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/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/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/apis-wings-eu/apis-wings-eu.biotools.json b/data/apis-wings-eu/apis-wings-eu.biotools.json new file mode 100644 index 0000000000000..00ddacd3b3bcf --- /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", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Statistical calculation", + "uri": "http://edamontology.org/operation_2238" + } + ] + } + ], + "homepage": "https://zenodo.org/record/7244070", + "language": [ + "R" + ], + "lastUpdate": "2022-12-30T06:50:52.737447Z", + "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/automorph/automorph.biotools.json b/data/automorph/automorph.biotools.json new file mode 100644 index 0000000000000..f40dfd90f93f6 --- /dev/null +++ b/data/automorph/automorph.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T02:31:15.864655Z", + "biotoolsCURIE": "biotools:automorph", + "biotoolsID": "automorph", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Daniel C. Alexander" + }, + { + "name": "Pearse A. Keane" + }, + { + "name": "Siegfried K. Wagner" + }, + { + "name": "Yukun Zhou" + } + ], + "description": "Automated Retinal Vascular Morphology Quantification via a Deep Learning Pipeline.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image annotation", + "uri": "http://edamontology.org/operation_3553" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Validation", + "uri": "http://edamontology.org/operation_2428" + } + ] + } + ], + "homepage": "https://github.com/rmaphoh/AutoMorph", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-20T02:31:15.867106Z", + "license": "Apache-2.0", + "name": "AutoMorph", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1167/tvst.11.7.12", + "metadata": { + "abstract": "© 2022 The Authors.Purpose: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases. Methods: AutoMorph consists of four functional modules: image preprocessing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement. Image quality grading and anatomical segmentation use the most recent deep learning techniques. We employ a model ensemble strategy to achieve robust results and analyze the prediction confidence to rectify false gradable cases in image quality grading. We externally validate the performance of each module on several independent publicly available datasets. Results: The EfficientNet-b4 architecture used in the image grading module achieves performance comparable to that of the state of the art for EyePACS-Q, with an F1-score of 0.86. The confidence analysis reduces the number of images incorrectly assessed as gradable by 76%. Binary vessel segmentation achieves an F1-score of 0.73 on AV-WIDE and 0.78 on DR HAGIS. Artery/vein scores are 0.66 on IOSTAR-AV, and disc segmentation achieves 0.94 in IDRID. Vascular morphology features measured from the AutoMorph segmentation map and expert annotation show good to excellent agreement. Conclusions: AutoMorph modules perform well even when external validation data show domain differences from training data (e.g., with different imaging devices). This fully automated pipeline can thus allow detailed, efficient, and comprehensive analysis of retinal vascular morphology on color fundus photographs. Translational Relevance: By making AutoMorph publicly available and open source, we hope to facilitate ophthalmic and systemic disease research, particularly in the emerging field of oculomics.", + "authors": [ + { + "name": "Alexander D.C." + }, + { + "name": "Chia M.A." + }, + { + "name": "Keane P.A." + }, + { + "name": "Struyven R." + }, + { + "name": "Wagner S.K." + }, + { + "name": "Woodward-Court P." + }, + { + "name": "Xu M." + }, + { + "name": "Zhao A." + }, + { + "name": "Zhou Y." + } + ], + "date": "2022-07-01T00:00:00Z", + "journal": "Translational Vision Science and Technology", + "title": "AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline" + }, + "pmcid": "PMC9290317", + "pmid": "35833885" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json b/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json new file mode 100644 index 0000000000000..f354db8f3a76b --- /dev/null +++ b/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json @@ -0,0 +1,67 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-06T14:15:37.670188Z", + "biotoolsCURIE": "biotools:best_Bam-Error-Stats-Tool", + "biotoolsID": "best_Bam-Error-Stats-Tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Daniel Liu, Daniel E. Cook" + } + ], + "description": "Bam Error Stats Tool (best): analysis of error types in aligned reads.\nbest is used to assess the quality of reads after aligning them to a reference assembly.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://github.com/google/best/blob/main/Usage.md" + } + ], + "download": [ + { + "note": "Github page", + "type": "Source code", + "url": "https://github.com/google/best", + "version": "0.1.0" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Sequence alignment validation", + "uri": "http://edamontology.org/operation_0447" + } + ] + } + ], + "homepage": "https://github.com/google/best", + "language": [ + "Other" + ], + "lastUpdate": "2023-01-06T14:15:37.673685Z", + "license": "MIT", + "maturity": "Emerging", + "name": "best", + "owner": "pauffret", + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + } + ], + "version": [ + "0.1.0" + ] +} diff --git a/data/blmm/blmm.biotools.json b/data/blmm/blmm.biotools.json new file mode 100644 index 0000000000000..d9b0912f09d76 --- /dev/null +++ b/data/blmm/blmm.biotools.json @@ -0,0 +1,68 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-17T00:36:31.619263Z", + "biotoolsCURIE": "biotools:blmm", + "biotoolsID": "blmm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "name": "Thomas E. Nichols", + "orcidid": "http://orcid.org/0000-0002-4516-5103" + }, + { + "name": "Thomas Maullin-Sapey", + "orcidid": "http://orcid.org/0000-0002-1890-330X" + } + ], + "description": "Parallelised Computing for Big Linear Mixed Models.\n\nWithin neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data", + "editPermission": { + "type": "private" + }, + "homepage": "https://github.com/TomMaullin/BLMM", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-17T00:36:31.621728Z", + "license": "Not licensed", + "name": "BLMM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1016/j.neuroimage.2022.119729", + "metadata": { + "abstract": "© 2022Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data. Yet in the large-n setting, such a voxel-wise deletion missing data strategy leads to severe shrinkage of the final analysis mask. To counter these issues, we describe the “Big” Linear Mixed Models (BLMM) toolbox, an efficient Python package for large-scale fMRI LMM analyses. BLMM is designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in our previous work, Maullin-Sapey and Nichols (2021).", + "authors": [ + { + "name": "Maullin-Sapey T." + }, + { + "name": "Nichols T.E." + } + ], + "date": "2022-12-01T00:00:00Z", + "journal": "NeuroImage", + "title": "BLMM: Parallelised computing for big linear mixed models" + }, + "pmid": "36336314" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Experimental design and studies", + "uri": "http://edamontology.org/topic_3678" + }, + { + "term": "Medical imaging", + "uri": "http://edamontology.org/topic_3384" + } + ] +} diff --git a/data/bp/bp.biotools.json b/data/bp/bp.biotools.json new file mode 100644 index 0000000000000..b89431aec74ab --- /dev/null +++ b/data/bp/bp.biotools.json @@ -0,0 +1,103 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-20T02:37:29.648861Z", + "biotoolsCURIE": "biotools:bp", + "biotoolsID": "bp", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "irinag@stat.tamu.edu", + "name": "Irina Gaynanova", + "orcidid": "http://orcid.org/0000-0002-4116-0268", + "typeEntity": "Person" + }, + { + "name": "Naresh M. Punjabi" + }, + { + "name": "John Schwenck", + "orcidid": "http://orcid.org/0000-0003-0821-9333" + } + ], + "description": "Blood Pressure Analysis in R.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://cran.r-project.org/web/packages/bp/bp.pdf" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Feature extraction", + "uri": "http://edamontology.org/operation_3937" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/johnschwenck/bp", + "language": [ + "R" + ], + "lastUpdate": "2023-01-20T02:37:29.651454Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://cran.r-project.org/web/packages/bp/index.html" + } + ], + "name": "bp", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1371/journal.pone.0268934", + "metadata": { + "abstract": "© 2022 Schwenck 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.Despite the world-wide prevalence of hypertension, there is a lack in open-source software for analyzing blood pressure data. The R package bp fills this gap by providing functionality for blood pressure data processing, visualization, and feature extraction. In addition to the comprehensive functionality, the package includes six sample data sets covering continuous arterial pressure data (AP), home blood pressure monitoring data (HBPM) and ambulatory blood pressure monitoring data (ABPM), making it easier for researchers to get started.", + "authors": [ + { + "name": "Gaynanova I." + }, + { + "name": "Punjabi N.M." + }, + { + "name": "Schwenck J." + } + ], + "date": "2022-09-01T00:00:00Z", + "journal": "PLoS ONE", + "title": "bp: Blood pressure analysis in R" + }, + "pmcid": "PMC9462781", + "pmid": "36083882" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Haematology", + "uri": "http://edamontology.org/topic_3408" + } + ] +} diff --git a/data/bphunter/bphunter.biotools.json b/data/bphunter/bphunter.biotools.json new file mode 100644 index 0000000000000..5eb9a405f30f7 --- /dev/null +++ b/data/bphunter/bphunter.biotools.json @@ -0,0 +1,153 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-01-18T22:21:50.061492Z", + "biotoolsCURIE": "biotools:bphunter", + "biotoolsID": "bphunter", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "pzhang@rockefeller.edu", + "name": "Peng Zhang", + "orcidid": "http://orcid.org/0000-0002-6129-567X", + "typeEntity": "Person" + }, + { + "name": "Jean-Laurent Casanova" + }, + { + "name": "Laurent Abel" + }, + { + "name": "Quentin Philippot" + } + ], + "description": "Genome-wide detection of human variants that disrupt intronic branchpoints.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein feature detection", + "uri": "http://edamontology.org/operation_3092" + }, + { + "term": "Sequence motif recognition", + "uri": "http://edamontology.org/operation_0239" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + } + ] + } + ], + "homepage": "https://github.com/casanova-lab/BPHunter", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-18T22:21:50.064032Z", + "license": "CC-BY-NC-ND-4.0", + "name": "BPHunter", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1073/pnas.2211194119", + "metadata": { + "abstract": "Copyright © 2022 the Author(s). Published by PNAS.Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.", + "authors": [ + { + "name": "Abel L." + }, + { + "name": "Boisson B." + }, + { + "name": "Casanova J.-L." + }, + { + "name": "Colobran R." + }, + { + "name": "Cooper D.N." + }, + { + "name": "Lei W.-T." + }, + { + "name": "Li J." + }, + { + "name": "Palacin P.S." + }, + { + "name": "Pan-Hammarstrom Q." + }, + { + "name": "Philippot Q." + }, + { + "name": "Puel A." + }, + { + "name": "Ren W." + }, + { + "name": "Stenson P.D." + }, + { + "name": "Zhang P." + }, + { + "name": "Zhang Q." + }, + { + "name": "Zhang S.-Y." + } + ], + "citationCount": 1, + "date": "2022-11-01T00:00:00Z", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "title": "Genome-wide detection of human variants that disrupt intronic branchpoints" + }, + "pmcid": "PMC9636908", + "pmid": "36306325" + } + ], + "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": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/bridgedb/bridgedb.biotools.json b/data/bridgedb/bridgedb.biotools.json index 69aa4cf641252..ba0ff57ef2b29 100644 --- a/data/bridgedb/bridgedb.biotools.json +++ b/data/bridgedb/bridgedb.biotools.json @@ -152,11 +152,11 @@ ] } ], - "homepage": "http://www.bridgedb.org/", + "homepage": "http://bridgedb.github.io/", "language": [ "Java" ], - "lastUpdate": "2022-04-20T05:51:55.558045Z", + "lastUpdate": "2023-01-12T10:40:58.210403Z", "license": "Apache-2.0", "maturity": "Mature", "name": "BridgeDb", @@ -170,7 +170,13 @@ { "doi": "10.1007/978-3-319-11964-9_7", "type": [ - "Other" + "Primary" + ] + }, + { + "doi": "10.3897/rio.8.e83031", + "type": [ + "Primary" ] }, { @@ -202,7 +208,7 @@ "name": "van Iersel M.P." } ], - "citationCount": 105, + "citationCount": 113, "date": "2010-01-04T00:00:00Z", "journal": "BMC Bioinformatics", "title": "The BridgeDb framework: Standardized access to gene, protein and metabolite identifier mapping services" @@ -236,6 +242,6 @@ ], "validated": 1, "version": [ - "3.0.13" + "3.0.18" ] } 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/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/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/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/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/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/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/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/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/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-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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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 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--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" + }, + 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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. 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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/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/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/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/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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"http://edamontology.org/operation_0314" + }, + { + "term": "Gene prediction", + "uri": "http://edamontology.org/operation_2454" + } + ] + } + ], + "homepage": "http://ibi.zju.edu.cn/EMLI/", + "lastUpdate": "2023-01-09T00:51:57.003869Z", + "name": "EMLI-ICC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1093/BIB/BBAC450", + "pmid": "36259363" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "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" + }, + { + "term": "Sequence analysis", + "uri": "http://edamontology.org/topic_0080" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/ensemblesplice/ensemblesplice.biotools.json 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": "http://edamontology.org/operation_0433" + } + ] + } + ], + "homepage": "https://github.com/OluwadareLab/EnsembleSplice", + "language": [ + "Python" + ], + "lastUpdate": "2023-01-09T00:47:43.600825Z", + "license": "Not licensed", + "name": "EnsembleSplice", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.1186/S12859-022-04971-W", + "metadata": { + "abstract": "© 2022, The Author(s).Background: Identifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. 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/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/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/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/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/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/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": 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"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/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/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/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/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/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", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + } + ] + } + ], + "homepage": "https://github.com/MaudeDavidLab/GMEmbeddings", + "language": [ + "Python", + "R", + "Shell" + ], + "lastUpdate": "2022-12-31T15:20:00.205757Z", + "license": "GPL-3.0", + "name": "GMEmbeddings", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Jennifer", + "publication": [ + { + "doi": "10.3389/FBINF.2022.828703", + "pmcid": "PMC9580954", + "pmid": "36304322" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + } + ] +} 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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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" ]