures and the avoidance of any input preprocessing. The experimental results have shown significant improvements in the computational times of SparkEC when compared to CloudEC for all the representative datasets and scenarios under evaluation, providing an average and maximum speedups of 4.9× and 11.9× , respectively, over its counterpart. Conclusion: As error correction can take excessive computational time, SparkEC provides a scalable solution for correcting large datasets. Due to its distributed implementation, SparkEC speed can increase with respect to the number of nodes in a cluster. Furthermore, the software is freely available under GPLv3 license and is compatible with different operating systems (Linux, Windows and macOS).", + "authors": [ + { + "name": "Exposito R.R." + }, + { + "name": "Martinez-Sanchez M." + }, + { + "name": "Tourino J." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "SparkEC: speeding up alignment-based DNA error correction tools" + }, + "pmcid": "PMC9639292", + "pmid": "36344928" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Software engineering", + "uri": "http://edamontology.org/topic_3372" + } + ] +} diff --git a/data/splnmtf/splnmtf.biotools.json b/data/splnmtf/splnmtf.biotools.json new file mode 100644 index 0000000000000..bf1c17814aa2c --- /dev/null +++ b/data/splnmtf/splnmtf.biotools.json @@ -0,0 +1,104 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:58:32.268997Z", + "biotoolsCURIE": "biotools:splnmtf", + "biotoolsID": "splnmtf", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Qi Dang" + }, + { + "name": "Xiaoying Liu" + }, + { + "name": "Shengli Xie", + "orcidid": "https://orcid.org/0000-0003-2041-5214" + }, + { + "name": "Yong Liang", + "orcidid": "https://orcid.org/0000-0002-4791-4307" + } + ], + "description": "Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + } + ] + } + ], + "homepage": "https://github.com/qi0906/SPLNMTF", + "language": [ + "MATLAB", + "Python" + ], + "lastUpdate": "2023-01-25T21:58:32.272531Z", + "license": "Not licensed", + "name": "SPLNMTF", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/TCBB.2022.3225300", + "metadata": { + "abstract": "IEEEDrug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.", + "authors": [ + { + "name": "Dang Q." + }, + { + "name": "Liang Y." + }, + { + "name": "Ling C." + }, + { + "name": "Liu X." + }, + { + "name": "Miao R." + }, + { + "name": "Ouyang D." + }, + { + "name": "Xie S." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics", + "title": "Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization" + }, + "pmid": "36445996" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/spot-disorder-single/spot-disorder-single.biotools.json b/data/spot-disorder-single/spot-disorder-single.biotools.json index 46c3ff1cda1e1..4e4142aaaf314 100644 --- a/data/spot-disorder-single/spot-disorder-single.biotools.json +++ b/data/spot-disorder-single/spot-disorder-single.biotools.json @@ -3,6 +3,7 @@ "additionDate": "2022-08-31T09:23:45.288170Z", "biotoolsCURIE": "biotools:spot-disorder-single", "biotoolsID": "spot-disorder-single", + "confidence_flag": "tool", "cost": "Free of charge", "credit": [ { @@ -12,6 +13,10 @@ { "email": "yaoqi.zhou@griffith.edu.au", "name": "Yaoqi Zhou" + }, + { + "name": "Jack Hanson", + "orcidid": "http://orcid.org/0000-0001-6956-6748" } ], "description": "SPOT-Disorder-Single is a single-sequence method that is more accurate than SPOT-Disorder (a profile-based method) for proteins with few homologous sequences and comparable for proteins in predicting long-disordered regions.", @@ -30,6 +35,20 @@ }, "function": [ { + "input": [ + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], "operation": [ { "term": "Homology-based gene prediction", @@ -39,10 +58,11 @@ } ], "homepage": "http://sparks-lab.org/server/spot-disorder-single/", - "lastUpdate": "2022-12-09T23:00:58.265155Z", + "lastUpdate": "2023-01-13T15:56:18.663132Z", "name": "SPOT-Disorder-Single", "operatingSystem": [ - "Linux" + "Linux", + "Windows" ], "owner": "daniela.mereuta", "publication": [ @@ -61,7 +81,7 @@ "name": "Zhou Y." } ], - "citationCount": 38, + "citationCount": 39, "date": "2018-11-26T00:00:00Z", "journal": "Journal of Chemical Information and Modeling", "title": "Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures" diff --git a/data/spot-disorder/spot-disorder.biotools.json b/data/spot-disorder/spot-disorder.biotools.json index f274230b6c327..c85b1b4b9b4cd 100644 --- a/data/spot-disorder/spot-disorder.biotools.json +++ b/data/spot-disorder/spot-disorder.biotools.json @@ -3,6 +3,7 @@ "additionDate": "2022-08-31T09:39:10.246272Z", "biotoolsCURIE": "biotools:spot-disorder", "biotoolsID": "spot-disorder", + "confidence_flag": "tool", "cost": "Free of charge", "credit": [ { @@ -40,6 +41,18 @@ "term": "Protein features", "uri": "http://edamontology.org/data_1277" } + }, + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] } ], "operation": [ @@ -51,10 +64,11 @@ } ], "homepage": "http://sparks-lab.org/server/spot-disorder/", - "lastUpdate": "2022-12-09T22:59:10.010460Z", + "lastUpdate": "2023-01-13T15:55:40.416695Z", "name": "SPOT-Disorder", "operatingSystem": [ - "Linux" + "Linux", + "Windows" ], "owner": "daniela.mereuta", "publication": [ @@ -76,7 +90,7 @@ "name": "Zhou Y." } ], - "citationCount": 179, + "citationCount": 181, "date": "2017-01-01T00:00:00Z", "journal": "Bioinformatics", "title": "Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks" diff --git a/data/spread_4/spread_4.biotools.json b/data/spread_4/spread_4.biotools.json new file mode 100644 index 0000000000000..7a823c418612b --- /dev/null +++ b/data/spread_4/spread_4.biotools.json @@ -0,0 +1,89 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:50:17.781436Z", + "biotoolsCURIE": "biotools:spread_4", + "biotoolsID": "spread_4", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "philippe.lemey@kuleuven.be", + "name": "Philippe Lemey", + "orcidid": "https://orcid.org/0000-0003-2826-5353", + "typeEntity": "Person", + "url": "https://orcid.org/0000-0003-2826-5353" + }, + { + "name": "Filip Bielejec" + }, + { + "name": "Guy Baele" + }, + { + "name": "Kanika D Nahata" + } + ], + "description": "Online visualisation of pathogen phylogeographic reconstructions.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Ancestral reconstruction", + "uri": "http://edamontology.org/operation_3745" + }, + { + "term": "Phylogenetic reconstruction", + "uri": "http://edamontology.org/operation_3478" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://spreadviz.org", + "lastUpdate": "2023-01-25T21:50:17.784360Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://beast.community/spread4" + } + ], + "name": "SPREAD 4", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/VE/VEAC088", + "pmcid": "PMC9615431", + "pmid": "36325034" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +} diff --git a/data/sspa_py/sspa_py.biotools.json b/data/sspa_py/sspa_py.biotools.json new file mode 100644 index 0000000000000..2513b3252dd81 --- /dev/null +++ b/data/sspa_py/sspa_py.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T00:54:45.074253Z", + "biotoolsCURIE": "biotools:sspa_py", + "biotoolsID": "sspa_py", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Cecilia Wieder", + "orcidid": "http://orcid.org/0000-0003-1548-4346" + }, + { + "name": "Rachel PJ Lai", + "orcidid": "http://orcid.org/0000-0003-3418-850X" + }, + { + "name": "Timothy Ebbels", + "orcidid": "http://orcid.org/0000-0002-3372-8423" + } + ], + "description": "sspa provides a Python interface for metabolomics pathway analysis. In addition to conventional methods over-representation analysis (ORA) and gene/metabolite set enrichment analysis (GSEA), it also provides a wide range of single-sample pathway analysis (ssPA) methods.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://cwieder.github.io/py-ssPA/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network visualisation", + "uri": "http://edamontology.org/operation_3925" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + } + ] + } + ], + "homepage": "https://pypi.org/project/sspa/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-20T00:54:45.076784Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://zenodo.org/record/7515344" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/cwieder/py-ssPA" + } + ], + "name": "ssPA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/s12859-022-05005-1", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures. Results: While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/), providing implementations of all the methods benchmarked in this study. Conclusion: This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.", + "authors": [ + { + "name": "Ebbels T.M.D." + }, + { + "name": "Lai R.P.J." + }, + { + "name": "Wieder C." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Single sample pathway analysis in metabolomics: performance evaluation and application" + }, + "pmcid": "PMC9664704", + "pmid": "36376837" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/starmap/starmap.biotools.json b/data/starmap/starmap.biotools.json new file mode 100644 index 0000000000000..9415913ef62b1 --- /dev/null +++ b/data/starmap/starmap.biotools.json @@ -0,0 +1,122 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:45:13.727287Z", + "biotoolsCURIE": "biotools:starmap", + "biotoolsID": "starmap", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Frank DiMaio" + }, + { + "name": "Vadim Kotov" + }, + { + "name": "Thomas C. Marlovits", + "orcidid": "http://orcid.org/0000-0001-8106-4038" + }, + { + "name": "Wolfgang Lugmayr", + "orcidid": "http://orcid.org/0000-0001-8501-8637" + } + ], + "description": "A user-friendly workflow for Rosetta-driven molecular structure refinement.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + }, + { + "term": "Molecular model refinement", + "uri": "http://edamontology.org/operation_0322" + }, + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + } + ] + } + ], + "homepage": "https://github.com/wlugmayr/chimerax-starmap", + "language": [ + "Perl", + "Python" + ], + "lastUpdate": "2023-01-25T21:45:13.729805Z", + "license": "BSD-2-Clause", + "name": "StarMap", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/S41596-022-00757-9", + "metadata": { + "abstract": "© 2022, Springer Nature Limited.Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.", + "authors": [ + { + "name": "DiMaio F." + }, + { + "name": "Goessweiner-Mohr N." + }, + { + "name": "Kotov V." + }, + { + "name": "Lugmayr W." + }, + { + "name": "Marlovits T.C." + }, + { + "name": "Wald J." + } + ], + "citationCount": 1, + "date": "2023-01-01T00:00:00Z", + "journal": "Nature Protocols", + "title": "StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement" + }, + "pmid": "36323866" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Electron microscopy", + "uri": "http://edamontology.org/topic_0611" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "NMR", + "uri": "http://edamontology.org/topic_0593" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + }, + { + "term": "X-ray diffraction", + "uri": "http://edamontology.org/topic_2828" + } + ] +} diff --git a/data/start-asap/start-asap.biotools.json b/data/start-asap/start-asap.biotools.json index c40672fb1d335..b7b763bb9a0c1 100644 --- a/data/start-asap/start-asap.biotools.json +++ b/data/start-asap/start-asap.biotools.json @@ -1,14 +1,29 @@ { + "accessibility": "Open access", "additionDate": "2020-05-01T10:10:02Z", "biotoolsCURIE": "biotools:start-asap", "biotoolsID": "start-asap", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Prepare the input directory for 'ASA3P', creating automatically a _config.xls_ file from the reads provided.\nRequires one or more reference files (.gbk recommended) and a directory with FASTQ files (.fq or .fastq, gzipped).\nMetadata can be supplied via command line or with a JSON file.", "editPermission": { "type": "private" }, "homepage": "http://github.com/quadram-institute-bioscience/start-asap/", - "lastUpdate": "2020-05-01T10:10:11Z", + "language": [ + "Perl" + ], + "lastUpdate": "2023-01-11T01:18:24.461157Z", + "license": "MIT", "name": "start-asap", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "telatin", + "toolType": [ + "Command-line tool" + ], "validated": 1 } diff --git a/data/stata/stata.biotools.json b/data/stata/stata.biotools.json new file mode 100644 index 0000000000000..c502a68175d23 --- /dev/null +++ b/data/stata/stata.biotools.json @@ -0,0 +1,60 @@ +{ + "additionDate": "2023-01-27T07:25:27.037484Z", + "biotoolsCURIE": "biotools:stata", + "biotoolsID": "stata", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "email": "service@stata.com", + "name": "StataCorp LLC", + "url": "https://www.stata.com/" + } + ], + "description": "Fast. Accurate. Easy to use. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and automated reporting", + "documentation": [ + { + "type": [ + "Installation instructions" + ], + "url": "https://www.stata.com/install-guide/" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://www.stata.com/order/dl/" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://www.stata.com/", + "lastUpdate": "2023-01-27T11:54:21.845757Z", + "link": [ + { + "note": "Technical Services\n\nhttps://www.stata.com/support/tech-support/contact/", + "type": [ + "Other", + "Other" + ], + "url": "https://www.stata.com/" + } + ], + "name": "Stata", + "owner": "iacs-biocomputacion", + "version": [ + "17" + ] +} diff --git a/data/steps_4.0/steps_4.0.biotools.json b/data/steps_4.0/steps_4.0.biotools.json new file mode 100644 index 0000000000000..d0ffb2f49fe2d --- /dev/null +++ b/data/steps_4.0/steps_4.0.biotools.json @@ -0,0 +1,132 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:39:25.786413Z", + "biotoolsCURIE": "biotools:steps_4.0", + "biotoolsID": "steps_4.0", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "erik@oist.jp", + "name": "Erik De Schutter", + "typeEntity": "Person" + }, + { + "name": "Felix Schürmann" + }, + { + "name": "Tristan Carel" + }, + { + "name": "Weiliang Chen" + } + ], + "description": "Fast and memory-efficient molecular simulations of neurons at the nanoscale.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Molecular dynamics", + "uri": "http://edamontology.org/operation_2476" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + } + ] + } + ], + "homepage": "http://steps.sourceforge.net/", + "lastUpdate": "2023-01-25T21:39:25.789374Z", + "name": "STEPS 4.0", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FNINF.2022.883742", + "metadata": { + "abstract": "Copyright © 2022 Chen, Carel, Awile, Cantarutti, Castiglioni, Cattabiani, Del Marmol, Hepburn, King, Kotsalos, Kumbhar, Lallouette, Melchior, Schürmann and De Schutter.Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.", + "authors": [ + { + "name": "Awile O." + }, + { + "name": "Cantarutti N." + }, + { + "name": "Carel T." + }, + { + "name": "Castiglioni G." + }, + { + "name": "Cattabiani A." + }, + { + "name": "Chen W." + }, + { + "name": "De Schutter E." + }, + { + "name": "Del Marmol B." + }, + { + "name": "Hepburn I." + }, + { + "name": "King J.G." + }, + { + "name": "Kotsalos C." + }, + { + "name": "Kumbhar P." + }, + { + "name": "Lallouette J." + }, + { + "name": "Melchior S." + }, + { + "name": "Schurmann F." + } + ], + "date": "2022-10-26T00:00:00Z", + "journal": "Frontiers in Neuroinformatics", + "title": "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale" + }, + "pmcid": "PMC9645802", + "pmid": "36387588" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Computer science", + "uri": "http://edamontology.org/topic_3316" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Neurobiology", + "uri": "http://edamontology.org/topic_3304" + } + ] +} diff --git a/data/sub-spia/sub-spia.biotools.json b/data/sub-spia/sub-spia.biotools.json index 9721aa3306a00..e24d5f0e09445 100644 --- a/data/sub-spia/sub-spia.biotools.json +++ b/data/sub-spia/sub-spia.biotools.json @@ -6,7 +6,22 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", + "credit": [ + { + "name": "Liangzhong Shen" + }, + { + "name": "Wenbin Liu" + }, + { + "name": "Xianbin Li" + }, + { + "name": "Xuequn Shang" + } + ], "description": "Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.”", "download": [ { @@ -39,7 +54,7 @@ "language": [ "R" ], - "lastUpdate": "2022-12-09T23:03:51.402102Z", + "lastUpdate": "2023-01-11T01:16:06.402897Z", "license": "MIT", "name": "sub-SPIA", "operatingSystem": [ diff --git a/data/sulfatlas/sulfatlas.biotools.json b/data/sulfatlas/sulfatlas.biotools.json index 516ab7e1b326a..c26f32555b9fd 100644 --- a/data/sulfatlas/sulfatlas.biotools.json +++ b/data/sulfatlas/sulfatlas.biotools.json @@ -1,26 +1,126 @@ { + "accessibility": "Open access", "additionDate": "2019-11-05T14:13:27Z", "biotoolsCURIE": "biotools:SulfAtlas", "biotoolsID": "SulfAtlas", - "collectionID": [ - "elixir-fr-sdp-2019" + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "gurvan@sb-roscoff.fr", + "name": "Gurvan Michel", + "orcidid": "https://orcid.org/0000-0002-3009-6205", + "typeEntity": "Person" + }, + { + "email": "tristan.barbeyron@sb-roscoff.fr", + "name": "Tristan Barbeyron", + "typeEntity": "Person" + }, + { + "name": "Mark Stam" + }, + { + "name": "Pernelle Lelièvre" + } ], - "description": "SulfAtlas describes the family and sub-families of structurally-related sulfatases. Sub-families are created based on phylogenetic analyses and essentially correspond to different substrate specificities.", + "description": "SulfAtlas describes the family and sub-families of structurally-related sulfatases. Sub-families are created based on phylogenetic analyses and essentially correspond to different substrate specificities", "editPermission": { "type": "private" }, - "homepage": "http://abims.sb-roscoff.fr/sulfatlas", - "lastUpdate": "2020-01-24T09:54:04Z", + "function": [ + { + "input": [ + { + "data": { + "term": "Protein sequence", + "uri": "http://edamontology.org/data_2976" + }, + "format": [ + { + "term": "FASTA", + "uri": "http://edamontology.org/format_1929" + } + ] + } + ], + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + } + ] + } + ], + "homepage": "https://sulfatlas.sb-roscoff.fr/", + "lastUpdate": "2023-01-25T21:30:47.860975Z", "name": "SulfAtlas", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "jison", + "publication": [ + { + "doi": "10.1093/NAR/GKAC977", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.SulfAtlas (https://sulfatlas.sb-roscoff.fr/) is a knowledge-based resource dedicated to a sequence-based classification of sulfatases. Currently four sulfatase families exist (S1-S4) and the largest family (S1, formylglycine-dependent sulfatases) is divided into subfamilies by a phylogenetic approach, each subfamily corresponding to either a single characterized specificity (or few specificities in some cases) or to unknown substrates. Sequences are linked to their biochemical and structural information according to an expert scrutiny of the available literature. Database browsing was initially made possible both through a keyword search engine and a specific sequence similarity (BLAST) server. In this article, we will briefly summarize the experimental progresses in the sulfatase field in the last 6 years. To improve and speed up the (sub)family assignment of sulfatases in (meta)genomic data, we have developed a new, freely-accessible search engine using Hidden Markov model (HMM) for each (sub)family. This new tool (SulfAtlas HMM) is also a key part of the internal pipeline used to regularly update the database. SulfAtlas resource has indeed significantly grown since its creation in 2016, from 4550 sequences to 162 430 sequences in August 2022.", + "authors": [ + { + "name": "Barbeyron T." + }, + { + "name": "Corre E." + }, + { + "name": "Hoebeke M." + }, + { + "name": "Lelievre P." + }, + { + "name": "Michel G." + }, + { + "name": "Stam M." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "SulfAtlas, the sulfatase database: state of the art and new developments" + }, + "pmcid": "PMC9825549", + "pmid": "36318251" + } + ], "toolType": [ "Database portal" ], "topic": [ { - "term": "Gene and protein families", - "uri": "http://edamontology.org/topic_0623" + "term": "Literature and language", + "uri": "http://edamontology.org/topic_3068" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Sequence sites, features and motifs", + "uri": "http://edamontology.org/topic_0160" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" } - ], - "validated": 1 + ] } diff --git a/data/supernatural_3.0/supernatural_3.0.biotools.json b/data/supernatural_3.0/supernatural_3.0.biotools.json new file mode 100644 index 0000000000000..f37ad7c84325f --- /dev/null +++ b/data/supernatural_3.0/supernatural_3.0.biotools.json @@ -0,0 +1,88 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:20:01.502420Z", + "biotoolsCURIE": "biotools:supernatural_3.0", + "biotoolsID": "supernatural_3.0", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "priyanka.banerjee@charite.de", + "name": "Priyanka Banerjee", + "orcidid": "https://orcid.org/0000-0001-8072-5594", + "typeEntity": "Person" + }, + { + "name": "Emanuel Kemmler" + }, + { + "name": "Kathleen Gallo" + }, + { + "name": "Robert Preissner" + } + ], + "description": "Database of natural products and natural product-based derivatives.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Natural product identification", + "uri": "http://edamontology.org/operation_3803" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "http://bioinf-applied.charite.de/supernatural_3", + "lastUpdate": "2023-01-25T21:20:01.505400Z", + "name": "SuperNatural 3.0", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1008", + "pmcid": "PMC9825600", + "pmid": "36399452" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Biochemistry", + "uri": "http://edamontology.org/topic_3292" + }, + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Infectious disease", + "uri": "http://edamontology.org/topic_3324" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/svafotate/svafotate.biotools.json b/data/svafotate/svafotate.biotools.json new file mode 100644 index 0000000000000..9304dbce8bc14 --- /dev/null +++ b/data/svafotate/svafotate.biotools.json @@ -0,0 +1,110 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:13:56.708086Z", + "biotoolsCURIE": "biotools:svafotate", + "biotoolsID": "svafotate", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "thomas.nicholas@utah.edu", + "name": "Thomas J. Nicholas", + "orcidid": "https://orcid.org/0000-0002-4198-6414", + "typeEntity": "Person" + }, + { + "name": "Aaron R. Quinlan" + }, + { + "name": "Michael J. Cormier" + } + ], + "description": "Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Parsing", + "uri": "http://edamontology.org/operation_1812" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Variant prioritisation", + "uri": "http://edamontology.org/operation_3226" + } + ] + } + ], + "homepage": "https://github.com/fakedrtom/SVAFotate", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T21:13:56.711465Z", + "license": "MIT", + "name": "SVAFotate", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05008-Y", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. Results: We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. Conclusions: Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease.", + "authors": [ + { + "name": "Cormier M.J." + }, + { + "name": "Nicholas T.J." + }, + { + "name": "Quinlan A.R." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate" + }, + "pmcid": "PMC9670370", + "pmid": "36384437" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Rare diseases", + "uri": "http://edamontology.org/topic_3325" + }, + { + "term": "Structural variation", + "uri": "http://edamontology.org/topic_3175" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/syblars/syblars.biotools.json b/data/syblars/syblars.biotools.json new file mode 100644 index 0000000000000..b7343cec2a69c --- /dev/null +++ b/data/syblars/syblars.biotools.json @@ -0,0 +1,118 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:09:35.972808Z", + "biotoolsCURIE": "biotools:syblars", + "biotoolsID": "syblars", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ivisatbilkent@gmail.com", + "name": "Ugur Dogrusoz", + "orcidid": "https://orcid.org/0000-0002-7153-0784", + "typeEntity": "Person" + }, + { + "name": "Perman Atayev" + }, + { + "name": "Yusuf Ziya Ozgul" + }, + { + "name": "Hasan Balci", + "orcidid": "https://orcid.org/0000-0001-8319-7758" + } + ], + "description": "A web service for laying out, rendering and mining biological maps in SBGN, SBML and more.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + }, + { + "term": "Pathway analysis", + "uri": "http://edamontology.org/operation_3928" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://syblars.cs.bilkent.edu.tr", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-25T21:09:35.976404Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/iVis-at-Bilkent/syblars" + } + ], + "name": "SyBLaRS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PCBI.1010635", + "metadata": { + "abstract": "© 2022 Balci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support. To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of wellknown graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest. We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools. SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.", + "authors": [ + { + "name": "Atayev P." + }, + { + "name": "Balci H." + }, + { + "name": "Dogrusoz U." + }, + { + "name": "Ozgul Y.Z." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS Computational Biology", + "title": "SyBLaRS: A web service for laying out, rendering and mining biological maps in SBGN, SBML and more" + }, + "pmcid": "PMC9704760", + "pmid": "36374853" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Molecular biology", + "uri": "http://edamontology.org/topic_3047" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Systems biology", + "uri": "http://edamontology.org/topic_2259" + } + ] +} diff --git a/data/syllable-pbwt/syllable-pbwt.biotools.json b/data/syllable-pbwt/syllable-pbwt.biotools.json new file mode 100644 index 0000000000000..53745b7258114 --- /dev/null +++ b/data/syllable-pbwt/syllable-pbwt.biotools.json @@ -0,0 +1,113 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T21:02:26.003314Z", + "biotoolsCURIE": "biotools:syllable-pbwt", + "biotoolsID": "syllable-pbwt", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "degui.zhi@uth.tmc.edu", + "name": "Degui Zhi", + "orcidid": "https://orcid.org/0000-0001-7754-1890", + "typeEntity": "Person" + }, + { + "email": "shzhang@cs.ucf.edu", + "name": "Shaojie Zhang", + "orcidid": "https://orcid.org/0000-0002-4051-5549", + "typeEntity": "Person" + }, + { + "name": "Ardalan Naseri" + }, + { + "name": "Victor Wang" + } + ], + "description": "Syllable-PBWT for space-efficient haplotype long-match query.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome indexing", + "uri": "http://edamontology.org/operation_3211" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://github.com/ZhiGroup/Syllable-PBWT", + "language": [ + "C++" + ], + "lastUpdate": "2023-01-25T21:02:26.006435Z", + "license": "Not licensed", + "name": "Syllable-PBWT", + "operatingSystem": [ + "Linux" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC734", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The positional Burrows-Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on the compressed syllabic panel, and leverages the polynomial rolling hash function for positional substring comparison. With the Syllable-PBWT data structures, we then present a long match query algorithm named Syllable-Query. RESULTS: Compared to the most time- and space-efficient previously published solution to the long match query problem, Syllable-Query reduced the memory use by a factor of over 100 on both the UK Biobank genotype data and the 1000 Genomes Project sequence data. Surprisingly, the smaller size of our syllabic data structures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster runtime than existing solutions. AVAILABILITY AND IMPLEMENTATION: https://github.com/ZhiGroup/Syllable-PBWT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Naseri A." + }, + { + "name": "Wang V." + }, + { + "name": "Zhang S." + }, + { + "name": "Zhi D." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Syllable-PBWT for space-efficient haplotype long-match query" + }, + "pmcid": "PMC9805553", + "pmid": "36440908" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Biobank", + "uri": "http://edamontology.org/topic_3337" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + } + ] +} diff --git a/data/synr/synr.biotools.json b/data/synr/synr.biotools.json new file mode 100644 index 0000000000000..52335f4d173e0 --- /dev/null +++ b/data/synr/synr.biotools.json @@ -0,0 +1,123 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T20:57:48.186008Z", + "biotoolsCURIE": "biotools:synr", + "biotoolsID": "synr", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "lowe.wilsson@tobii.com", + "name": "Lowe Wilsson", + "orcidid": "http://orcid.org/0000-0003-1283-8399", + "typeEntity": "Person" + }, + { + "name": "Janina Neufeld", + "orcidid": "https://orcid.org/0000-0002-7743-526X" + }, + { + "name": "Tessa M. van Leeuwen", + "orcidid": "https://orcid.org/0000-0001-7810-6348" + } + ], + "description": "Explore synesthesia consistency test data, calculate consistency scores, and classify participant data as valid or invalid.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://datalowe.github.io/synr/" + }, + { + "type": [ + "User manual" + ], + "url": "https://cran.r-project.org/web/packages/synr/synr.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Anonymisation", + "uri": "http://edamontology.org/operation_3283" + }, + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/datalowe/synr", + "language": [ + "R" + ], + "lastUpdate": "2023-01-25T20:57:48.189517Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://cran.r-project.org/web/packages/synr/index.html" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/datalowe/synr-article-material" + } + ], + "name": "synr", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3758/S13428-022-02007-Y", + "metadata": { + "abstract": "© 2022, The Author(s).Synesthesia is a phenomenon where sensory stimuli or cognitive concepts elicit additional perceptual experiences. For instance, in a commonly studied type of synesthesia, stimuli such as words written in black font elicit experiences of other colors, e.g., red. In order to objectively verify synesthesia, participants are asked to choose colors for repeatedly presented stimuli and the consistency of their choices is evaluated (consistency test). Previously, there has been no publicly available and easy-to-use tool for analyzing consistency test results. Here, the R package synr is introduced, which provides an efficient interface for exploring consistency test data and applying common procedures for analyzing them. Importantly, synr also implements a novel method enabling identification of participants whose scores cannot be interpreted, e.g., who only give black or red color responses. To this end, density-based spatial clustering of applications with noise (DBSCAN) is applied in conjunction with a measure of spread in 3D space. An application of synr with pre-existing openly accessible data illustrating how synr is used in practice is presented. Also included is a comparison of synr’s data validation procedure and human ratings, which found that synr had high correspondence with human ratings and outperformed human raters in situations where human raters were easily mislead. Challenges for widespread adoption of synr as well as suggestions for using synr within the field of synesthesia and other areas of psychological research are discussed.", + "authors": [ + { + "name": "Neufeld J." + }, + { + "name": "Wilsson L." + }, + { + "name": "van Leeuwen T.M." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Behavior Research Methods", + "title": "synr: An R package for handling synesthesia consistency test data" + }, + "pmid": "36357762" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + } + ] +} diff --git a/data/tabix/tabix.biotools.json b/data/tabix/tabix.biotools.json index 78e5f94e30c30..caf362cf744b7 100644 --- a/data/tabix/tabix.biotools.json +++ b/data/tabix/tabix.biotools.json @@ -1,7 +1,15 @@ { + "accessibility": "Open access", "additionDate": "2021-04-22T01:06:12Z", "biotoolsCURIE": "biotools:tabix", "biotoolsID": "tabix", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Heng Li" + } + ], "description": "Tabix indexes a TAB-delimited genome position file in.tab.bgz and creates an index file (in.tab.bgz.tbi or in.tab.bgz.csi) when region is absent from the command-line. The input data file must be position sorted and compressed by bgzip which has a gzip(1) like interface.\n\nAfter indexing, tabix is able to quickly retrieve data lines overlapping regions specified in the format \"chr:beginPos-endPos\". (Coordinates specified in this region format are 1-based and inclusive.)\n\nFast data retrieval also works over network if URI is given as a file name and in this case the index file will be downloaded if it is not present locally.\n\nThe tabix (.tbi) and BAI index formats can handle individual chromosomes up to 512 Mbp (2^29 bases) in length. If your input file might contain data lines with begin or end positions greater than that, you will need to use a CSI index.", "editPermission": { "authors": [ @@ -54,8 +62,13 @@ } ], "homepage": "http://www.htslib.org/doc/tabix.html", - "lastUpdate": "2022-12-09T23:06:48.591627Z", + "lastUpdate": "2023-01-11T01:11:56.773187Z", "name": "tabix", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "leipzig", "toolType": [ "Command-line tool" diff --git a/data/tableau/tableau.biotools.json b/data/tableau/tableau.biotools.json new file mode 100644 index 0000000000000..a027ab1358531 --- /dev/null +++ b/data/tableau/tableau.biotools.json @@ -0,0 +1,48 @@ +{ + "additionDate": "2023-01-27T13:02:30.702295Z", + "biotoolsCURIE": "biotools:tableau", + "biotoolsID": "tableau", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "url": "https://www.tableau.com/support/help" + } + ], + "description": "Tableau is a visual analytics platform transforming the way we use data to solve problems—empowering people and organizations to make the most of their data. \n\n\n\nAs the market-leading choice for modern business intelligence, our analytics platform makes it easier for people to explore and manage data, and faster to discover and share insights that can change businesses and the world.\n\nEverything we do is driven by our mission to help people see and understand data, which is why our products are designed to put the user first—whether they’re an analyst, data scientist, student, teacher, executive, or business user. From connection through collaboration, Tableau is the most powerful, secure, and flexible end-to-end analytics platform.", + "documentation": [ + { + "type": [ + "API documentation" + ], + "url": "https://www.tableau.com/developer/tools" + } + ], + "download": [ + { + "note": "Homepage to Tableau", + "type": "Other", + "url": "https://www.tableau.com/products/trial" + } + ], + "editPermission": { + "type": "public" + }, + "homepage": "https://www.tableau.com/", + "lastUpdate": "2023-01-27T13:02:40.106361Z", + "link": [ + { + "note": "Homepage to Tableau", + "type": [ + "Other" + ], + "url": "https://www.tableau.com/" + } + ], + "name": "Tableau", + "owner": "iacs-biocomputacion", + "version": [ + "2022.4" + ] +} diff --git a/data/tadmaster/tadmaster.biotools.json b/data/tadmaster/tadmaster.biotools.json new file mode 100644 index 0000000000000..6f4cdc5f44e70 --- /dev/null +++ b/data/tadmaster/tadmaster.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T14:17:21.780949Z", + "biotoolsCURIE": "biotools:tadmaster", + "biotoolsID": "tadmaster", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ooluwada@uccs.edu", + "name": "Oluwatosin Oluwadare", + "orcidid": "https://orcid.org/0000-0002-5264-2342", + "typeEntity": "Person" + }, + { + "name": "Allen Westcott" + }, + { + "name": "Sean Higgins" + }, + { + "name": "Victor Akpokiro" + } + ], + "description": "A comprehensive web-based tool for the analysis of topologically associated domains.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://tadmaster.io", + "language": [ + "C++", + "MATLAB", + "Python", + "R", + "Shell" + ], + "lastUpdate": "2023-01-25T14:17:21.784047Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/OluwadareLab/TADMaster" + } + ], + "name": "TADMaster", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05020-2", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Chromosome conformation capture and its derivatives have provided substantial genetic data for understanding how chromatin self-organizes. These techniques have identified regions of high intrasequence interactions called topologically associated domains (TADs). TADs are structural and functional units that shape chromosomes and influence genomic expression. Many of these domains differ across cell development and can be impacted by diseases. Thus, analysis of the identified domains can provide insight into genome regulation. Hence, there are many approaches to identifying such domains across many cell lines. Despite the availability of multiple tools for TAD detection, TAD callers' speed, flexibility, result inconsistency, and reproducibility remain challenges in this research area. Results: In this work, we developed a computational webserver called TADMaster that provides an analysis suite to directly evaluate the concordance level and robustness of two or more TAD data on any given genome region. The suite provides multiple visual and quantitative metrics to compare the identified domains' number, size, and various comparisons of shared domains, domain boundaries, and domain overlap. Conclusions: TADMaster is an efficient and easy-to-use web application that provides a set of consensus and unique TADs to inform the choice of TADs. It can be accessed at http://tadmaster.io and is also available as a containerized application that can be deployed and run locally on any platform or operating system.", + "authors": [ + { + "name": "Akpokiro V." + }, + { + "name": "Higgins S." + }, + { + "name": "Oluwadare O." + }, + { + "name": "Westcott A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "TADMaster: a comprehensive web-based tool for the analysis of topologically associated domains" + }, + "pmcid": "PMC9636664", + "pmid": "36333787" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Chromosome conformation capture", + "uri": "http://edamontology.org/topic_3940" + }, + { + "term": "DNA", + "uri": "http://edamontology.org/topic_0654" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + } + ] +} diff --git a/data/taguchiarray/taguchiarray.biotools.json b/data/taguchiarray/taguchiarray.biotools.json deleted file mode 100644 index 8972d22a05c96..0000000000000 --- a/data/taguchiarray/taguchiarray.biotools.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "accessibility": "Open access (with restrictions)", - "additionDate": "2021-05-05T11:11:32Z", - "biotoolsCURIE": "biotools:taguchiarray", - "biotoolsID": "taguchiarray", - "collectionID": [ - "File Exchange", - "MATLAB" - ], - "cost": "Free of charge (with restrictions)", - "credit": [ - { - "name": "Chixin Xiao", - "typeEntity": "Person", - "typeRole": [ - "Primary contact" - ], - "url": "https://www.mathworks.com/matlabcentral/profile/6298843-chixin-xiao" - } - ], - "description": "This algorithm provides the Orthogonal(Taguchi) Array with inputs: Q (the number of the levels) and N (the number of the factors).", - "download": [ - { - "type": "Screenshot", - "url": "https://www.mathworks.com//matlabcentral/images/default_screenshot.jpg" - } - ], - "editPermission": { - "type": "private" - }, - "homepage": "https://www.mathworks.com/matlabcentral/fileexchange/71628-taguchiarray", - "language": [ - "MATLAB" - ], - "lastUpdate": "2021-05-22T13:07:42Z", - "name": "TaguchiArray", - "operatingSystem": [ - "Linux", - "Mac", - "Windows" - ], - "owner": "zsmag19", - "toolType": [ - "Library" - ], - "version": [ - "2.0.0" - ] -} diff --git a/data/talend_open_studio/talend_open_studio.biotools.json b/data/talend_open_studio/talend_open_studio.biotools.json new file mode 100644 index 0000000000000..7c5699ef7a73d --- /dev/null +++ b/data/talend_open_studio/talend_open_studio.biotools.json @@ -0,0 +1,58 @@ +{ + "additionDate": "2023-01-26T09:59:34.956729Z", + "biotoolsCURIE": "biotools:talend_open_studio", + "biotoolsID": "talend_open_studio", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Ehtisham Zaidi, Sharat Menon, Robert Thanaraj, Nina Showell,", + "url": "https://www.talend.com/lp/gartner-magic-quadrant-data-integration/" + } + ], + "description": "Talend Open Studio for Data Integration is free-to-download software to kickstart your first data integration and ETL projects.\n\nFeatures:\n\n Free open source Apache license\n RDBMS connectors: Oracle, Teradata, Microsoft SQL server\n SaaS connectors: Marketo, Salesforce, NetSuite\n Packaged apps: SAP, Microsoft Dynamics, Sugar CRM", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://res.cloudinary.com/talend/image/upload/v1625103406/docs/talend-support-portal_u5oxsc.pdf" + } + ], + "download": [ + { + "type": "API specification", + "url": "https://api.talend.com/apis/" + }, + { + "type": "Downloads page", + "url": "https://www.talend.com/lp/open-studio-for-data-integration/" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "https://www.talend.com/products/talend-open-studio/", + "lastUpdate": "2023-01-26T09:59:42.044277Z", + "link": [ + { + "type": [ + "Software catalogue" + ], + "url": "https://www.talend.com/products/talend-open-studio/" + } + ], + "name": "Talend Open Studio", + "owner": "iacs-biocomputacion" +} diff --git a/data/tampa/tampa.biotools.json b/data/tampa/tampa.biotools.json new file mode 100644 index 0000000000000..9af30fd903602 --- /dev/null +++ b/data/tampa/tampa.biotools.json @@ -0,0 +1,13 @@ +{ + "additionDate": "2023-01-07T02:04:10.610574Z", + "biotoolsCURIE": "biotools:tampa", + "biotoolsID": "tampa", + "description": "TAMPA (Taxonomic metagenome profiling evaluation) , is a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community.", + "editPermission": { + "type": "private" + }, + "homepage": "https://github.com/dkoslicki/TAMPA", + "lastUpdate": "2023-01-07T02:04:22.881318Z", + "name": "TAMPA", + "owner": "vsarwal" +} diff --git a/data/tarean/tarean.biotools.json b/data/tarean/tarean.biotools.json index 6e7f6d585b613..f938feeceb5aa 100644 --- a/data/tarean/tarean.biotools.json +++ b/data/tarean/tarean.biotools.json @@ -70,7 +70,7 @@ } ], "homepage": "http://repeatexplorer.org/?page_id=826", - "lastUpdate": "2022-12-09T23:09:39.192373Z", + "lastUpdate": "2023-01-11T01:03:30.556619Z", "license": "GPL-3.0", "link": [ { @@ -88,6 +88,11 @@ ], "maturity": "Mature", "name": "TAREAN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "kavonrtep", "publication": [ { @@ -114,7 +119,7 @@ "name": "Vrbova I." } ], - "citationCount": 125, + "citationCount": 127, "date": "2017-07-01T00:00:00Z", "journal": "Nucleic Acids Research", "title": "TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads" @@ -123,6 +128,9 @@ "pmid": "28402514" } ], + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Sequence composition, complexity and repeats", diff --git a/data/tatdb/tatdb.biotools.json b/data/tatdb/tatdb.biotools.json new file mode 100644 index 0000000000000..bef4520dd24e5 --- /dev/null +++ b/data/tatdb/tatdb.biotools.json @@ -0,0 +1,96 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T14:14:00.128258Z", + "biotoolsCURIE": "biotools:tatdb", + "biotoolsID": "tatdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "agrigoriev@camden.rutgers.edu", + "name": "Andrey Grigoriev", + "orcidid": "https://orcid.org/0000-0002-3227-532X", + "typeEntity": "Person" + }, + { + "name": "Lingyu Guan" + } + ], + "description": "A database of Ago1-mediated targets of transfer RNA fragments.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "miRNA target prediction", + "uri": "http://edamontology.org/operation_0463" + } + ] + } + ], + "homepage": "https://grigoriev-lab.camden.rutgers.edu/tatdb", + "lastUpdate": "2023-01-25T14:14:00.130793Z", + "name": "tatDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1018", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.tRNA-derived fragments (tRFs) are a class of emerging post-transcriptional regulators of gene expression likely binding to the transcripts of target genes. However, only a few tRFs targets have been experimentally validated, making it hard to extrapolate the functions or binding mechanisms of tRFs. The paucity of resources supporting the identification of the targets of tRFs creates a bottleneck in the fast-developing field. We have previously analyzed chimeric reads in crosslinked Argonaute1-RNA complexes to help infer the guide-target pairs and binding mechanisms of multiple tRFs based on experimental data in human HEK293 cells. To efficiently disseminate these results to the research community, we designed a web-based database tatDB (targets of tRFs DataBase) populated with close to 250 000 experimentally determined guide-target pairs with ∼23 000 tRF isoforms. tatDB has a user-friendly interface with flexible query options/filters allowing one to obtain comprehensive information on given tRFs (or targets). Modes of interactions are supported by secondary structures of potential guide-target hybrids and binding motifs, essential for understanding the targeting mechanisms of tRFs. Further, we illustrate the value of the database on an example of hypothesis-building for a tRFs potentially involved in the lifecycle of the SARS-CoV-2 virus. tatDB is freely accessible at https://grigoriev-lab.camden.rutgers.edu/tatdb.", + "authors": [ + { + "name": "Grigoriev A." + }, + { + "name": "Guan L." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "tatDB: a database of Ago1-mediated targets of transfer RNA fragments" + }, + "pmcid": "PMC9825446", + "pmid": "36350638" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/tedd/tedd.biotools.json b/data/tedd/tedd.biotools.json new file mode 100644 index 0000000000000..961b1ce0337cc --- /dev/null +++ b/data/tedd/tedd.biotools.json @@ -0,0 +1,139 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T14:11:13.541547Z", + "biotoolsCURIE": "biotools:tedd", + "biotoolsID": "tedd", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "elvisdong@cuhk.edu.hk", + "name": "Zirui Dong", + "orcidid": "https://orcid.org/0000-0002-3626-6500", + "typeEntity": "Person" + }, + { + "email": "richardchoy@cuhk.edu.hk", + "name": "Kwong Wai Choy", + "orcidid": "https://orcid.org/0000-0002-3616-6200", + "typeEntity": "Person" + }, + { + "name": "Cong Tan" + }, + { + "name": "Ziheng Zhou" + } + ], + "description": "A database of temporal gene expression patterns during multiple developmental periods in human and model organisms.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Expression profile clustering", + "uri": "http://edamontology.org/operation_0313" + }, + { + "term": "Gene expression QTL analysis", + "uri": "http://edamontology.org/operation_3232" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + } + ] + } + ], + "homepage": "https://TEDD.obg.cuhk.edu.hk/", + "lastUpdate": "2023-01-25T14:11:13.566942Z", + "name": "TEDD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC978", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene-gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/.", + "authors": [ + { + "name": "Bellgard M." + }, + { + "name": "Cao Y." + }, + { + "name": "Chau M.H.K." + }, + { + "name": "Chen X." + }, + { + "name": "Choy K.W." + }, + { + "name": "Dong Z." + }, + { + "name": "Jiang X." + }, + { + "name": "Ke Z." + }, + { + "name": "Kwok Y.K." + }, + { + "name": "Leung T.Y." + }, + { + "name": "Tan C." + }, + { + "name": "Zhou Z." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms" + }, + "pmcid": "PMC9825605", + "pmid": "36350663" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Chromosome conformation capture", + "uri": "http://edamontology.org/topic_3940" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + } + ] +} diff --git a/data/termviewer/termviewer.biotools.json b/data/termviewer/termviewer.biotools.json new file mode 100644 index 0000000000000..7ae3ad22d94f9 --- /dev/null +++ b/data/termviewer/termviewer.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T13:58:07.743525Z", + "biotoolsCURIE": "biotools:termviewer", + "biotoolsID": "termviewer", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "wangk@chop.edu", + "name": "Kai Wang", + "orcidid": "https://orcid.org/0000-0002-5585-982X" + }, + { + "name": "James M. Havrilla" + }, + { + "name": "Anna Nixon", + "orcidid": "https://orcid.org/0000-0002-6273-4275" + }, + { + "name": "Li Fang", + "orcidid": "https://orcid.org/0000-0001-5456-0255" + } + ], + "description": "A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + } + ] + } + ], + "homepage": "https://github.com/WGLab/TermViewer", + "language": [ + "JavaScript", + "Python", + "Shell" + ], + "lastUpdate": "2023-01-25T13:58:07.746041Z", + "license": "Not licensed", + "name": "TermViewer", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1002/CBDV.202200805", + "metadata": { + "abstract": "© 2022 Wiley-VHCA AG, Zurich, Switzerland.Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.", + "authors": [ + { + "name": "Fang L." + }, + { + "name": "Havrilla J.M." + }, + { + "name": "Nixon A." + }, + { + "name": "Wang K." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Chemistry and Biodiversity", + "title": "Termviewer – A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation" + }, + "pmid": "36328766" + } + ], + "toolType": [ + "Command-line tool", + "Web application" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Medical informatics", + "uri": "http://edamontology.org/topic_3063" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Ontology and terminology", + "uri": "http://edamontology.org/topic_0089" + } + ] +} diff --git a/data/teslatoto/teslatoto.biotools.json b/data/teslatoto/teslatoto.biotools.json deleted file mode 100644 index 7a3aec2686527..0000000000000 --- a/data/teslatoto/teslatoto.biotools.json +++ /dev/null @@ -1,57 +0,0 @@ -{ - "additionDate": "2022-01-02T02:06:28.669675Z", - "biotoolsCURIE": "biotools:teslatoto", - "biotoolsID": "teslatoto", - "description": "TESLATOTO Merupakan Bandar Togel Online terpercaya dengan diskon mencapai 66% menyediakan beragam jenis permainan hanya menggunakan 1 userid", - "editPermission": { - "type": "private" - }, - "homepage": "https://139.59.99.87/", - "lastUpdate": "2022-01-02T02:10:48.167860Z", - "link": [ - { - "note": "Daftar Sekarang - TeslaToto", - "type": [ - "Service" - ], - "url": "https://cutt.ly/DaftarTeslaToto" - }, - { - "note": "Facebook - TeslaToto", - "type": [ - "Social media" - ], - "url": "https://cutt.ly/FbTeslatoto" - }, - { - "note": "Instagram - TeslaToto", - "type": [ - "Social media" - ], - "url": "https://cutt.ly/IGTeslaToto" - }, - { - "note": "Live Chat 24 Jam", - "type": [ - "Helpdesk" - ], - "url": "https://cutt.ly/LiveChatTeslaToto" - }, - { - "note": "Promo-Promo TeslaToto", - "type": [ - "Software catalogue" - ], - "url": "https://139.59.99.87/promotion.php" - }, - { - "note": "WhatsApp - TeslaToto", - "type": [ - "Social media" - ], - "url": "https://cutt.ly/WATeslaToto" - } - ], - "name": "TeslaToto", - "owner": "TeslaToto" -} diff --git a/data/tiedie/tiedie.biotools.json b/data/tiedie/tiedie.biotools.json index 43b431826a391..5ef3899c44bb2 100644 --- a/data/tiedie/tiedie.biotools.json +++ b/data/tiedie/tiedie.biotools.json @@ -6,7 +6,22 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", + "credit": [ + { + "name": "Daniel E Carlin" + }, + { + "name": "David Haussler" + }, + { + "name": "Evan O Paull" + }, + { + "name": "Joshua M Stuart" + } + ], "description": "Application of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.", "editPermission": { "type": "public" @@ -17,7 +32,7 @@ "Python", "R" ], - "lastUpdate": "2022-12-09T23:11:53.120115Z", + "lastUpdate": "2023-01-11T00:57:02.907360Z", "license": "GPL-3.0", "name": "TieDie", "operatingSystem": [ @@ -51,13 +66,18 @@ "name": "Stuart J.M." } ], - "citationCount": 124, + "citationCount": 126, "date": "2013-11-01T00:00:00Z", "journal": "Bioinformatics", "title": "Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)" - } + }, + "pmcid": "PMC3799471", + "pmid": "23986566" } ], + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Molecular interactions, pathways and networks", diff --git a/data/timedb/timedb.biotools.json b/data/timedb/timedb.biotools.json new file mode 100644 index 0000000000000..056157fa540f3 --- /dev/null +++ b/data/timedb/timedb.biotools.json @@ -0,0 +1,149 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T02:00:12.496062Z", + "biotoolsCURIE": "biotools:timedb", + "biotoolsID": "timedb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "shuaicli@cityu.edu.hk", + "name": "Shuaicheng Li", + "orcidid": "https://orcid.org/0000-0001-6246-6349", + "typeEntity": "Person" + }, + { + "name": "Haoyue Cheng" + }, + { + "name": "Lingxi Chen", + "orcidid": "https://orcid.org/0000-0002-5229-7470" + }, + { + "name": "Xueying Wang", + "orcidid": "https://orcid.org/0000-0001-8979-3737" + } + ], + "description": "Tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deisotoping", + "uri": "http://edamontology.org/operation_3629" + }, + { + "term": "Expression correlation analysis", + "uri": "http://edamontology.org/operation_3463" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://timedb.deepomics.org", + "lastUpdate": "2023-01-25T02:00:12.498631Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/deepomicslab/TIMEDB" + } + ], + "name": "TIMEDB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1006", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.", + "authors": [ + { + "name": "Chen L." + }, + { + "name": "Cheng H." + }, + { + "name": "Dong J." + }, + { + "name": "Lai Z." + }, + { + "name": "Li S." + }, + { + "name": "Liu D." + }, + { + "name": "Liu W." + }, + { + "name": "Shi S." + }, + { + "name": "Wang X." + }, + { + "name": "Zhang W." + }, + { + "name": "Zhang Y." + }, + { + "name": "Zhao B." + }, + { + "name": "Zhou C." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization" + }, + "pmcid": "PMC9825442", + "pmid": "36399488" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Allergy, clinical immunology and immunotherapeutics", + "uri": "http://edamontology.org/topic_3400" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/timothy/timothy.biotools.json b/data/timothy/timothy.biotools.json index e6e4e8c0e6ecf..ef2e3122797b3 100644 --- a/data/timothy/timothy.biotools.json +++ b/data/timothy/timothy.biotools.json @@ -6,14 +6,23 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", + "credit": [ + { + "name": "Maciej Cytowski" + }, + { + "name": "Zuzanna Szymanska" + } + ], "description": "Timothy is a novel large scale parallel computational model allowing 3-D simulations of cell colonies growing and interacting with variable environment in previously unavailable tissue scale.\nThe cells are modeled as individuals located in the lattice-free 3-D space. The model incorporates cellular environment modelled in a continuous manner, mathematical description based on partial differential equations is formulated for selected important components of the environment. Discrete and continuous formulations are efficiently coupled in one model and allow considerations on different scales: sub-cellular, cellular and tissue scale.\nHigh parallel scalability achieved allows simulation of up to 109 individual cells. This large scale computational approach allows for simulations to be carried out over realistic spatial scales up to 1cm in size i.e. the tissue scale.", "documentation": [ { "type": [ - "Other" + "General" ], - "url": "https://timothy.icm.edu.pl/examples.html" + "url": "https://timothy.icm.edu.pl/doc/index.html" } ], "download": [ @@ -50,8 +59,8 @@ "language": [ "C" ], - "lastUpdate": "2022-12-09T23:14:06.662181Z", - "license": "gnuplot", + "lastUpdate": "2023-01-10T16:57:19.384628Z", + "license": "GPL-2.0", "name": "Timothy", "operatingSystem": [ "Linux" @@ -77,6 +86,9 @@ } } ], + "toolType": [ + "Desktop application" + ], "topic": [ { "term": "Cell biology", diff --git a/data/tmalphafold_database/tmalphafold_database.biotools.json b/data/tmalphafold_database/tmalphafold_database.biotools.json new file mode 100644 index 0000000000000..5b4cc1eab248b --- /dev/null +++ b/data/tmalphafold_database/tmalphafold_database.biotools.json @@ -0,0 +1,155 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:54:39.779541Z", + "biotoolsCURIE": "biotools:tmalphafold_database", + "biotoolsID": "tmalphafold_database", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "tusnady.gabor@ttk.hu", + "name": "Gábor E Tusnády", + "orcidid": "https://orcid.org/0000-0001-8105-0285", + "typeEntity": "Person" + }, + { + "name": "András Zeke" + }, + { + "name": "Csongor Gerdán" + }, + { + "name": "Laszlo Dobson" + }, + { + "name": "Levente I Szekeres" + }, + { + "name": "Tamás Langó" + } + ], + "description": "Membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Gene name", + "uri": "http://edamontology.org/data_2299" + } + }, + { + "data": { + "term": "Organism name", + "uri": "http://edamontology.org/data_2909" + } + }, + { + "data": { + "term": "Protein name", + "uri": "http://edamontology.org/data_1009" + } + }, + { + "data": { + "term": "UniProt accession", + "uri": "http://edamontology.org/data_3021" + } + } + ], + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Protein structure prediction", + "uri": "http://edamontology.org/operation_0474" + }, + { + "term": "Transmembrane protein prediction", + "uri": "http://edamontology.org/operation_0269" + }, + { + "term": "Transmembrane protein visualisation", + "uri": "http://edamontology.org/operation_2241" + } + ] + } + ], + "homepage": "https://tmalphafold.ttk.hu/", + "lastUpdate": "2023-01-25T01:54:39.782165Z", + "name": "TmAlphaFold database", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC928", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3D predictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/.", + "authors": [ + { + "name": "Dobson L." + }, + { + "name": "Gerdan C." + }, + { + "name": "Lango T." + }, + { + "name": "Szekeres L.I." + }, + { + "name": "Tusnady G.E." + }, + { + "name": "Zeke A." + } + ], + "citationCount": 1, + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures" + }, + "pmcid": "PMC9825488", + "pmid": "36318239" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Lipids", + "uri": "http://edamontology.org/topic_0153" + }, + { + "term": "Membrane and lipoproteins", + "uri": "http://edamontology.org/topic_0820" + }, + { + "term": "Protein structure analysis", + "uri": "http://edamontology.org/topic_2814" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + } + ] +} diff --git a/data/tmodbase/tmodbase.biotools.json b/data/tmodbase/tmodbase.biotools.json new file mode 100644 index 0000000000000..4d7778f8a904c --- /dev/null +++ b/data/tmodbase/tmodbase.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:49:13.313220Z", + "biotoolsCURIE": "biotools:tmodbase", + "biotoolsID": "tmodbase", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "zhengll33@mail.sysu.edu.cn", + "name": "Ling-Ling Zheng", + "orcidid": "https://orcid.org/0000-0002-7152-1095", + "typeEntity": "Person" + }, + { + "name": "Hao-Tian Lei" + }, + { + "name": "Zhang-Hao Wang" + }, + { + "name": "Liang-Hu Qu", + "orcidid": "https://orcid.org/0000-0003-3657-2863" + } + ], + "description": "Deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + }, + { + "term": "tRNA gene prediction", + "uri": "http://edamontology.org/operation_0464" + } + ] + } + ], + "homepage": "https://www.tmodbase.com/", + "lastUpdate": "2023-01-25T01:49:13.315749Z", + "name": "tModBase", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1087", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.", + "authors": [ + { + "name": "Lei H.-T." + }, + { + "name": "Li B." + }, + { + "name": "Mei S.-Q." + }, + { + "name": "Qu L.-H." + }, + { + "name": "Sun Y." + }, + { + "name": "Wang Z.-H." + }, + { + "name": "Yang J.-H." + }, + { + "name": "Zheng L.-L." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "tModBase: deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data" + }, + "pmcid": "PMC9825477", + "pmid": "36408909" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json b/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json deleted file mode 100644 index fb7182b9d788c..0000000000000 --- a/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "additionDate": "2020-05-13T22:04:27Z", - "biotoolsCURIE": "biotools:tool_recommender_system_in_galaxy_using_deep_learning", - "biotoolsID": "tool_recommender_system_in_galaxy_using_deep_learning", - "description": "A model is developed to recommend tools, by analysing workflows composed by researchers on the European Galaxy server, using a deep learning approach. The model is used to recommend tools in Galaxy.", - "editPermission": { - "type": "private" - }, - "homepage": "https://github.com/anuprulez/galaxy_tool_recommendation", - "lastUpdate": "2022-12-09T23:16:07.929079Z", - "name": "Tool recommender system in Galaxy using deep learning", - "owner": "admin", - "topic": [ - { - "term": "Machine learning", - "uri": "http://edamontology.org/topic_3474" - }, - { - "term": "Workflows", - "uri": "http://edamontology.org/topic_0769" - } - ] -} diff --git a/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json b/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json index f6955dd3b1e20..8ebf2bab930f7 100644 --- a/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json +++ b/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json @@ -7,6 +7,7 @@ "File Exchange", "MATLAB" ], + "confidence_flag": "tool", "cost": "Free of charge (with restrictions)", "credit": [ { @@ -32,7 +33,8 @@ "language": [ "MATLAB" ], - "lastUpdate": "2021-05-21T18:49:30Z", + "lastUpdate": "2023-01-10T16:40:51.348990Z", + "license": "Not licensed", "name": "Toolkits for monte carlo dose simulation and visualization", "operatingSystem": [ "Linux", diff --git a/data/tourmaline/tourmaline.biotools.json b/data/tourmaline/tourmaline.biotools.json index cfb1ddb87b930..f498e4abeb9f2 100644 --- a/data/tourmaline/tourmaline.biotools.json +++ b/data/tourmaline/tourmaline.biotools.json @@ -1,17 +1,34 @@ { + "accessibility": "Open access", "additionDate": "2022-06-02T18:35:00.890528Z", "biotoolsCURIE": "biotools:tourmaline", "biotoolsID": "tourmaline", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Tourmaline is an amplicon sequence processing workflow for Illumina sequence data that uses QIIME 2 and the software packages it wraps. Tourmaline manages commands, inputs, and outputs using the Snakemake workflow management system.", "editPermission": { "type": "private" }, "homepage": "https://github.com/aomlomics/tourmaline", - "lastUpdate": "2022-12-09T23:42:50.683190Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T16:35:14.498312Z", "license": "BSD-3-Clause", "name": "Tourmaline", + "operatingSystem": [ + "Linux", + "Mac" + ], "owner": "lukethompson", + "toolType": [ + "Command-line tool" + ], "topic": [ + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + }, { "term": "Workflows", "uri": "http://edamontology.org/topic_0769" diff --git a/data/toxric/toxric.biotools.json b/data/toxric/toxric.biotools.json new file mode 100644 index 0000000000000..bdf08b2285535 --- /dev/null +++ b/data/toxric/toxric.biotools.json @@ -0,0 +1,135 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:43:42.708561Z", + "biotoolsCURIE": "biotools:toxric", + "biotoolsID": "toxric", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "boxc@bmi.ac.cn", + "name": "Xiaochen Bo", + "orcidid": "https://orcid.org/0000-0003-1911-7922", + "typeEntity": "Person" + }, + { + "email": "hes1224@163.com", + "name": "Song He", + "orcidid": "https://orcid.org/0000-0002-4136-6151", + "typeEntity": "Person" + }, + { + "name": "Bowei Yan" + }, + { + "name": "Lianlian Wu", + "orcidid": "https://orcid.org/0000-0002-9611-4488" + } + ], + "description": "A comprehensive database of toxicological data and benchmarks.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Chemical name (synonymous)", + "uri": "http://edamontology.org/data_1001" + } + }, + { + "data": { + "term": "SMILES string", + "uri": "http://edamontology.org/data_2301" + } + } + ], + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://toxric.bioinforai.tech/", + "lastUpdate": "2023-01-25T01:43:42.711220Z", + "name": "TOXRIC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC1074", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.", + "authors": [ + { + "name": "Bo X." + }, + { + "name": "Han J." + }, + { + "name": "He S." + }, + { + "name": "Li R." + }, + { + "name": "Wu L." + }, + { + "name": "Xiao J." + }, + { + "name": "Yan B." + } + ], + "date": "2023-01-06T00:00:00Z", + "journal": "Nucleic acids research", + "title": "TOXRIC: a comprehensive database of toxicological data and benchmarks" + }, + "pmcid": "PMC9825425", + "pmid": "36400569" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Drug metabolism", + "uri": "http://edamontology.org/topic_3375" + }, + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Toxicology", + "uri": "http://edamontology.org/topic_2840" + } + ] +} diff --git a/data/tps/tps.biotools.json b/data/tps/tps.biotools.json index 85e1bd5055651..62d0a923fc03e 100644 --- a/data/tps/tps.biotools.json +++ b/data/tps/tps.biotools.json @@ -6,14 +6,25 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", - "description": "TPS is a tool for combining time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications.", - "download": [ + "credit": [ + { + "email": "gitter@biostat.wisc.edu", + "name": "Anthony Gitter", + "orcidid": "https://orcid.org/0000-0002-5324-9833" + }, + { + "name": "Ali Sinan Köksal" + }, + { + "name": "Jasmin Fisher" + }, { - "type": "Source code", - "url": "https://github.com/koksal/tps" + "name": "Kirsten Beck" } ], + "description": "TPS is a tool for combining time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications.", "editPermission": { "type": "public" }, @@ -47,7 +58,7 @@ "Python", "Scala" ], - "lastUpdate": "2022-12-09T23:45:43.254496Z", + "lastUpdate": "2023-01-10T16:26:59.255265Z", "license": "MIT", "name": "TPS", "operatingSystem": [ @@ -98,7 +109,7 @@ "name": "Wolf-Yadlin A." } ], - "citationCount": 14, + "citationCount": 15, "date": "2018-09-25T00:00:00Z", "journal": "Cell Reports", "title": "Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data" diff --git a/data/tracespipe/tracespipe.biotools.json b/data/tracespipe/tracespipe.biotools.json index 8dd41401a61ec..eada9a73e23d0 100644 --- a/data/tracespipe/tracespipe.biotools.json +++ b/data/tracespipe/tracespipe.biotools.json @@ -3,6 +3,7 @@ "additionDate": "2020-07-21T16:42:49Z", "biotoolsCURIE": "biotools:tracespipe", "biotoolsID": "tracespipe", + "confidence_flag": "tool", "cost": "Free of charge", "description": "A hybrid pipeline for reconstruction and analysis of viral and host genomes at multi-organ level.", "editPermission": { @@ -12,7 +13,7 @@ "language": [ "Shell" ], - "lastUpdate": "2022-12-09T23:47:27.419610Z", + "lastUpdate": "2023-01-10T16:21:34.602836Z", "license": "GPL-3.0", "maturity": "Emerging", "name": "TRACESPipe", diff --git a/data/tractoinferno/tractoinferno.biotools.json b/data/tractoinferno/tractoinferno.biotools.json new file mode 100644 index 0000000000000..0ee38040edb4f --- /dev/null +++ b/data/tractoinferno/tractoinferno.biotools.json @@ -0,0 +1,138 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:37:41.121024Z", + "biotoolsCURIE": "biotools:tractoinferno", + "biotoolsID": "tractoinferno", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "philippe.poulin2@usherbrooke.ca", + "name": "Philippe Poulin", + "orcidid": "https://orcid.org/0000-0002-0116-4352", + "typeEntity": "Person" + }, + { + "name": "Guillaume Theaud" + }, + { + "name": "Pierre-Marc Jodoin" + }, + { + "name": "Maxime Descoteaux", + "orcidid": "http://orcid.org/0000-0002-8191-2129" + } + ], + "description": "A large-scale, open-source, multi-site database for machine learning dMRI tractography.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://github.com/scil-vital/TractoInferno/", + "language": [ + "Python", + "Shell" + ], + "lastUpdate": "2023-01-25T01:37:41.123791Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "http://github.com/ppoulin91/tractoinferno_compute_sh_flow" + }, + { + "type": [ + "Repository" + ], + "url": "http://github.com/ppoulin91/tractoinferno_tracking_flow" + } + ], + "name": "TractoInferno", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/S41597-022-01833-1", + "metadata": { + "abstract": "© 2022, The Author(s).TractoInferno is the world’s largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.", + "authors": [ + { + "name": "Bore A." + }, + { + "name": "Descoteaux M." + }, + { + "name": "Guay S." + }, + { + "name": "Jodoin P.-M." + }, + { + "name": "Poulin P." + }, + { + "name": "Renauld E." + }, + { + "name": "Rheault F." + }, + { + "name": "St-Onge E." + }, + { + "name": "Theaud G." + }, + { + "name": "de Beaumont L." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography" + }, + "pmcid": "PMC9700736", + "pmid": "36433966" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "MRI", + "uri": "http://edamontology.org/topic_3444" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Neurobiology", + "uri": "http://edamontology.org/topic_3304" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/transposable_element_finder/transposable_element_finder.biotools.json b/data/transposable_element_finder/transposable_element_finder.biotools.json new file mode 100644 index 0000000000000..828d4d7c4cffd --- /dev/null +++ b/data/transposable_element_finder/transposable_element_finder.biotools.json @@ -0,0 +1,109 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T14:08:26.197963Z", + "biotoolsCURIE": "biotools:transposable_element_finder", + "biotoolsID": "transposable_element_finder", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "miyao@affrc.go.jp", + "name": "Akio Miyao", + "typeEntity": "Person" + }, + { + "name": "Utako Yamanouchi" + } + ], + "description": "Finding active transposable elements from next generation sequencing data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Recombination detection", + "uri": "http://edamontology.org/operation_0451" + }, + { + "term": "Sorting", + "uri": "http://edamontology.org/operation_3802" + }, + { + "term": "Transposon prediction", + "uri": "http://edamontology.org/operation_0427" + } + ] + } + ], + "homepage": "https://github.com/akiomiyao/tef", + "language": [ + "Perl" + ], + "lastUpdate": "2023-01-25T14:08:26.201083Z", + "license": "Not licensed", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://akiomiyao.github.io/tef/" + } + ], + "name": "TEF", + "operatingSystem": [ + "Linux" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05011-3", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Detection of newly transposed events by transposable elements (TEs) from next generation sequence (NGS) data is difficult, due to their multiple distribution sites over the genome containing older TEs. The previously reported Transposon Insertion Finder (TIF) detects TE transpositions on the reference genome from NGS short reads using end sequences of target TE. TIF requires the sequence of target TE and is not able to detect transpositions for TEs with an unknown sequence. Result: The new algorithm Transposable Element Finder (TEF) enables the detection of TE transpositions, even for TEs with an unknown sequence. TEF is a finding tool of transposed TEs, in contrast to TIF as a detection tool of transposed sites for TEs with a known sequence. The transposition event is often accompanied with a target site duplication (TSD). Focusing on TSD, two algorithms to detect both ends of TE, TSDs and target sites are reported here. One is based on the grouping with TSDs and direct comparison of k-mers from NGS without similarity search. The other is based on the junction mapping of TE end sequence candidates. Both methods succeed to detect both ends and TSDs of known active TEs in several tests with rice, Arabidopsis and Drosophila data and discover several new TEs in new locations. PCR confirmed the detected transpositions of TEs in several test cases in rice. Conclusions: TEF detects transposed TEs with TSDs as a result of TE transposition, sequences of both ends and their inserted positions of transposed TEs by direct comparison of NGS data between two samples. Genotypes of transpositions are verified by counting of junctions of head and tail, and non-insertion sequences in NGS reads. TEF is easy to run and independent of any TE library, which makes it useful to detect insertions from unknown TEs bypassed by common TE annotation pipelines.", + "authors": [ + { + "name": "Miyao A." + }, + { + "name": "Yamanouchi U." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Transposable element finder (TEF): finding active transposable elements from next generation sequencing data" + }, + "pmcid": "PMC9682801", + "pmid": "36418944" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Mobile genetic elements", + "uri": "http://edamontology.org/topic_0798" + }, + { + "term": "PCR experiment", + "uri": "http://edamontology.org/topic_3519" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/transvod/transvod.biotools.json b/data/transvod/transvod.biotools.json new file mode 100644 index 0000000000000..93617d8461c13 --- /dev/null +++ b/data/transvod/transvod.biotools.json @@ -0,0 +1,108 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:10:21.044022Z", + "biotoolsCURIE": "biotools:transvod", + "biotoolsID": "transvod", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Dacheng Tao", + "orcidid": "https://orcid.org/0000-0001-7225-5449" + }, + { + "name": "Lizhuang Ma", + "orcidid": "https://orcid.org/0000-0003-1653-4341" + }, + { + "name": "Qianyu Zhou", + "orcidid": "https://orcid.org/0000-0002-5331-050X" + }, + { + "name": "Xiangtai Li", + "orcidid": "https://orcid.org/0000-0002-0550-8247" + } + ], + "description": "End-to-End Video Object Detection With Spatial-Temporal Transformers.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Query and retrieval", + "uri": "http://edamontology.org/operation_0224" + } + ] + } + ], + "homepage": "https://github.com/SJTU-LuHe/TransVOD", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T01:10:21.046555Z", + "license": "Apache-2.0", + "name": "TransVOD", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1109/TPAMI.2022.3223955", + "metadata": { + "abstract": "IEEEDetection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has not been well explored. In this paper, we present TransVOD, the first end-to-end video object detection system based on simple yet effective spatial-temporal Transformer architectures. The first goal of this paper is to streamline the pipeline of current VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow model, relation networks. Besides, benefited from the object query design in DETR, our method does not need post-processing methods such as Seq-NMS. In particular, we present a temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal transformer consists of two components: Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3 %-4 % mAP) on the ImageNet VID dataset. TransVOD yields comparable performances on the benchmark of ImageNet VID. Then, we present two improved versions of TransVOD including TransVOD++ and TransVOD Lite. The former fuses object-level information into object query via dynamic convolution while the latter models the entire video clips as the output to speed up the inference time. We give detailed analysis of all three models in the experiment part. In particular, our proposed TransVOD++ sets a new state-of-the-art record in terms of accuracy on ImageNet VID with 90.0 % mAP. Our proposed TransVOD Lite also achieves the best speed and accuracy trade-off with 83.7 % mAP while running at around 30 FPS on a single V100 GPU device. Code and models are available at https://github.com/SJTU-LuHe/TransVOD.", + "authors": [ + { + "name": "Cheng G." + }, + { + "name": "He L." + }, + { + "name": "Li X." + }, + { + "name": "Ma L." + }, + { + "name": "Tao D." + }, + { + "name": "Tong Y." + }, + { + "name": "Yang Y." + }, + { + "name": "Zhou Q." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "IEEE Transactions on Pattern Analysis and Machine Intelligence", + "title": "TransVOD: End-to-End Video Object Detection With Spatial-Temporal Transformers" + }, + "pmid": "36417746" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "RNA immunoprecipitation", + "uri": "http://edamontology.org/topic_3794" + } + ] +} diff --git a/data/treat_web/treat_web.biotools.json b/data/treat_web/treat_web.biotools.json new file mode 100644 index 0000000000000..142e90baea990 --- /dev/null +++ b/data/treat_web/treat_web.biotools.json @@ -0,0 +1,142 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T01:02:00.849234Z", + "biotoolsCURIE": "biotools:treat_web", + "biotoolsID": "treat_web", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "biozy@ict.ac.cn", + "name": "Yi Zhao", + "typeEntity": "Person" + }, + { + "email": "budechao@ict.ac.cn", + "name": "Dechao Bu", + "typeEntity": "Person" + }, + { + "email": "crs@ibp.ac.cn", + "name": "Runsheng Chen", + "typeEntity": "Person" + }, + { + "name": "Yufan Luo" + } + ], + "description": "Therapeutic RNAs exploration inspired by artificial intelligence technology.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Peptide immunogenicity prediction", + "uri": "http://edamontology.org/operation_0252" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + } + ] + } + ], + "homepage": "https://rna.org.cn/treat", + "lastUpdate": "2023-01-25T01:02:00.851786Z", + "name": "TREAT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.CSBJ.2022.10.011", + "metadata": { + "abstract": "© 2022 The AuthorsRecent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.", + "authors": [ + { + "name": "Bu D." + }, + { + "name": "Chen R." + }, + { + "name": "He Z." + }, + { + "name": "Huo P." + }, + { + "name": "Jiaxin Q." + }, + { + "name": "Liu L." + }, + { + "name": "Luo Y." + }, + { + "name": "Wang Z." + }, + { + "name": "Wu Y." + }, + { + "name": "Zhang D." + }, + { + "name": "Zhang S." + }, + { + "name": "Zhao L." + }, + { + "name": "Zhao Y." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Computational and Structural Biotechnology Journal", + "title": "TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology" + }, + "pmcid": "PMC9589171", + "pmid": "36320935" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Immunogenetics", + "uri": "http://edamontology.org/topic_3930" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + } + ] +} diff --git a/data/twineqtl/twineqtl.biotools.json b/data/twineqtl/twineqtl.biotools.json new file mode 100644 index 0000000000000..975f398fb2747 --- /dev/null +++ b/data/twineqtl/twineqtl.biotools.json @@ -0,0 +1,142 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T01:04:26.604216Z", + "biotoolsCURIE": "biotools:twineqtl", + "biotoolsID": "twineqtl", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "fzou@bios.unc.edu", + "name": "Fei Zou", + "typeEntity": "Person" + }, + { + "name": "Andrey A. Shabalin" + }, + { + "name": "Zhaoyu Yin" + }, + { + "name": "Kai Xia", + "orcidid": "http://orcid.org/0000-0003-1326-0891" + }, + { + "name": "Wonil Chung", + "orcidid": "http://orcid.org/0000-0002-5766-6247" + } + ], + "description": "Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Collapsing methods", + "uri": "http://edamontology.org/operation_3791" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + }, + { + "term": "Regression analysis", + "uri": "http://edamontology.org/operation_3659" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "https://github.com/andreyshabalin/TwinEQTL", + "language": [ + "R" + ], + "lastUpdate": "2023-01-20T01:04:26.606629Z", + "license": "Not licensed", + "name": "TwinEQTL", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/genetics/iyac088", + "metadata": { + "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved.We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model for twin genome-wide association study data. Instead of analyzing all twin samples together with linear mixed-effects model, TwinEQTL first splits twin samples into 2 independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the 2 nonindependent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between 2 dependent test statistics at each single-nucleotide polymorphism is independent of its minor allele frequency. Thus, the correlation is constant across all single-nucleotide polymorphisms. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared with the gold-standard linear mixed-effects models. To accommodate expression quantitative loci analysis with twin subjects, we further implement TwinEQTL into an R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for genome-wide association study and expression quantitative loci analysis with twin samples.", + "authors": [ + { + "name": "Chung W." + }, + { + "name": "Gilmore J.H." + }, + { + "name": "Santelli R.C." + }, + { + "name": "Shabalin A.A." + }, + { + "name": "Styner M." + }, + { + "name": "Sullivan P.F." + }, + { + "name": "Wright F.A." + }, + { + "name": "Xia K." + }, + { + "name": "Yin Z." + }, + { + "name": "Zou F." + } + ], + "date": "2022-08-01T00:00:00Z", + "journal": "Genetics", + "title": "TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies" + }, + "pmcid": "PMC9339336", + "pmid": "35689615" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Mathematics", + "uri": "http://edamontology.org/topic_3315" + }, + { + "term": "Microarray experiment", + "uri": "http://edamontology.org/topic_3518" + } + ] +} diff --git a/data/twobitinfo/twobitinfo.biotools.json b/data/twobitinfo/twobitinfo.biotools.json index 3f1e3056c449f..cda7c2500b324 100644 --- a/data/twobitinfo/twobitinfo.biotools.json +++ b/data/twobitinfo/twobitinfo.biotools.json @@ -1,16 +1,41 @@ { + "accessibility": "Open access", "additionDate": "2021-04-22T19:54:37Z", "biotoolsCURIE": "biotools:twobitinfo", "biotoolsID": "twobitinfo", "collectionID": [ "ucsc-utilities" ], + "confidence_flag": "tool", + "cost": "Free of charge", "description": "get information about sequences in a .2bit file", "editPermission": { "type": "private" }, "homepage": "http://hgdownload.cse.ucsc.edu/admin/exe/", - "lastUpdate": "2021-04-22T19:57:50Z", + "lastUpdate": "2023-01-10T16:14:50.457425Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/ucscGenomeBrowser/kent/tree/master/src/utils/twoBitInfo" + } + ], "name": "twobitinfo", - "owner": "leipzig" + "operatingSystem": [ + "Linux", + "Mac" + ], + "owner": "leipzig", + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ] } diff --git a/data/ultraliser/ultraliser.biotools.json b/data/ultraliser/ultraliser.biotools.json new file mode 100644 index 0000000000000..4da47818350ed --- /dev/null +++ b/data/ultraliser/ultraliser.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T00:50:44.329594Z", + "biotoolsCURIE": "biotools:ultraliser", + "biotoolsID": "ultraliser", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "marwan.abdellah@epfl.ch", + "name": "Marwan Abdellah", + "orcidid": "https://orcid.org/0000-0002-7549-9657", + "typeEntity": "Person" + }, + { + "email": "felix.schuermann@epfl.ch", + "name": "Felix Schürmann", + "typeEntity": "Person" + }, + { + "name": "Henry Markram" + }, + { + "name": "Juan José García Cantero" + } + ], + "description": "A framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://github.com/BlueBrain/Ultraliser/wiki" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Modelling and simulation", + "uri": "http://edamontology.org/operation_2426" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://github.com/BlueBrain/Ultraliser", + "language": [ + "C++" + ], + "lastUpdate": "2023-01-25T00:50:44.332131Z", + "license": "GPL-3.0", + "name": "Ultraliser", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC491", + "pmid": "36434788" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Anatomy", + "uri": "http://edamontology.org/topic_3067" + }, + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Mathematics", + "uri": "http://edamontology.org/topic_3315" + }, + { + "term": "Neurobiology", + "uri": "http://edamontology.org/topic_3304" + } + ] +} diff --git a/data/umic/umic.biotools.json b/data/umic/umic.biotools.json index f6b724d1be7a9..8177fcb932569 100644 --- a/data/umic/umic.biotools.json +++ b/data/umic/umic.biotools.json @@ -3,6 +3,7 @@ "additionDate": "2021-05-05T19:18:02Z", "biotoolsCURIE": "biotools:umic", "biotoolsID": "umic", + "confidence_flag": "tool", "cost": "Free of charge", "credit": [ { @@ -13,6 +14,15 @@ "typeRole": [ "Primary contact" ] + }, + { + "name": "Anastasia Chatzidimitriou" + }, + { + "name": "Maria Christina Maniou" + }, + { + "name": "Maria Tsagiopoulou" } ], "description": "UMIc is an alignment free framework serving as a pre-processing step of FASTQ files for deduplication and correction of reads building consensus sequences from each UMI. The tool takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences, and produces FASTQ files that contain the corrected sequences (without the UMI) and their quality.", @@ -44,7 +54,7 @@ "language": [ "R" ], - "lastUpdate": "2022-12-09T23:49:58.250735Z", + "lastUpdate": "2023-01-10T15:47:28.890180Z", "license": "MIT", "maturity": "Emerging", "name": "UMIc", @@ -83,15 +93,21 @@ "name": "Tsagiopoulou M." } ], + "citationCount": 1, "date": "2021-05-28T00:00:00Z", "journal": "Frontiers in Genetics", "title": "UMIc: A Preprocessing Method for UMI Deduplication and Reads Correction" }, + "pmcid": "PMC8193862", + "pmid": "34122513", "type": [ "Method" ] } ], + "toolType": [ + "Library" + ], "version": [ "1.0" ] diff --git a/data/uroccomp/uroccomp.biotools.json b/data/uroccomp/uroccomp.biotools.json index 0c6c3fb0e45ba..09dfce75a5222 100644 --- a/data/uroccomp/uroccomp.biotools.json +++ b/data/uroccomp/uroccomp.biotools.json @@ -7,6 +7,7 @@ "File Exchange", "MATLAB" ], + "confidence_flag": "tool", "cost": "Free of charge (with restrictions)", "credit": [ { @@ -42,7 +43,7 @@ "language": [ "MATLAB" ], - "lastUpdate": "2022-12-09T23:48:59.665877Z", + "lastUpdate": "2023-01-10T15:53:26.534377Z", "license": "GPL-3.0", "link": [ { diff --git a/data/usagi/usagi.biotools.json b/data/usagi/usagi.biotools.json new file mode 100644 index 0000000000000..d5907330c56fa --- /dev/null +++ b/data/usagi/usagi.biotools.json @@ -0,0 +1,54 @@ +{ + "additionDate": "2023-01-26T08:58:40.143653Z", + "biotoolsCURIE": "biotools:usagi", + "biotoolsID": "usagi", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Observational Health Data Sciences and Informatics OHDSI", + "url": "https://www.ohdsi.org/" + } + ], + "description": "Usagi is a software tool used to help in the process of mapping codes from a source system into terminologies, preferably standard ones, stored in the Observational Medical Outcomes Partnership (OMOP) Vocabulary (http://www.ohdsi.org/data-standardization/vocabulary-resources/). The word Usagi is Japanese for rabbit and was named after the first mapping exercise it was used for; mapping source codes used in a Japanese dataset into OMOP Vocabulary concepts.", + "documentation": [ + { + "type": [ + "Installation instructions" + ], + "url": "https://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:usagi" + } + ], + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/OHDSI/Usagi" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Mapping", + "uri": "http://edamontology.org/operation_2429" + } + ] + } + ], + "homepage": "https://www.ohdsi.org/analytic-tools/usagi/", + "lastUpdate": "2023-01-26T09:15:23.263977Z", + "link": [ + { + "type": [ + "Software catalogue" + ], + "url": "https://www.ohdsi.org/analytic-tools/usagi/" + } + ], + "name": "Usagi", + "owner": "iacs-biocomputacion" +} diff --git a/data/usat/usat.biotools.json b/data/usat/usat.biotools.json new file mode 100644 index 0000000000000..f8095de230569 --- /dev/null +++ b/data/usat/usat.biotools.json @@ -0,0 +1,111 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T01:15:43.518565Z", + "biotoolsCURIE": "biotools:usat", + "biotoolsID": "usat", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "Xuewen.wang@unthscc.edu", + "name": "Xuewen Wang", + "orcidid": "http://orcid.org/0000-0003-2820-9255", + "typeEntity": "Person" + }, + { + "name": "Bruce Budowle" + }, + { + "name": "Jianye Ge" + } + ], + "description": "A Bioinformatic Toolkit to Facilitate Interpretation and Comparative Visualization of Tandem Repeat Sequences.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Haplotype mapping", + "uri": "http://edamontology.org/operation_0487" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/XuewenWangUGA/USAT", + "language": [ + "Java", + "Shell" + ], + "lastUpdate": "2023-01-20T01:17:37.689993Z", + "license": "LGPL-2.1", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/Ge-Lab/USAT" + } + ], + "name": "USAT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/s12859-022-05021-1", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Tandem repeats (TR), highly variable genomic variants, are widely used in individual identification, disease diagnostics, and evolutionary studies. The recent advances in sequencing technologies and bioinformatic tools facilitate calling TR haplotypes genome widely. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision in characterizing TR haplotypes. The need to identify the differences at the single nucleotide level between or among TR haplotypes with an easy-use bioinformatic tool is essential. Results: In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which takes TR haplotype output from existing tools to perform allele size conversion, sequence comparison of haplotypes, figure plotting, comparison for allele distribution, and interactive visualization. An exemplary application of USAT for analysis of the CODIS core STR loci for DNA forensics with benchmarking human individuals demonstrated the capabilities of USAT. USAT has user-friendly graphic interfaces and runs fast in major computing operating systems with parallel computing enabled. Conclusion: USAT is a user-friendly bioinformatics software for interpretation, visualization, and comparisons of TRs.", + "authors": [ + { + "name": "Budowle B." + }, + { + "name": "Ge J." + }, + { + "name": "Wang X." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "USAT: a bioinformatic toolkit to facilitate interpretation and comparative visualization of tandem repeat sequences" + }, + "pmcid": "PMC9675219", + "pmid": "36402991" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Sequence composition, complexity and repeats", + "uri": "http://edamontology.org/topic_0157" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/vaeda/vaeda.biotools.json b/data/vaeda/vaeda.biotools.json new file mode 100644 index 0000000000000..7356f48d9cfa8 --- /dev/null +++ b/data/vaeda/vaeda.biotools.json @@ -0,0 +1,102 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T00:33:52.016648Z", + "biotoolsCURIE": "biotools:vaeda", + "biotoolsID": "vaeda", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "kostka@pitt.edu", + "name": "Dennis Kostka", + "orcidid": "https://orcid.org/0000-0002-1460-5487", + "typeEntity": "Person" + }, + { + "name": "Hannah Schriever" + } + ], + "description": "Vaeda computationally annotates doublets in single-cell RNA sequencing data.", + "download": [ + { + "type": "Container file", + "url": "https://doi.org/10.5281/zenodo.7199783" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + } + ] + } + ], + "homepage": "https://github.com/kostkalab/vaeda", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-25T00:33:52.019192Z", + "license": "MIT", + "name": "vaeda", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC720", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology's utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation, computational methods to annotate and remove doublets form scRNA-seq datasets are needed. RESULTS: We introduce vaeda (Variational Auto-Encoder for Doublet Annotation), a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to 16 benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. Altogether, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. AVAILABILITY AND IMPLEMENTATION: Vaeda is available at https://github.com/kostkalab/vaeda, and the version used for the results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Kostka D." + }, + { + "name": "Schriever H." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "Vaeda computationally annotates doublets in single-cell RNA sequencing data" + }, + "pmcid": "PMC9805559", + "pmid": "36342203" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/variantspark/variantspark.biotools.json b/data/variantspark/variantspark.biotools.json index 12f0105286ef3..c1cc702e9c818 100644 --- a/data/variantspark/variantspark.biotools.json +++ b/data/variantspark/variantspark.biotools.json @@ -1,15 +1,53 @@ { + "accessibility": "Open access", "additionDate": "2020-04-30T12:25:08Z", "biotoolsCURIE": "biotools:variantspark", "biotoolsID": "variantspark", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "VariantSpark is a tailored Apache Spark-based machine learning framework that creates insights from high-dimensional data, including genomics and clinical data. VariantSpark’s machine learning method overcomes the limitation of traditional approaches that requires data to be eliminated or identifying only independent markers. Especially complex events are triggered by multiple contributing factors. VariantSpark is able to detect such sets of interacting features thereby identifying more accurate predictive markers. VariantSpark builds on the Random Forest Machine Learning method, which allows to interrogate the tree-based models and identify which features contributed in what proportion to the overall prediction outcome. We also provide a visualization engine that shows the interplay between features and their label association.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://variantspark.readthedocs.io/en/latest/index.html" + } + ], "editPermission": { "type": "private" }, "homepage": "https://bioinformatics.csiro.au/variantspark/", - "lastUpdate": "2022-12-09T23:50:59.063091Z", + "language": [ + "JavaScript" + ], + "lastUpdate": "2023-01-10T15:43:14.003083Z", + "license": "MIT", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://aws.amazon.com/marketplace/pp/prodview-pgna4dj6xqqde" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/aehrc/VariantSpark" + } + ], "name": "VariantSpark", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "ntwine", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Machine learning", diff --git a/data/vcfpop/vcfpop.biotools.json b/data/vcfpop/vcfpop.biotools.json new file mode 100644 index 0000000000000..febb195207458 --- /dev/null +++ b/data/vcfpop/vcfpop.biotools.json @@ -0,0 +1,140 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-23T10:12:35.663506Z", + "biotoolsCURIE": "biotools:vcfpop", + "biotoolsID": "vcfpop", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Baoguo Li" + }, + { + "name": "Bing Yang" + }, + { + "name": "Jincuo Ao" + }, + { + "name": "Kang Huang", + "orcidid": "https://orcid.org/0000-0002-8357-117X" + } + ], + "description": "Performing population genetics analyses for polyploids and anisoploids based on next-generation sequencing variant calling dataset.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Ecological modelling", + "uri": "http://edamontology.org/operation_3946" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "http://github.com/huangkang1987/vcfpop", + "language": [ + "C", + "C++" + ], + "lastUpdate": "2023-01-23T10:12:35.665990Z", + "license": "GPL-3.0", + "name": "VCFPOP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1111/1755-0998.13744", + "metadata": { + "abstract": "© 2022 John Wiley & Sons Ltd.Polyploids are cells or organisms with a genome consisting of more than two sets of homologous chromosomes. Polyploid plants have important traits that facilitate speciation and are thus often model systems for evolutionary, molecular ecology and agricultural studies. However, due to their unusual mode of inheritance and double-reduction, diploid models of population genetic analysis cannot properly be applied to autopolyploids. To overcome this problem, we developed a software package entitled vcfpop to perform a variety of population genetic analyses for autopolyploids, such as parentage analysis, analysis of molecular variance, principal coordinates analysis, hierarchical clustering analysis and Bayesian clustering. We used three data sets to evaluate the capability of vcfpop to analyse large data sets on a desktop computer. This software is freely available at http://github.com/huangkang1987/vcfpop.", + "authors": [ + { + "name": "Ao J." + }, + { + "name": "Cui Y." + }, + { + "name": "Dunn D.W." + }, + { + "name": "He S." + }, + { + "name": "Huang K." + }, + { + "name": "Kong Y." + }, + { + "name": "Li B." + }, + { + "name": "Li N." + }, + { + "name": "Li W." + }, + { + "name": "Li W." + }, + { + "name": "Li Y." + }, + { + "name": "Shen Y." + }, + { + "name": "Wang D." + }, + { + "name": "Yang B." + } + ], + "date": "2022-01-01T00:00:00Z", + "journal": "Molecular Ecology Resources", + "title": "vcfpop: Performing population genetics analyses for autopolyploids and aneuploids based on next-generation sequencing data sets" + }, + "pmid": "36458971" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Ecology", + "uri": "http://edamontology.org/topic_0610" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Population genetics", + "uri": "http://edamontology.org/topic_3056" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/vechat/vechat.biotools.json b/data/vechat/vechat.biotools.json new file mode 100644 index 0000000000000..e6e539032efda --- /dev/null +++ b/data/vechat/vechat.biotools.json @@ -0,0 +1,107 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T00:16:14.913180Z", + "biotoolsCURIE": "biotools:vechat", + "biotoolsID": "vechat", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "a.schoenhuth@cwi.nl", + "name": "Alexander Schönhuth", + "orcidid": "https://orcid.org/0000-0003-3529-0856", + "typeEntity": "Person" + }, + { + "name": "Xiao Luo" + }, + { + "name": "Xiongbin Kang" + } + ], + "description": "Correcting errors in long reads using variation graphs.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "De-novo assembly", + "uri": "http://edamontology.org/operation_0524" + }, + { + "term": "Genome assembly", + "uri": "http://edamontology.org/operation_0525" + }, + { + "term": "Haplotype mapping", + "uri": "http://edamontology.org/operation_0487" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Sequencing error detection", + "uri": "http://edamontology.org/operation_3195" + } + ] + } + ], + "homepage": "https://github.com/HaploKit/vechat", + "language": [ + "C++", + "Python" + ], + "lastUpdate": "2023-01-25T00:16:14.915840Z", + "license": "GPL-3.0", + "name": "VeChat", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1038/S41467-022-34381-8", + "metadata": { + "abstract": "© 2022, The Author(s).Error correction is the canonical first step in long-read sequencing data analysis. Current self-correction methods, however, are affected by consensus sequence induced biases that mask true variants in haplotypes of lower frequency showing in mixed samples. Unlike consensus sequence templates, graph-based reference systems are not affected by such biases, so do not mistakenly mask true variants as errors. We present VeChat, as an approach to implement this idea: VeChat is based on variation graphs, as a popular type of data structure for pangenome reference systems. Extensive benchmarking experiments demonstrate that long reads corrected by VeChat contain 4 to 15 (Pacific Biosciences) and 1 to 10 times (Oxford Nanopore Technologies) less errors than when being corrected by state of the art approaches. Further, using VeChat prior to long-read assembly significantly improves the haplotype awareness of the assemblies. VeChat is an easy-to-use open-source tool and publicly available at https://github.com/HaploKit/vechat.", + "authors": [ + { + "name": "Kang X." + }, + { + "name": "Luo X." + }, + { + "name": "Schonhuth A." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Nature Communications", + "title": "VeChat: correcting errors in long reads using variation graphs" + }, + "pmcid": "PMC9636371", + "pmid": "36333324" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/vfuzz/vfuzz.biotools.json b/data/vfuzz/vfuzz.biotools.json deleted file mode 100644 index 6273db44580ad..0000000000000 --- a/data/vfuzz/vfuzz.biotools.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "additionDate": "2021-01-18T10:56:20Z", - "biotoolsCURIE": "biotools:vfuzz", - "biotoolsID": "vfuzz", - "confidence_flag": "tool", - "description": "Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs.\n\nFuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable", - "editPermission": { - "type": "private" - }, - "homepage": "https://github.com/guidovranken/vfuzz", - "lastUpdate": "2021-03-20T12:30:29Z", - "name": "VFuzz", - "owner": "zsmag19", - "publication": [ - { - "doi": "10.1109/TCYB.2020.3013675", - "pmid": "32946405" - } - ] -} diff --git a/data/vgsc-db/vgsc-db.biotools.json b/data/vgsc-db/vgsc-db.biotools.json new file mode 100644 index 0000000000000..c06e42393f1af --- /dev/null +++ b/data/vgsc-db/vgsc-db.biotools.json @@ -0,0 +1,127 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-25T00:11:11.356221Z", + "biotoolsCURIE": "biotools:vgsc-db", + "biotoolsID": "vgsc-db", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "panpeichen@zju.edu.cn", + "name": "Peichen Pan", + "typeEntity": "Person" + }, + { + "email": "tingjunhou@zju.edu.cn", + "name": "Tingjun Hou", + "typeEntity": "Person" + }, + { + "name": "Gaoang Wang" + }, + { + "name": "Jiahui Yu" + } + ], + "description": "An online database of voltage-gated sodium channels.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "http://cadd.zju.edu.cn/vgsc/", + "lastUpdate": "2023-01-25T00:11:11.359587Z", + "name": "VGSC-DB", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S13321-022-00655-Y", + "metadata": { + "abstract": "© 2022, The Author(s).As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target for drug design and discovery. Many subtype-selective modulators targeting VGSCs have been reported, and some of them have been approved for clinical applications. However, the drug design resources related to VGSCs are insufficient, especially the lack of accurate and extensive compound data toward VGSCs. To fulfill this demand, we develop the Voltage-gated Sodium Channels Database (VGSC-DB). VGSC-DB is the first open-source database for VGSCs, which provides open access to 6055 data records, including 3396 compounds from 173 references toward nine subtypes of Navs (Nav1.1 ~ Nav1.9). A total of 28 items of information is included in each data record, including the chemical structure, biological activity (IC50/EC50), target, binding site, organism, chemical and physical properties, etc. VGSC-DB collects the data from small-molecule compounds, toxins and various derivatives. Users can search the information of compounds by text or structure, and the advanced search function is also supported to realize batch query. VGSC-DB is freely accessible at http://cadd.zju.edu.cn/vgsc/, and all the data can be downloaded in XLSX/SDF file formats. Graphical Abstract: [Figure not available: see fulltext.].", + "authors": [ + { + "name": "Cao D." + }, + { + "name": "Du H." + }, + { + "name": "Hou T." + }, + { + "name": "Liu Y." + }, + { + "name": "Pan P." + }, + { + "name": "Shen C." + }, + { + "name": "Wang G." + }, + { + "name": "Yu J." + }, + { + "name": "Zhang X." + }, + { + "name": "Zhang Y." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "Journal of Cheminformatics", + "title": "VGSC-DB: an online database of voltage-gated sodium channels" + }, + "pmcid": "PMC9628066", + "pmid": "36320030" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Medicinal chemistry", + "uri": "http://edamontology.org/topic_0209" + }, + { + "term": "Preclinical and clinical studies", + "uri": "http://edamontology.org/topic_3379" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/vilon/vilon.biotools.json b/data/vilon/vilon.biotools.json new file mode 100644 index 0000000000000..80e64a2987a1d --- /dev/null +++ b/data/vilon/vilon.biotools.json @@ -0,0 +1,94 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T23:51:23.303826Z", + "biotoolsCURIE": "biotools:vilon", + "biotoolsID": "vilon", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "nar21@kreil.org", + "name": "David P Kreil", + "orcidid": "https://orcid.org/0000-0001-7538-2056", + "typeEntity": "Person" + }, + { + "email": "eric.kolaczyk@mcgill.ca", + "name": "Eric D Kolaczyk", + "typeEntity": "Person" + }, + { + "name": "Alexander D Aldoshin" + }, + { + "name": "Swati Singh" + }, + { + "name": "Maciej M Kańduła", + "orcidid": "https://orcid.org/0000-0001-5727-1491" + } + ], + "description": "Multi-layer network approach to data integration demonstrated for patient stratification.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Expression profile clustering", + "uri": "http://edamontology.org/operation_0313" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + } + ] + } + ], + "homepage": "https://github.com/data-int/vilon/", + "language": [ + "Perl", + "R" + ], + "lastUpdate": "2023-01-24T23:51:23.306639Z", + "license": "GPL-3.0", + "name": "ViLoN", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/NAR/GKAC988", + "pmid": "36395816" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Personalised medicine", + "uri": "http://edamontology.org/topic_3577" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/vimrt/vimrt.biotools.json b/data/vimrt/vimrt.biotools.json new file mode 100644 index 0000000000000..ffe84068e5465 --- /dev/null +++ b/data/vimrt/vimrt.biotools.json @@ -0,0 +1,157 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T23:43:12.519115Z", + "biotoolsCURIE": "biotools:vimrt", + "biotoolsID": "vimrt", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "xyzhang@tongji.edu.cn", + "name": "Xiaoyan Zhang", + "orcidid": "https://orcid.org/0000-0003-1562-0045", + "typeEntity": "Person" + }, + { + "email": "nadger_wang@139.com", + "name": "Ying Wang", + "typeEntity": "Person" + }, + { + "name": "Fanglin Tan" + }, + { + "name": "Yuantao Tong" + } + ], + "description": "A text-mining tool and search engine for automated virus mutation recognition.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Disease name", + "uri": "http://edamontology.org/data_3668" + } + }, + { + "data": { + "term": "Gene name", + "uri": "http://edamontology.org/data_2299" + } + }, + { + "data": { + "term": "Virus identifier", + "uri": "http://edamontology.org/data_2913" + } + } + ], + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Small molecule design", + "uri": "http://edamontology.org/operation_4009" + }, + { + "term": "Text mining", + "uri": "http://edamontology.org/operation_0306" + } + ] + } + ], + "homepage": "http://bmtongji.cn:1225/mutation/index", + "lastUpdate": "2023-01-24T23:43:12.522495Z", + "name": "ViMRT", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAC721", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. RESULTS: We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. AVAILABILITY AND IMPLEMENTATION: ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.", + "authors": [ + { + "name": "Cheng S." + }, + { + "name": "Crabbe M.J.C." + }, + { + "name": "Fang M." + }, + { + "name": "Huang D." + }, + { + "name": "Huang H." + }, + { + "name": "Tan F." + }, + { + "name": "Tong Y." + }, + { + "name": "Wang Y." + }, + { + "name": "Wei Z." + }, + { + "name": "Xie Y." + }, + { + "name": "Zhang X." + }, + { + "name": "Zhang Z." + }, + { + "name": "Zong H." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "ViMRT: a text-mining tool and search engine for automated virus mutation recognition" + }, + "pmcid": "PMC9805560", + "pmid": "36342236" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Data mining", + "uri": "http://edamontology.org/topic_3473" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Medicine", + "uri": "http://edamontology.org/topic_3303" + }, + { + "term": "Natural language processing", + "uri": "http://edamontology.org/topic_0218" + }, + { + "term": "Vaccinology", + "uri": "http://edamontology.org/topic_3966" + } + ] +} diff --git a/data/virmutsig/virmutsig.biotools.json b/data/virmutsig/virmutsig.biotools.json new file mode 100644 index 0000000000000..c18f2d91db61e --- /dev/null +++ b/data/virmutsig/virmutsig.biotools.json @@ -0,0 +1,147 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T15:50:49.569918Z", + "biotoolsCURIE": "biotools:virmutsig", + "biotoolsID": "virmutsig", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "alex.graudenzi@ibfm.cnr.it", + "name": "Alex Graudenzi", + "orcidid": "https://orcid.org/0000-0001-5452-1918", + "typeEntity": "Person" + }, + { + "email": "daniele.ramazzotti@unimib.it", + "name": "Daniele Ramazzotti", + "orcidid": "https://orcid.org/0000-0002-6087-2666", + "typeEntity": "Person" + }, + { + "email": "d.maspero@campus.unimib.it", + "name": "Davide Maspero", + "typeEntity": "Person" + }, + { + "name": "Danilo Porro" + }, + { + "name": "Fabrizio Angaroni" + }, + { + "name": "Rocco Piazza" + } + ], + "description": "Discovery and assignment of viral mutational signatures from sequencing data.", + "download": [ + { + "type": "Container file", + "url": "https://hub.docker.com/r/dcblab/virmutsig_img" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Data retrieval", + "uri": "http://edamontology.org/operation_2422" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + }, + { + "term": "de Novo sequencing", + "uri": "http://edamontology.org/operation_3644" + } + ] + } + ], + "homepage": "https://github.com/BIMIB-DISCo/VirMutSig", + "language": [ + "R" + ], + "lastUpdate": "2023-01-24T15:50:49.573238Z", + "license": "Apache-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/BIMIB-DISCo/VirMutSig.git" + } + ], + "name": "VirMutSig", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/J.XPRO.2021.100911", + "metadata": { + "abstract": "© 2021 The Author(s)We describe the procedures to perform the following: (1) the de novo discovery of mutational signatures from raw sequencing data of viral samples and (2) the association of existing viral mutational signatures to the samples of a given dataset. The goal is to identify and characterize the nucleotide substitution patterns related to the mutational processes that underlie the origination of variants in viral genomes. The VirMutSig protocol is available at this link: https://github.com/BIMIB-DISCo/VirMutSig. For complete information on the theoretical aspects of this protocol, please refer to Graudenzi et al. (2021).", + "authors": [ + { + "name": "Angaroni F." + }, + { + "name": "Graudenzi A." + }, + { + "name": "Maspero D." + }, + { + "name": "Piazza R." + }, + { + "name": "Porro D." + }, + { + "name": "Ramazzotti D." + } + ], + "citationCount": 1, + "date": "2021-12-17T00:00:00Z", + "journal": "STAR Protocols", + "title": "VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data" + }, + "pmcid": "PMC9680118" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Systems biology", + "uri": "http://edamontology.org/topic_2259" + } + ] +} diff --git a/data/visit/visit.biotools.json b/data/visit/visit.biotools.json deleted file mode 100644 index 457b4acefa730..0000000000000 --- a/data/visit/visit.biotools.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "additionDate": "2021-05-27T09:24:40Z", - "biotoolsCURIE": "biotools:visit", - "biotoolsID": "visit", - "description": "VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool.", - "editPermission": { - "type": "private" - }, - "function": [ - { - "operation": [ - { - "term": "Visualisation", - "uri": "http://edamontology.org/operation_0337" - } - ] - } - ], - "homepage": "https://wci.llnl.gov/simulation/computer-codes/visit", - "lastUpdate": "2022-12-09T23:52:25.998587Z", - "name": "VisIt", - "owner": "Kigaard", - "topic": [ - { - "term": "Zoology", - "uri": "http://edamontology.org/topic_3500" - } - ] -} diff --git a/data/vital_sqi/vital_sqi.biotools.json b/data/vital_sqi/vital_sqi.biotools.json new file mode 100644 index 0000000000000..a8749b79f8c79 --- /dev/null +++ b/data/vital_sqi/vital_sqi.biotools.json @@ -0,0 +1,141 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T15:41:50.410913Z", + "biotoolsCURIE": "biotools:vital_sqi", + "biotoolsID": "vital_sqi", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "khoaldv@oucru.org", + "name": "Van-Khoa D. Le", + "typeEntity": "Person" + }, + { + "name": "David Clifton" + }, + { + "name": "Hai Bich Ho" + }, + { + "name": "Pantelis Georgiou" + } + ], + "description": "Signal quality control pipeline for electrocardiogram and photoplethysmogram.", + "documentation": [ + { + "type": [ + "General", + "User manual" + ], + "url": "https://vitalsqi.readthedocs.io/en/latest/?badge=latest" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://pypi.org/project/vital-sqi/", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-24T15:41:50.413468Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/meta00/vital_sqi" + } + ], + "name": "vital_sqi", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FPHYS.2022.1020458", + "metadata": { + "abstract": "Copyright © 2022 Le, Ho, Karolcik, Hernandez, Greeff, Nguyen, Phan, Le, Thwaites, Georgiou and Clifton.Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.", + "authors": [ + { + "name": "Clifton D." + }, + { + "name": "Georgiou P." + }, + { + "name": "Greeff H." + }, + { + "name": "Hernandez B." + }, + { + "name": "Ho H.B." + }, + { + "name": "Karolcik S." + }, + { + "name": "Le T.P." + }, + { + "name": "Le V.-K.D." + }, + { + "name": "Nguyen V.H." + }, + { + "name": "Phan N.Q.K." + }, + { + "name": "Thwaites L." + } + ], + "date": "2022-11-11T00:00:00Z", + "journal": "Frontiers in Physiology", + "title": "vital_sqi: A Python package for physiological signal quality control" + }, + "pmcid": "PMC9692103", + "pmid": "36439252" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/vitviz/vitviz.biotools.json b/data/vitviz/vitviz.biotools.json new file mode 100644 index 0000000000000..8c4441f0288bf --- /dev/null +++ b/data/vitviz/vitviz.biotools.json @@ -0,0 +1,109 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T15:35:14.493636Z", + "biotoolsCURIE": "biotools:vitviz", + "biotoolsID": "vitviz", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "stefania.savoi@unito.it", + "name": "Stefania Savoi", + "typeEntity": "Person" + }, + { + "email": "tomas.matus@uv.es", + "name": "José Tomás Matus", + "typeEntity": "Person" + }, + { + "name": "Antonio Santiago" + }, + { + "name": "Luis Orduña" + } + ], + "description": "Transcriptomic and metabolomic integration as a resource in grapevine to study fruit metabolite quality traits.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Deposition", + "uri": "http://edamontology.org/operation_3431" + }, + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://tomsbiolab.com/vitviz", + "lastUpdate": "2023-01-24T15:35:14.496536Z", + "name": "VitViz", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FPLS.2022.937927", + "metadata": { + "abstract": "Copyright © 2022 Savoi, Santiago, Orduña and Matus.Transcriptomics and metabolomics are methodologies being increasingly chosen to perform molecular studies in grapevine (Vitis vinifera L.), focusing either on plant and fruit development or on interaction with abiotic or biotic factors. Currently, the integration of these approaches has become of utmost relevance when studying key plant physiological and metabolic processes. The results from these analyses can undoubtedly be incorporated in breeding programs whereby genes associated with better fruit quality (e.g., those enhancing the accumulation of health-promoting compounds) or with stress resistance (e.g., those regulating beneficial responses to environmental transition) can be used as selection markers in crop improvement programs. Despite the vast amount of data being generated, integrative transcriptome/metabolome meta-analyses (i.e., the joint analysis of several studies) have not yet been fully accomplished in this species, mainly due to particular specificities of metabolomic studies, such as differences in data acquisition (i.e., different compounds being investigated), unappropriated and unstandardized metadata, or simply no deposition of data in public repositories. These meta-analyses require a high computational capacity for data mining a priori, but they also need appropriate tools to explore and visualize the integrated results. This perspective article explores the universe of omics studies conducted in V. vinifera, focusing on fruit-transcriptome and metabolome analyses as leading approaches to understand berry physiology, secondary metabolism, and quality. Moreover, we show how omics data can be integrated in a simple format and offered to the research community as a web resource, giving the chance to inspect potential gene-to-gene and gene-to-metabolite relationships that can later be tested in hypothesis-driven research. In the frame of the activities promoted by the COST Action CA17111 INTEGRAPE, we present the first grapevine transcriptomic and metabolomic integrated database (TransMetaDb) developed within the Vitis Visualization (VitViz) platform (https://tomsbiolab.com/vitviz). This tool also enables the user to conduct and explore meta-analyses utilizing different experiments, therefore hopefully motivating the community to generate Findable, Accessible, Interoperable and Reusable (F.A.I.R.) data to be included in the future.", + "authors": [ + { + "name": "Matus J.T." + }, + { + "name": "Orduna L." + }, + { + "name": "Santiago A." + }, + { + "name": "Savoi S." + } + ], + "date": "2022-10-20T00:00:00Z", + "journal": "Frontiers in Plant Science", + "title": "Transcriptomic and metabolomic integration as a resource in grapevine to study fruit metabolite quality traits" + }, + "pmcid": "PMC9630917", + "pmid": "36340350" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Proteomics", + "uri": "http://edamontology.org/topic_0121" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/vsl2/vsl2.biotools.json b/data/vsl2/vsl2.biotools.json deleted file mode 100644 index a8e402d34edce..0000000000000 --- a/data/vsl2/vsl2.biotools.json +++ /dev/null @@ -1,73 +0,0 @@ -{ - "accessibility": "Open access", - "additionDate": "2022-08-31T10:04:03.014683Z", - "biotoolsCURIE": "biotools:vsl2", - "biotoolsID": "vsl2", - "cost": "Free of charge", - "credit": [ - { - "email": "zoran@ist.temple.edu", - "name": "Zoran Obradovic" - } - ], - "description": "VSL2 is a predictor model that is applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by other disorder predictors.", - "editPermission": { - "authors": [ - "damiano.piovesan", - "tlazar" - ], - "type": "group" - }, - "function": [ - { - "operation": [ - { - "term": "Protein disorder prediction", - "uri": "http://edamontology.org/operation_3904" - } - ] - } - ], - "homepage": "http://www.ist.temple.edu/disprot/predictorVSL2.php", - "lastUpdate": "2022-12-09T23:53:44.772424Z", - "name": "VSL2", - "operatingSystem": [ - "Linux" - ], - "owner": "daniela.mereuta", - "publication": [ - { - "doi": "10.1186/1471-2105-7-208", - "metadata": { - "abstract": "Background: Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. Results: We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (≤30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. Conclusion: The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php. © 2006 Peng et al; licensee BioMed Central Ltd.", - "authors": [ - { - "name": "Dunker A.K." - }, - { - "name": "Obradovic Z." - }, - { - "name": "Peng K." - }, - { - "name": "Radivojac P." - }, - { - "name": "Vucetic S." - } - ], - "citationCount": 675, - "date": "2006-04-17T00:00:00Z", - "journal": "BMC Bioinformatics", - "title": "Length-dependent prediction of protein in intrinsic disorder" - }, - "type": [ - "Primary" - ] - } - ], - "toolType": [ - "Command-line tool" - ] -} diff --git a/data/vulcan_mapper/vulcan_mapper.biotools.json b/data/vulcan_mapper/vulcan_mapper.biotools.json index 5195e597a3832..c71808c0c63c4 100644 --- a/data/vulcan_mapper/vulcan_mapper.biotools.json +++ b/data/vulcan_mapper/vulcan_mapper.biotools.json @@ -1,7 +1,10 @@ { + "accessibility": "Open access", "additionDate": "2021-08-23T17:59:56Z", "biotoolsCURIE": "biotools:vulcan_mapper", "biotoolsID": "vulcan_mapper", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Improved long-read mapping and structural variant calling via dual-mode alignment", "editPermission": { "type": "private" @@ -39,9 +42,21 @@ } ], "homepage": "https://gitlab.com/treangenlab/vulcan", - "lastUpdate": "2022-12-09T23:56:53.813295Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T13:55:45.761138Z", + "license": "MIT", "name": "Vulcan", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "yilei_fu", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Mapping", diff --git a/data/watchdog/watchdog.biotools.json b/data/watchdog/watchdog.biotools.json index 0af3296152733..19f53106ce21e 100644 --- a/data/watchdog/watchdog.biotools.json +++ b/data/watchdog/watchdog.biotools.json @@ -1,15 +1,58 @@ { + "accessibility": "Open access", "additionDate": "2020-04-24T15:51:24Z", "biotoolsCURIE": "biotools:watchdog", "biotoolsID": "watchdog", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Workflow management system for the automated and distributed analysis of large-scale experimental data", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://klugem.github.io/watchdog/Watchdog-manual.html" + } + ], "editPermission": { "type": "private" }, "homepage": "https://www.bio.ifi.lmu.de/watchdog", - "lastUpdate": "2022-12-09T23:58:20.392967Z", + "language": [ + "Java" + ], + "lastUpdate": "2023-01-10T13:45:54.299616Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://anaconda.org/bioconda/watchdog-wms" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/klugem/watchdog" + }, + { + "type": [ + "Repository" + ], + "url": "https://hub.docker.com/r/klugem/watchdog-wms/" + } + ], "name": "Watchdog", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "CarolineFriedel", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "Workflows", diff --git a/data/wenda_gpu/wenda_gpu.biotools.json b/data/wenda_gpu/wenda_gpu.biotools.json new file mode 100644 index 0000000000000..5d107ca47d631 --- /dev/null +++ b/data/wenda_gpu/wenda_gpu.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T01:28:37.565416Z", + "biotoolsCURIE": "biotools:wenda_gpu", + "biotoolsID": "wenda_gpu", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Jacob R. Gardner" + }, + { + "name": "Ariel A. Hippen", + "orcidid": "http://orcid.org/0000-0001-9336-6543" + }, + { + "name": "Casey S. Greene", + "orcidid": "http://orcid.org/0000-0001-8713-9213" + }, + { + "name": "Jake Crawford", + "orcidid": "http://orcid.org/0000-0001-6207-0782" + } + ], + "description": "Fast domain adaptation method for building prediction models on genomic data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Fold recognition", + "uri": "http://edamontology.org/operation_0303" + }, + { + "term": "PTM site prediction", + "uri": "http://edamontology.org/operation_0417" + } + ] + } + ], + "homepage": "https://github.com/greenelab/wenda_gpu/", + "language": [ + "Python", + "Shell" + ], + "lastUpdate": "2023-01-20T01:28:37.567973Z", + "license": "BSD-3-Clause", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/greenelab/wenda_gpu_paper/" + } + ], + "name": "wenda_gpu", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/bioinformatics/btac663", + "metadata": { + "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Domain adaptation allows for the development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets. RESULTS: We developed wenda_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net. AVAILABILITY AND IMPLEMENTATION: wenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", + "authors": [ + { + "name": "Crawford J." + }, + { + "name": "Gardner J.R." + }, + { + "name": "Greene C.S." + }, + { + "name": "Hippen A.A." + } + ], + "date": "2022-11-15T00:00:00Z", + "journal": "Bioinformatics (Oxford, England)", + "title": "wenda_gpu: fast domain adaptation for genomic data" + }, + "pmcid": "PMC9665854", + "pmid": "36193991" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/wgdtree/wgdtree.biotools.json b/data/wgdtree/wgdtree.biotools.json new file mode 100644 index 0000000000000..17fae7c0c6ea0 --- /dev/null +++ b/data/wgdtree/wgdtree.biotools.json @@ -0,0 +1,117 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T15:27:22.780978Z", + "biotoolsCURIE": "biotools:wgdtree", + "biotoolsID": "wgdtree", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "daliberles@temple.edu", + "name": "David A. Liberles", + "orcidid": "https://orcid.org/0000-0003-3487-8826", + "typeEntity": "Person" + }, + { + "name": "C. Nicholas Henry" + }, + { + "name": "Claire S. Probst" + }, + { + "name": "Yuying Rong" + } + ], + "description": "A phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene tree construction", + "uri": "http://edamontology.org/operation_0553" + }, + { + "term": "Phylogenetic tree reconciliation", + "uri": "http://edamontology.org/operation_3947" + }, + { + "term": "Species tree construction", + "uri": "http://edamontology.org/operation_0544" + }, + { + "term": "Tree dating", + "uri": "http://edamontology.org/operation_3942" + } + ] + } + ], + "homepage": "https://github.com/cnickh/wgdtree", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-24T15:27:22.783680Z", + "license": "MIT", + "name": "WGDTree", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-05042-W", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Multiple processes impact the probability of retention of individual genes following whole genome duplication (WGD) events. In analyzing two consecutive whole genome duplication events that occurred in the lineage leading to Atlantic salmon, a new phylogenetic statistical analysis was developed to examine the contingency of retention in one event based upon retention in a previous event. This analysis is intended to evaluate mechanisms of duplicate gene retention and to provide software to generate the test statistic for any genome with pairs of WGDs in its history. Results: Here a software package written in Python, ‘WGDTree’ for the analysis of duplicate gene retention following whole genome duplication events is presented. Using gene tree-species tree reconciliation to label gene duplicate nodes and differentiate between WGD and SSD duplicates, the tool calculates a statistic based upon the conditional probability of a gene duplicate being retained after a second whole genome duplication dependent upon the retention status after the first event. The package also contains methods for the simulation of gene trees with WGD events. After running simulations, the accuracy of the placement of events has been determined to be high. The conditional probability statistic has been calculated for Phalaenopsis equestris on a monocot species tree with a pair of consecutive WGD events on its lineage, showing the applicability of the method. Conclusions: A new software tool has been created for the analysis of duplicate genes in examination of retention mechanisms. The software tool has been made available on the Python package index and the source code can be found on GitHub here: https://github.com/cnickh/wgdtree.", + "authors": [ + { + "name": "Henry C.N." + }, + { + "name": "Liberles D.A." + }, + { + "name": "Miraszek J.L." + }, + { + "name": "Piper K." + }, + { + "name": "Probst C.S." + }, + { + "name": "Rong Y." + }, + { + "name": "Wilson A.E." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "WGDTree: a phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events" + }, + "pmcid": "PMC9701042", + "pmid": "36434497" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Genomics", + "uri": "http://edamontology.org/topic_0622" + }, + { + "term": "Phylogenetics", + "uri": "http://edamontology.org/topic_3293" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + } + ] +} diff --git a/data/wgsim/wgsim.biotools.json b/data/wgsim/wgsim.biotools.json index 8b65b595ae275..b02a2424fe686 100644 --- a/data/wgsim/wgsim.biotools.json +++ b/data/wgsim/wgsim.biotools.json @@ -1,15 +1,26 @@ { + "accessibility": "Open access", "additionDate": "2021-04-22T01:04:54Z", "biotoolsCURIE": "biotools:wgsim", "biotoolsID": "wgsim", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Wgsim is a small tool for simulating sequence reads from a reference genome.\nIt is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL)\npolymorphisms, and simulate reads with uniform substitution sequencing errors.\nIt does not generate INDEL sequencing errors, but this can be partly\ncompensated by simulating INDEL polymorphisms.\n\nWgsim outputs the simulated polymorphisms, and writes the true read coordinates\nas well as the number of polymorphisms and sequencing errors in read names.\nOne can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl\nthat comes with the package.", "editPermission": { "type": "private" }, "homepage": "https://github.com/lh3/wgsim", - "lastUpdate": "2022-12-09T23:59:21.316937Z", + "language": [ + "C", + "Perl" + ], + "lastUpdate": "2023-01-10T13:41:28.362051Z", + "license": "Not licensed", "name": "wgsim", "owner": "leipzig", + "toolType": [ + "Command-line tool" + ], "topic": [ { "term": "DNA polymorphism", diff --git a/data/whatismygene/whatismygene.biotools.json b/data/whatismygene/whatismygene.biotools.json index e5c66686558bc..129e7853d5869 100644 --- a/data/whatismygene/whatismygene.biotools.json +++ b/data/whatismygene/whatismygene.biotools.json @@ -1,15 +1,26 @@ { + "accessibility": "Open access", "additionDate": "2022-07-26T10:48:58.836208Z", "biotoolsCURIE": "biotools:whatismygene", "biotoolsID": "whatismygene", + "confidence_flag": "tool", + "cost": "Free of charge", "description": "Web-based gene enrichment tools based on a huge underlying database of transcriptomic, proteomic (and other-omic) studies.", "editPermission": { "type": "private" }, "homepage": "https://whatismygene.com", - "lastUpdate": "2022-12-10T00:01:07.198150Z", + "lastUpdate": "2023-01-10T13:39:03.987777Z", "name": "Whatismygene", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], "owner": "whatismygene", + "toolType": [ + "Database portal" + ], "topic": [ { "term": "Proteomics", diff --git a/data/white_rabbit/white_rabbit.biotools.json b/data/white_rabbit/white_rabbit.biotools.json new file mode 100644 index 0000000000000..a7be81d48f9a7 --- /dev/null +++ b/data/white_rabbit/white_rabbit.biotools.json @@ -0,0 +1,52 @@ +{ + "additionDate": "2023-01-25T11:01:19.528168Z", + "biotoolsCURIE": "biotools:white_rabbit", + "biotoolsID": "white_rabbit", + "collectionID": [ + "IMPaCT-Data" + ], + "credit": [ + { + "name": "Observational Health Data Sciences and Informatics OHDSI", + "typeEntity": "Consortium" + } + ], + "description": "WhiteRabbit is a software tool to help prepare for ETLs (Extraction, Transformation, Loading) of longitudinal healthcare databases into the OMOP Common Data Model (CDM). The source data can be in comma-separated text files, or in a database (MySQL, SQL Server, ORACLE, PostgreSQL); the CDM will be in a database (MySQL, SQL Server, PostgreSQL).\n\nThe main function of WhiteRabbit is to perform a scan of the source data, providing detailed information on the tables, fields, and values that appear in a field. This scan will generate a report that can be used as a reference when designing the ETL, for instance when using the Rabbit-In-a-Hat tool. Rabbit-In-a-Hat uses the scan document and displays source data information through a graphical user interface to allow a user to connect source data structure to the CDM data structure. The function of Rabbit-In-a-Hat is to generate documentation for the ETL process, not generate code to create an ETL.", + "download": [ + { + "type": "Downloads page", + "url": "https://github.com/OHDSI/WhiteRabbit" + } + ], + "editPermission": { + "authors": [ + "iacs-biocomputacion" + ], + "type": "public" + }, + "function": [ + { + "operation": [ + { + "term": "Data handling", + "uri": "http://edamontology.org/operation_2409" + } + ] + } + ], + "homepage": "http://ohdsi.github.io/WhiteRabbit/", + "lastUpdate": "2023-01-26T09:14:55.520702Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://www.ohdsi.org/analytic-tools/whiterabbit-for-etl-design/" + } + ], + "name": "White Rabbit", + "owner": "iacs-biocomputacion", + "version": [ + "v0.10.7" + ] +} diff --git a/data/wrmxpress/wrmxpress.biotools.json b/data/wrmxpress/wrmxpress.biotools.json new file mode 100644 index 0000000000000..2a0c6917fbfad --- /dev/null +++ b/data/wrmxpress/wrmxpress.biotools.json @@ -0,0 +1,128 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-24T15:15:44.911811Z", + "biotoolsCURIE": "biotools:wrmxpress", + "biotoolsID": "wrmxpress", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mzamanian@wisc.edu", + "name": "Mostafa Zamanian", + "orcidid": "https://orcid.org/0000-0001-9233-1760", + "typeEntity": "Person" + }, + { + "name": "Kendra J. Gallo" + }, + { + "name": "John D. Chan", + "orcidid": "https://orcid.org/0000-0003-4986-972X" + }, + { + "name": "Nicolas J. Wheeler", + "orcidid": "https://orcid.org/0000-0002-5909-4190" + } + ], + "description": "A modular package for high-throughput image analysis of parasitic and free-living worms.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "http://www.zamanianlab.org/ZamanianLabDocs/pipelines_wrmxpress/" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Editing", + "uri": "http://edamontology.org/operation_3096" + }, + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Parsing", + "uri": "http://edamontology.org/operation_1812" + } + ] + } + ], + "homepage": "https://github.com/zamanianlab/wrmXpress", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-24T15:15:44.914479Z", + "license": "MIT", + "name": "wrmXpress", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/JOURNAL.PNTD.0010937", + "metadata": { + "abstract": "© 2022 Wheeler et al.Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating rele-vant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of pheno-types, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds prom-ise for enabling collaboration among investigators with diverse interests.", + "authors": [ + { + "name": "Chan J.D." + }, + { + "name": "Gallo K.J." + }, + { + "name": "Rehborg E.J.G." + }, + { + "name": "Ryan K.T." + }, + { + "name": "Wheeler N.J." + }, + { + "name": "Zamanian M." + } + ], + "date": "2022-11-01T00:00:00Z", + "journal": "PLoS Neglected Tropical Diseases", + "title": "wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms" + }, + "pmcid": "PMC9718391", + "pmid": "36399491" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/yalla/yalla.biotools.json b/data/yalla/yalla.biotools.json index 5ff193169db42..300ef92d3dbe0 100644 --- a/data/yalla/yalla.biotools.json +++ b/data/yalla/yalla.biotools.json @@ -6,7 +6,19 @@ "collectionID": [ "PerMedCoE" ], + "confidence_flag": "tool", "cost": "Free of charge", + "credit": [ + { + "name": "James Sharpe" + }, + { + "name": "Miquel Marin-Riera" + }, + { + "name": "Philipp Germann" + } + ], "description": "GPU-Powered Spheroid Models for Mesenchyme and Epithelium", "download": [ { @@ -18,9 +30,16 @@ "type": "public" }, "homepage": "https://github.com/germannp/yalla", - "lastUpdate": "2022-04-28T14:15:52.083477Z", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-10T13:35:10.043473Z", "license": "MIT", "name": "yalla", + "operatingSystem": [ + "Linux", + "Mac" + ], "owner": "tntiniak", "publication": [ { @@ -38,13 +57,17 @@ "name": "Sharpe J." } ], - "citationCount": 9, + "citationCount": 14, "date": "2019-03-27T00:00:00Z", "journal": "Cell Systems", "title": "ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium" - } + }, + "pmid": "30904379" } ], + "toolType": [ + "Command-line tool" + ], "version": [ "1.0" ]