From a91ab4e60e82bfd27600120f9bf79d06780622ad Mon Sep 17 00:00:00 2001 From: Tools Platform Ecosystem bot Date: Sun, 3 Sep 2023 01:09:56 +0000 Subject: [PATCH] import bio.tools data on Sun Sep 3 01:09:56 UTC 2023 --- data/ape/ape.biotools.json | 93 ++---- ...r_coarse-grained_flexibility.biotools.json | 291 +++++++++++++++++ ...cular_interaction_potentials.biotools.json | 295 +++++++++++++++++ ...molecular_structure_checking.biotools.json | 263 +++++++++++++++ ...ion_free_energy_calculations.biotools.json | 10 +- ...rials_protein-ligand_docking.biotools.json | 14 +- ...n_conformational_transitions.biotools.json | 305 ++++++++++++++++++ ...otein_md_setup_amber_version.biotools.json | 23 +- .../biotools-linter.biotools.json | 41 +++ data/cardinal/cardinal.biotools.json | 116 ++++--- ..._interaction_potentials_cmip.biotools.json | 203 ++++++++++++ data/dbaqp-snp/dbaqp-snp.biotools.json | 101 ++++++ data/destin2/destin2.biotools.json | 85 +++++ data/endure/endure.biotools.json | 119 +++++++ data/fameta/fameta.biotools.json | 121 +++++++ data/genesignet/genesignet.biotools.json | 119 +++++++ data/gentle/gentle.biotools.json | 60 ++++ data/gpcr-sas/gpcr-sas.biotools.json | 18 ++ data/grnsight/grnsight.biotools.json | 8 +- data/gvc/gvc.biotools.json | 111 +++++++ data/impute2/impute2.biotools.json | 70 ++-- data/impute_5/impute_5.biotools.json | 126 ++++++++ data/intlim/intlim.biotools.json | 93 ++++++ data/ip4gs/ip4gs.biotools.json | 129 ++++++++ data/kargva/kargva.biotools.json | 114 +++++++ data/macpepdb/macpepdb.biotools.json | 101 ------ data/medipipe/medipipe.biotools.json | 94 ++++++ data/mols_2.0/mols_2.0.biotools.json | 109 +++++++ .../neuropred-plm/neuropred-plm.biotools.json | 82 +++++ data/newlife/newlife.biotools.json | 13 + .../nf-core_isoseq.biotools.json | 146 +++++++++ data/ordb/ordb.biotools.json | 42 +++ data/pangpcr/pangpcr.biotools.json | 48 +++ data/peaks2utr/peaks2utr.biotools.json | 116 +++++++ data/pickaxe/pickaxe.biotools.json | 135 ++++++++ .../pimangmoneyaward.biotools.json | 16 + data/plantltrdb/plantltrdb.biotools.json | 99 ++++++ data/pym2aia/pym2aia.biotools.json | 131 ++++++++ data/redfold/redfold.biotools.json | 112 +++++++ data/respond-cam/respond-cam.biotools.json | 101 ++++++ data/rscanner/rscanner.biotools.json | 120 +++++++ data/scannotate/scannotate.biotools.json | 80 +++++ data/scardock/scardock.biotools.json | 115 +++++++ data/scntimpute/scntimpute.biotools.json | 21 ++ data/scvelo/scvelo.biotools.json | 90 +++++- data/sdm2/sdm2.biotools.json | 82 +++++ data/shapeit5/shapeit5.biotools.json | 130 ++++++++ data/shic/shic.biotools.json | 252 +++++++++++++++ data/snapshot/snapshot.biotools.json | 146 +++++++++ .../spheroidanalyser.biotools.json | 97 ++++++ data/star/star.biotools.json | 63 +++- data/uctcrdb/uctcrdb.biotools.json | 99 ++++++ 52 files changed, 5300 insertions(+), 268 deletions(-) create mode 100644 data/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility.biotools.json create mode 100644 data/bioexcel_building_blocks_tutorials_molecular_interaction_potentials/bioexcel_building_blocks_tutorials_molecular_interaction_potentials.biotools.json create mode 100644 data/bioexcel_building_blocks_tutorials_molecular_structure_checking/bioexcel_building_blocks_tutorials_molecular_structure_checking.biotools.json create mode 100644 data/bioexcel_building_blocks_tutorials_protein_conformational_transitions/bioexcel_building_blocks_tutorials_protein_conformational_transitions.biotools.json create mode 100644 data/biotools-linter/biotools-linter.biotools.json create mode 100644 data/classical_molecular_interaction_potentials_cmip/classical_molecular_interaction_potentials_cmip.biotools.json create mode 100644 data/dbaqp-snp/dbaqp-snp.biotools.json create mode 100644 data/destin2/destin2.biotools.json create mode 100644 data/endure/endure.biotools.json create mode 100644 data/fameta/fameta.biotools.json create mode 100644 data/genesignet/genesignet.biotools.json create mode 100644 data/gentle/gentle.biotools.json create mode 100644 data/gpcr-sas/gpcr-sas.biotools.json create mode 100644 data/gvc/gvc.biotools.json create mode 100644 data/impute_5/impute_5.biotools.json create mode 100644 data/intlim/intlim.biotools.json create mode 100644 data/ip4gs/ip4gs.biotools.json create mode 100644 data/kargva/kargva.biotools.json delete mode 100644 data/macpepdb/macpepdb.biotools.json create mode 100644 data/medipipe/medipipe.biotools.json create mode 100644 data/mols_2.0/mols_2.0.biotools.json create mode 100644 data/neuropred-plm/neuropred-plm.biotools.json create mode 100644 data/newlife/newlife.biotools.json create mode 100644 data/nf-core_isoseq/nf-core_isoseq.biotools.json create mode 100644 data/ordb/ordb.biotools.json create mode 100644 data/pangpcr/pangpcr.biotools.json create mode 100644 data/peaks2utr/peaks2utr.biotools.json create mode 100644 data/pickaxe/pickaxe.biotools.json create mode 100644 data/pimangmoneyaward/pimangmoneyaward.biotools.json create mode 100644 data/plantltrdb/plantltrdb.biotools.json create mode 100644 data/pym2aia/pym2aia.biotools.json create mode 100644 data/redfold/redfold.biotools.json create mode 100644 data/respond-cam/respond-cam.biotools.json create mode 100644 data/rscanner/rscanner.biotools.json create mode 100644 data/scannotate/scannotate.biotools.json create mode 100644 data/scardock/scardock.biotools.json create mode 100644 data/scntimpute/scntimpute.biotools.json create mode 100644 data/sdm2/sdm2.biotools.json create mode 100644 data/shapeit5/shapeit5.biotools.json create mode 100644 data/shic/shic.biotools.json create mode 100644 data/snapshot/snapshot.biotools.json create mode 100644 data/spheroidanalyser/spheroidanalyser.biotools.json create mode 100644 data/uctcrdb/uctcrdb.biotools.json diff --git a/data/ape/ape.biotools.json b/data/ape/ape.biotools.json index bb28661aaa45a..51dbd1578fc28 100644 --- a/data/ape/ape.biotools.json +++ b/data/ape/ape.biotools.json @@ -1,23 +1,41 @@ { + "accessibility": "Open access", "additionDate": "2021-01-18T10:10:48Z", "biotoolsCURIE": "biotools:ape", "biotoolsID": "ape", "confidence_flag": "tool", + "cost": "Free of charge", "credit": [ { - "email": "a.l.lamprecht@uu.nl", + "email": "anna-lena.lamprecht@uni-potsdam.de", "name": "Anna-Lena Lamprecht", "orcidid": "https://orcid.org/0000-0003-1953-5606", "typeEntity": "Person" }, { - "email": "v.kasalica@uu.nl", + "email": "v.kasalica@esciencecenter.nl", "name": "Vedran Kasalica", "orcidid": "https://orcid.org/0000-0002-0097-1056", "typeEntity": "Person" } ], "description": "APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints.", + "documentation": [ + { + "type": [ + "API documentation" + ], + "url": "https://ape-framework.readthedocs.io/" + } + ], + "download": [ + { + "note": "Download APE java library or the CLI", + "type": "Software package", + "url": "https://mvnrepository.com/artifact/io.github.sanctuuary/APE/2.1.7", + "version": "2.1.7" + } + ], "editPermission": { "type": "private" }, @@ -35,11 +53,11 @@ ] } ], - "homepage": "https://ape-framework.readthedocs.io/en/latest/", + "homepage": "https://github.com/sanctuuary/APE/", "language": [ "Java" ], - "lastUpdate": "2021-05-27T08:09:08Z", + "lastUpdate": "2023-08-30T12:49:28.699851Z", "license": "Apache-2.0", "link": [ { @@ -49,53 +67,21 @@ "url": "https://github.com/sanctuuary/ape" } ], + "maturity": "Mature", "name": "APE", - "owner": "Kigaard", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "VKasalica", "publication": [ { "doi": "10.1007/978-3-030-50436-6_34", - "metadata": { - "abstract": "© The Author(s) 2020.Automated workflow composition is bound to take the work with scientific workflows to the next level. On top of today’s comprehensive eScience infrastructure, it enables the automated generation of possible workflows for a given specification. However, functionality for automated workflow composition tends to be integrated with one of the many available workflow management systems, and is thus difficult or impossible to apply in other environments. Therefore we have developed APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints. The workflows can further be transformed into executable implementations and/or exported into standard workflow formats. In this paper we describe APE v1.0 and discuss lessons learned from applications in bioinformatics and geosciences.", - "authors": [ - { - "name": "Kasalica V." - }, - { - "name": "Lamprecht A.-L." - } - ], - "citationCount": 2, - "date": "2020-01-01T00:00:00Z", - "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", - "title": "Ape: A command-line tool and api for automated workflow composition" - }, "pmcid": "PMC7304703" }, { "doi": "10.1021/ACS.JPROTEOME.0C00983", - "metadata": { - "abstract": "© 2021 The Authors. Published by American Chemical Society.The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving \"into the wild\"opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.", - "authors": [ - { - "name": "Ison J." - }, - { - "name": "Kasalica V." - }, - { - "name": "Lamprecht A.-L." - }, - { - "name": "Palmblad M." - }, - { - "name": "Schwammle V." - } - ], - "date": "2021-04-02T00:00:00Z", - "journal": "Journal of Proteome Research", - "title": "APE in the Wild: Automated Exploration of Proteomics Workflows in the bio.tools Registry" - }, "pmcid": "PMC8041394", "pmid": "33720735" } @@ -113,26 +99,13 @@ "term": "Ontology and terminology", "uri": "http://edamontology.org/topic_0089" }, - { - "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": "Sequence analysis", - "uri": "http://edamontology.org/topic_0080" - }, { "term": "Workflows", "uri": "http://edamontology.org/topic_0769" } ], - "validated": 1 + "validated": 1, + "version": [ + "2.1.7" + ] } diff --git a/data/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility.biotools.json b/data/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility.biotools.json new file mode 100644 index 0000000000000..8a70160017830 --- /dev/null +++ b/data/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility/bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility.biotools.json @@ -0,0 +1,291 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-29T10:55:17.540852Z", + "biotoolsCURIE": "biotools:bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility", + "biotoolsID": "bioexcel_building_blocks_tutorials_macromolecular_coarse-grained_flexibility", + "collectionID": [ + "BioExcel" + ], + "cost": "Free of charge", + "credit": [ + { + "email": "adam.hospital@irbbarcelona.org", + "name": "Adam Hospital", + "orcidid": "https://orcid.org/0000-0002-8291-8071", + "typeEntity": "Person", + "typeRole": [ + "Developer", + "Documentor", + "Maintainer", + "Primary contact" + ], + "url": "https://www.irbbarcelona.org/en/research/adam-hospital" + }, + { + "email": "genis.bayarri@irbbarcelona.org", + "name": "Genís Bayarri", + "orcidid": "https://orcid.org/0000-0003-0513-0288", + "typeEntity": "Person", + "typeRole": [ + "Contributor", + "Developer", + "Maintainer" + ], + "url": "https://www.irbbarcelona.org/es/research/genis-bayarri" + }, + { + "name": "BioExcel CoE", + "typeEntity": "Consortium", + "typeRole": [ + "Provider" + ], + "url": "https://bioexcel.eu/" + } + ], + "description": "This tutorial aims to illustrate the process of generating protein conformational ensembles from 3D structures using Coarse-Grained tools from the FlexServ server and analysing its molecular flexibility", + "documentation": [ + { + "note": "Read The Docs workflow documentation", + "type": [ + "Other" + ], + "url": "https://biobb-wf-flexserv.readthedocs.io/en/latest/index.html" + } + ], + "download": [ + { + "note": "BioExcel Binder", + "type": "VM image", + "url": "https://bioexcel-binder.tsi.ebi.ac.uk/v2/gh/bioexcel/biobb_wf_flexserv/main?filepath=biobb_wf_flexserv%2Fnotebooks%2Fbiobb_wf_flexserv.ipynb", + "version": "1.0" + }, + { + "type": "Source code", + "url": "https://github.com/bioexcel/biobb_wf_flexserv", + "version": "1.0" + } + ], + "editPermission": { + "type": "private" + }, + "elixirNode": [ + "Spain", + "UK" + ], + "elixirPlatform": [ + "Interoperability", + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Protein 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"uri": "http://edamontology.org/format_3874" + }, + { + "term": "PDB", + "uri": "http://edamontology.org/format_1476" + } + ] + } + ] + } + ], + "homepage": "https://mmb.irbbarcelona.org/biobb/workflows/tutorials/flexserv", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-29T10:55:17.543854Z", + "license": "Apache-2.0", + "link": [ + { + "note": "Main web site (BioBB Workflows)", + "type": [ + "Other" + ], + "url": "https://mmb.irbbarcelona.org/biobb/workflows#macromolecular-coarse-grained-flexibility" + }, + { + "note": "Source Code in GitHub", + "type": [ + "Repository" + ], + "url": "https://github.com/bioexcel/biobb_wf_flexserv" + }, + { + "note": "Tutorial in HTML", + "type": [ + "Other" + ], + "url": "https://mmb.irbbarcelona.org/biobb/workflows/tutorials/flexserv" + }, + { + "note": "Tutorial in Read The Docs", + "type": [ + "Other" + ], + "url": "https://biobb-wf-flexserv.readthedocs.io/en/latest/index.html" + } + ], + "maturity": "Emerging", + "name": "BioExcel Building Blocks tutorials: Macromolecular Coarse-Grained Flexibility", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "adam.hospital@irbbarcelona.org", + "publication": [ + { + "doi": "10.1038/s41597-019-0177-4", + "metadata": { + "abstract": "In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", + "authors": [ + { + "name": "Andrio P." + }, + { + "name": "Badia R.M." + }, + { + "name": "Codo L." + }, + { + "name": "Conejero J." + }, + { + "name": "Del Pino M." + }, + { + "name": "Gelpi J.L." + }, + { + "name": "Goble C." + }, + { + "name": "Hospital A." + }, + { + "name": "Jorda L." + }, + { + "name": "Lezzi D." + }, + { + "name": "Orozco M." + }, + { + "name": "Soiland-Reyes S." + } + ], + "citationCount": 33, + "date": "2019-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows" + }, + "note": "BioExcel Building Blocks (BioBB)", + "pmcid": "PMC6736963", + "pmid": "31506435", + "type": [ + "Other" + ] + } + 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BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", + "abstract": "In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", "authors": [ { "name": "Andrio P." @@ -254,7 +254,7 @@ "name": "Soiland-Reyes S." } ], - "citationCount": 4, + "citationCount": 33, "date": "2019-12-01T00:00:00Z", "journal": "Scientific Data", "title": "BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows" diff --git a/data/bioexcel_building_blocks_tutorials_protein-ligand_docking/bioexcel_building_blocks_tutorials_protein-ligand_docking.biotools.json b/data/bioexcel_building_blocks_tutorials_protein-ligand_docking/bioexcel_building_blocks_tutorials_protein-ligand_docking.biotools.json index a6b52f96950e1..868922b09b933 100644 --- a/data/bioexcel_building_blocks_tutorials_protein-ligand_docking/bioexcel_building_blocks_tutorials_protein-ligand_docking.biotools.json +++ b/data/bioexcel_building_blocks_tutorials_protein-ligand_docking/bioexcel_building_blocks_tutorials_protein-ligand_docking.biotools.json @@ -120,6 +120,10 @@ { "term": "Protein-ligand docking", "uri": "http://edamontology.org/operation_0482" + }, + { + "term": "Structure visualisation", + "uri": "http://edamontology.org/operation_0570" } ], "output": [ @@ -142,7 +146,7 @@ "language": [ "Python" ], - "lastUpdate": "2021-11-30T11:25:10.770488Z", + "lastUpdate": "2023-08-31T07:34:58.172062Z", "license": "Apache-2.0", "link": [ { @@ -207,7 +211,7 @@ { "doi": "10.1038/s41597-019-0177-4", "metadata": { - "abstract": "© 2019, The Author(s).In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", + "abstract": "In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. 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BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", "authors": [ { "name": "Andrio P." @@ -246,7 +250,7 @@ "name": "Soiland-Reyes S." } ], - "citationCount": 8, + "citationCount": 33, "date": "2019-12-01T00:00:00Z", "journal": "Scientific Data", "title": "BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows" @@ -280,6 +284,10 @@ "Workflow" ], "topic": [ + { + "term": "Data visualisation", + "uri": "http://edamontology.org/topic_0092" + }, { "term": "Molecular modelling", "uri": "http://edamontology.org/topic_2275" diff --git a/data/bioexcel_building_blocks_tutorials_protein_conformational_transitions/bioexcel_building_blocks_tutorials_protein_conformational_transitions.biotools.json 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Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", + "authors": [ + { + "name": "Andrio P." + }, + { + "name": "Badia R.M." + }, + { + "name": "Codo L." + }, + { + "name": "Conejero J." + }, + { + "name": "Del Pino M." + }, + { + "name": "Gelpi J.L." + }, + { + "name": "Goble C." + }, + { + "name": "Hospital A." + }, + { + "name": "Jorda L." + }, + { + "name": "Lezzi D." + }, + { + "name": "Orozco M." + }, + { + "name": "Soiland-Reyes S." + } + ], + "citationCount": 33, + "date": "2019-12-01T00:00:00Z", + "journal": "Scientific Data", + "title": "BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows" + }, + "note": "BioExcel Building Blocks (BioBB)", + "pmcid": "PMC6736963", + "pmid": "31506435", + "type": [ + "Other" + ] + }, + { + "doi": "10.1093/bioinformatics/btt324", + "metadata": { + "abstract": "Motivation: A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, ...) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD).Availability: Freely available on the web at http://mmb.irbbarcelona.org/GOdMD.Contact: or modesto@mmb.pcb.ub.esSupplementary information: Supplementary data are available at Bioinformatics online. © 2013 The Author 2013. Published by Oxford University Press.", + "authors": [ + { + "name": "Emperador A." + }, + { + "name": "Hospital A." + }, + { + "name": "Orozco M." + }, + { + "name": "Sfriso P." + } + ], + "citationCount": 24, + "date": "2013-08-15T00:00:00Z", + "journal": "Bioinformatics", + "title": "Exploration of conformational transition pathways from coarse-grained simulations" + }, + "note": "GOdMD method", + "pmid": "23740746", + "type": [ + "Other" + ] + } + ], + "relation": [ + { + "biotoolsID": "biobb", + "type": "uses" + }, + { + "biotoolsID": "godmd", + "type": "uses" + }, + { + "biotoolsID": "water", + "type": "uses" + } + ], + "toolType": [ + "Workflow" + ], + "topic": [ + { + "term": "Biomolecular simulation", + "uri": "http://edamontology.org/topic_3892" + }, + { + "term": "Data visualisation", + "uri": "http://edamontology.org/topic_0092" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein sites, features and motifs", + "uri": "http://edamontology.org/topic_3510" + }, + { + "term": "Protein structure analysis", + "uri": "http://edamontology.org/topic_2814" + }, + { + "term": "Protein structure analysis", + "uri": "http://edamontology.org/topic_2814" + } + ], + "version": [ + "1.0" + ] +} diff --git a/data/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version.biotools.json b/data/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version.biotools.json index bf9d4d665fe51..731fc17fc3459 100644 --- a/data/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version.biotools.json +++ b/data/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version/bioexcel_building_blocks_tutorials_protein_md_setup_amber_version.biotools.json @@ -53,6 +53,11 @@ } ], "download": [ + { + "note": "BioExcel Binder", + "type": "VM image", + "url": "https://bioexcel-binder.tsi.ebi.ac.uk/v2/gh/bioexcel/biobb_wf_amber_md_setup/master?filepath=%2Fbiobb_wf_amber_md_setup%2Fnotebooks%2Fmdsetup%2Fbiobb_amber_setup_notebook.ipynb" + }, { "type": "Source code", "url": "https://github.com/bioexcel/biobb_wf_amber_md_setup" @@ -96,6 +101,14 @@ { "term": "Protein structure analysis", "uri": "http://edamontology.org/operation_2406" + }, + { + "term": "Structure visualisation", + "uri": "http://edamontology.org/operation_0570" + }, + { + "term": "Trajectory visualization", + "uri": "http://edamontology.org/operation_3890" } ], "output": [ @@ -134,7 +147,7 @@ "language": [ "Python" ], - "lastUpdate": "2021-11-30T11:39:19.846860Z", + "lastUpdate": "2023-08-31T07:40:34.818218Z", "license": "Apache-2.0", "link": [ { @@ -171,7 +184,7 @@ { "doi": "10.1038/s41597-019-0177-4", "metadata": { - "abstract": "© 2019, The Author(s).In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", + "abstract": "In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.", "authors": [ { "name": "Andrio P." @@ -210,7 +223,7 @@ "name": "Soiland-Reyes S." } ], - "citationCount": 8, + "citationCount": 33, "date": "2019-12-01T00:00:00Z", "journal": "Scientific Data", "title": "BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows" @@ -236,6 +249,10 @@ "Workflow" ], "topic": [ + { + "term": "Data visualisation", + "uri": "http://edamontology.org/topic_0092" + }, { "term": "Molecular dynamics", "uri": "http://edamontology.org/topic_0176" diff --git a/data/biotools-linter/biotools-linter.biotools.json b/data/biotools-linter/biotools-linter.biotools.json new file mode 100644 index 0000000000000..0b578a5d1219f --- /dev/null +++ b/data/biotools-linter/biotools-linter.biotools.json @@ -0,0 +1,41 @@ +{ + "additionDate": "2023-08-29T11:42:48.137925Z", + "biotoolsCURIE": "biotools:biotools-linter", + "biotoolsID": "biotools-linter", + "cost": "Free of charge", + "description": "A rule-based checker for the bio.tools database.", + "download": [ + { + "type": "Source code", + "url": "https://github.com/3top1a/biotools-linter" + } + ], + "editPermission": { + "type": "private" + }, + "homepage": "https://biotools-linter.biodata.ceitec.cz/", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-29T12:14:31.178291Z", + "license": "MIT", + "maturity": "Emerging", + "name": "biotools-linter", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "FilipRusz", + "toolType": [ + "Command-line tool", + "Web API", + "Web service" + ], + "topic": [ + { + "term": "Data quality management", + "uri": "http://edamontology.org/topic_3572" + } + ] +} diff --git a/data/cardinal/cardinal.biotools.json b/data/cardinal/cardinal.biotools.json index 3e8a318553753..beb083550dfc2 100644 --- a/data/cardinal/cardinal.biotools.json +++ b/data/cardinal/cardinal.biotools.json @@ -33,6 +33,7 @@ ], "editPermission": { "authors": [ + "jannikwitte", "proteomics.bio.tools" ], "type": "group" @@ -42,10 +43,14 @@ "input": [ { "data": { - "term": "Mass spectrometry data", - "uri": "http://edamontology.org/data_2536" + "term": "Image", + "uri": "http://edamontology.org/data_2968" }, "format": [ + { + "term": "ibd", + "uri": "http://edamontology.org/format_3839" + }, { "term": "imzML metadata file", "uri": "http://edamontology.org/format_3682" @@ -54,21 +59,85 @@ } ], "operation": [ + { + "term": "Heat map generation", + "uri": "http://edamontology.org/operation_0531" + }, + { + "term": "Mass spectrum visualisation", + "uri": "http://edamontology.org/operation_3694" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ], + "output": [ + { + "data": { + "term": "Image", + "uri": "http://edamontology.org/data_2968" + }, + "format": [ + { + "term": "PNG", + "uri": "http://edamontology.org/format_3603" + }, + { + "term": "SVG", + "uri": "http://edamontology.org/format_3604" + } + ] + } + ] + }, + { + "input": [ + { + "data": { + "term": "Image", + "uri": "http://edamontology.org/data_2968" + }, + "format": [ + { + "term": "ibd", + "uri": "http://edamontology.org/format_3839" + }, + { + "term": "imzML metadata file", + "uri": "http://edamontology.org/format_3682" + } + ] + } + ], + "operation": [ + { + "term": "Mass spectra calibration", + "uri": "http://edamontology.org/operation_3627" + }, { "term": "Spectral analysis", "uri": "http://edamontology.org/operation_3214" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" } ], "output": [ { "data": { - "term": "Plot", - "uri": "http://edamontology.org/data_2884" + "term": "Image", + "uri": "http://edamontology.org/data_2968" }, "format": [ { - "term": "Image format", - "uri": "http://edamontology.org/format_3547" + "term": "ibd", + "uri": "http://edamontology.org/format_3839" + }, + { + "term": "imzML metadata file", + "uri": "http://edamontology.org/format_3682" } ] } @@ -79,7 +148,7 @@ "language": [ "R" ], - "lastUpdate": "2019-07-20T17:40:56Z", + "lastUpdate": "2023-08-30T09:33:42.175400Z", "license": "Artistic-2.0", "link": [ { @@ -99,39 +168,6 @@ "publication": [ { "doi": "10.1093/bioinformatics/btv146", - "metadata": { - "abstract": "© The Author 2015. Published by Oxford University Press.Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to predefined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.", - "authors": [ - { - "name": "Bemis K.D." - }, - { - "name": "Eberlin L.S." - }, - { - "name": "Ferreira C." - }, - { - "name": "Harry A." - }, - { - "name": "Mallick P." - }, - { - "name": "Stolowitz M." - }, - { - "name": "Van De Ven S.M." - }, - { - "name": "Vitek O." - } - ], - "citationCount": 105, - "date": "2015-07-15T00:00:00Z", - "journal": "Bioinformatics", - "title": "Cardinal: An R package for statistical analysis of mass spectrometry-based imaging experiments" - }, "pmcid": "PMC4495298", "pmid": "25777525", "type": [ diff --git a/data/classical_molecular_interaction_potentials_cmip/classical_molecular_interaction_potentials_cmip.biotools.json b/data/classical_molecular_interaction_potentials_cmip/classical_molecular_interaction_potentials_cmip.biotools.json new file mode 100644 index 0000000000000..9936d5cbbccff --- /dev/null +++ b/data/classical_molecular_interaction_potentials_cmip/classical_molecular_interaction_potentials_cmip.biotools.json @@ -0,0 +1,203 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T07:29:49.062944Z", + "biotoolsCURIE": "biotools:classical_molecular_interaction_potentials_cmip", + "biotoolsID": "classical_molecular_interaction_potentials_cmip", + "collectionID": [ + "BSC", + "BioExcel" + ], + "cost": "Free of charge", + "credit": [ + { + "email": "gelpi@ub.edu", + "name": "Josep Lluís Gelpí", + "orcidid": "https://orcid.org/0000-0002-0566-7723", + "typeEntity": "Person", + "typeRole": [ + "Developer", + "Documentor", + "Maintainer", + "Primary contact", + "Support" + ], + "url": "https://www.bsc.es/es/gelpi-josep" + } + ], + "description": "CMIP performs a series of analysis centered in the obtention of molecular interaction potentials using classic formalisms:\nElectrostatic interactions through coulombic potentials with several dielectric options or Poisson-Boltzmann equation\nVan de Waals potentials computed from the Lennard-Jones expression\nElectrostatic Solvation energy evaluated from PB potentials\nHydrophobic Solvation from surface analysis with a variety of alternative scales.", + "documentation": [ + { + "note": "CMIP User Manual", + "type": [ + "User manual" + ], + "url": "https://mmb.irbbarcelona.org/gitlab/gelpi/CMIP/-/blob/master/CMIP.man" + } + ], + "download": [ + { + "note": "BioConda package", + "type": "Software package", + "url": "https://anaconda.org/bioconda/cmip" + }, + { + "note": "Source Code in GitLab", + "type": "Source code", + "url": "https://mmb.irbbarcelona.org/gitlab/gelpi/CMIP" + } + ], + "editPermission": { + "authors": [ + "gelpi@ub.edu" + ], + "type": "group" + }, + "elixirNode": [ + "Spain" + ], + "elixirPlatform": [ + "Interoperability", + "Tools" + ], + "function": [ + { + "input": [ + { + "data": { + "term": "Protein structure", + "uri": "http://edamontology.org/data_1460" + }, + "format": [ + { + "term": "PDB", + "uri": "http://edamontology.org/format_1476" + } + ] + } + ], + "operation": [ + { + "term": "Protein property calculation", + "uri": "http://edamontology.org/operation_0250" + }, + { + "term": "Residue contact prediction", + "uri": "http://edamontology.org/operation_0272" + }, + { + "term": "Residue interaction calculation", + "uri": "http://edamontology.org/operation_0248" + } + ], + "output": [ + { + "data": { + "term": "Protein interaction data", + "uri": "http://edamontology.org/data_0906" + }, + "format": [ + { + "term": "Binary format", + "uri": "http://edamontology.org/format_2333" + }, + { + "term": "dat", + "uri": "http://edamontology.org/format_1637" + } + ] + } + ] + } + ], + "homepage": "https://mmb.irbbarcelona.org/gitlab/gelpi/CMIP", + "language": [ + "Fortran" + ], + "lastUpdate": "2023-08-31T07:30:28.315262Z", + "license": "Apache-2.0", + "link": [ + { + "note": "Source Code in GitLab", + "type": [ + "Repository" + ], + "url": "https://mmb.irbbarcelona.org/gitlab/gelpi/CMIP" + } + ], + "maturity": "Mature", + "name": "Classical Molecular Interaction Potentials (CMIP)", + "operatingSystem": [ + "Linux", + "Mac" + ], + "owner": "adam.hospital@irbbarcelona.org", + "publication": [ + { + "doi": "10.1002/prot.1159", + "metadata": { + "abstract": "The latest version of the classical molecular interaction potential (CMIP) has the ability to predict the position of crystallographic waters in several proteins with great accuracy. This article analyzes the ability of the CMIP functional to improve the setup procedure of the molecular system in molecular dynamics (MD) simulations of proteins. To this end, the CMIP strategy is used to include both water molecules and counterions in different protein systems. The structural details of the configurations sampled from trajectories obtained using the CMIP setup procedure are compared with those obtained from trajectories derived from a standard equilibration process. The results show that standard MD simulations can lead to artifactual results, which are avoided using the CMIP setup procedure. Because the CMIP is easy to implement at a low computational cost, it can be very useful in obtaining reliable MD trajectories. © 2001 Wiley-Liss, Inc.", + "authors": [ + { + "name": "Barril X." + }, + { + "name": "Cirera J." + }, + { + "name": "De La Cruz X." + }, + { + "name": "Gelpi J.L." + }, + { + "name": "Kalko S.G." + }, + { + "name": "Luque F.J." + }, + { + "name": "Orozco M." + } + ], + "citationCount": 86, + "date": "2001-12-01T00:00:00Z", + "journal": "Proteins: Structure, Function and Genetics", + "title": "Classical molecular interaction potentials: Improved setup procedure in molecular dynamics simulations of proteins" + }, + "pmid": "11746690", + "type": [ + "Primary" + ] + } + ], + "relation": [ + { + "biotoolsID": "biobb", + "type": "includedIn" + }, + { + "biotoolsID": "bioexcel_building_blocks_tutorials_molecular_interaction_potentials", + "type": "includedIn" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "Protein interactions", + "uri": "http://edamontology.org/topic_0128" + }, + { + "term": "Structure analysis", + "uri": "http://edamontology.org/topic_0081" + } + ], + "version": [ + "2.6.1" + ] +} diff --git a/data/dbaqp-snp/dbaqp-snp.biotools.json b/data/dbaqp-snp/dbaqp-snp.biotools.json new file mode 100644 index 0000000000000..b377b2db46d6e --- /dev/null +++ b/data/dbaqp-snp/dbaqp-snp.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T13:52:19.501334Z", + "biotoolsCURIE": "biotools:dbaqp-snp", + "biotoolsID": "dbaqp-snp", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "rsankar@iitk.ac.in", + "name": "Ramasubbu Sankararamakrishnan", + "orcidid": "https://orcid.org/0000-0002-8527-5614", + "typeEntity": "Person" + } + ], + "description": "Database of missense single-nucleotide polymorphisms in human aquaporins.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "PCR primer design", + "uri": "http://edamontology.org/operation_0308" + }, + { + "term": "SNP annotation", + "uri": "http://edamontology.org/operation_3661" + }, + { + "term": "SNP detection", + "uri": "http://edamontology.org/operation_0484" + }, + { + "term": "Variant effect prediction", + "uri": "http://edamontology.org/operation_0331" + } + ] + } + ], + "homepage": "http://bioinfo.iitk.ac.in/dbAQP-SNP", + "lastUpdate": "2023-08-30T13:52:19.503823Z", + "name": "dbAQP-SNP", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/DATABASE/BAAD012", + "metadata": { + "abstract": "Aquaporins and aquaglyceroporins belong to the superfamily of major intrinsic proteins (MIPs), and they transport water and other neutral solutes such as glycerol. These channel proteins are involved in vital physiological processes and are implicated in several human diseases. Experimentally determined structures of MIPs from diverse organisms reveal a unique hour-glass fold with six transmembrane helices and two half-helices. MIP channels have two constrictions formed by Asn-Pro-Ala (NPA) motifs and aromatic/arginine selectivity filters (Ar/R SFs). Several reports have found associations among single-nucleotide polymorphisms (SNPs) in human aquaporins (AQPs) with diseases in specific populations. In this study, we have compiled 2798 SNPs that give rise to missense mutations in 13 human AQPs. To understand the nature of missense substitutions, we have systematically analyzed the pattern of substitutions. We found several examples in which substitutions could be considered as non-conservative that include small to big or hydrophobic to charged residues. We also analyzed these substitutions in the context of structure. We have identified SNPs that occur in NPA motifs or Ar/R SFs, and they will most certainly disrupt the structure and/or transport properties of human AQPs. We found 22 examples in which missense SNP substitutions that are mostly non-conservative in nature have given rise to pathogenic conditions as found in the Online Mendelian Inheritance in Man database. It is most likely that not all missense SNPs in human AQPs will result in diseases. However, understanding the effect of missense SNPs on the structure and function of human AQPs is important. In this direction, we have developed a database dbAQP-SNP that contains information about all 2798 SNPs. This database has several features and search options that can help the user to find SNPs in specific positions of human AQPs including the functionally and/or structurally important regions. dbAQP-SNP (http://bioinfo.iitk.ac.in/dbAQP-SNP) is freely available to the academic community. Database URL http://bioinfo.iitk.ac.in/dbAQP-SNP", + "authors": [ + { + "name": "Dande R." + }, + { + "name": "Sankararamakrishnan R." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Database", + "title": "dbAQP-SNP: a database of missense single-nucleotide polymorphisms in human aquaporins" + }, + "pmcid": "PMC10010469", + "pmid": "36913438" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "DNA polymorphism", + "uri": "http://edamontology.org/topic_2885" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Human biology", + "uri": "http://edamontology.org/topic_2815" + }, + { + "term": "Pathology", + "uri": "http://edamontology.org/topic_0634" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/destin2/destin2.biotools.json b/data/destin2/destin2.biotools.json new file mode 100644 index 0000000000000..4fda3bedcbcac --- /dev/null +++ b/data/destin2/destin2.biotools.json @@ -0,0 +1,85 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:28:43.180338Z", + "biotoolsCURIE": "biotools:destin2", + "biotoolsID": "destin2", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yuchaoj@email.unc.edu", + "name": "Yuchao Jiang", + "typeEntity": "Person" + }, + { + "name": "Jin Seok Lee", + "typeEntity": "Person" + }, + { + "name": "Peter Y. Guan", + "typeEntity": "Person" + } + ], + "description": "Integrative and cross-modality analysis of single-cell chromatin accessibility data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + } + ] + } + ], + "homepage": "https://github.com/yuchaojiang/Destin2", + "language": [ + "R" + ], + "lastUpdate": "2023-08-30T14:28:43.220366Z", + "name": "Destin2", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/FGENE.2023.1089936", + "pmcid": "PMC9981783", + "pmid": "36873935" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Chromosome conformation capture", + "uri": "http://edamontology.org/topic_3940" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Statistics and probability", + "uri": "http://edamontology.org/topic_2269" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/endure/endure.biotools.json b/data/endure/endure.biotools.json new file mode 100644 index 0000000000000..5c91c5a47183c --- /dev/null +++ b/data/endure/endure.biotools.json @@ -0,0 +1,119 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:47:51.978306Z", + "biotoolsCURIE": "biotools:endure", + "biotoolsID": "endure", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "georg.kuenze@uni-leipzig.de", + "name": "Georg Künze", + "typeEntity": "Person" + }, + { + "name": "Felipe Engelberger", + "typeEntity": "Person" + }, + { + "name": "Jonathan D Zakary", + "typeEntity": "Person" + } + ], + "description": "Directing Protein Design Choices by Per-Residue Energy Breakdown Analysis with an Interactive Web Application.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Backbone modelling", + "uri": "http://edamontology.org/operation_0479" + }, + { + "term": "Molecular surface calculation", + "uri": "http://edamontology.org/operation_0387" + }, + { + "term": "Protein design", + "uri": "http://edamontology.org/operation_4008" + }, + { + "term": "Salt bridge calculation", + "uri": "http://edamontology.org/operation_1839" + } + ] + } + ], + "homepage": "http://endure.kuenzelab.org", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-31T14:47:51.980951Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/kuenzelab/ENDURE" + } + ], + "name": "ENDURE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/fmolb.2023.1178035", + "metadata": { + "abstract": "Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.", + "authors": [ + { + "name": "Engelberger F." + }, + { + "name": "Kunze G." + }, + { + "name": "Zakary J.D." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Frontiers in Molecular Biosciences", + "title": "Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application" + }, + "pmcid": "PMC10204868", + "pmid": "37228581" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Enzymes", + "uri": "http://edamontology.org/topic_0821" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Protein folding, stability and design", + "uri": "http://edamontology.org/topic_0130" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/fameta/fameta.biotools.json b/data/fameta/fameta.biotools.json new file mode 100644 index 0000000000000..ffba14c211b78 --- /dev/null +++ b/data/fameta/fameta.biotools.json @@ -0,0 +1,121 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T13:53:52.199946Z", + "biotoolsCURIE": "biotools:fameta", + "biotoolsID": "fameta", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "agustin.lahoz@uv.es", + "name": "Agustín Lahoz", + "orcidid": "https://orcid.org/0000-0001-7232-0626", + "typeEntity": "Person" + } + ], + "description": "Mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Metabolic network modelling", + "uri": "http://edamontology.org/operation_3660" + }, + { + "term": "Metabolic pathway prediction", + "uri": "http://edamontology.org/operation_3929" + }, + { + "term": "SILAC", + "uri": "http://edamontology.org/operation_3638" + } + ] + } + ], + "homepage": "http://www.fameta.es", + "language": [ + "R" + ], + "lastUpdate": "2023-08-30T13:53:52.202450Z", + "license": "GPL-2.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://CRAN.R-project.org/package=FAMetA" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/maialba3/FAMetA" + } + ], + "name": "FAMetA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIB/BBAD064", + "metadata": { + "abstract": "The use of stable isotope tracers and mass spectrometry (MS) is the gold standard method for the analysis of fatty acid (FA) metabolism. Yet, current state-of-the-art tools provide limited and difficult-to-interpret information about FA biosynthetic routes. Here we present FAMetA, an R package and a web-based application (www.fameta.es) that uses 13C mass isotopologue profiles to estimate FA import, de novo lipogenesis, elongation and desaturation in a user-friendly platform. The FAMetA workflow covers the required functionalities needed for MS data analyses. To illustrate its utility, different in vitro and in vivo experimental settings are used in which FA metabolism is modified. Thanks to the comprehensive characterization of FA biosynthesis and the easy-to-interpret graphical representations compared to previous tools, FAMetA discloses unnoticed insights into how cells reprogram their FA metabolism and, when combined with FASN, SCD1 and FADS2 inhibitors, it enables the identification of new FAs by the metabolic reconstruction of their synthesis route.", + "authors": [ + { + "name": "Alcoriza-Balaguer M.I." + }, + { + "name": "Benet M." + }, + { + "name": "Garcia-Canaveras J.C." + }, + { + "name": "Juan-Vidal O." + }, + { + "name": "Lahoz A." + } + ], + "date": "2023-03-01T00:00:00Z", + "journal": "Briefings in Bioinformatics", + "title": "FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism" + }, + "pmcid": "PMC10025582", + "pmid": "36857618" + } + ], + "toolType": [ + "Library", + "Web application" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Lipids", + "uri": "http://edamontology.org/topic_0153" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Proteomics experiment", + "uri": "http://edamontology.org/topic_3520" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/genesignet/genesignet.biotools.json b/data/genesignet/genesignet.biotools.json new file mode 100644 index 0000000000000..ca822e739f1b2 --- /dev/null +++ b/data/genesignet/genesignet.biotools.json @@ -0,0 +1,119 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:05:27.225322Z", + "biotoolsCURIE": "biotools:genesignet", + "biotoolsID": "genesignet", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "przytyck@ncbi.nlm.nih.gov", + "name": "Teresa M. Przytycka", + "orcidid": "https://orcid.org/0000-0002-6261-277X", + "typeEntity": "Person" + }, + { + "name": "Bayarbaatar Amgalan", + "typeEntity": "Person" + }, + { + "name": "Damian Wojtowicz", + "typeEntity": "Person" + }, + { + "name": "Yoo-Ah Kim", + "typeEntity": "Person" + } + ], + "description": "Influence network model uncovers relations between biological processes and mutational signatures.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Metabolic network modelling", + "uri": "http://edamontology.org/operation_3660" + }, + { + "term": "Named-entity and concept recognition", + "uri": "http://edamontology.org/operation_3280" + }, + { + "term": "Network visualisation", + "uri": "http://edamontology.org/operation_3925" + } + ] + } + ], + "homepage": "https://github.com/ncbi/GeneSigNet", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-31T14:05:27.227979Z", + "name": "GENESIGNET", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1186/S13073-023-01162-X", + "metadata": { + "abstract": "Background: There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures. Methods: To gain insights into these relationships, we developed a network-based method, named GeneSigNet that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Results: Applying GeneSigNet to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GeneSigNet also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GeneSigNet also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. Conclusions: GeneSigNet provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GeneSigNet method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.", + "authors": [ + { + "name": "Amgalan B." + }, + { + "name": "Kim Y.-A." + }, + { + "name": "Przytycka T.M." + }, + { + "name": "Wojtowicz D." + } + ], + "citationCount": 1, + "date": "2023-12-01T00:00:00Z", + "journal": "Genome Medicine", + "title": "Influence network model uncovers relations between biological processes and mutational signatures" + }, + "pmcid": "PMC9987115", + "pmid": "36879282" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "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/gentle/gentle.biotools.json b/data/gentle/gentle.biotools.json new file mode 100644 index 0000000000000..e8ed333f7758f --- /dev/null +++ b/data/gentle/gentle.biotools.json @@ -0,0 +1,60 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-09-02T07:29:28.160089Z", + "biotoolsCURIE": "biotools:gentle", + "biotoolsID": "gentle", + "cost": "Free of charge", + "description": "Software for DNA and amino acid editing, database management, plasmid maps, restriction and ligation, alignments, sequencer data import, calculators, gel image display, PCR, and much more.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://en.wikibooks.org/wiki/GENtle" + } + ], + "editPermission": { + "type": "private" + }, + "homepage": "http://gentle.magnusmanske.de/", + "language": [ + "C++" + ], + "lastUpdate": "2023-09-02T07:39:00.374787Z", + "license": "GPL-2.0", + "link": [ + { + "type": [ + "Issue tracker" + ], + "url": "https://github.com/GENtle-persons/gentle-m/issues" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/GENtle-persons/gentle-m" + } + ], + "maturity": "Legacy", + "name": "GENtle", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "smoe", + "topic": [ + { + "term": "Genetic engineering", + "uri": "http://edamontology.org/topic_3912" + }, + { + "term": "PCR experiment", + "uri": "http://edamontology.org/topic_3519" + } + ], + "version": [ + "1.9.4" + ] +} diff --git a/data/gpcr-sas/gpcr-sas.biotools.json b/data/gpcr-sas/gpcr-sas.biotools.json new file mode 100644 index 0000000000000..9c8c64c5fe0db --- /dev/null +++ b/data/gpcr-sas/gpcr-sas.biotools.json @@ -0,0 +1,18 @@ +{ + "additionDate": "2023-08-30T12:39:58.333143Z", + "biotoolsCURIE": "biotools:gpcr-sas", + "biotoolsID": "gpcr-sas", + "description": "The G Protein-Coupled Receptors-Sequence Analysis and Statistics (GPCR-SAS) web application provides a set of tools to perform comparative analysis of sequence positions between receptors, based on a curated structural-informed multiple sequence alignment. The analysis tools include: (i) percentage of occurrence of an amino acid or motif and entropy at a position or range of positions, (ii) covariance of two positions, (iii) correlation between two amino acids in two positions (or two sequence motifs in two ranges of positions), and (iv) snake-plot representation for a specific receptor or for the consensus sequence of a group of selected receptors. The analysis of conservation of residues and motifs across transmembrane (TM) segments may guide the design of more selective ligands or help to rationalize activation mechanisms, among others. As an example, here we analyze the amino acids of the \"transmission switch\", that initiates receptor activation following ligand binding.", + "editPermission": { + "type": "private" + }, + "homepage": "http://lmc.uab.cat/gpcrsas/", + "lastUpdate": "2023-08-30T12:39:58.335527Z", + "name": "GPCR-SAS", + "owner": "laasfeld", + "publication": [ + { + "doi": "10.1371/journal.pone.0199843" + } + ] +} diff --git a/data/grnsight/grnsight.biotools.json b/data/grnsight/grnsight.biotools.json index 843462f8ddf01..c9a92a6fd46e5 100644 --- a/data/grnsight/grnsight.biotools.json +++ b/data/grnsight/grnsight.biotools.json @@ -206,7 +206,7 @@ "language": [ "JavaScript" ], - "lastUpdate": "2022-12-12T23:17:57.261128Z", + "lastUpdate": "2023-08-29T16:51:15.145002Z", "license": "BSD-3-Clause", "link": [ { @@ -228,7 +228,7 @@ { "doi": "10.7717/peerj-cs.85", "metadata": { - "abstract": "© 2016 Dahlquist et al.GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight's diagrams are based on D3.js's force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop.When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (http://dondi.github.io/ GRNsight/) and code (https://github.com/dondi/GRNsight) are available under the open source BSD license.", + "abstract": "GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight's diagrams are based on D3.js's force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop.When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (http://dondi.github.io/ GRNsight/) and code (https://github.com/dondi/GRNsight) are available under the open source BSD license.", "authors": [ { "name": "Anguiano N.A." @@ -262,7 +262,7 @@ ] }, { - "doi": "10.5281/zenodo.1287498", + "doi": "10.5281/zenodo.7411630", "type": [ "Other" ] @@ -283,6 +283,6 @@ ], "validated": 1, "version": [ - "6.0.4" + "6.0.7" ] } diff --git a/data/gvc/gvc.biotools.json b/data/gvc/gvc.biotools.json new file mode 100644 index 0000000000000..3b461d54b8e4e --- /dev/null +++ b/data/gvc/gvc.biotools.json @@ -0,0 +1,111 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T13:56:28.415430Z", + "biotoolsCURIE": "biotools:gvc", + "biotoolsID": "gvc", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "adhisant@tnt.uni-hannover.de", + "name": "Yeremia Gunawan Adhisantoso", + "typeEntity": "Person" + } + ], + "description": "Efficient random access compression for gene sequence variations.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome indexing", + "uri": "http://edamontology.org/operation_3211" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Sorting", + "uri": "http://edamontology.org/operation_3802" + }, + { + "term": "Variant calling", + "uri": "http://edamontology.org/operation_3227" + } + ] + } + ], + "homepage": "https://github.com/sXperfect/gvc/", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-30T13:56:28.417920Z", + "name": "GVC", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1186/S12859-023-05240-0", + "metadata": { + "abstract": "Background: In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.", + "authors": [ + { + "name": "Adhisantoso Y.G." + }, + { + "name": "Ohm J.-R." + }, + { + "name": "Ostermann J." + }, + { + "name": "Rohlfing C." + }, + { + "name": "Tunev V." + }, + { + "name": "Voges J." + } + ], + "date": "2023-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "GVC: efficient random access compression for gene sequence variations" + }, + "pmcid": "PMC10044409", + "pmid": "36978010" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "GWAS study", + "uri": "http://edamontology.org/topic_3517" + }, + { + "term": "Gene expression", + "uri": "http://edamontology.org/topic_0203" + }, + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Oncology", + "uri": "http://edamontology.org/topic_2640" + } + ] +} diff --git a/data/impute2/impute2.biotools.json b/data/impute2/impute2.biotools.json index 9a2fba321503a..0db21257baebf 100644 --- a/data/impute2/impute2.biotools.json +++ b/data/impute2/impute2.biotools.json @@ -23,14 +23,31 @@ "url": "https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#options" } ], + "download": [ + { + "note": "Linux (x86_64) Static Executable", + "type": "Binaries", + "url": "https://mathgen.stats.ox.ac.uk/impute/impute_v2.3.2_x86_64_static.tgz", + "version": "2.3.2" + } + ], "editPermission": { "authors": [ - "animalandcropgenomics" + "animalandcropgenomics", + "billsfriend" ], "type": "group" }, "function": [ { + "input": [ + { + "data": { + "term": "Sequence variations", + "uri": "http://edamontology.org/data_3498" + } + } + ], "operation": [ { "term": "Imputation", @@ -40,6 +57,14 @@ "term": "Phasing", "uri": "http://edamontology.org/operation_3454" } + ], + "output": [ + { + "data": { + "term": "Sequence variations", + "uri": "http://edamontology.org/data_3498" + } + } ] } ], @@ -47,7 +72,7 @@ "language": [ "Perl" ], - "lastUpdate": "2019-09-04T08:38:12Z", + "lastUpdate": "2023-08-30T02:34:05.305800Z", "license": "GPL-3.0", "name": "IMPUTE2", "operatingSystem": [ @@ -59,48 +84,12 @@ "publication": [ { "doi": "10.1371/journal.pgen.1000529", - "metadata": { - "abstract": "Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%-20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions. © 2009 Howie et al.", - "authors": [ - { - "name": "Donnelly P." - }, - { - "name": "Howie B.N." - }, - { - "name": "Marchini J." - } - ], - "citationCount": 2509, - "date": "2009-06-01T00:00:00Z", - "journal": "PLoS Genetics", - "title": "A flexible and accurate genotype imputation method for the next generation of genome-wide association studies" - }, "type": [ "Primary" ] }, { "doi": "10.1534/g3.111.001198", - "metadata": { - "abstract": "Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package. © 2011 Howie et al.", - "authors": [ - { - "name": "Howie B." - }, - { - "name": "Marchini J." - }, - { - "name": "Stephens M." - } - ], - "citationCount": 620, - "date": "2011-11-01T00:00:00Z", - "journal": "G3: Genes, Genomes, Genetics", - "title": "Genotype imputation with thousands of genomes" - }, "type": [ "Primary" ] @@ -115,5 +104,8 @@ "uri": "http://edamontology.org/topic_3056" } ], - "validated": 1 + "validated": 1, + "version": [ + "2.3.2" + ] } diff --git a/data/impute_5/impute_5.biotools.json b/data/impute_5/impute_5.biotools.json new file mode 100644 index 0000000000000..e08bb0e4a3cdb --- /dev/null +++ b/data/impute_5/impute_5.biotools.json @@ -0,0 +1,126 @@ +{ + "additionDate": "2023-09-02T14:20:31.584436Z", + "biotoolsCURIE": "biotools:impute_5", + "biotoolsID": "impute_5", + "cost": "Free of charge (with restrictions)", + "credit": [ + { + "email": "jonathan.marchini@regeneron.com", + "name": "Jonathan Marchini", + "orcidid": "https://orcid.org/0000-0003-0610-8322", + "url": "https://jmarchini.org/" + } + ], + "description": "IMPUTE 5 is a genotype imputation method that can scale to reference panels with millions of samples.", + "download": [ + { + "note": "dropbox link", + "type": "Software package", + "url": "https://www.dropbox.com/sh/mwnceyhir8yze2j/AADbzP6QuAFPrj0Z9_I1RSmla?dl=0", + "version": "1.1.5" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Sequence variations", + "uri": "http://edamontology.org/data_3498" + }, + "format": [ + { + "term": "BCF", + "uri": "http://edamontology.org/format_3020" + }, + { + "term": "VCF", + "uri": "http://edamontology.org/format_3016" + } + ] + } + ], + "operation": [ + { + "term": "Imputation", + "uri": "http://edamontology.org/operation_3557" + } + ], + "output": [ + { + "data": { + "term": "Sequence variations", + "uri": "http://edamontology.org/data_3498" + }, + "format": [ + { + "term": "BCF", + "uri": "http://edamontology.org/format_3020" + }, + { + "term": "VCF", + "uri": "http://edamontology.org/format_3016" + } + ] + } + ] + } + ], + "homepage": "https://jmarchini.org/software/#impute-5", + "lastUpdate": "2023-09-02T14:20:31.588771Z", + "license": "Other", + "name": "IMPUTE 5", + "operatingSystem": [ + "Linux" + ], + "owner": "billsfriend", + "publication": [ + { + "doi": "10.1371/journal.pgen.1009049", + "metadata": { + "abstract": "Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model. Using the HRC reference panel, which has *65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.", + "authors": [ + { + "name": "Delaneau O." + }, + { + "name": "Marchini J." + }, + { + "name": "Rubinacci S." + } + ], + "citationCount": 38, + "date": "2020-11-16T00:00:00Z", + "journal": "PLoS Genetics", + "title": "Genotype imputation using the Positional Burrows Wheeler Transform" + }, + "pmcid": "PMC7704051", + "pmid": "33196638", + "type": [ + "Primary" + ] + } + ], + "relation": [ + { + "biotoolsID": "impute2", + "type": "isNewVersionOf" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Population genetics", + "uri": "http://edamontology.org/topic_3056" + } + ], + "version": [ + "1.1.5" + ] +} diff --git a/data/intlim/intlim.biotools.json b/data/intlim/intlim.biotools.json new file mode 100644 index 0000000000000..f56e970e804f3 --- /dev/null +++ b/data/intlim/intlim.biotools.json @@ -0,0 +1,93 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:45:33.979119Z", + "biotoolsCURIE": "biotools:intlim", + "biotoolsID": "intlim", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "ewy.mathe@nih.gov", + "name": "Ewy A Mathé", + "typeEntity": "Person" + }, + { + "name": "Kyle D Spencer", + "typeEntity": "Person" + }, + { + "name": "Tara Eicher", + "typeEntity": "Person" + } + ], + "description": "Identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Correlation", + "uri": "http://edamontology.org/operation_3465" + }, + { + "term": "Statistical calculation", + "uri": "http://edamontology.org/operation_2238" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://intlim.ncats.io/", + "language": [ + "R" + ], + "lastUpdate": "2023-08-30T14:45:33.981748Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/ncats/IntLIM" + } + ], + "name": "IntLIM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIOADV/VBAD009", + "pmcid": "PMC10010601", + "pmid": "36922980" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/ip4gs/ip4gs.biotools.json b/data/ip4gs/ip4gs.biotools.json new file mode 100644 index 0000000000000..24398f621c0dd --- /dev/null +++ b/data/ip4gs/ip4gs.biotools.json @@ -0,0 +1,129 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:20:21.287835Z", + "biotoolsCURIE": "biotools:ip4gs", + "biotoolsID": "ip4gs", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "qchengray@cau.edu.cn", + "name": "Qian Cheng", + "typeEntity": "Person" + }, + { + "email": "wanshi0066@126.com", + "name": "Shuqin Jiang", + "typeEntity": "Person" + }, + { + "name": "Ran Fu", + "typeEntity": "Person" + }, + { + "name": "Shan Jiang", + "typeEntity": "Person" + }, + { + "name": "Tong Li", + "typeEntity": "Person" + }, + { + "name": "Xiangfeng Wang", + "typeEntity": "Person" + } + ], + "description": "Platform for genomic selection, performing streamlined GS analysis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Dimensionality reduction", + "uri": "http://edamontology.org/operation_3935" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + }, + { + "term": "Genotyping", + "uri": "http://edamontology.org/operation_3196" + }, + { + "term": "Scatter plot plotting", + "uri": "http://edamontology.org/operation_2940" + } + ] + } + ], + "homepage": "https://github.com/furan2019/IP4GSdata", + "language": [ + "R" + ], + "lastUpdate": "2023-08-31T14:20:21.290405Z", + "name": "IP4GS", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/FPLS.2023.1131493", + "metadata": { + "abstract": "Genomic selection (GS), a strategy to use genotypes to predict phenotypes via statistical or machine learning models, has become a routine practice in plant breeding programs. GS can speed up the genetic gain by reducing phenotyping costs and/or shortening the breeding cycles. GS analysis is complicated involving data clean up and formatting, training and test population analysis, model selection and evaluation, and parameter optimization. In addition, GS analysis also requires some programming skills and knowledge of statistical modeling. Thus, we need a more practical GS tools for breeders. To alleviate this difficulty, we developed the web-based platform IP4GS (https://ngdc.cncb.ac.cn/ip4gs/), which offers a user-friendly interface to perform GS analysis simply through point-and-click actions. IP4GS currently includes seven commonly used models, eleven evaluation metrics, and visualization modules, offering great convenience for plant breeders with limited bioinformatics knowledge to apply GS analysis.", + "authors": [ + { + "name": "Cheng Q." + }, + { + "name": "Fu R." + }, + { + "name": "Jiang S." + }, + { + "name": "Jiang S." + }, + { + "name": "Li T." + }, + { + "name": "Wang X." + } + ], + "citationCount": 1, + "date": "2023-01-01T00:00:00Z", + "journal": "Frontiers in Plant Science", + "title": "IP4GS: Bringing genomic selection analysis to breeders" + }, + "pmcid": "PMC10025548", + "pmid": "36950355" + } + ], + "toolType": [ + "Desktop application" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Genotype and phenotype", + "uri": "http://edamontology.org/topic_0625" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + } + ] +} diff --git a/data/kargva/kargva.biotools.json b/data/kargva/kargva.biotools.json new file mode 100644 index 0000000000000..d83b1493f57f3 --- /dev/null +++ b/data/kargva/kargva.biotools.json @@ -0,0 +1,114 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:26:10.621881Z", + "biotoolsCURIE": "biotools:kargva", + "biotoolsID": "kargva", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "m.prosperi@ufl.edu", + "name": "Mattia Prosperi", + "typeEntity": "Person" + }, + { + "name": "Christina Boucher", + "typeEntity": "Person" + }, + { + "name": "Noelle Noyes", + "typeEntity": "Person" + }, + { + "name": "Simone Marini", + "typeEntity": "Person" + } + ], + "description": "K-mer Antibiotic Resistance Gene Variant Analyzer.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Antimicrobial resistance prediction", + "uri": "http://edamontology.org/operation_3482" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + }, + { + "term": "k-mer counting", + "uri": "http://edamontology.org/operation_3472" + } + ] + } + ], + "homepage": "https://github.com/DataIntellSystLab/KARGVA", + "language": [ + "Java" + ], + "lastUpdate": "2023-08-30T14:26:10.624682Z", + "license": "MIT", + "name": "KARGVA", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/FMICB.2023.1060891", + "metadata": { + "abstract": "Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer – KARGVA – an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2 and 86.6% accuracy within 1 and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.", + "authors": [ + { + "name": "Boucher C." + }, + { + "name": "Marini S." + }, + { + "name": "Noyes N." + }, + { + "name": "Prosperi M." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Frontiers in Microbiology", + "title": "The K-mer antibiotic resistance gene variant analyzer (KARGVA)" + }, + "pmcid": "PMC10027697", + "pmid": "36960290" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Genetic variation", + "uri": "http://edamontology.org/topic_0199" + }, + { + "term": "Mapping", + "uri": "http://edamontology.org/topic_0102" + }, + { + "term": "Metagenomics", + "uri": "http://edamontology.org/topic_3174" + }, + { + "term": "Microbial ecology", + "uri": "http://edamontology.org/topic_3697" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/macpepdb/macpepdb.biotools.json b/data/macpepdb/macpepdb.biotools.json deleted file mode 100644 index e55901c12846a..0000000000000 --- a/data/macpepdb/macpepdb.biotools.json +++ /dev/null @@ -1,101 +0,0 @@ -{ - "accessibility": "Open access", - "additionDate": "2021-05-28T06:43:30Z", - "biotoolsCURIE": "biotools:macpepdb", - "biotoolsID": "macpepdb", - "description": "A Database to Quickly Access All Tryptic Peptides of the UniProtKB.", - "editPermission": { - "type": "private" - }, - "function": [ - { - "operation": [ - { - "term": "PTM localisation", - "uri": "http://edamontology.org/operation_3755" - }, - { - "term": "Peptide database search", - "uri": "http://edamontology.org/operation_3646" - }, - { - "term": "Protein molecular weight calculation", - "uri": "http://edamontology.org/operation_0398" - }, - { - "term": "Tag-based peptide identification", - "uri": "http://edamontology.org/operation_3643" - } - ] - } - ], - "homepage": "https://macpepdb.mpc.rub.de/", - "lastUpdate": "2021-05-28T06:43:30Z", - "name": "MaCPepDB", - "operatingSystem": [ - "Linux", - "Mac", - "Windows" - ], - "owner": "Niclaskn", - "publication": [ - { - "doi": "10.1021/ACS.JPROTEOME.0C00967", - "metadata": { - "abstract": "© 2021 The Authors. Published by American Chemical Society.Protein sequence databases play a crucial role in the majority of the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB serves as one of the major sources, as it combines the information of several smaller databases and enriches the entries with additional biological information. For the identification of peptides in a sample by tandem mass spectra, as generated by data-dependent acquisition, protein sequence databases provide the basis for most spectrum identification search engines. In addition, for targeted proteomics approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), knowledge of the peptide sequences, their masses, and whether they are unique for a protein is essential. Because most bottom-up proteomics approaches use trypsin to cleave the proteins in a sample, the tryptic peptides contained in a protein database are of great interest. We present a database, called MaCPepDB (mass-centric peptide database), that consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts of UniProtKB. This database is especially designed to not only allow queries of peptide sequences and return the respective information about connected proteins and thus whether a peptide is unique but also allow queries of specific masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational modifications can be considered in a query as well as different mass deviations for posttranslational modifications. Hence the database can be used by a sequence query not only to, for example, check in which proteins of the UniProt database a tryptic peptide can be found but also to find possibly interfering peptides in PRM/SRM experiments using the mass query. The complete database contains currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version 2020_03), for which a single query usually takes less than 1 s. For easy exploration of the data, a web interface was developed. A REST application programming interface (API) for programmatic and workflow access is also available at https://macpepdb.mpc.rub.de.", - "authors": [ - { - "name": "Barkovits K." - }, - { - "name": "Eisenacher M." - }, - { - "name": "Marcus K." - }, - { - "name": "Roocke S." - }, - { - "name": "Uszkoreit J." - }, - { - "name": "Winkelhardt D." - }, - { - "name": "Wulf M." - } - ], - "date": "2021-04-02T00:00:00Z", - "journal": "Journal of Proteome Research", - "title": "MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB" - }, - "pmid": "33724838" - } - ], - "toolType": [ - "Web application" - ], - "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": "Sequence analysis", - "uri": "http://edamontology.org/topic_0080" - }, - { - "term": "Small molecules", - "uri": "http://edamontology.org/topic_0154" - } - ] -} diff --git a/data/medipipe/medipipe.biotools.json b/data/medipipe/medipipe.biotools.json new file mode 100644 index 0000000000000..1ceccbeb2815f --- /dev/null +++ b/data/medipipe/medipipe.biotools.json @@ -0,0 +1,94 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:46:31.552282Z", + "biotoolsCURIE": "biotools:medipipe", + "biotoolsID": "medipipe", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "trevor.pugh@utoronto.ca", + "name": "Trevor J Pugh", + "orcidid": "https://orcid.org/0000-0002-8073-5888", + "typeEntity": "Person" + }, + { + "email": "hansenhe@uhnresearch.ca", + "name": "Housheng Hansen He", + "typeEntity": "Person" + }, + { + "email": "yzeng@uhnresearch.ca", + "name": "Yong Zeng", + "typeEntity": "Person" + }, + { + "name": "yzeng@uhnresearch.ca", + "typeEntity": "Person" + } + ], + "description": "Automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + }, + { + "term": "Sequencing quality control", + "uri": "http://edamontology.org/operation_3218" + } + ] + } + ], + "homepage": "https://github.com/yzeng-lol/MEDIPIPE", + "lastUpdate": "2023-08-30T14:46:31.554749Z", + "license": "MIT", + "name": "MEDIPIPE", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/bioinformatics/btad423", + "pmcid": "PMC10348834", + "pmid": "37402621" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Computer science", + "uri": "http://edamontology.org/topic_3316" + }, + { + "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": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/mols_2.0/mols_2.0.biotools.json b/data/mols_2.0/mols_2.0.biotools.json new file mode 100644 index 0000000000000..71b0ca8e8aa4a --- /dev/null +++ b/data/mols_2.0/mols_2.0.biotools.json @@ -0,0 +1,109 @@ +{ + "additionDate": "2023-08-30T07:09:33.335720Z", + "biotoolsCURIE": "biotools:mols_2.0", + "biotoolsID": "mols_2.0", + "description": "MOLS 2.0 is a free and open-source software package for Peptide Modeling and Protein-Ligand Induced-Fit Docking developed in Prof. N. Gautham's Lab at the Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Tamil Nadu, India.", + "download": [ + { + "type": "Downloads page", + "url": "https://sourceforge.net/projects/mols2-0/files/" + } + ], + "editPermission": { + "type": "private" + }, + "homepage": "https://sourceforge.net/projects/mols2-0/", + "lastUpdate": "2023-08-30T07:15:47.695715Z", + "name": "MOLS 2.0", + "owner": "sampaul", + "publication": [ + { + "doi": "10.1007/s00894-016-3106-x", + "metadata": { + "abstract": "We previously developed an algorithm to perform conformational searches of proteins and peptides, and to perform the docking of ligands to protein receptors. In order to identify optimal conformations and docked poses, this algorithm uses mutually orthogonal Latin squares (MOLS) to rationally sample the vast conformational (or docking) space, and then analyzes this relatively small sample using a variant of mean field theory. The conformational search part of the algorithm was denoted MOLS 1.0. The docking portion of the algorithm, which allows only “flexible ligand/rigid receptor” docking, was denoted MOLSDOCK. Both are FORTRAN-based command-line-only molecular docking computer programs, though a GUI was developed later for MOLS 1.0. Both the conformational search and the rigid receptor docking parts of the algorithm have been extensively validated. We have now further enhanced the capabilities of the program by incorporating “induced fit” side-chain receptor flexibility for docking peptide ligands. Benchmarking and extensive testing is now being carried out for the flexible receptor portion of the docking. Additionally, to make both the peptide conformational search and docking algorithms (the latter including both flexible ligand/rigid receptor and flexible ligand/flexible receptor techniques) more accessible to the research community, we have developed MOLS 2.0, which incorporates a new Java-based graphical user interface (GUI). Here, we give a detailed description of MOLS 2.0. The source code and binary for MOLS 2.0 are distributed free (under a GNU Lesser General Public License) to the scientific community. They are freely available for download at https://sourceforge.net/projects/mols2-0/files/.", + "authors": [ + { + "name": "Gautham N." + }, + { + "name": "Paul D.S." + } + ], + "citationCount": 17, + "date": "2016-10-01T00:00:00Z", + "journal": "Journal of Molecular Modeling", + "title": "MOLS 2.0: software package for peptide modeling and protein–ligand docking" + }, + "pmid": "27638416", + "type": [ + "Primary" + ] + }, + { + "doi": "10.1007/s00894-022-05413-3", + "metadata": { + "abstract": "iMOLSDOCK is an induced-fit docking algorithm that uses the mutually orthogonal Latin squares (MOLS) sampling technique. Here, we describe the updates made to iMOLSDOCK in order to increase receptor flexibility, improve the scoring system, and speed up calculation. With a dataset of 35 peptide-protein complexes, the PepSet benchmark dataset of 80 peptide-protein complexes, and the Astex Diverse set, which uses nonpeptide small molecules as ligands, iMOLSDOCK has been benchmarked and validated. Flexible residues are now able to deviate from the starting position by a maximum of 3.0 Å due to the increased receptor flexibility. The ranking effectiveness of iMOLSDOCK has increased by 24% once the scoring system was improved. Additionally, iMOLSDOCK has been compared to Gold v5.2.1, HPEPDOCK, AutoDock CrankPep v1.0, AutoDock Vina, HADDOCK, PatchDock, and RosettaLigand. For induced-fit peptide-protein docking, iMOLSDOCK achieved success rates of 6%, 37%, and 89% at the top 1, 10, and 100 levels. At the top 1, 10, and 100 levels, iMOLSDOCK had success rates for small molecule-protein docking of 14%, 31%, and 49%. The computation time for peptide docking was lowered by two orders of magnitude, and for nonpeptide small molecule docking, it was roughly 14 times faster due to code optimization in the iMOLSDOCK docking tool. Source code and binary of iMOLSDOCK could be obtained from https://sourceforge.net/projects/mols2-0/files/.", + "authors": [ + { + "name": "Karthe P." + }, + { + "name": "Paul D.S." + } + ], + "citationCount": 1, + "date": "2023-01-01T00:00:00Z", + "journal": "Journal of Molecular Modeling", + "title": "Improved docking of peptides and small molecules in iMOLSDOCK" + }, + "pmid": "36536252", + "type": [ + "Benchmarking study" + ] + }, + { + "doi": "10.1007/s10822-018-0152-8", + "metadata": { + "abstract": "We have earlier reported the iMOLSDOCK technique to perform ‘induced-fit’ peptide–protein docking. iMOLSDOCK uses the mutually orthogonal Latin squares (MOLSs) technique to sample the conformation and the docking pose of the small molecule ligand and also the flexible residues of the receptor protein, and arrive at the optimum pose and conformation. In this paper we report the extension carried out in iMOLSDOCK to dock nonpeptide small molecule ligands to receptor proteins. We have benchmarked and validated iMOLSDOCK with a dataset of 34 protein–ligand complexes as well as with Astex Diverse dataset, with nonpeptide small molecules as ligands. We have also compared iMOLSDOCK with other flexible receptor docking tools GOLD v5.2.1 and AutoDock Vina. The results obtained show that the method works better than these two algorithms, though it consumes more computer time. The source code and binary of MOLS 2.0 (under a GNU Lesser General Public License) are freely available for download at https://sourceforge.net/projects/mols2-0/files/.", + "authors": [ + { + "name": "Gautham N." + }, + { + "name": "Sam Paul D." + } + ], + "citationCount": 3, + "date": "2018-09-01T00:00:00Z", + "journal": "Journal of Computer-Aided Molecular Design", + "title": "Protein–small molecule docking with receptor flexibility in iMOLSDOCK" + }, + "pmid": "30128925", + "type": [ + "Benchmarking study" + ] + }, + { + "doi": "10.1016/j.jmgm.2017.03.008", + "metadata": { + "abstract": "We have earlier reported the MOLSDOCK technique to perform rigid receptor/flexible ligand docking. The method uses the MOLS method, developed in our laboratory. In this paper we report iMOLSDOCK, the ‘flexible receptor’ extension we have carried out to the algorithm MOLSDOCK. iMOLSDOCK uses mutually orthogonal Latin squares (MOLS) to sample the conformation and the docking pose of the ligand and also the flexible residues of the receptor protein. The method then uses a variant of the mean field technique to analyze the sample to arrive at the optimum. We have benchmarked and validated iMOLSDOCK with a dataset of 44 peptide-protein complexes with peptides. We have also compared iMOLSDOCK with other flexible receptor docking tools GOLD v5.2.1 and AutoDock Vina. The results obtained show that the method works better than these two algorithms, though it consumes more computer time.", + "authors": [ + { + "name": "Gautham N." + }, + { + "name": "Paul D.S." + } + ], + "citationCount": 5, + "date": "2017-06-01T00:00:00Z", + "journal": "Journal of Molecular Graphics and Modelling", + "title": "iMOLSDOCK: Induced-fit docking using mutually orthogonal Latin squares (MOLS)" + }, + "pmid": "28365533", + "type": [ + "Method" + ] + } + ] +} diff --git a/data/neuropred-plm/neuropred-plm.biotools.json b/data/neuropred-plm/neuropred-plm.biotools.json new file mode 100644 index 0000000000000..cf8d0bda5dc25 --- /dev/null +++ b/data/neuropred-plm/neuropred-plm.biotools.json @@ -0,0 +1,82 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T13:59:38.109797Z", + "biotoolsCURIE": "biotools:neuropred-plm", + "biotoolsID": "neuropred-plm", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "yanw@hust.edu.cn", + "name": "Zhidong Xue", + "typeEntity": "Person" + }, + { + "email": "zdxue@hust.edu.cn", + "name": "Yan Wang", + "typeEntity": "Person" + } + ], + "description": "Interpretable and robust model for neuropeptide prediction by protein language model.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Network analysis", + "uri": "http://edamontology.org/operation_3927" + }, + { + "term": "Protein modelling", + "uri": "http://edamontology.org/operation_0477" + } + ] + } + ], + "homepage": "https://github.com/isyslab-hust/NeuroPred-PLM", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-30T13:59:38.112618Z", + "license": "MIT", + "link": [ + { + "type": [ + "Other" + ], + "url": "https://pypi.org/project/NeuroPredPLM/" + } + ], + "name": "NeuroPred-PLM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIB/BBAD077", + "pmid": "36892166" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Drug development", + "uri": "http://edamontology.org/topic_3373" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/newlife/newlife.biotools.json b/data/newlife/newlife.biotools.json new file mode 100644 index 0000000000000..22eaa9ec5adc5 --- /dev/null +++ b/data/newlife/newlife.biotools.json @@ -0,0 +1,13 @@ +{ + "additionDate": "2023-08-31T06:34:54.637580Z", + "biotoolsCURIE": "biotools:newlife", + "biotoolsID": "newlife", + "description": "newtools reads two input sequences and writes their optimal global sequence alignment to file", + "editPermission": { + "type": "private" + }, + "homepage": "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=bio.tools%E5%8A%9F%E8%83%BD%E7%BB%93%E6%9E%84%E5%9B%BE&btnG=", + "lastUpdate": "2023-08-31T06:34:54.641120Z", + "name": "newlife", + "owner": "Shengyu" +} diff --git a/data/nf-core_isoseq/nf-core_isoseq.biotools.json b/data/nf-core_isoseq/nf-core_isoseq.biotools.json new file mode 100644 index 0000000000000..fde5fc604e7b8 --- /dev/null +++ b/data/nf-core_isoseq/nf-core_isoseq.biotools.json @@ -0,0 +1,146 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:39:51.889276Z", + "biotoolsCURIE": "biotools:nf-core_isoseq", + "biotoolsID": "nf-core_isoseq", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "sguizard@ed.ac.uk", + "name": "Sébastien Guizard", + "orcidid": "https://orcid.org/0000-0001-5116-4150", + "typeEntity": "Person" + }, + { + "name": "Jacqueline Smith", + "typeEntity": "Person" + }, + { + "name": "Katarzyna Miedzinska", + "typeEntity": "Person" + } + ], + "description": "Simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://isoseq.how" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Genome annotation", + "uri": "http://edamontology.org/operation_0362" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + }, + { + "term": "Transcriptome assembly", + "uri": "http://edamontology.org/operation_3258" + } + ] + } + ], + "homepage": "https://nf-co.re/isoseq", + "language": [ + "Groovy", + "Python" + ], + "lastUpdate": "2023-08-31T14:39:51.891944Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/PacificBiosciences/IsoSeq" + }, + { + "type": [ + "Repository" + ], + "url": "https://github.com/nf-core/isoseq" + } + ], + "name": "nf-core isoseq", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAD150", + "metadata": { + "abstract": "Motivation: Iso-Seq RNA long-read sequencing enables the identification of full-length transcripts and isoforms, removing the need for complex analysis such as transcriptome assembly. However, the raw sequencing data need to be processed in a series of steps before annotation is complete. Here, we present nf-core/isoseq, a pipeline for automatic read processing and genome annotation. Following nf-core guidelines, the pipeline has few dependencies and can be run on any of platforms.", + "authors": [ + { + "name": "Archibald A." + }, + { + "name": "Davey M." + }, + { + "name": "Guizard S." + }, + { + "name": "Kuo R.I." + }, + { + "name": "Miedzinska K." + }, + { + "name": "Smith J." + }, + { + "name": "Smith J." + }, + { + "name": "Watson M." + } + ], + "date": "2023-05-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing" + }, + "pmcid": "PMC10199315", + "pmid": "36961337" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "RNA splicing", + "uri": "http://edamontology.org/topic_3320" + }, + { + "term": "Sequence assembly", + "uri": "http://edamontology.org/topic_0196" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + }, + { + "term": "Workflows", + "uri": "http://edamontology.org/topic_0769" + } + ] +} diff --git a/data/ordb/ordb.biotools.json b/data/ordb/ordb.biotools.json new file mode 100644 index 0000000000000..d14c7013c1a2b --- /dev/null +++ b/data/ordb/ordb.biotools.json @@ -0,0 +1,42 @@ +{ + "additionDate": "2023-08-30T11:41:28.797963Z", + "biotoolsCURIE": "biotools:ordb", + "biotoolsID": "ordb", + "description": "The Olfactory Receptor Database is a central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. To deal with the very large OR gene family, we have constructed an algorithm that automatically downloads sequences from web sources such as GenBank and SWISS-PROT into the database. The algorithm uses hypertext markup language (HTML) parsing techniques that extract information relevant to ORDB. The information is then correlated with the metadata in the ORDB knowledge base to encode the unstructured text extracted into the structured format compliant with the database architecture, entity attribute value with classes and relationship (EAV/CR), which supports the SenseLab project as a whole. Three population methods: batch, automatic and semi-automatic population are discussed. The data is imported into the database using extensible markup language (XML).", + "editPermission": { + "type": "public" + }, + "homepage": "https://senselab.med.yale.edu/ordb", + "lastUpdate": "2023-08-30T11:41:28.802934Z", + "name": "ORDB", + "owner": "laasfeld", + "publication": [ + { + "doi": "10.1093/nar/30.1.354", + "metadata": { + "abstract": "The Olfactory Receptor Database (ORDB; http:// senselab.med.yale.edu/senselab/ordb) is a central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. To deal with the very large OR gene family, we have constructed an algorithm that automatically down-loads sequences from web sources such as GenBank and SWISS-PROT into the database. The algorithm uses hypertext markup language (HTML) parsing techniques that extract information relevant to ORDB. The information is then correlated with the metadata in the ORDB knowledge base to encode the unstructured text extracted into the structured format compliant with the database architecture, entity attribute value with classes and relationship (EAV/CR), which supports the SenseLab project as a whole. Three population methods: batch, automatic and semi-automatic population are discussed. The data is imported into the database using extensible markup language (XML).", + "authors": [ + { + "name": "Crasto C." + }, + { + "name": "Marenco L." + }, + { + "name": "Miller P." + }, + { + "name": "Shepherd G." + } + ], + "citationCount": 68, + "date": "2002-01-01T00:00:00Z", + "journal": "Nucleic Acids Research", + "title": "Olfactory Receptor Database: A metadata-driven automated population from sources of gene and protein sequences" + }, + "type": [ + "Primary" + ] + } + ] +} diff --git a/data/pangpcr/pangpcr.biotools.json b/data/pangpcr/pangpcr.biotools.json new file mode 100644 index 0000000000000..f76fb7744ba97 --- /dev/null +++ b/data/pangpcr/pangpcr.biotools.json @@ -0,0 +1,48 @@ +{ + "additionDate": "2023-08-30T12:13:35.064036Z", + "biotoolsCURIE": "biotools:pangpcr", + "biotoolsID": "pangpcr", + "description": "PanGPCR docks the compound of interest to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound’s GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side-Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand.", + "editPermission": { + "type": "public" + }, + "homepage": "https://gpcrpanel.cmdm.tw/index.html", + "lastUpdate": "2023-08-30T12:13:35.067187Z", + "name": "PanGPCR", + "owner": "laasfeld", + "publication": [ + { + "doi": "10.1093/bioinformatics/btaa766", + "metadata": { + "abstract": "Summary: Drug discovery targeting G protein-coupled receptors (GPCRs), the largest known class of therapeutic targets, is challenging. To facilitate the rapid discovery and development of GPCR drugs, we built a system, PanGPCR, to predict multiple potential GPCR targets and their expression locations in the tissues, side effects and possible repurposing of GPCR drugs. With PanGPCR, the compound of interest is docked to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound s GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side- Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand.", + "authors": [ + { + "name": "Ho M.-Y." + }, + { + "name": "Hsu M.-T." + }, + { + "name": "Liu L.-C." + }, + { + "name": "Su B.-H." + }, + { + "name": "Tseng Y.J." + }, + { + "name": "Wang S.-Y." + } + ], + "citationCount": 1, + "date": "2021-04-15T00:00:00Z", + "journal": "Bioinformatics", + "title": "PanGPCR: Predictions for multiple targets, repurposing and side effects PanGPCR: Predictions for multiple targets, repurposing and side effects" + }, + "type": [ + "Primary" + ] + } + ] +} diff --git a/data/peaks2utr/peaks2utr.biotools.json b/data/peaks2utr/peaks2utr.biotools.json new file mode 100644 index 0000000000000..9e205dba1064d --- /dev/null +++ b/data/peaks2utr/peaks2utr.biotools.json @@ -0,0 +1,116 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T13:02:12.759270Z", + "biotoolsCURIE": "biotools:peaks2utr", + "biotoolsID": "peaks2utr", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "thomasdan.otto@glasgow.ac.uk", + "name": "Thomas D Otto", + "orcidid": "https://orcid.org/0000-0002-1246-7404", + "typeEntity": "Person" + }, + { + "name": "Kathryn Crouch", + "typeEntity": "Person" + }, + { + "name": "William Haese-Hill", + "typeEntity": "Person" + } + ], + "description": "Tool that annotates 3' untranslated regions (UTR) for a given set of aligned sequencing reads in BAM format, and canonical annotation in GFF or GTF format.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Gene expression profiling", + "uri": "http://edamontology.org/operation_0314" + }, + { + "term": "Peak calling", + "uri": "http://edamontology.org/operation_3222" + }, + { + "term": "Splitting", + "uri": "http://edamontology.org/operation_3359" + } + ] + } + ], + "homepage": "https://pypi.org/project/peaks2utr", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-31T13:02:12.761770Z", + "license": "GPL-3.0", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/haessar/peaks2utr" + } + ], + "name": "peaks2utr", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAD112", + "metadata": { + "abstract": "Summary: Annotation of nonmodel organisms is an open problem, especially the detection of untranslated regions (UTRs). Correct annotation of UTRs is crucial in transcriptomic analysis to accurately capture the expression of each gene yet is mostly overlooked in annotation pipelines. Here we present peaks2utr, an easy-to-use Python command line tool that uses the UTR enrichment of single-cell technologies, such as 10× Chromium, to accurately annotate 3′ UTRs for a given canonical annotation.", + "authors": [ + { + "name": "Crouch K." + }, + { + "name": "Haese-Hill W." + }, + { + "name": "Otto T.D." + } + ], + "date": "2023-03-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "peaks2utr: a robust Python tool for the annotation of 3′ UTRs" + }, + "pmcid": "PMC10008064", + "pmid": "36864613" + } + ], + "toolType": [ + "Command-line tool" + ], + "topic": [ + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Model organisms", + "uri": "http://edamontology.org/topic_0621" + }, + { + "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" + } + ] +} diff --git a/data/pickaxe/pickaxe.biotools.json b/data/pickaxe/pickaxe.biotools.json new file mode 100644 index 0000000000000..61923bafbf841 --- /dev/null +++ b/data/pickaxe/pickaxe.biotools.json @@ -0,0 +1,135 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:51:49.486984Z", + "biotoolsCURIE": "biotools:pickaxe", + "biotoolsID": "pickaxe", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "k-tyo@northwestern.edu", + "name": "Keith E. J. Tyo", + "orcidid": "https://orcid.org/0000-0002-2342-0687", + "typeEntity": "Person" + }, + { + "name": "Jonathan Strutz", + "typeEntity": "Person" + }, + { + "name": "Kevin M Shebek", + "typeEntity": "Person" + }, + { + "name": "Linda J Broadbelt", + "typeEntity": "Person" + } + ], + "description": "Prediction of novel metabolic reactions.", + "documentation": [ + { + "type": [ + "User manual" + ], + "url": "https://mine-database.readthedocs.io/en/latest/pickaxe_run.html" + } + ], + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + }, + { + "term": "Metabolic network modelling", + "uri": "http://edamontology.org/operation_3660" + }, + { + "term": "Metabolic pathway prediction", + "uri": "http://edamontology.org/operation_3929" + }, + { + "term": "Natural product identification", + "uri": "http://edamontology.org/operation_3803" + } + ] + } + ], + "homepage": "https://pypi.org/project/minedatabase/", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-31T14:51:49.489457Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/tyo-nu/pickaxe_paper" + } + ], + "name": "Pickaxe", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1186/S12859-023-05149-8", + "metadata": { + "abstract": "Background: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.", + "authors": [ + { + "name": "Broadbelt L.J." + }, + { + "name": "Shebek K.M." + }, + { + "name": "Strutz J." + }, + { + "name": "Tyo K.E.J." + } + ], + "date": "2023-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Pickaxe: a Python library for the prediction of novel metabolic reactions" + }, + "pmcid": "PMC10031857", + "pmid": "36949401" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Endocrinology and metabolism", + "uri": "http://edamontology.org/topic_3407" + }, + { + "term": "Enzymes", + "uri": "http://edamontology.org/topic_0821" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + }, + { + "term": "Molecular interactions, pathways and networks", + "uri": "http://edamontology.org/topic_0602" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/pimangmoneyaward/pimangmoneyaward.biotools.json b/data/pimangmoneyaward/pimangmoneyaward.biotools.json new file mode 100644 index 0000000000000..f0f16bb1ae762 --- /dev/null +++ b/data/pimangmoneyaward/pimangmoneyaward.biotools.json @@ -0,0 +1,16 @@ +{ + "additionDate": "2023-09-02T15:53:45.114443Z", + "biotoolsCURIE": "biotools:PimangMoneyAward", + "biotoolsID": "PimangMoneyAward", + "description": "피망 머니상.010 9331 6906.한게임 머니상.카톡:m6906 한게임 머니상.전번01093316906.카톡:m6906피망머니 상. is the leading representative money award that sells and purchases Pmang Game Money.", + "editPermission": { + "type": "private" + }, + "homepage": "https://www.gangdong.shop", + "lastUpdate": "2023-09-02T15:53:58.529024Z", + "name": "PimangMoneyAward", + "owner": "arena", + "version": [ + "beta 1" + ] +} diff --git a/data/plantltrdb/plantltrdb.biotools.json b/data/plantltrdb/plantltrdb.biotools.json new file mode 100644 index 0000000000000..3ab69a1de90be --- /dev/null +++ b/data/plantltrdb/plantltrdb.biotools.json @@ -0,0 +1,99 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:55:39.133431Z", + "biotoolsCURIE": "biotools:plantltrdb", + "biotoolsID": "plantltrdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "achraf.elalalli@um6p.ma", + "name": "Achraf El Allali", + "typeEntity": "Person" + }, + { + "email": "morad.mokthar@um6p.ma", + "name": "Morad M. Mokhtar", + "typeEntity": "Person" + }, + { + "name": "Alsamman M Alsamman", + "typeEntity": "Person" + } + ], + "description": "An interactive database for 195 plant species LTR-retrotransposons.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Chimera detection", + "uri": "http://edamontology.org/operation_0450" + }, + { + "term": "Scaffolding", + "uri": "http://edamontology.org/operation_3216" + }, + { + "term": "Sequence trimming", + "uri": "http://edamontology.org/operation_3192" + } + ] + } + ], + "homepage": "https://bioinformatics.um6p.ma/PlantLTRdb", + "language": [ + "Perl", + "R" + ], + "lastUpdate": "2023-08-30T14:55:39.135862Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/agc-bioinformatics/PlantLTRdb" + } + ], + "name": "PlantLTRdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/FPLS.2023.1134627", + "pmcid": "PMC10025401", + "pmid": "36950350" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Agricultural science", + "uri": "http://edamontology.org/topic_3810" + }, + { + "term": "Biological databases", + "uri": "http://edamontology.org/topic_3071" + }, + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Plant biology", + "uri": "http://edamontology.org/topic_0780" + }, + { + "term": "Structural variation", + "uri": "http://edamontology.org/topic_3175" + } + ] +} diff --git a/data/pym2aia/pym2aia.biotools.json b/data/pym2aia/pym2aia.biotools.json new file mode 100644 index 0000000000000..c81dca34bf5e1 --- /dev/null +++ b/data/pym2aia/pym2aia.biotools.json @@ -0,0 +1,131 @@ +{ + "additionDate": "2023-08-30T09:06:30.320364Z", + "biotoolsCURIE": "biotools:pym2aia", + "biotoolsID": "pym2aia", + "credit": [ + { + "name": "Jonas Cordes", + "orcidid": "https://orcid.org/0000-0003-3148-4282" + } + ], + "description": "pyM²aia is a Python package for memory-efficient access and processing of mass spectrometry image data. The I/O functionality is derived from the interactive desktop application M²aia. Special features are the batch generator utilities for deep learning applications.", + "documentation": [ + { + "type": [ + "General" + ], + "url": "https://data.jtfc.de/pym2aia/sphinx-build/html/index.html" + } + ], + "download": [ + { + "type": "Source code", + "url": "https://github.com/m2aia/pym2aia" + } + ], + "editPermission": { + "type": "public" + }, + "function": [ + { + "input": [ + { + "data": { + "term": "Image", + "uri": "http://edamontology.org/data_2968" + }, + "format": [ + { + "term": "ibd", + "uri": "http://edamontology.org/format_3839" + }, + { + "term": "imzML metadata file", + "uri": "http://edamontology.org/format_3682" + } + ] + } + ], + "operation": [ + { + "term": "Mass spectrum visualisation", + "uri": "http://edamontology.org/operation_3694" + }, + { + "term": "Standardisation and normalisation", + "uri": "http://edamontology.org/operation_3435" + } + ], + "output": [ + { + "data": { + "term": "Image", + "uri": "http://edamontology.org/data_2968" + }, + "format": [ + { + "term": "PNG", + "uri": "http://edamontology.org/format_3603" + }, + { + "term": "SVG", + "uri": "http://edamontology.org/format_3604" + } + ] + } + ] + } + ], + "homepage": "https://m2aia.de/pym2aia.html", + "language": [ + "C++", + "Python" + ], + "lastUpdate": "2023-08-30T09:09:08.519629Z", + "license": "BSD-3-Clause", + "link": [ + { + "type": [ + "Issue tracker", + "Repository" + ], + "url": "https://github.com/m2aia/pym2aia" + } + ], + "name": "pyM2aia", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "jannikwitte", + "publication": [ + { + "doi": "10.1093/gigascience/giab049", + "note": "Cordes J; Enzlein T; Marsching C; Hinze M; Engelhardt S; Hopf C; Wolf I (July, 2021): M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data https://doi.org/10.1093/gigascience/giab049" + } + ], + "relation": [ + { + "biotoolsID": "m2aia", + "type": "uses" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Bioinformatics", + "uri": "http://edamontology.org/topic_0091" + }, + { + "term": "Data visualisation", + "uri": "http://edamontology.org/topic_0092" + }, + { + "term": "Metabolomics", + "uri": "http://edamontology.org/topic_3172" + } + ] +} diff --git a/data/redfold/redfold.biotools.json b/data/redfold/redfold.biotools.json new file mode 100644 index 0000000000000..4837b9d639d1d --- /dev/null +++ b/data/redfold/redfold.biotools.json @@ -0,0 +1,112 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:29:14.056121Z", + "biotoolsCURIE": "biotools:redfold", + "biotoolsID": "redfold", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "aky3100@mail.ncyu.edu.tw", + "name": "Chun-Chi Chen", + "typeEntity": "Person" + }, + { + "name": "Yi-Ming Chan", + "typeEntity": "Person" + } + ], + "description": "RNA secondary structure prediction using residual encoder-decoder network.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "RNA inverse folding", + "uri": "http://edamontology.org/operation_0483" + }, + { + "term": "RNA secondary structure alignment", + "uri": "http://edamontology.org/operation_0502" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + }, + { + "term": "RNA structure prediction", + "uri": "http://edamontology.org/operation_2441" + }, + { + "term": "Structure visualisation", + "uri": "http://edamontology.org/operation_0570" + } + ] + } + ], + "homepage": "https://redfold.ee.ncyu.edu.tw", + "lastUpdate": "2023-08-31T14:29:14.058465Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/aky3100/REDfold" + } + ], + "name": "REDfold", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1186/S12859-023-05238-8", + "metadata": { + "abstract": "Background: As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is O(N3) ; it becomes O(N6) for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis. Results: In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.", + "authors": [ + { + "name": "Chan Y.-M." + }, + { + "name": "Chen C.-C." + } + ], + "date": "2023-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network" + }, + "pmcid": "PMC10044938", + "pmid": "36977986" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Functional, regulatory and non-coding RNA", + "uri": "http://edamontology.org/topic_0659" + }, + { + "term": "Gene transcripts", + "uri": "http://edamontology.org/topic_3512" + }, + { + "term": "Nucleic acid structure analysis", + "uri": "http://edamontology.org/topic_0097" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + } + ] +} diff --git a/data/respond-cam/respond-cam.biotools.json b/data/respond-cam/respond-cam.biotools.json new file mode 100644 index 0000000000000..900247c1f61cc --- /dev/null +++ b/data/respond-cam/respond-cam.biotools.json @@ -0,0 +1,101 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T12:43:58.622230Z", + "biotoolsCURIE": "biotools:respond-cam", + "biotoolsID": "respond-cam", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "mxu1@cs.cmu.edu", + "name": "Min Xu", + "typeEntity": "Person" + }, + { + "name": "Bo Zhou", + "typeEntity": "Person" + }, + { + "name": "Guannan Zhao", + "typeEntity": "Person" + } + ], + "description": "Analyzing Deep Models for 3D Imaging Data by Visualizations.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Image analysis", + "uri": "http://edamontology.org/operation_3443" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/xulabs/aitom/tree/master/aitom/segmentation/respond_cam", + "language": [ + "Python" + ], + "lastUpdate": "2023-08-31T12:43:58.624858Z", + "name": "Respond-CAM", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1007/978-3-030-00928-1_55", + "metadata": { + "abstract": "The convolutional neural network (CNN) has become a powerful tool for various biomedical image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In this paper, we present a novel algorithm, Respond-weighted Class Activation Mapping (Respond-CAM), for making CNN-based models interpretable by visualizing input regions that are important for predictions, especially for biomedical 3D imaging data inputs. Our method uses the gradients of any target concept (e.g. the score of target class) that flow into a convolutional layer. The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept. We prove a preferable sum-to-score property of the Respond-CAM and verify its significant improvement on 3D images from the current state-of-the-art approach. Our tests on Cellular Electron Cryo-Tomography 3D images show that Respond-CAM achieves superior performance on visualizing the CNNs with 3D biomedical image inputs, and is able to get reasonably good results on visualizing the CNNs with natural image inputs. The Respond-CAM is an efficient and reliable approach for visualizing the CNN machinery, and is applicable to a wide variety of CNN model families and image analysis tasks. Our code is available at: https://github.com/xulabs/projects/tree/master/respond_cam.", + "authors": [ + { + "name": "Jiang R." + }, + { + "name": "Wang K." + }, + { + "name": "Xu M." + }, + { + "name": "Zhao G." + }, + { + "name": "Zhou B." + } + ], + "citationCount": 30, + "date": "2018-01-01T00:00:00Z", + "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", + "title": "Respond-CAM: Analyzing deep models for 3D imaging data by visualizations" + }, + "pmcid": "PMC10028588", + "pmid": "36951805" + } + ], + "toolType": [ + "Script" + ], + "topic": [ + { + "term": "Data visualisation", + "uri": "http://edamontology.org/topic_0092" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + } + ] +} diff --git a/data/rscanner/rscanner.biotools.json b/data/rscanner/rscanner.biotools.json new file mode 100644 index 0000000000000..4549a5d7eb684 --- /dev/null +++ b/data/rscanner/rscanner.biotools.json @@ -0,0 +1,120 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:26:43.364459Z", + "biotoolsCURIE": "biotools:rscanner", + "biotoolsID": "rscanner", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "anna.pyle@yale.edu", + "name": "Anna Marie Pyle", + "typeEntity": "Person" + }, + { + "name": "Gandhar Mahadeshwar", + "typeEntity": "Person" + }, + { + "name": "Han Wan", + "typeEntity": "Person" + }, + { + "name": "Rafael de Cesaris Araujo Tavares", + "typeEntity": "Person" + }, + { + "name": "Zion R Perry", + "typeEntity": "Person" + } + ], + "description": "Rapid assessment and visualization of RNA structure content.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "RNA secondary structure alignment", + "uri": "http://edamontology.org/operation_0502" + }, + { + "term": "RNA secondary structure prediction", + "uri": "http://edamontology.org/operation_0278" + }, + { + "term": "Structure visualisation", + "uri": "http://edamontology.org/operation_0570" + } + ] + } + ], + "homepage": "https://github.com/pylelab/RSCanner", + "language": [ + "R" + ], + "lastUpdate": "2023-08-31T14:26:43.367250Z", + "name": "RSCanner", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIOINFORMATICS/BTAD111", + "metadata": { + "abstract": "Motivation: The increasing availability of RNA structural information that spans many kilobases of transcript sequence imposes a need for tools that can rapidly screen, identify, and prioritize structural modules of interest. Results: We describe RNA Structural Content Scanner (RSCanner), an automated tool that scans RNA transcripts for regions that contain high levels of secondary structure and then classifies each region for its relative propensity to adopt stable or dynamic structures. RSCanner then generates an intuitive heatmap enabling users to rapidly pinpoint regions likely to contain a high or low density of discrete RNA structures, thereby informing downstream functional or structural investigation.", + "authors": [ + { + "name": "Mahadeshwar G." + }, + { + "name": "Perry Z.R." + }, + { + "name": "Pyle A.M." + }, + { + "name": "Tavares R.D.C.A." + }, + { + "name": "Wan H." + } + ], + "date": "2023-03-01T00:00:00Z", + "journal": "Bioinformatics", + "title": "RSCanner: rapid assessment and visualization of RNA structure content" + }, + "pmcid": "PMC10017096", + "pmid": "36857576" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Nucleic acid structure analysis", + "uri": "http://edamontology.org/topic_0097" + }, + { + "term": "RNA", + "uri": "http://edamontology.org/topic_0099" + }, + { + "term": "Structure prediction", + "uri": "http://edamontology.org/topic_0082" + }, + { + "term": "Transcription factors and regulatory sites", + "uri": "http://edamontology.org/topic_0749" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/scannotate/scannotate.biotools.json b/data/scannotate/scannotate.biotools.json new file mode 100644 index 0000000000000..7f16d58097969 --- /dev/null +++ b/data/scannotate/scannotate.biotools.json @@ -0,0 +1,80 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:30:42.252185Z", + "biotoolsCURIE": "biotools:scannotate", + "biotoolsID": "scannotate", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "xuekui@uvic.ca", + "name": "Xuekui Zhang", + "orcidid": "https://orcid.org/0000-0003-4728-2343", + "typeEntity": "Person" + }, + { + "email": "li.xing@math.usask.ca", + "name": "Li Xing", + "typeEntity": "Person" + } + ], + "description": "Automated cell-type annotation tool for single-cell RNA-sequencing data.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Differential gene expression profiling", + "uri": "http://edamontology.org/operation_3223" + }, + { + "term": "Dot plot plotting", + "uri": "http://edamontology.org/operation_0490" + }, + { + "term": "Essential dynamics", + "uri": "http://edamontology.org/operation_3891" + } + ] + } + ], + "homepage": "https://cran.r-project.org/package=scAnnotate", + "language": [ + "R" + ], + "lastUpdate": "2023-08-30T14:30:42.254744Z", + "license": "GPL-3.0", + "name": "scAnnotate", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1093/BIOADV/VBAD030", + "pmcid": "PMC10027414", + "pmid": "36949780" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Machine learning", + "uri": "http://edamontology.org/topic_3474" + }, + { + "term": "RNA-Seq", + "uri": "http://edamontology.org/topic_3170" + } + ] +} diff --git a/data/scardock/scardock.biotools.json b/data/scardock/scardock.biotools.json new file mode 100644 index 0000000000000..55df882836788 --- /dev/null +++ b/data/scardock/scardock.biotools.json @@ -0,0 +1,115 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T12:58:48.040741Z", + "biotoolsCURIE": "biotools:scardock", + "biotoolsID": "scardock", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "senliu.ctgu@gmail.com", + "name": "Sen Liu", + "orcidid": "https://orcid.org/0000-0001-5182-7241", + "typeEntity": "Person" + }, + { + "name": "Lingyu Zeng", + "typeEntity": "Person" + }, + { + "name": "Qi Song", + "typeEntity": "Person" + }, + { + "name": "Qiang Zheng", + "typeEntity": "Person" + } + ], + "description": "Web server for screening covalent ligands based on our SCAR strategy.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Protein structure validation", + "uri": "http://edamontology.org/operation_0321" + }, + { + "term": "Protein-protein docking", + "uri": "http://edamontology.org/operation_3899" + }, + { + "term": "Virtual screening", + "uri": "http://edamontology.org/operation_3938" + } + ] + } + ], + "homepage": "https://scardock.com/", + "lastUpdate": "2023-08-31T12:58:48.043152Z", + "link": [ + { + "type": [ + "Other" + ], + "url": "http://www.liugroup.site/scardock/" + } + ], + "name": "SCARdock", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1021/ACSOMEGA.2C08147", + "metadata": { + "abstract": "Background: Covalent drugs have been intentionally discarded historically due to the concern of off-target side effects, but the past decade has seen a fast resurgence of the discovery of covalent drugs. Compared to noncovalent ligands, covalent ligands might have better biochemical efficiency, lower patient burden, less dosing frequency, less drug resistance, and improved target specificity. Results: Previously, we proposed the steric-clashes alleviating receptor (SCAR) strategy for screening and repurposing covalent inhibitors. To help the discovery of covalent ligands targeting protein targets, we have developed a web server dedicated to providing the SCARdock protocol to general users. Along with this server, we presented three high-quality data sets for the discovery of covalent ligands: a manually curated data set containing 954 high-quality complex structures of covalent ligands and proteins, a manually curated data set of 68 experimentally confirmed covalent warheads targeting 11 different residues, and a prefiltered, classified, and ready-to-use data set of 690,018 entries of purchasable virtual compounds containing these experimentally verified warheads. Conclusions: The SCARdock server and the accompanied data sets would be of great value to the discovery of covalent ligands and are available freely at http://www.liugroup.site/scardock/ or https://scardock.com.", + "authors": [ + { + "name": "Liu S." + }, + { + "name": "Song Q." + }, + { + "name": "Zeng L." + }, + { + "name": "Zheng Q." + } + ], + "date": "2023-03-21T00:00:00Z", + "journal": "ACS Omega", + "title": "SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands" + }, + "pmcid": "PMC10034828", + "pmid": "36969452" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Molecular modelling", + "uri": "http://edamontology.org/topic_2275" + }, + { + "term": "NMR", + "uri": "http://edamontology.org/topic_0593" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + } + ] +} diff --git a/data/scntimpute/scntimpute.biotools.json b/data/scntimpute/scntimpute.biotools.json new file mode 100644 index 0000000000000..ba6889ada3647 --- /dev/null +++ b/data/scntimpute/scntimpute.biotools.json @@ -0,0 +1,21 @@ +{ + "additionDate": "2023-08-28T11:48:44.064979Z", + "biotoolsCURIE": "biotools:scNTImpute", + "biotoolsID": "scNTImpute", + "description": "scNTImpute is an imputation model for scRNA-seq data. 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It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell’s position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.", + "authors": [ + { + "name": "Bergen V." + }, + { + "name": "Lange M." + }, + { + "name": "Peidli S." + }, + { + "name": "Theis F.J." + }, + { + "name": "Wolf F.A." + } + ], + "citationCount": 700, + "date": "2020-12-01T00:00:00Z", + "journal": "Nature Biotechnology", + "title": "Generalizing RNA velocity to transient cell states through dynamical modeling" + }, + "pmid": "32747759", + "type": [ + "Primary" + ] } ], + "toolType": [ + "Library" + ], "topic": [ { "term": "Cell biology", @@ -56,5 +131,8 @@ "uri": "http://edamontology.org/topic_3170" } ], - "validated": 1 + "validated": 1, + "version": [ + "0.2.5" + ] } diff --git a/data/sdm2/sdm2.biotools.json b/data/sdm2/sdm2.biotools.json new file mode 100644 index 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} + ] + } + ], + "operation": [ + { + "term": "Conversion", + "uri": "http://edamontology.org/operation_3434" + } + ], + "output": [ + { + "data": { + "term": "Sequence distance matrix", + "uri": "http://edamontology.org/data_0870" + }, + "format": [ + { + "term": "mega", + "uri": "http://edamontology.org/format_1991" + } + ] + } + ] + } + ], + "homepage": "https://github.com/magnuspalmblad/shic", + "lastUpdate": "2023-08-31T15:52:52.675315Z", + "license": "MIT", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/magnuspalmblad/shic" + } + ], + "name": "shic", + "owner": "n.m.palmblad@lumc.nl", + "toolType": [ + "Script" + ] +} diff --git a/data/snapshot/snapshot.biotools.json b/data/snapshot/snapshot.biotools.json new file mode 100644 index 0000000000000..1fdff6b86df2c --- /dev/null +++ b/data/snapshot/snapshot.biotools.json @@ -0,0 +1,146 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:23:43.060202Z", + "biotoolsCURIE": "biotools:snapshot", + "biotoolsID": "snapshot", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "rch8@psu.edu", + "name": "Ross C. Hardison", + "orcidid": "https://orcid.org/0000-0003-4084-7516", + "typeEntity": "Person" + }, + { + "email": "guanjuexiang@gmail.com", + "name": "Guanjue Xiang", + "typeEntity": "Person" + }, + { + "name": "Belinda Giardine", + "typeEntity": "Person" + }, + { + "name": "Chen Sun", + "typeEntity": "Person" + }, + { + "name": "Cheryl A. Keller", + "typeEntity": "Person" + }, + { + "name": "Lin An", + "typeEntity": "Person" + } + ], + "description": "Package for clustering and visualizing epigenetic history during cell differentiation.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Clustering", + "uri": "http://edamontology.org/operation_3432" + }, + { + "term": "Filtering", + "uri": "http://edamontology.org/operation_3695" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "https://github.com/guanjue/Snapshot", + "language": [ + "Python", + "R" + ], + "lastUpdate": "2023-08-31T14:23:43.062842Z", + "license": "MIT", + "name": "Snapshot", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.1186/S12859-023-05223-1", + "metadata": { + "abstract": "Background: Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. Results: To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis. Conclusion: The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot.", + "authors": [ + { + "name": "An L." + }, + { + "name": "Anderson S.M." + }, + { + "name": "Bodine D." + }, + { + "name": "Giardine B." + }, + { + "name": "Hardison R.C." + }, + { + "name": "Heuston E.F." + }, + { + "name": "Keller C.A." + }, + { + "name": "Kirby M." + }, + { + "name": "Sun C." + }, + { + "name": "Xiang G." + }, + { + "name": "Zhang Y." + } + ], + "date": "2023-12-01T00:00:00Z", + "journal": "BMC Bioinformatics", + "title": "Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation" + }, + "pmcid": "PMC10026520", + "pmid": "36941541" + } + ], + "toolType": [ + "Library" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Epigenomics", + "uri": "http://edamontology.org/topic_3173" + }, + { + "term": "Epistasis", + "uri": "http://edamontology.org/topic_3974" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + }, + { + "term": "Transcriptomics", + "uri": "http://edamontology.org/topic_3308" + } + ] +} diff --git a/data/spheroidanalyser/spheroidanalyser.biotools.json b/data/spheroidanalyser/spheroidanalyser.biotools.json new file mode 100644 index 0000000000000..01cec5f2791ab --- /dev/null +++ b/data/spheroidanalyser/spheroidanalyser.biotools.json @@ -0,0 +1,97 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-30T14:35:15.041339Z", + "biotoolsCURIE": "biotools:spheroidanalyser", + "biotoolsID": "spheroidanalyser", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "l.f.stead@leeds.ac.uk", + "name": "Lucy F. Stead", + "typeEntity": "Person" + }, + { + "name": "Joseph N. Wilkinson", + "typeEntity": "Person" + }, + { + "name": "Rhiannon Barrow", + "typeEntity": "Person" + } + ], + "description": "Online platform for analyzing data from 3D spheroids or organoids grown in 96-well plates.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Aggregation", + "uri": "http://edamontology.org/operation_3436" + }, + { + "term": "Box-Whisker plot plotting", + "uri": "http://edamontology.org/operation_2943" + }, + { + "term": "Dot plot plotting", + "uri": "http://edamontology.org/operation_0490" + }, + { + "term": "Quantification", + "uri": "http://edamontology.org/operation_3799" + } + ] + } + ], + "homepage": "https://spheroidanalyser.leeds.ac.uk", + "language": [ + "R" + ], + "lastUpdate": "2023-08-30T14:35:15.043789Z", + "link": [ + { + "type": [ + "Repository" + ], + "url": "https://github.com/GliomaGenomics" + } + ], + "name": "SpheroidAnalyseR", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.14440/JBM.2022.388", + "pmcid": "PMC10040300", + "pmid": "36992918" + } + ], + "toolType": [ + "Web application" + ], + "topic": [ + { + "term": "Drug discovery", + "uri": "http://edamontology.org/topic_3336" + }, + { + "term": "Imaging", + "uri": "http://edamontology.org/topic_3382" + }, + { + "term": "Physiology", + "uri": "http://edamontology.org/topic_3300" + }, + { + "term": "Sequencing", + "uri": "http://edamontology.org/topic_3168" + } + ] +} diff --git a/data/star/star.biotools.json b/data/star/star.biotools.json index 5200ae24af2f5..ce034e172e5fc 100644 --- a/data/star/star.biotools.json +++ b/data/star/star.biotools.json @@ -1,4 +1,5 @@ { + "accessibility": "Open access", "additionDate": "2017-01-13T13:16:43Z", "biotoolsCURIE": "biotools:star", "biotoolsID": "star", @@ -19,25 +20,70 @@ "documentation": [ { "type": [ - "General" + "Release notes" ], "url": "https://github.com/alexdobin/STAR/releases" + }, + { + "type": [ + "User manual" + ], + "url": "https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf" } ], "editPermission": { "authors": [ "animalandcropgenomics", + "billsfriend", "hmenager" ], "type": "group" }, "function": [ { + "input": [ + { + "data": { + "term": "RNA sequence", + "uri": "http://edamontology.org/data_3495" + }, + "format": [ + { + "term": "FASTQ", + "uri": "http://edamontology.org/format_1930" + } + ] + } + ], "operation": [ { "term": "Sequence alignment", "uri": "http://edamontology.org/operation_0292" } + ], + "output": [ + { + "data": { + "term": "Gene expression matrix", + "uri": "http://edamontology.org/data_3112" + } + }, + { + "data": { + "term": "Nucleic acid sequence alignment", + "uri": "http://edamontology.org/data_1383" + }, + "format": [ + { + "term": "BAM", + "uri": "http://edamontology.org/format_2572" + }, + { + "term": "SAM", + "uri": "http://edamontology.org/format_2573" + } + ] + } ] } ], @@ -45,7 +91,7 @@ "language": [ "C++" ], - "lastUpdate": "2021-09-28T07:28:50.397510Z", + "lastUpdate": "2023-08-31T03:31:28.075202Z", "license": "GPL-3.0", "maturity": "Mature", "name": "STAR", @@ -58,7 +104,7 @@ { "doi": "10.1038/nmeth.4106", "metadata": { - "abstract": "© 2017 Nature America, inc., part of springer Nature. All rights reserved.Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data. We performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. We found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.", + "abstract": "Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data. We performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. We found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.", "authors": [ { "name": "Baruzzo G." @@ -79,7 +125,7 @@ "name": "Kim E.J." } ], - "citationCount": 115, + "citationCount": 147, "date": "2017-02-01T00:00:00Z", "journal": "Nature Methods", "title": "Simulation-based comprehensive benchmarking of RNA-seq aligners" @@ -123,7 +169,7 @@ "name": "Zaleski C." } ], - "citationCount": 12650, + "citationCount": 21778, "date": "2013-01-01T00:00:00Z", "journal": "Bioinformatics", "title": "STAR: Ultrafast universal RNA-seq aligner" @@ -149,7 +195,7 @@ "name": "Stadler P.F." } ], - "citationCount": 52, + "citationCount": 63, "date": "2014-07-01T00:00:00Z", "journal": "Bioinformatics", "title": "Lacking alignments? The next-generation sequencing mapper segemehl revisited" @@ -173,5 +219,8 @@ "uri": "http://edamontology.org/topic_3308" } ], - "validated": 1 + "validated": 1, + "version": [ + "2.7.11" + ] } diff --git a/data/uctcrdb/uctcrdb.biotools.json b/data/uctcrdb/uctcrdb.biotools.json new file mode 100644 index 0000000000000..06bb1c881eabe --- /dev/null +++ b/data/uctcrdb/uctcrdb.biotools.json @@ -0,0 +1,99 @@ +{ + "accessibility": "Open access", + "additionDate": "2023-08-31T14:36:25.271452Z", + "biotoolsCURIE": "biotools:uctcrdb", + "biotoolsID": "uctcrdb", + "confidence_flag": "tool", + "cost": "Free of charge", + "credit": [ + { + "email": "jian_zhang@tju.edu.cn", + "name": "Jian Zhang", + "typeEntity": "Person" + }, + { + "name": "Shiwen Shan", + "typeEntity": "Person" + }, + { + "name": "Yunsheng Dou", + "typeEntity": "Person" + } + ], + "description": "T cell receptor sequence database with online analysis functions.", + "editPermission": { + "type": "private" + }, + "function": [ + { + "operation": [ + { + "term": "Database search", + "uri": "http://edamontology.org/operation_2421" + }, + { + "term": "Visualisation", + "uri": "http://edamontology.org/operation_0337" + } + ] + } + ], + "homepage": "http://uctcrdb.cn/", + "lastUpdate": "2023-08-31T14:36:25.274088Z", + "name": "UcTCRdb", + "operatingSystem": [ + "Linux", + "Mac", + "Windows" + ], + "owner": "Pub2Tools", + "publication": [ + { + "doi": "10.3389/FIMMU.2023.1158295", + "metadata": { + "abstract": "Unlike conventional major histocompatibility complex (MHC) class I and II molecules reactive T cells, the unconventional T cell subpopulations recognize various non-polymorphic antigen-presenting molecules and are typically characterized by simplified patterns of T cell receptors (TCRs), rapid effector responses and ‘public’ antigen specificities. Dissecting the recognition patterns of the non-MHC antigens by unconventional TCRs can help us further our understanding of the unconventional T cell immunity. The small size and irregularities of the released unconventional TCR sequences are far from high-quality to support systemic analysis of unconventional TCR repertoire. Here we present UcTCRdb, a database that contains 669,900 unconventional TCRs collected from 34 corresponding studies in humans, mice, and cattle. In UcTCRdb, users can interactively browse TCR features of different unconventional T cell subsets in different species, search and download sequences under different conditions. Additionally, basic and advanced online TCR analysis tools have been integrated into the database, which will facilitate the study of unconventional TCR patterns for users with different backgrounds. UcTCRdb is freely available at http://uctcrdb.cn/.", + "authors": [ + { + "name": "Dou Y." + }, + { + "name": "Shan S." + }, + { + "name": "Zhang J." + } + ], + "date": "2023-01-01T00:00:00Z", + "journal": "Frontiers in Immunology", + "title": "UcTCRdb: An unconventional T cell receptor sequence database with online analysis functions" + }, + "pmcid": "PMC10040587", + "pmid": "36993970" + } + ], + "toolType": [ + "Database portal" + ], + "topic": [ + { + "term": "Cell biology", + "uri": "http://edamontology.org/topic_2229" + }, + { + "term": "Data mining", + "uri": "http://edamontology.org/topic_3473" + }, + { + "term": "Immunoproteins and antigens", + "uri": "http://edamontology.org/topic_2830" + }, + { + "term": "Small molecules", + "uri": "http://edamontology.org/topic_0154" + }, + { + "term": "Zoology", + "uri": "http://edamontology.org/topic_3500" + } + ] +}