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biotools-import on Sun Jan 22 01:08:35 UTC 2023 #20

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biotools-import on Sun Jan 22 01:08:35 UTC 2023 #20

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98 changes: 98 additions & 0 deletions data/3dpolys-le/3dpolys-le.biotools.json
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{
"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": "[email protected]",
"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"
}
]
}
129 changes: 129 additions & 0 deletions data/airrscape/airrscape.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-01-17T00:16:20.718687Z",
"biotoolsCURIE": "biotools:airrscape",
"biotoolsID": "airrscape",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "[email protected]",
"name": "Eric Waltari",
"orcidid": "http://orcid.org/0000-0001-6930-9645",
"typeEntity": "Person"
},
{
"email": "[email protected]",
"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"
}
]
}
147 changes: 147 additions & 0 deletions data/ampbenchmark/ampbenchmark.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-01-20T02:22:08.626953Z",
"biotoolsCURIE": "biotools:ampbenchmark",
"biotoolsID": "ampbenchmark",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "[email protected]",
"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"
}
]
}
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