clusterProfilerA universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results.https://bioconductor.org/packages/clusterProfilerclusterprofilerbiotools:clusterprofiler4.20.0RRID:SCR_016884rridLibraryCommand-line toolhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3053GeneticsLinuxWindowsMacRArtistic-2.0BioConductorFree of chargehttp://edamontology.org/operation_2436Gene-set enrichment analysishttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/data_1027Gene ID (NCBI)http://edamontology.org/format_2330Textual formathttps://guangchuangyu.github.io/clusterProfilerMirrorhttps://yulab-smu.top/contribution-knowledge-mining/Source codehttps://bioconductor.org/packages/clusterProfilerUser manual10.1016/j.xinn.2021.10014110.1016/j.xinn.2024.10072210.1038/s41596-024-01020-z10.1089/omi.2011.0118Chun-Hui GaoPersonContributorGiovanni Dall'OlioPersonContributorGuangchuang YuPersonDeveloperMaintainerLi-Gen WangPersonContributorMeijun ChenPersonContributorWanqian WeiPersonContributorXiao LuoPersonContributorBRAINPackage for calculating aggregated isotopic distributions and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm.https://bioconductor.org/packages/BRAINbrainbiotools:brain1.58.0RRID:SCR_003018rridCommand-line toolLibraryhttp://edamontology.org/topic_3370Analytical chemistryhttp://edamontology.org/topic_0153Lipidomicshttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_3520Proteomics experimenthttp://edamontology.org/topic_2269Statistics and probabilityMacWindowsLinuxRGPL-2.0BioConductorProteomicshttp://edamontology.org/operation_3632Isotopic distributions calculationhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_3441Plottinghttp://edamontology.org/data_1857Atomic occupancyhttp://edamontology.org/format_3554R file formathttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_3554R file formathttps://bioconductor.org/packages/release/bioc/src/contrib/BRAIN_1.58.0.tar.gzSource codehttps://bioconductor.org/packages/BRAINUser manualbiostringsuses10.1021/ac303439mPrimaryPiotr Dittwaldwith contributions of Dirk Valkenborg and Jurgen ClaesenMetaCytoThis package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database.https://bioconductor.org/packages/MetaCytometacytobiotools:metacyto1.34.0RRID:SCR_016415rridLibraryCommand-line toolhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_2269Statistics and probabilityMacWindowsLinuxRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3432Clusteringhttp://bioconductor.org/packages/MetaCyto/Mirrorhttps://git.bioconductor.org/packages/MetaCytoRepositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/MetaCyto_1.34.0.tar.gzSource codehttps://bioconductor.org/packages/MetaCytoUser manualAtul J. ButteChethan JujjavarapuSanchita BhattacharyaZicheng HuDiffBindCompute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.https://bioconductor.org/packages/DiffBinddiffbindbiotools:diffbind3.22.2RRID:SCR_012918rridCommand-line toolLibraryhttp://edamontology.org/topic_3169ChIP-seqMacWindowsLinuxRArtistic-2.0BioConductorhttp://edamontology.org/operation_3677Differential binding analysishttps://bioconductor.org/packages/DiffBind/Source codehttps://bioconductor.org/packages/DiffBindUser manualGord BrownPersonDeveloperRory StarkPersonDeveloperMaintainervariancePartitionRecent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.https://bioconductor.org/packages/dreamletvariancepartitionbiotools:variancepartition1.10.0RRID:SCR_019204rridLibraryCommand-line toolhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRArtistic-2.0BioConductorhttp://edamontology.org/operation_3565RNA-seq time series data analysishttp://edamontology.org/operation_0315Gene expression comparisonhttp://edamontology.org/data_3112Gene expression matrixhttp://edamontology.org/format_2058Gene expression report formathttps://DiseaseNeurogenomics.github.io/dreamletSource codehttps://bioconductor.org/packages/dreamletUser manual10.1101/2023.03.17.533005Gabriel HoffmanPersonDeveloperMaintainerSpliceMapThis package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly.https://bioconductor.org/packages/Rbowtiesplicemapbiotools:splicemap1.52.0RRID:SCR_009650rridCommand-line toolhttp://edamontology.org/topic_3170RNA-SeqLinuxC++PythonArtistic-2.0BioConductorMaturehttp://edamontology.org/operation_3198Read mappinghttps://github.com/fmicompbio/RbowtieSource codehttps://bioconductor.org/packages/RbowtieUser manual10.1093/nar/gkq21110.1186/gb-2009-10-3-r25Anita LerchPersonDeveloperFlorian HahnePersonDeveloperMichael StadlerPersonDeveloperMaintainernf-core-proteinfamiliesnf-core/proteinfamilies is a bioinformatics pipeline that generates protein families from amino acid sequences and/or updates existing families with new sequences. It takes a protein fasta file as input, clusters the sequences and then generates protein family Hiden Markov Models (HMMs) along with their multiple sequence alignments (MSAs). Optionally, paths to existing family HMMs and MSAs can be given (must have matching base filenames one-to-one) in order to update with new sequences in case of matching hits.https://github.com/nf-core/proteinfamiliesnf-core_proteinfamiliesbiotools:nf-core_proteinfamilies2.0.02.1.02.2.02.5.0RRID:SCR_027374rrid1.3.0Workflowhttp://edamontology.org/topic_0623Gene and protein familiesGroovyMITnf-coreMatureFree of chargeOpen accesshttps://github.com/nf-core/proteinfamiliesRepository10.1101/2025.08.12.670010DICHROWEBA server that supports several different algorithms for the analysis of Circular Dichroism (CD) spectra for the prediction of protein secondary structure. Results also contain a graphical comparison of calculated versus experimental results.http://dichroweb.cryst.bbk.ac.uk/html/home.shtmldichrowebbiotools:dichrowebRRID:SCR_018125rridWeb applicationhttp://edamontology.org/topic_3542Protein secondary structurehttp://edamontology.org/topic_1317Structural biologyhttp://edamontology.org/topic_2814Protein structure analysishttp://edamontology.org/topic_0078ProteinsLinuxWindowsMachttp://edamontology.org/operation_2488Protein secondary structure comparisonhttp://edamontology.org/operation_0319Protein secondary structure assignmenthttp://edamontology.org/operation_0267Protein secondary structure predictionhttp://edamontology.org/operation_2416Protein secondary structure analysishttp://dichroweb.cryst.bbk.ac.uk/html/userguide_dichroweb.shtmlGeneral10.1093/nar/gkh37115215473PMC44150910.1093/bioinformatics/18.1.21111836237Contact Formhttp://dichroweb.cryst.bbk.ac.uk/html/bugreport.shtmlPersonPrimary contactIntEnzThe recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB) on the nomenclature and classification of enzyme-catalysed reactions.http://www.ebi.ac.uk/intenzintenzbiotools:intenzRRID:SCR_002992rridDatabase portalhttp://edamontology.org/topic_0821EnzymesLinuxWindowsMacDataEMBLhttp://edamontology.org/operation_0224Query and retrievalhttp://www.ebi.ac.uk/intenz/faq.jspGeneral10.1093/nar/gkh11914681451PMC308853PrimaryEMBL-EBI DatabasesInstituteProviderhttp://www.ebi.ac.uk/intenz/contact.jspPersonPrimary contactFolkloreDeterministic, rule-based variant interpretation platform for clinical genetics laboratories. Automates ACMG/AMP 2015 classification using a Bayesian point-based framework (Tavtigian et al. 2018) with BayesDel ClinGen SVI-calibrated thresholds (Pejaver et al. 2022). Integrates 8 reference databases (gnomAD v4.1, ClinVar, dbNSFP 4.9c, SpliceAI, gnomAD Constraint, HPO, ClinGen, Ensembl VEP). Analyzes nuclear and mtDNA variants, structural and copy-number variants (SV/CNV), with trio/family and cohort analysis. Supports HPO-based phenotype matching, biomedical literature mining across 2M+ PubMed publications, and structured clinical report generation. AI assists in evidence synthesis but does not make classification decisions. EU-hosted on dedicated infrastructure in Helsinki, Finland (GDPR-compliant).https://folklore.helena.bioFolklorebiotools:Folklore3.39.1RRID:SCR_028669rrid3.39.1Web applicationWeb APIhttp://edamontology.org/topic_3574Human geneticshttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_3325Rare diseaseshttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3063Medical informaticsLinuxPythonProprietaryMatureCommercialRestricted accesshttp://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_3498Sequence variationshttp://edamontology.org/format_3016VCFhttp://edamontology.org/operation_3197Genetic variation analysishttp://edamontology.org/operation_0305Literature searchhttp://edamontology.org/operation_0362Genome annotationhttps://github.com/helena-bioinformatics/folklore-mcpRepositoryPublic Apache-2.0 MCP protocol adapter source; the Folklore SaaS platform and clinical interpretation backend remain proprietary.https://api.helena.bio/folklore/v1/mcpServicePublic remote endpoint for Folklore Clinical Variant Interpretation MCP version 1.2.2.https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.helena-bioinformatics%2Ffolklore&version=latestSoftware catalogueOfficial MCP Registry entry: io.github.helena-bioinformatics/folklore.https://folklore.helena.bio/integrationsOtherOfficial Folklore integrations and MCP client connection page.https://api.helena.bio/folklore/v1/healthTechnical monitoringPublic health and discovery metadata endpoint for the Folklore MCP service.https://github.com/helena-bioinformatics/folklore-mcpSource codeApache-2.0 standalone MCP protocol adapter. Excludes proprietary clinical interpretation logic, private data, credentials and operational infrastructure.1.2.2https://folklore.helena.bio/docsGeneralComplete production documentation covering every threshold, database version, and classification rule. Intended for clinical geneticists, laboratory directors, accreditation auditors, and bioinformaticians.https://folklore.helena.bio/how-it-worksGeneralSeven-stage analysis pipeline (quality control, annotation, classification, phenotype matching, literature, screening, interpretation) transforming a raw VCF into a clinician-ready report, each stage producing traceable, auditable output.https://folklore.helena.bio/methodologyGeneralACMG/AMP 2015 classification methodology (Richards et al. 2015) via Bayesian point-based system (Tavtigian et al. 2018), BayesDel ClinGen SVI-calibrated thresholds (Pejaver et al. 2022), and SpliceAI aligned to ClinGen SVI 2023 (Walker et al. 2023). Optional ClinGen VCEP overlay for ~50-60 genes. Strictly evidence-based, no ML determines pathogenicity.https://folklore.helena.bio/methodology/mtdnaGeneralMitochondrial DNA variant classification under the ClinGen Mitochondrial Disease Working Group (MMDWG) 2020 specification (McCormick et al. 2020). Operates as an independent module from the nuclear ACMG/AMP pipeline; every variant carries an explicit framework provenance label. Strength tiers follow ClinGen mtDNA VCEP v1.0.0.https://folklore.helena.bio/methodology/family-analysisGeneralInheritance-aware evidence from trio (proband + both parents), duo, and proband-plus-sibling analyses. Implements ClinGen SVI 2018 de novo PS2/PM6 (PMID 29543229), Jarvik & Browning 2016 LOD segregation framework (PMID 27236918), and ClinGen SVI 2021 PP1 strength bands. Augments the existing ACMG/AMP classification without re-calling variants.https://folklore.helena.bio/methodology/svGeneralStructural and copy-number variant evaluation under the Riggs 2020 joint ACMG/ClinGen technical standard. Documents the point-based loss and gain metrics, dosage-sensitivity evidence, and five-tier classification, with reference data and documented limitations.https://folklore.helena.bio/screening-methodologyGeneralPrioritizes classified variants for clinical review. After ACMG classification determines what each variant is, screening determines which to review first based on patient-specific clinical relevance. Evaluates seven independent dimensions, applies clinical profile boosts, and produces a four-tier priority ranking with transparent, visible score components.https://folklore.helena.bio/docs/folklore-connectorAPI documentationCanonical connector guide for Folklore Clinical Variant Interpretation MCP (io.github.helena-bioinformatics/folklore), version 1.2.2.https://github.com/helena-bioinformatics/folklore-mcp#readmeGeneralPublic Apache-2.0 MCP protocol adapter README. The Folklore SaaS platform and clinical interpretation backend remain proprietary.https://github.com/helena-bioinformatics/folklore-mcp/blob/main/CHANGELOG.mdRelease notesRelease history for the public Folklore MCP protocol adapter.10.5281/zenodo.21105027Preprint1.0Methodological framework for real-world performance studies of the platform's variant classification, demonstrated on three internal validation cohorts.10.5281/zenodo.21189571Preprint1.0A Deterministic Classification Core with an Agentic Interpretation Layer: An Architecture for Auditable, Human-Gated Clinical Variant Analysis10.5281/zenodo.21763096PreprintRobustness and cross-cohort concordance of developmental regulons in endometrial carcinoma. Authors: Draga Toncheva, Vasil Sgurev and Vladimir Mitev.10.5281/zenodo.21922952Other1.2.2Archived public Apache-2.0 Folklore MCP adapter release. All-version DOI: 10.5281/zenodo.21922951.10.5281/zenodo.22093164Other1.3.1Archived public Apache-2.0 Folklore MCP adapter release with four read-only tools, including semantic Literature Corpus search. All-version DOI: 10.5281/zenodo.21922951.10.3389/fgene.2026.1925492MethodMitev V. A methodological framework for real-world performance studies of clinical variant classification platforms at early organizational stages. Frontiers in Genetics 17:1925492, published 14 September 2026. Method demonstrated on 97 cases across three cohorts; independent validation remains future work.Helena Bioinformaticscontact@helena.biohttps://helena.bioInstituteDeveloperBowtieThis package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly.https://bioconductor.org/packages/Rbowtiebowtiebiotools:bowtie1.52.0RRID:SCR_005476rridCommand-line toolhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_0102Mappinghttp://edamontology.org/topic_0196Sequence assemblyhttp://edamontology.org/topic_3168SequencingLinuxMacWindowsC++Artistic-2.0galaxyPasteurBioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_0292Sequence alignmenthttp://edamontology.org/data_0006Datahttp://edamontology.org/format_1929FASTAhttp://edamontology.org/format_2331HTMLhttp://edamontology.org/data_0006Datahttp://edamontology.org/format_1933FASTQ-solexahttp://edamontology.org/format_1932FASTQ-sangerhttp://edamontology.org/format_1931FASTQ-illuminahttps://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/odoppelt/bowtie/bowtie_wrapper/0.12.7Galaxy servicehttps://github.com/fmicompbio/RbowtieSource codehttps://bioconductor.org/packages/RbowtieUser manual10.1093/nar/gkq21110.1186/gb-2009-10-3-r25Anita LerchPersonDeveloperFlorian HahnePersonDeveloperMichael StadlerPersonDeveloperMaintainerFolkloreDeterministic, rule-based variant interpretation platform for clinical genetics laboratories. Automates ACMG/AMP 2015 classification using a Bayesian point-based framework (Tavtigian et al. 2018) with BayesDel ClinGen SVI-calibrated thresholds (Pejaver et al. 2022). Integrates 8 reference databases (gnomAD v4.1, ClinVar, dbNSFP 4.9c, SpliceAI, gnomAD Constraint, HPO, ClinGen, Ensembl VEP). Analyzes nuclear and mtDNA variants, structural and copy-number variants (SV/CNV), with trio/family and cohort analysis. Supports HPO-based phenotype matching, biomedical literature mining across 2M+ PubMed publications, and structured clinical report generation. AI assists in evidence synthesis but does not make classification decisions. EU-hosted on dedicated infrastructure in Helsinki, Finland (GDPR-compliant).https://folklore.helena.bioHelixInsightbiotools:HelixInsight3.39.1RRID:SCR_028669rrid3.39.1Web APIWeb applicationhttp://edamontology.org/topic_3574Human geneticshttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_3325Rare diseaseshttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3063Medical informaticsLinuxPythonProprietaryMatureCommercialRestricted accesshttp://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_3498Sequence variationshttp://edamontology.org/format_3016VCFhttp://edamontology.org/operation_3197Genetic variation analysishttp://edamontology.org/operation_0305Literature searchhttp://edamontology.org/operation_0362Genome annotationhttps://github.com/helena-bioinformatics/folklore-mcpRepositoryPublic Apache-2.0 MCP protocol adapter source; the Folklore SaaS platform and clinical interpretation backend remain proprietary.https://api.helena.bio/folklore/v1/mcpServicePublic remote endpoint for Folklore Clinical Variant Interpretation MCP version 1.2.2.https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.helena-bioinformatics%2Ffolklore&version=latestSoftware catalogueOfficial MCP Registry entry: io.github.helena-bioinformatics/folklore.https://folklore.helena.bio/integrationsOtherOfficial Folklore integrations and MCP client connection page.https://api.helena.bio/folklore/v1/healthTechnical monitoringPublic health and discovery metadata endpoint for the Folklore MCP service.https://github.com/helena-bioinformatics/folklore-mcpSource codeApache-2.0 standalone MCP protocol adapter. Excludes proprietary clinical interpretation logic, private data, credentials and operational infrastructure.1.2.2https://folklore.helena.bio/docsGeneralComplete production documentation covering every threshold, database version, and classification rule. Intended for clinical geneticists, laboratory directors, accreditation auditors, and bioinformaticians.https://folklore.helena.bio/how-it-worksGeneralSeven-stage analysis pipeline (quality control, annotation, classification, phenotype matching, literature, screening, interpretation) transforming a raw VCF into a clinician-ready report, each stage producing traceable, auditable output.https://folklore.helena.bio/methodologyGeneralACMG/AMP 2015 classification methodology (Richards et al. 2015) via Bayesian point-based system (Tavtigian et al. 2018), BayesDel ClinGen SVI-calibrated thresholds (Pejaver et al. 2022), and SpliceAI aligned to ClinGen SVI 2023 (Walker et al. 2023). Optional ClinGen VCEP overlay for ~50-60 genes. Strictly evidence-based, no ML determines pathogenicity.https://folklore.helena.bio/methodology/mtdnaGeneralMitochondrial DNA variant classification under the ClinGen Mitochondrial Disease Working Group (MMDWG) 2020 specification (McCormick et al. 2020). Operates as an independent module from the nuclear ACMG/AMP pipeline; every variant carries an explicit framework provenance label. Strength tiers follow ClinGen mtDNA VCEP v1.0.0.https://folklore.helena.bio/methodology/family-analysisGeneralInheritance-aware evidence from trio (proband + both parents), duo, and proband-plus-sibling analyses. Implements ClinGen SVI 2018 de novo PS2/PM6 (PMID 29543229), Jarvik & Browning 2016 LOD segregation framework (PMID 27236918), and ClinGen SVI 2021 PP1 strength bands. Augments the existing ACMG/AMP classification without re-calling variants.https://folklore.helena.bio/methodology/svGeneralStructural and copy-number variant evaluation under the Riggs 2020 joint ACMG/ClinGen technical standard. Documents the point-based loss and gain metrics, dosage-sensitivity evidence, and five-tier classification, with reference data and documented limitations.https://folklore.helena.bio/screening-methodologyGeneralPrioritizes classified variants for clinical review. After ACMG classification determines what each variant is, screening determines which to review first based on patient-specific clinical relevance. Evaluates seven independent dimensions, applies clinical profile boosts, and produces a four-tier priority ranking with transparent, visible score components.https://folklore.helena.bio/docs/folklore-connectorAPI documentationCanonical connector guide for Folklore Clinical Variant Interpretation MCP (io.github.helena-bioinformatics/folklore), version 1.2.2.https://github.com/helena-bioinformatics/folklore-mcp#readmeGeneralPublic Apache-2.0 MCP protocol adapter README. The Folklore SaaS platform and clinical interpretation backend remain proprietary.https://github.com/helena-bioinformatics/folklore-mcp/blob/main/CHANGELOG.mdRelease notesRelease history for the public Folklore MCP protocol adapter.10.5281/zenodo.21105027Preprint1.0Methodological framework for real-world performance studies of the platform's variant classification, demonstrated on three internal validation cohorts.10.5281/zenodo.21189571Preprint1.0A Deterministic Classification Core with an Agentic Interpretation Layer: An Architecture for Auditable, Human-Gated Clinical Variant Analysis10.5281/zenodo.21763096PreprintRobustness and cross-cohort concordance of developmental regulons in endometrial carcinoma. Authors: Draga Toncheva, Vasil Sgurev and Vladimir Mitev.10.5281/zenodo.21922952Other1.2.2Archived public Apache-2.0 Folklore MCP adapter release. All-version DOI: 10.5281/zenodo.21922951.10.5281/zenodo.22093164Other1.3.1Archived public Apache-2.0 Folklore MCP adapter release with four read-only tools, including semantic Literature Corpus search. All-version DOI: 10.5281/zenodo.21922951.Helena Bioinformaticscontact@helena.biohttps://helena.bioInstituteDeveloperRsubreadAlignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads.https://bioconductor.org/packages/Rsubreadrsubreadbiotools:rsubread2.26.0RRID:SCR_016945rridLibraryCommand-line toolhttp://edamontology.org/topic_0204Gene regulationhttp://edamontology.org/topic_3053Geneticshttp://edamontology.org/topic_3168SequencingWindowsLinuxMacRGPL-3.0BioConductorhttp://edamontology.org/operation_2403Sequence analysishttp://edamontology.org/data_1383Sequence alignment (nucleic acid)http://edamontology.org/format_2572BAMhttp://edamontology.org/format_2573SAMhttp://bioconductor.org/packages/release/bioc/html/Rsubread.htmlMirrorhttp://bioconductor.org/packages/RsubreadSource codehttps://bioconductor.org/packages/RsubreadUser manual10.1093/nar/gkz114Wei ShiYang Liao and Gordon K Smyth with contributions from Jenny DaiReactomePAThis package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team.https://bioconductor.org/packages/ReactomePAreactomepabiotools:reactomepa1.56.0RRID:SCR_019316rridLibraryCommand-line toolhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_0602Molecular interactions, pathways and networksMacWindowsLinuxRGPL-2.0BioConductorhttp://edamontology.org/operation_2497Pathway or network analysishttp://edamontology.org/data_1027Gene ID (NCBI)http://edamontology.org/format_2330Textual formathttps://guangchuangyu.github.io/ReactomePAMirrorhttps://yulab-smu.top/contribution-knowledge-mining/Source codehttps://bioconductor.org/packages/ReactomePAUser manual10.1039/C5MB00663EGuangchuang YuPersonDeveloperMaintainerVladislav PetyukPersonContributorRdisopIn high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set.https://bioconductor.org/packages/Rdisoprdisopbiotools:rdisop1.72.0RRID:SCR_000453rridLibraryCommand-line toolhttp://edamontology.org/topic_3520Proteomics experimentMacWindowsLinuxRGPL-2.0ProteomicsBioConductorhttp://edamontology.org/operation_3214Spectral analysishttps://github.com/sneumann/RdisopMirrorhttps://github.com/sneumann/RdisopSource codehttps://bioconductor.org/packages/RdisopUser manualRuses10.1007/s00453-007-0162-810.1093/bioinformatics/btm63110.1093/bioinformatics/btn603Anton PervukhinPersonDeveloperJan LisecPersonContributorMiao YuPersonContributorRoberto CanteriPersonContributorSteffen NeumannPersonDeveloperMaintainerDEXSeqThe package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.https://bioconductor.org/packages/DEXSeqdexseqbiotools:dexseq1.58.0RRID:SCR_012823rridLibraryCommand-line toolhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRGPL-3.0-or-laterBioConductorhttp://edamontology.org/operation_3501Enrichment analysishttp://edamontology.org/operation_0446Exonic splicing enhancer predictionhttps://bioconductor.org/packages/release/bioc/src/contrib/DEXSeq_1.58.0.tar.gzSource codehttps://bioconductor.org/packages/DEXSeqUser manual10.1073/pnas.130720211010.1101/gr.133744.111Alejandro ReyesPersonDeveloperHugo GrusonPersonMaintainerSimon AndersPersonDeveloperDESeq2Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.https://bioconductor.org/packages/DESeq2deseq2biotools:deseq21.52.0RRID:SCR_015687rridCommand-line toolhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRLGPL-3.0-or-laterde.NBIBioConductorMatureFree of chargehttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/operation_3680RNA-Seq analysishttp://edamontology.org/data_3917Count matrixhttp://edamontology.org/format_3751DSVhttps://github.com/Bioconductor-mirror/DESeq2/tree/release-3.2Repositoryhttps://github.com/thelovelab/DESeq2Source codehttps://bioconductor.org/packages/DESeq2User manual10.1186/s13059-014-0550-8Anqi ZhuPersonContributorCZIFunding agencyContributorConstantin Ahlmann-EltzePersonContributorKwame ForbesPersonContributorMichael LovePersonDeveloperMaintainerNIH NHGRIFunding agencyContributorNikolaos IgnatiadisPersonContributorRADIANT EU FP7Funding agencyContributorRaphael RosselliniPersonContributorSimon AndersPersonContributorDeveloperWolfgang HuberPersonContributorDeveloperCiteFuseCiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.https://bioconductor.org/packages/CiteFuseCiteFusebiotools:CiteFuse1.24.0RRID:SCR_019321rridhttp://edamontology.org/topic_3512Gene transcriptshttp://edamontology.org/topic_0108Protein expressionhttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_3308TranscriptomicsGPL-3.0BioConductorhttp://edamontology.org/operation_3799Quantificationhttp://edamontology.org/operation_2495Expression analysishttp://edamontology.org/operation_0337Visualisationhttps://github.com/SydneyBioX/CiteFuse/issuesIssue trackerhttps://bioconductor.org/packages/release/bioc/html/CiteFuse.htmlOtherhttps://github.com/SydneyBioX/CiteFuse/Repositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/CiteFuse_1.24.0.tar.gzSource codehttps://bioconductor.org/packages/CiteFuseUser manual10.1093/bioinformatics/btaa282Hani KimPersonDeveloperYingxin LinPersonDeveloperMaintainerCATALYSTCATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.https://bioconductor.org/packages/CATALYSTcatalystbiotools:catalyst1.36.0RRID:SCR_017127rridLibraryhttp://edamontology.org/topic_2269Statistics and probabilityWindowsLinuxMacRGPL-2.0BioConductorhttp://edamontology.org/operation_3214Spectral analysishttp://edamontology.org/operation_3435Standardisation and normalisationhttp://bioconductor.org/packages/CATALYST/Mirrorhttps://github.com/HelenaLC/CATALYSTMirrorhttps://git.bioconductor.org/packages/CATALYSTRepositoryhttps://github.com/HelenaLC/CATALYSTSource codehttps://bioconductor.org/packages/CATALYSTUser manual10.1016/j.cels.2018.02.01010.12688/f1000research.11622.310.12688/f1000research.26073.2Bernd BodenmillerFunding agencyContributorHelena L. CrowellPersonDeveloperMaintainerMark D. RobinsonPersonContributorDeveloperStéphane ChevrierPersonDeveloperVito R.T. ZanotelliPersonDeveloperbiomaRtIn recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (<https://www.ensembl.org/info/data/biomart/index.html>). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintained by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining.https://bioconductor.org/packages/biomaRtbiomartbiotools:biomart2.68.0RRID:SCR_019214rridCommand-line toolhttp://edamontology.org/topic_3077Data acquisitionhttp://edamontology.org/topic_3489Database managementhttp://edamontology.org/topic_0621Model organismshttp://edamontology.org/topic_0602Molecular interactions, pathways and networkshttp://edamontology.org/topic_3500ZoologyMacWindowsLinuxRArtistic-2.0de.NBIBioConductorBioconductorMatureFree of chargehttp://edamontology.org/operation_2422Data retrievalhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/operation_0360Structural similarity searchhttp://edamontology.org/operation_2421Database searchhttps://github.com/Huber-group-EMBL/biomaRtSource codehttps://huber-group-embl.github.io/biomaRt/Source codehttps://bioconductor.org/packages/biomaRtUser manual10.1038/nprot.2009.9710.1093/bioinformatics/bti525Francois PepinPersonContributorGerman Network for Bioinformatics Infrastructure - de.NBIFunding agencyContributorHugo GrusonPersonContributorMaintainerMike SmithPersonContributorSean DavisPersonContributorSteffen DurinckPersonDeveloperVince S BuffaloPersonContributorWolfgang HuberPersonDeveloperNoodleNoodle is a public, read-only web application for semantic discovery and exploration of biomedical literature sourced from PubMed, Europe PMC, and OpenAlex. It accepts natural-language research questions and presents ranked publications together with gene, genetic variant, and phenotype views. Publication pages provide provenance, identifiers, source links, indexed concepts, and navigable semantic-neighbourhood graphs. The companion Noodle Biomedical Literature Discovery MCP exposes the same public discovery domain through a read-only, source-linked Model Context Protocol interface with bounded citation and semantic graph traversal. Semantic proximity, citation distance, co-mention, and search rank are discovery signals and do not establish scientific evidence, causality, diagnosis, or treatment.https://noodle.helena.bio/noodlebiotools:noodle0.2.0RRID:SCR_028920rridWeb APIWeb applicationhttp://edamontology.org/topic_0091BioinformaticsFreewareMatureFree of chargeOpen accesshttp://edamontology.org/operation_0305Literature searchSemantic retrieval and navigation of biomedical publications by research question, gene, variant, phenotype, and related-publication neighbourhood.https://www.helena.bio/OtherDeveloped and operated by Helena Bioinformatics.https://github.com/helena-bioinformatics/noodle-mcpRepositoryPublic Apache-2.0 source for the standalone Noodle Biomedical Literature Discovery MCP adapter.https://api.helena.bio/noodle/v1/mcpServicePublic read-only Streamable HTTP endpoint for Noodle Biomedical Literature Discovery MCP version 0.2.0.https://registry.modelcontextprotocol.io/v0/servers?search=io.github.helena-bioinformatics%2FnoodleSoftware catalogueOfficial MCP Registry entry: io.github.helena-bioinformatics/noodle.https://noodle.helena.bio/integrationsOtherOfficial Noodle integrations and MCP client connection page.https://api.helena.bio/noodle/v1/healthTechnical monitoringPublic health endpoint for the Noodle MCP service.https://github.com/helena-bioinformatics/noodle-mcpSource codeApache-2.0 standalone MCP protocol adapter. Excludes private corpus implementation, credentials, private data, and operational infrastructure.0.2.0https://noodle.helena.bio/methodologyGeneralPublic methodology, retrieval boundary, semantic-proximity limitations, and provenance policy.https://noodle.helena.bio/data-sourcesOtherAttributed biomedical literature sources, source roles, update boundaries, and correction handling.https://noodle.helena.bio/editorial-policyGovernanceEditorial, correction, attribution, and content-governance policy.https://noodle.helena.bio/mcpAPI documentationCanonical connector and agent-selection guide for Noodle Biomedical Literature Discovery MCP version 0.2.0.https://github.com/helena-bioinformatics/noodle-mcp#readmeGeneralPublic Apache-2.0 MCP adapter README, tool surface, graph boundary, and connection guidance.https://github.com/helena-bioinformatics/noodle-mcp/blob/main/CHANGELOG.mdRelease notesRelease history for the public Noodle MCP protocol adapter.HelixInsightuses10.5281/zenodo.221519901.0Public technical service description, methodology boundary, provenance, graph contract, and citation metadata for Noodle.10.5281/zenodo.22166486Other0.2.0Archived public Apache-2.0 Noodle Biomedical Literature Discovery MCP adapter release. All-version DOI: 10.5281/zenodo.22166485.Helena Bioinformaticscontact@helena.biohttps://www.helena.bio/InstitutePrimary contactDeveloperMaintainerProviderSupportPrimary developer, maintainer, service provider, and support contact for Noodle.UTRdbDatabase of Untranslated regions (UTRs).http://utrdb.ba.itb.cnr.it/utrdbbiotools:utrdbRRID:SCR_005868rridDatabase portalhttp://edamontology.org/topic_3308Transcriptomicshttp://edamontology.org/topic_3047Molecular biologyLinuxWindowsMacELIXIR-ITA-CNRMatureFree of chargehttp://edamontology.org/operation_0224Query and retrievalhttp://utrdb.ba.itb.cnr.it/General10.1093/nar/gkp90219880380PMC2808995PrimaryELIXIR-ITA-CNRInstituteProviderGraziano Pesolegraziano.pesole@uniba.itPersonPrimary contacttopGOtopGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.https://bioconductor.org/packages/topGOtopgobiotools:topgo2.64.0RRID:SCR_014798rridLibraryCommand-line toolhttp://edamontology.org/topic_3473Data mininghttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_0089Ontology and terminologyhttp://edamontology.org/topic_2269Statistics and probabilityWindowsLinuxMacRLGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_3672Gene functional annotationhttp://edamontology.org/operation_2436Gene-set enrichment analysishttps://github.com/federicomarini/topGOSource codehttps://bioconductor.org/packages/topGOUser manual10.18129/B9.bioc.topGOAdrian AlexaPersonDeveloperFederico MariniPersonMaintainerJörg RahnenführerPersonDevelopertimecourseFunctions for data analysis and graphical displays for developmental microarray time course data.https://bioconductor.org/packages/timecoursetimecoursebiotools:timecourse1.84.0RRID:SCR_000077rridLibraryCommand-line toolhttp://edamontology.org/topic_3518Microarray experimentWindowsLinuxMacRLGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_2238Statistical calculationhttp://www.bioconductor.orgMirrorhttp://www.bioconductor.orgSource codehttps://bioconductor.org/packages/timecourseUser manual10.18129/B9.bioc.timecourseYu Chuan TaiSynergyFinder Plus (SynergyFinder+)Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (http://www.synergyfinder.org) for users who prefer graphical user interface.https://bioconductor.org/packages/synergyfindersynergyfinderbiotools:synergyfinder3.20.0RRID:SCR_019318rridWeb applicationBioinformatics portalhttp://edamontology.org/topic_3307Computational biologyMacLinuxWindowsRBioConductorhttp://edamontology.org/operation_2238Statistical calculationhttps://www.helsinki.fi/en/researchgroups/network-pharmacology-for-precision-medicineHelpdeskNETWORK PHARMACOLOGY FOR PRECISION MEDICINE, FACULTY OF MEDICINE, UNIVERSITY OF HELSINKIhttps://twitter.com/SynergyFinderSocial mediaTwitter sitehttps://tangsoftwarelab.shinyapps.io/synergyfinder/Mirrorhttp://www.synergyfinder.orgSource codehttps://bioconductor.org/packages/synergyfinderUser manualdrugcombusedBy10.1016/j.gpb.2022.01.004Jing TangPersonDeveloperShuyu ZhengPersonDeveloperMaintainerMUSCLE (EBI)MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences.https://bioconductor.org/packages/musclemuscle_ebibiotools:muscle_ebi3.54.0RRID:SCR_011812rridWeb applicationWeb servicehttp://edamontology.org/topic_0080Sequence analysisWindowsLinuxMacUnlicenseEBI ToolsJob Dispatcher ToolsBioConductorMUSCLEhttp://edamontology.org/operation_0492Multiple sequence alignmenthttp://edamontology.org/data_2044Sequencehttps://www.ebi.ac.uk/about/contact/support/job-dispatcher-servicesHelpdeskhttp://www.drive5.com/muscle/Source codehttps://bioconductor.org/packages/muscleUser manualmuscleuses10.1093/nar/gkh340Algorithm by Robert C. Edgar. R port by Alex T. Kalinka.fgseaThe package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.https://bioconductor.org/packages/fgseafgseabiotools:fgsea1.38.0RRID:SCR_020938rridLibraryCommand-line toolhttp://edamontology.org/topic_3053GeneticsMacLinuxWindowsRMITBioConductorhttp://edamontology.org/operation_2436Gene-set enrichment analysishttps://github.com/alserglab/fgsea/Repositoryhttps://github.com/alserglab/fgsea/Source codehttps://bioconductor.org/packages/fgseaUser manual10.1101/060012Alexey SergushichevPersonDeveloperMaintainerGennady KorotkevichPersonDeveloperNikita GolikovPersonDeveloperNikita GusakPersonContributorNikolay BudinPersonContributorVladimir SukhovPersonDeveloperZieman MarkPersonContributorensembldbThe package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates.https://bioconductor.org/packages/ensembldbensembldbbiotools:ensembldb2.36.1RRID:SCR_019103rridLibraryCommand-line toolhttp://edamontology.org/topic_3071Data managementWindowsLinuxMacRLGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/operation_0361Sequence annotationhttps://github.com/jotsetung/ensembldbMirrorhttps://github.com/jorainer/ensembldbSource codehttps://bioconductor.org/packages/ensembldbUser manual10.1093/bioinformatics/btz031Johannes RainerLaurent Gatto Christian Weichenberger and Boyu Yu.Sebastian GibbBIOSZENBIOSZEN is an open-source R package and modular Shiny application for reproducible analysis and visualization of experimental biological data from Excel or CSV files. It supports statistical testing, control-based normalization, replicate-aware quality control, and customizable scientific plots.
Its microbial growth-curve module analyzes optical-density time series and automatically extracts quantitative growth parameters, including maximum specific growth rate (µMax), doubling time, lag time, maximum optical density (ODmax), time to maximum growth, area under the curve (AUC), and initial optical density (OD0).
Results can be exported as processed datasets, statistical summaries, analysis metadata, scientific graphics, and editable PowerPoint figures.https://bioszen.github.io/BIOSZEN/bioszenbiotools:bioszen2.1.2RRID:SCR_028902rridDesktop applicationWeb applicationhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_2269Statistics and probabilityhttp://edamontology.org/topic_3301MicrobiologyWindowsMacRGPL-3.0Free of chargeOpen accesshttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_0337VisualisationStatistical analysis, control-based normalization, replicate-aware quality control, and customizable visualization of experimental biological data from Excel or CSV files.http://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_2945Data analysisAnalysis and visualization of microbial optical-density time series, including extraction of uMax, max_percap_time, doubling time, lag time, ODmax, max_time, AUC, OD0, and related growth parameters.https://github.com/bioszen/BIOSZENRepositorySource code, example files, documentation, and development history.https://github.com/bioszen/BIOSZEN/issuesIssue trackerBug reports, installation problems, and feature suggestions.https://bioszen-test.share.connect.posit.cloudServiceLimited online demonstration of the BIOSZEN Shiny application.https://zenodo.org/records/18217210Downloads pageArchived release files and permanent software record.2.1.2https://bioszen.r-universe.dev/BIOSZENSoftware packageInstallable R package for BIOSZEN 2.1.2, including access to the Shiny application.2.1.2https://github.com/bioszen/BIOSZEN#readmeGeneralUser manualQuick start guidehttps://github.com/bioszen/BIOSZEN/blob/main/inst/app/www/MANUAL_EN.mdUser manualComplete BIOSZEN user manual in English.https://github.com/bioszen/BIOSZEN#citationCitation instructions10.5281/zenodo.18217210Primaryall versions10.5281/zenodo.22117454Primary2.1.2Benjamín Szenfeldbioszenf@gmail.comhttps://orcid.org/0009-0003-4702-4149PersonDeveloperMaintainerPrimary contactCognitionBioChemA structural pharmacology workbench for cognition-related CNS targets, built so that a displayed number must trace to a computation. Runs Boltz-2 locally for structure prediction, validates chemistry with RDKit, enforces a provenance record on every value, and reports eight studies pre-registered under content hashes before any data was seen. Its headline result is negative: designed peptides did not separate from composition-matched shuffles of their own amino acids.https://github.com/hopejsh/CognitionBioChemcognitionbiochembiotools:cognitionbiochem1.0.0doi:10.5281/zenodo.22032684doiconcept DOI, resolves to the latest releasedoi:10.5281/zenodo.22032685doi1.0.0RRID:SCR_028851rridWeb applicationCommand-line toolhttp://edamontology.org/topic_0082Structure predictionhttp://edamontology.org/topic_2814Protein structure analysishttp://edamontology.org/topic_2275Molecular modellinghttp://edamontology.org/topic_3336Drug discoveryMacLinuxPythonJavaScriptApache-2.0MatureFree of chargeOpen accesshttp://edamontology.org/operation_0474Protein structure predictionhttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_1929FASTABoltz-2 v2.2.1 runs locally; every predicted structure is retained under content-addressed custody with its confidence files.http://edamontology.org/operation_2428Validationhttp://edamontology.org/data_1460Protein structurehttp://edamontology.org/format_1477mmCIFBackbone geometry, per-residue pLDDT and interface PAE are read from the model's own file; a value that was never computed renders as a label.http://edamontology.org/operation_2480Structure analysishttp://edamontology.org/data_2301SMILES stringhttp://edamontology.org/format_1196SMILESRDKit validation: parsing, molecular formula, InChIKey and stereochemistry completeness. A SMILES that fails to parse is shown as unverified.https://github.com/hopejsh/CognitionBioChemRepositoryhttps://github.com/hopejsh/CognitionBioChem/issuesIssue trackerhttps://github.com/hopejsh/CognitionBioChem/archive/refs/tags/v1.0.0.tar.gzSource code1.0.0https://github.com/hopejsh/CognitionBioChem#readmeGeneralhttps://github.com/hopejsh/CognitionBioChem/blob/main/docs/REGISTRATION.mdCitation instructionshttps://github.com/hopejsh/CognitionBioChem/blob/main/NOTICETerms of useThe `license` field above is Apache-2.0, which covers this project's code, but the repository is not single-licensed. It redistributes third-party scientific data that keeps its own terms: CC BY 4.0 (UniProt, AlphaFold DB), CC BY-SA 3.0 (ChEMBL-derived files -- SHARE-ALIKE, so reuse carries an obligation onward), CC0 1.0 (RCSB depositions) and MIT (Boltz-2 model outputs). NOTICE lists which files fall under which.Seung Ho Jungseung.jung@gmail.comhttps://orcid.org/0000-0001-7914-5306PersonDeveloperMaintainerPhonaLabPhonaLab is a browser-based platform for acoustic analysis of voice recordings, aimed at speech-language pathologists, voice clinicians, and researchers. It computes validated multiparametric acoustic indices — including the Acoustic Voice Quality Index (AVQI), Acoustic Breathiness Index (ABI), smoothed cepstral peak prominence (CPPS), and glottal-to-noise excitation ratio (GNE) — from sustained-vowel and connected-speech recordings, using Praat algorithms via the Parselmouth interface. Audio is processed in memory and not stored. Interface available in English, Brazilian Portuguese, and Spanish.https://www.phonalab.com/phonalabbiotools:phonalabRRID:SCR_028845rridDOI:10.5281/zenodo.21987370doiWeb applicationhttp://edamontology.org/topic_3303Medicinehttp://edamontology.org/topic_3063Medical informaticshttp://edamontology.org/topic_0092Data visualisationPythonJavaScriptProprietaryEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_0337Visualisationhttps://www.instagram.com/phonalab.voice/Social mediahttps://doi.org/10.1016/j.jvoice.2026.04.009Citation instructionsPeer-reviewed publication describing algorithm verification and concurrent validity of the platform's acoustic voice quality indices.https://www.phonalab.com/guidesTraining materialExplanatory guides on acoustic voice measures (CPPS, GNE, MPT/DSI) and clinical workflows.https://www.phonalab.com/termsTerms of useAudio is processed in memory and is not stored on the server.https://www.phonalab.com/faqFAQ10.1016/j.jvoice.2026.04.00942049572Primary10.1016/j.jvoice.2026.07.05142603770MethodJorge Carlos Lucerolucero@unb.brhttps://lucerojc.github.io/https://orcid.org/0000-0003-0597-3808PersonPrimary contactDeveloperProfessor of Computer Science at University of Brasília (Brazil)PhonaLab Inova Simplessupport@phonalab.comhttps://www.phonalab/comInstituteProviderConspectaConspecta is a browser-based research platform that brings microscopy image analysis, flow cytometry, molecular biology, sample tracking, and publication-ready figures into one connected workspace. It replaces the patchwork of disconnected tools most labs assemble, so a lab's data, samples, and results stay linked from experiment to figure with full traceability. Built for imaging-heavy and flow-heavy biology labs, new PIs, and early-stage biotech. Research-focused, not regulated or clinical. Free for you and one collaborator, every workspace included. Paid plans open it to the whole lab and add additional storage, AI object detection, external integrations via API, and bring-your-own AI assistant integration.https://conspecta.bioconspectabiotools:conspectaRRID:SCR_028623rridWeb applicationhttp://edamontology.org/topic_3382Imaginghttp://edamontology.org/topic_3385Light microscopyhttp://edamontology.org/topic_3934Cytometryhttp://edamontology.org/topic_3047Molecular biologyLinuxMacWindowsProprietaryEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_0346Sequence similarity searchhttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/operation_0292Sequence alignmenthttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_3935Dimensionality reductionhttps://conspecta.bio/guide/User manualPySMACKSPython computational framework for analysis of single-molecule FRET datahttps://github.com/molcretb/PySMACKSpysmacksbiotools:pysmacks1.0.0RRID:SCR_028788rridLibraryhttp://edamontology.org/topic_3306Biophysicshttp://edamontology.org/topic_3383BioimagingLinuxWindowsPythonMITEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3443Image analysisRun the GUI of PySMACKSPySMACKS_GUIhttps://github.com/molcretb/PySMACKSRepositoryhttps://github.com/molcretb/PySMACKS/issuesIssue trackerhttps://molcretb.github.io/PySMACKS/OtherDocumentationhttps://pypi.org/project/PySMACKS/Software package1.0.0b2https://molcretb.github.io/PySMACKSUser manualGeneralInstallation instructionsBastien Molcrettebastien.molcrette@unibas.chhttps://schmid.chemie.unibas.ch/en/https://orcid.org/0000-0002-5995-5376PersonPrimary contactDeveloperDocumentorMaintainerContributorfull-stack developer of PySMACKSSonja Schmidsonja.schmid@unibas.chhttps://schmid.chemie.unibas.ch/en/https://orcid.org/0000-0002-3710-5602PersonSupportTeam leader at the University of Basel, in which PySMACKS is developedClustVisWeb tool which allows users to upload their own data and easily create Principal Component Analysis (PCA) plots and heatmaps. Data can be uploaded as a file or by copy-pasteing it to the text box.http://biit.cs.ut.ee/clustvisclustvisbiotools:clustvis1RRID:SCR_017133rridWeb applicationhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_2269Statistics and probabilityLinuxWindowsMacRGPL-3.0Elixir-EEFree of chargeToolsEstoniahttp://edamontology.org/operation_0313Gene expression clusteringhttp://edamontology.org/operation_2942Treemap visualisationhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_2939Principal component plottinghttp://edamontology.org/operation_0531Heat map generationhttp://edamontology.org/data_3111Processed microarray datahttp://edamontology.org/format_2330Textual formathttp://edamontology.org/format_3033Matrix formathttp://edamontology.org/data_3113Sample annotationhttp://edamontology.org/format_2330Textual formathttp://edamontology.org/format_3033Matrix formathttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_2330Textual formathttp://edamontology.org/format_3033Matrix formathttps://github.com/taunometsalu/ClustVisRepositoryA code repository in github.https://hub.docker.com/r/taunometsalu/clustvis/Container fileDocker imagehttps://biit.cs.ut.ee/clustvis/General10.1093/nar/gkv46825969447PMC4489295PrimaryELIXIR-EEInstituteProviderTauno Metsalutauno.metsalu@ut.eePersonPrimary contactbiit.support@ut.eePersonPrimary contactJGI Genome PortalThe JGI Genome Portal provides a unified access point to all JGI genomic databases and analytical tools. A user can find all DOE JGI sequencing projects and their status, search for and download assemblies and annotations of sequenced genomes, and interactively explore those genomes and compare them with other sequenced microbes, fungi, plants or metagenomes using specialized systems tailored to each particular class of organisms.http://genome.jgi.doe.gov/jgi_genome_portalbiotools:jgi_genome_portalRRID:SCR_002383rridDatabase portalhttp://edamontology.org/topic_3174Metagenomicshttp://edamontology.org/topic_0780Plant biologyhttp://edamontology.org/topic_0196Sequence assemblyhttp://edamontology.org/topic_0621Model organismsLinuxWindowsMacJavahttp://edamontology.org/operation_0525Genome assemblyhttp://edamontology.org/operation_0310Sequence assemblyhttp://edamontology.org/operation_3216Scaffoldinghttp://edamontology.org/operation_0362Genome annotationhttps://jgi.doe.gov/user-program-info/General10.1093/nar/gkr94722110030PMC3245080I.V. Grigorievivgrigoriev@lbl.govPersonPrimary contactAutoDock VinaAutoDock Vina is a new open-source program for drug discovery, molecular docking and virtual screening, offering multi-core capability, high performance and enhanced accuracy and ease of use.http://vina.scripps.edu/autodock_vinabiotools:autodock_vina1.1.2RRID:SCR_011958rridDesktop applicationhttp://edamontology.org/topic_3336Drug discoveryhttp://edamontology.org/topic_2275Molecular modellinghttp://edamontology.org/topic_2275Molecular dockingLinuxWindowsMacC++http://edamontology.org/operation_0478Molecular dockinghttp://www.mybiosoftware.com/autodock-vina-1-1-2-molecular-docking-virtual-screening-program.htmlSoftware cataloguehttp://vina.scripps.edu/manual.htmlUser manual10.1002/jcc.2133419499576PMC3041641Primaryhttp://olegtrott.com/PersonPrimary contactPHYLIPComprehensive set of programs for phylogenetic analyses; available for PC and Mac; source code available for easy compiling in UNIX.http://evolution.genetics.washington.edu/phylip.htmlphylipbiotools:phylipRRID: SCR_006244rridWeb applicationhttp://edamontology.org/topic_3293Phylogeneticshttp://edamontology.org/topic_0084Phylogenyhttp://edamontology.org/topic_0621Model organismshttp://edamontology.org/topic_0623Gene and protein familieshttp://edamontology.org/topic_3300PhysiologyLinuxWindowsMacCBSD-3-ClauseAnimal and Crop Genomicshttp://edamontology.org/operation_0552Phylogenetic tree bootstrappinghttp://edamontology.org/operation_0557Phylogenetic tree distances calculationhttp://edamontology.org/operation_0547Phylogenetic tree generation (maximum likelihood and Bayesian methods)http://edamontology.org/operation_0545Phylogenetic tree generation (parsimony methods)http://edamontology.org/operation_0324Phylogenetic tree analysishttp://evolution.genetics.washington.edu/phylip/phylip.htmlGeneral10.1371/journal.pone.001399921085574PMC298155310.1385/1-59259-192-2:24310547839Joe Felsensteinjoe@gs.washington.eduhttp://evolution.gs.washington.edu/felsenstein.htmlPersonPrimary contactBionocularsLife science search engine providing access to 80M+ articles, from PubMed and OpenAlex, through complementary search pathways.
Combines classical keyword search with AI-assisted exploration, including semantic keyword grouping, knowledge graph visualization, and AI-driven summarization. All AI-generated insights are traceable to source articles. Designed to support transparent, user-steered literature exploration for biology and biomedical research. Currently in early access.https://www.bionoculars.com/bionocularsbiotools:bionocularsRRID:SCR_028217rridWeb applicationWeb servicehttp://edamontology.org/topic_3344Biomedical sciencehttp://edamontology.org/topic_3303Medicinehttp://edamontology.org/topic_3070Biologyhttp://edamontology.org/topic_0218Natural language processinghttp://edamontology.org/topic_0089Ontology and terminologyhttp://edamontology.org/topic_3068Literature and languageMacLinuxWindowsProprietaryEmergingCommercialOpen access (with restrictions)http://edamontology.org/operation_0305Literature searchhttp://edamontology.org/operation_0224Query and retrievalhttps://www.bionoculars.com/tutorialQuick start guidehttps://www.bionoculars.com/blog/early-accessRelease notesbio.toolsThe community registry of computational tools and data resources for biosciences.https://bio.toolsbio.toolsbiotools:bio.toolsRRID:SCR_014695rridWeb APIDatabase portalWeb applicationhttp://edamontology.org/topic_0089Ontology and terminologyhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_3307Computational biologyJavaScriptPythonGPL-3.0EmergingFree of chargeOpen accessToolsDenmarkhttp://edamontology.org/operation_2422Data retrievalhttp://edamontology.org/data_3671Texthttp://edamontology.org/format_1964plain text format (unformatted)https://biotools.readthedocs.io/en/latest/support.html?highlight=helpHelpdeskhttps://github.com/bio-tools/biotoolsregistry/issuesIssue trackerhttps://biotools.readthedocs.io/en/latest/contributors_guide.html#mailing-listMailing listhttps://github.com/bio-tools/biotoolsregistry/Repositoryhttps://github.com/bio-tools/biotoolsRegistrySource codehttps://biotools.readthedocs.io/en/latest/api_usage_guide.htmlAPI documentationAPI usage guidehttps://biotools.readthedocs.io/en/latest/api_reference.htmlAPI documentationAPI Referencehttps://biotools.readthedocs.io/en/latest/publications.html#citationCitation instructionshttps://biotools.readthedocs.io/en/latest/contributors_guide.htmlContributions policyhttps://biotools.readthedocs.io/en/latest/Generalhttps://biotools.readthedocs.io/en/latest/governance.htmlGovernancehttps://biotools.readthedocs.io/en/latest/curators_guide.htmlUser manualCurators Guide10.1093/nar/gkv1116Primary10.12688/f1000research.12974.1Other10.1093/gigascience/gix022Other10.1007/s10009-015-0392-zOtherContributorshttps://biotools.readthedocs.io/en/latest/contributors.htmlPersonRandom Number LabAn interactive set of tools including randomization of data (even stratified random sampling) and random assignments for studies, generation of random numbers following specific probability distributions, generation of random and quasi-random point distributions and sampling patterns, noise in one or two dimensions with adjustable spectral properties, random walks and Lévy flights. Own data tables can be provided for random sampling in a privacy-friendly way, as data processing takes place directly in the browser and no data is transferred to anywhere else. The Random Number Lab also offers some educational material and worksheets and can be used for scientific and educational purposes.https://randomnumberlab.orgrandom_number_labbiotools:random_number_labRRID:SCR_028010rridWeb applicationhttp://edamontology.org/topic_2269Statistics and probabilityhttp://edamontology.org/topic_3678Experimental design and studiesLinuxiOSMacAndroidWindowsJavaScriptMatureFree of chargehttp://edamontology.org/operation_0364Random sequence generationGeneration of sequences of random numbers or elements, spatial point patterns, random walkshttp://edamontology.org/operation_0337VisualisationVisualization of random distributions and random processeshttp://edamontology.org/operation_2409Data handlingRandomization and permutation of data in a privacy-preserving wayThomas Amthorhello@randomnumberlab.orghttps://orcid.org/0000-0002-1456-6131PersonDeveloperMaintainerPSIPREDThe PSIPRED Protein Analysis Workbench unites many available analysis tools into a single web based framework. An excellent tool for prediction of secondary structure, with access to GenTHREADER for protein fold recognition and MEMSAT-2 transmembrane topology prediction.http://bioinf.cs.ucl.ac.uk/psipred/psipredbiotools:psipredRRID:SCR_018546rridWeb applicationhttp://edamontology.org/topic_0082Structure predictionhttp://edamontology.org/topic_0736Protein folds and structural domainshttp://edamontology.org/topic_3542Protein secondary structurehttp://edamontology.org/topic_0130Protein folding, stability and designhttp://edamontology.org/topic_2814Protein structure analysisLinuxWindowsMachttp://edamontology.org/operation_0469Protein secondary structure prediction (turns)http://edamontology.org/operation_0303Protein fold recognitionhttp://edamontology.org/operation_0267Protein secondary structure predictionhttp://bioinf.cs.ucl.ac.uk/index.php?id=779General10.1093/bioinformatics/16.4.4041086904110.1006/jmbi.1999.30911049386810.1093/nar/gkt38123748958PMC3692098psipred teampsipred@cs.ucl.ac.ukPersonPrimary contactSWISS-2DPAGEData on proteins identified on various 2-D PAGE and SDS-PAGE reference maps.http://www.expasy.org/ch2d/swiss-2dpagebiotools:swiss-2dpageRRID:SCR_006946rridDatabase portalhttp://edamontology.org/topic_0121ProteomicsLinuxWindowsMacProteomicshttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_0989Protein identifierhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_3021UniProt accessionhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_3021UniProt accessionhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_2374Spot serial numberhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_2791Reference map name (SWISS-2DPAGE)http://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_2791Reference map name (SWISS-2DPAGE)http://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_2374Spot serial numberhttp://world-2dpage.expasy.org/swiss-2dpage/docs/manch2d.htmlGeneral10.1002/pmic.20030083015274128PrimarySIB Swiss Institute of BioinformaticsInstituteContributorhttp://world-2dpage.expasy.org/contactPersonPrimary contactSpatial ProteomicsEnd-to-end spatial proteomics analysis workflow with QuPath integration. Our workflow consists of cell segmentation, batch correction, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization.https://github.com/BioDepot/SpatialProteomicsspatial_proteomicsbiotools:spatial_proteomicsRRID:SCR_027835rridWorkflowPythonRMITFree of chargehttps://youtu.be/vHjdCAZQhfEOtherDemo videohttps://github.com/BioDepot/SpatialProteomicsSource code10.1101/2025.05.23.655879Other1Ling-Hong Hunglhhung@uw.eduhttps://orcid.org/0000-0002-5209-2248DeveloperKa Yee Yeungkayee@uw.eduhttps://orcid.org/0000-0002-1754-7577Primary contactBryce Fukudafukudab@uw.eduhttps://orcid.org/0009-0003-7203-5261DeveloperMassBankMassBank is a public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains spectra from EI‐MS, fast atom bombardment MS, electrospray ionization (ESI)‐MSn data of thousands of authentic standards, EI‐MS and other‐MS data of thousands of volatile natural and synthetic compounds, and ESI‐MS2 data of synthetic drugs contributed by research groups worldwide. ESI‐MS2 data were analyzed under nonstandardized, independent experimental conditions. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass‐to‐charge ratio are optimized to the ESI‐MS2 data. MassBank is useful for the identification of chemical compounds and the publication of experimental data.https://massbank.eu/massbankbiotools:massbankdoi:10.25504/FAIRsharing.dk451adoirrid:SCR_015535rridDatabase portalhttp://edamontology.org/topic_3172MetabolomicsMatureFree of chargeMetabolomicsGermanyhttps://massbank.eu/ServiceEuropean MassBank sitehttps://github.com/MassBank/Otherhttps://github.com/MassBank/MassBank-web/Source code10.1002/jms.177720623627PrimaryThe MassBank ProjectProjectDeveloperFunded by JST-BIRD 2006-2010de.NBI MASH10.13039/501100002347ProjectFunding under FKZ 031L0107NORMAN AssociationProteinProspectorA suite of protein identification tools.http://prospector.ucsf.eduproteinprospectorbiotools:proteinprospectorRRID:SCR_014558rridWeb applicationhttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_3520Proteomics experimenthttp://edamontology.org/topic_3489Database managementLinuxWindowsMacms-utilsProteomicshttp://edamontology.org/operation_2929Protein fragment weight comparisonhttp://edamontology.org/operation_3647Blind peptide database searchhttp://ms-utils.orgSoftware cataloguehttp://prospector.ucsf.edu/prospector/html/instruct/allman.htmUser manual10.1074/mcp.d500002-mcp20015937296PrimaryAdminppadmin@cgl.ucsf.eduPersonMaintainerwebmaster@ms-utils.orghttp://ms-utils.orgPersonDocumentorSILVA rRNA databaseSILVA provides comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukaryota).https://www.arb-silva.desilvabiotools:silvaRRID:SCR_006423rridWeb applicationDatabase portalhttp://edamontology.org/topic_0659Functional, regulatory and non-coding RNAhttp://edamontology.org/topic_0080Sequence analysishttp://edamontology.org/topic_3293Phylogeneticshttp://edamontology.org/topic_0637Taxonomyhttp://edamontology.org/topic_3050Biodiversityhttp://edamontology.org/topic_3301Microbiologyhttp://edamontology.org/topic_0632Probes and primersCC-BY-4.0de.NBIde.NBI-biodataDSMZ Digital DiversityMatureFree of chargeOpen accessDataGermanyhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_1097Sequence accession (nucleic acid)http://edamontology.org/format_1964plain text format (unformatted)http://edamontology.org/data_1868Taxonhttp://edamontology.org/format_1964plain text format (unformatted)http://edamontology.org/data_1046Strain namehttp://edamontology.org/format_1964plain text format (unformatted)http://edamontology.org/data_1088Article IDhttp://edamontology.org/format_1964plain text format (unformatted)http://edamontology.org/data_2909Organism namehttp://edamontology.org/format_1964plain text format (unformatted)The search and retrieval functions of the SILVA website can be used to build custom subsets of sequences. In addition to simple searches e.g. for accession numbers, organism names, taxonomic entities, or publication DOI/PubMed IDs, complex queries over several database fields using constraints such as sequence length or quality values are possible. The results can be sorted according to accession numbers, organism names, sequence length, sequence and alignment quality and Pintail values. The search results show accession number, organism name, sequence length, sequence quality values, taxonomic classifications, and links to view the full sequence record on SILVA and on ENA. Sequences found via search and added to download cart can be downloaded as FASTA and ARB files.http://edamontology.org/operation_2419Primer and probe designhttp://edamontology.org/data_2977Nucleic acid sequencehttp://edamontology.org/format_1207nucleotidehttp://edamontology.org/data_1240PCR primershttp://edamontology.org/format_1207nucleotideThe SILVA Probe Match and Evaluation Tool detects and displays all occurrences of a given probe or primer sequence in the SILVA datasets.
TestPrime allows you to evaluate the performance of primer pairs by running an in silico PCR on the SILVA databases. From the results of the PCR, TestPrime computes coverages for each taxonomic group in all of the taxonomies offered by SILVA.http://edamontology.org/operation_0492Multiple sequence alignmenthttp://edamontology.org/operation_0496Global alignmenthttp://edamontology.org/operation_3460Taxonomic classificationhttp://edamontology.org/operation_0547Phylogenetic inference (maximum likelihood and Bayesian methods)http://edamontology.org/data_2977Nucleic acid sequencehttp://edamontology.org/format_1984FASTA-alnThe Alignment, Classification and Tree Service (ACT) of SILVA allows to align and optionally classify sequences using the SILVA Incremental Aligner (SINA). From aligned sequences, (optionally) phylogenetic trees can be inferred using FastTree or RAxML.https://www.arb-silva.de/browser/ServiceSILVA Taxonomy Browserhttps://www.arb-silva.de/search/ServiceSILVA metadata searchhttps://www.arb-silva.de/aligner/ServiceACT: Alignment, Classification and Tree Servicehttps://www.arb-silva.de/search/testprobe/ServiceSILVA Probe Match and Evaluation Toolhttps://www.arb-silva.de/search/testprime/ServiceSILVA Primer Evaluation Toolhttps://treeviewer.arb-silva.de/ServiceWeb-based viewer for the SILVA guide treeshttps://www.arb-silva.de/download/archive/Downloads pageSILVA dataset archivehttps://www.arb-silva.de/silva-license-information/Terms of usehttps://www.arb-silva.de/contact/Citation instructionshttps://www.arb-silva.de/documentation/Generalhttps://www.arb-silva.de/documentation/faqs/FAQsilvangsusedByd3hubincludedIn10.1093/nar/gks121923193283PMC3531112Primary10.1093/nar/gkt120924293649PMC3965112Other10.1093/nar/gkm86417947321PMC2175337Other10.1093/bioinformatics/bts25222556368PMC3389763Other10.1016/j.jbiotec.2017.06.119828648396Review10.1186/s12859-017-1841-3OtherLeibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultureshub@dsmz.dehttps://www.dsmz.degrid.420081.f02tyer376InstituteProviderSILVA Teamcontact@arb-silva.dehttps://www.arb-silva.de/contact/team/grid.507782.f027z9pz32DivisionPrimary contactBioSimulationsBioSimulations is a web application for sharing and re-using biomodels, simulations, and visualizations of simulations results. BioSimulations supports a wide range of modeling frameworks (e.g., kinetic, constraint-based, and logical modeling), model formats (e.g., BNGL, CellML, SBML), and simulation tools (e.g., COPASI, libRoadRunner/tellurium, NFSim, VCell). BioSimulations aims to help researchers discover published models that might be useful for their research and quickly try them via a simple web-based interface.https://www.biosimulations.org/biosimulationsbiotools:biosimulationsRRID:SCR_018733rridCommand-line toolWeb APIWeb applicationWeb servicehttp://edamontology.org/topic_2259Systems biologyhttp://edamontology.org/topic_0602Molecular interactions, pathways and networksJavaScriptPythonMITEmergingFree of chargeOpen accesshttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_2426Modelling and simulationhttp://edamontology.org/data_0950Mathematical modelhttp://edamontology.org/format_2585SBMLhttp://edamontology.org/format_3240CellMLhttps://github.com/reproducible-biomedical-modeling/Biosimulations/issuesIssue trackerhttps://github.com/reproducible-biomedical-modeling/BiosimulationsRepositoryhttps://api.biosimulations.org/Servicehttps://docs.biosimulations.orgGeneralhttps://api.biosimulations.org/API documentationhttps://docs.biosimulations.org/developers/conduct/Code of conducthttps://docs.biosimulations.org/about/terms/Terms of userunbiosimulationsusesbiosimulatorsusesbiomodelsusessbmlusesbionetgenusescobrapyusescopasiuseslibroadrunnerusesJonathan Karrkarr@mssm.eduhttps://www.karrlab.orghttps://orcid.org/0000-0002-2605-5080PersonDeveloperBilal Shaikhhttps://orcid.org/0000-0001-5801-5510PersonDeveloperHuman Microbiome CompendiumCollection of human microbiome sequencing data that pairs subject metadata with relative abundance data derived from amplicon and shotgun metagenomic sequencing processed via uniform pipelines.https://microbiomap.orghuman_microbiome_compendiumbiotools:human_microbiome_compendiumRRID:SCR_026991rridBioinformatics portalhttp://edamontology.org/topic_3174Metagenomicshttp://edamontology.org/topic_3697Microbial ecologyhttp://edamontology.org/topic_4012FAIR datahttp://edamontology.org/topic_3174Metagenomicshttp://edamontology.org/topic_3293PhylogeneticsRPythonCC-BY-2.0microbiomeEmergingFree of chargeOpen accessDatahttps://microbiomap.orgOtherHomepage of the project with links to multiple tools and datasetshttps://doi.org/10.5281/zenodo.8186993RepositoryLink to the most recent release of the compendium datasethttps://doi.org/10.5281/zenodo.8186993Biological dataDOI links to the most recent releasehttps://zenodo.org/records/15122187/files/README.mdUser manual10.1016/j.cell.2024.12.01739848248PMC11848717Primary1.1.0Richard Abdillrabdill@uchicago.eduhttps://blekhmanlab.orghttps://orcid.org/0000-0001-9565-5832PersonPrimary contactISfinderIt is a dedicated insertion sequence (IS) database which assigns names to individual ISs to maintain a coherent nomenclature, an IS repositaory including >3000 individual ISs from both bacteria and archaea and provides a basis for IS classification. Each IS is indexed in ISfinder with various associated pieces of information and classified into a group or family to provide some insight into its phylogeny.http://www-is.biotoul.frisfinderbiotools:isfinderRRID:SCR_003020rridDatabase portalhttp://edamontology.org/topic_3068Literature and languagehttp://edamontology.org/topic_0084Phylogenyhttp://edamontology.org/topic_0798Mobile genetic elementshttp://edamontology.org/topic_0621Model organismshttp://edamontology.org/topic_3168SequencingLinuxWindowsMacSQLToolsFrancehttp://edamontology.org/operation_3211Genome indexinghttp://edamontology.org/operation_3349Bibliography generationhttp://edamontology.org/operation_2436Gene-set enrichment analysishttps://www-is.biotoul.fr/general_information.phpGeneral10.1093/nar/gkp94719906702PMC2808865Patricia SiguierPatricia.Siguier@ibcg.biotoul.frPersonPrimary contactPharosPharos is the web interface for data collected by the Illuminating the Druggable Genome initiative. Target, disease and ligand information are collected and displayed.
You are using an outdated browser. Please upgrade your browser to improve your experience.https://pharos.nih.gov/pharosbiotools:pharosRRID: SCR_016924rridhttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_0080Sequence analysishttp://edamontology.org/topic_0634Pathologyhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/operation_3436Aggregationhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_2422Data retrievalhttps://pharos.nih.gov/apiAPI documentation10.1093/nar/gkaa99333156327PMC7778974Tudor I Opreatoprea@salud.unm.eduPerson