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{ "count": 4014, "next": "?page=2", "previous": null, "list": [ { "name": "SLiM", "description": "Evolutionary simulation framework that combines a powerful engine for population genetic simulations with the capability of modeling arbitrarily complex evolutionary scenarios. 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Complete source code is on GitHub, but most platforms have an installer anyway; see the manual, chapter 2, for installation instructions.", "version": null }, { "url": "https://github.com/MesserLab/SLiM/releases/latest", "type": "Downloads page", "note": "The GitHub page for the current release version, to obtain full source code.", "version": null } ], "documentation": [ { "url": "http://benhaller.com/slim/SLiM_Manual.pdf", "type": [ "User manual" ], "note": "The manual for SLiM itself" }, { "url": "http://benhaller.com/slim/Eidos_Manual.pdf", "type": [ "User manual" ], "note": "The manual for Eidos, the scripting language used by SLiM" }, { "url": "http://benhaller.com/slim/SLiMEidosRefSheets.zip", "type": [ "Quick start guide" ], "note": "Quick reference sheets for SLiM and Eidos" } ], "publication": [ { "doi": "10.1093/molbev/msy228", "pmid": "30517680", "pmcid": "PMC6389312", "type": [ "Primary" ], "version": "3.0", "note": "B.C. Haller, P.W. Messer. (2019). SLiM 3: Forward genetic simulations beyond the Wright–Fisher Model. Molecular Biology and Evolution 36(3), 632–637.", "metadata": { "title": "SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model", "abstract": "", "date": "2019-03-01T00:00:00Z", "citationCount": 541, "authors": [], "journal": "Molecular Biology and Evolution" } }, { "doi": "10.1093/molbev/msy237", "pmid": "30590560", "pmcid": "PMC6501880", "type": [ "Method" ], "version": "3.0", "note": "B.C. Haller, P.W. Messer. (2019). Evolutionary modeling in SLiM 3 for beginners. Molecular Biology and Evolution 36(5), 1101–1109.", "metadata": { "title": "Evolutionary Modeling in SLiM 3 for Beginners", "abstract": "", "date": "2019-05-01T00:00:00Z", "citationCount": 13, "authors": [], "journal": "Molecular Biology and Evolution" } }, { "doi": "10.1111/1755-0998.12968", "pmid": "30565882", "pmcid": "PMC6393187", "type": [ "Primary" ], "version": "3.0", "note": "B.C. Haller, J. Galloway, J. Kelleher, P.W. Messer, P.L. Ralph. (2019). Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. Molecular Ecology Resources 19(2), 552–566.", "metadata": { "title": "Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes", "abstract": "", "date": "2019-03-01T00:00:00Z", "citationCount": 123, "authors": [], "journal": "Molecular Ecology Resources" } }, { "doi": "10.1101/2025.08.07.669155", "pmid": "40832315", "pmcid": "PMC12363870", "type": [ "Primary" ], "version": "5.0", "note": "BC Haller, PL Ralph, PW Messer. (2025). SLiM 5: Eco-evolutionary simulations across multiple chromosomes and full genomes. bioRxiv, 2025.08. 07.669155", "metadata": null }, { "doi": "10.1086/723601", "pmid": "37130229", "pmcid": "PMC10793872", "type": [ "Primary" ], "version": "4.0", "note": "B.C. Haller, P.W. Messer. (2023). SLiM 4: Multispecies eco-evolutionary modeling. 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], "note": null }, { "url": "https://www.jalview.org/development/jalview_develop/", "type": [ "Other" ], "note": "Latest development version" }, { "url": "https://source.jalview.org/crucible/browse/jalview", "type": [ "Repository" ], "note": null }, { "url": "https://twitter.com/Jalview", "type": [ "Social media" ], "note": "Twitter feed" }, { "url": "https://www.youtube.com/channel/UCIjpnvZB770yz7ftbrJ0tfw", "type": [ "Social media" ], "note": "YouTube training videos" } ], "download": [ { "url": "https://www.jalview.org/download", "type": "Downloads page", "note": null, "version": null }, { "url": "https://www.jalview.org/download/source/", "type": "Source code", "note": null, "version": null }, { "url": "https://www.jalview.org/download/?os=all", "type": "Binaries", "note": "Binaries for all platforms", "version": null }, { "url": "https://www.jalview.org/favicon.svg", "type": "Icon", "note": null, "version": null }, { "url": "https://www.jalview.org/download/other/jar/", "type": 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VAPOR is provided with a fasta file of full-length sequences (> 20,000) for a given segment, a set of reads, and attempts to retrieve a reference that is closest to the sample strain.", "homepage": "https://github.com/wm75/vaporup", "biotoolsID": "vapor", "biotoolsCURIE": "biotools:vapor", "version": [ "1.0.3" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" }, { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_3198", "term": "Read mapping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/connor-lab/vapor", "type": [ "Other" ], "note": "Original VAPOR repository from which versions prior to 1.0.3 were released" } ], "download": [ { "url": "https://pypi.org/project/vaporup/#files", "type": "Software package", "note": null, "version": null }, { "url": "https://doi.org/10.5281/zenodo.15364147", "type": "Biological data", "note": "A curated collection of reference sequences for each influenza A genome segment that can be used with the tool for classification of sequences from this virus", "version": null } ], "documentation": [], "publication": [ { "doi": "10.1093/bioinformatics/btz814", "pmid": "31693070", "pmcid": "PMC7703727", "type": [], "version": null, "note": null, "metadata": { "title": "Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications", "abstract": "Motivation: Influenza viruses represent a global public health burden due to annual epidemics and pandemic potential. Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation and detection of strains of non-human origin. Results: Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome sequencing samples with a mean of > 99:8% identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. VAPOR has the potential to improve the robustness of bioinformatics pipelines for surveillance and could be adapted to other RNA viruses.", "date": "2020-01-01T00:00:00Z", "citationCount": 7, "authors": [ { "name": "Southgate J.A." }, { "name": "Bull M.J." }, { "name": "Brown C.M." }, { "name": "Watkins J." }, { "name": "Corden S." }, { "name": "Southgate B." }, { "name": "Moore C." }, { "name": "Connor T.R." } ], "journal": "Bioinformatics" } } ], "credit": [], "owner": "Kigaard", "additionDate": "2020-01-14T09:34:01Z", "lastUpdate": "2025-08-28T14:32:58.093703Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-CZ", "wm75" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "EssSubgraph", "description": "EssSubgraph is a model algorithm that integrates omics data and network data to predict essential genes.", "homepage": "https://github.com/wenmm/EssSubgraph/tree/main", "biotoolsID": "esssubgraph", "biotoolsCURIE": 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"hudie.luoluo@gmail.com", "url": null, "orcidid": "https://orcid.org/0009-0006-1600-1210", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Susan Carpenter", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-5600-5404", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Karen McGinnis", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-9564-8146", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Andrew Nelson", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-9896-1739", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Keriayn Smith", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-4351-2765", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Tian Hong", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-8212-7050", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact" ], "note": null } ], "owner": "haimei", "additionDate": "2025-08-20T17:28:51.309591Z", "lastUpdate": "2025-08-20T18:13:15.065957Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MacSyLib", "description": "a Python package library that allow to model and detect macromolecular systems, genetic pathways… by similarity search in prokaryotes datasets.", "homepage": "https://github.com/gem-pasteur/macsylib", "biotoolsID": "macsylib", "biotoolsCURIE": "biotools:macsylib", "version": [ "1.0.1" ], "otherID": [], "relation": [ { "biotoolsID": "macsyfinder", "type": "usedBy" } ], "function": [ { "operation": [ { "uri": 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"Other" ], "note": null }, { "url": "https://macsylib.readthedocs.io/en/latest/", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://github.com/gem-pasteur/macsylib", "type": "Source code", "note": null, "version": "1.0.1" }, { "url": "https://pypi.org/project/MacSyLib/", "type": "Software package", "note": null, "version": "1.0.1" } ], "documentation": [ { "url": "https://macsylib.readthedocs.io/en/latest/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.24072/pcjournal.250", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes", "abstract": "Complex cellular functions are usually encoded by a set of genes in one or a few orga-nized genetic loci in microbial genomes. Macromolecular System Finder (MacSyFinder) is a program that uses these properties to model and then annotate cellular functions in microbial genomes. This is done by integrating the identification of each individual gene at the level of the molecular system. We hereby present a major release of MacSyFinder (version 2) coded in Python 3. The code was improved and rationalized to facilitate future maintainability. Several new features were added to allow more flexible modelling of the systems. We introduce a more intuitive and comprehensive search engine to identify all the best candidate systems and sub-optimal ones that respect the models’ constraints. We also introduce the novel macsydata companion tool that enables the easy installation and broad distribution of the models developed for MacSyFinder (macsy-models) from GitHub repositories. Finally, we have updated and improved MacSyFinder popular mod-els: TXSScan to identify protein secretion systems, TFFscan to identify type IV filaments, CONJscan to identify conjugative systems, and CasFinder to identify CRISPR associated proteins. MacSyFinder and the updated models are available at: https://github.com/gem-pasteur/macsyfinder.", "date": "2023-01-01T00:00:00Z", "citationCount": 43, "authors": [ { "name": "Neron B." }, { "name": "Denise R." }, { "name": "Coluzzi C." }, { "name": "Touchon M." }, { "name": "Rocha E.P.C." }, { "name": "Abby S.S." } ], "journal": "Peer Community Journal" } } ], "credit": [ { "name": "Institut Pasteur", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": "0495fxg12", "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null }, { "name": "Microbial Evolutionary Genomics Unit", "email": null, "url": "https://research.pasteur.fr/en/team/microbial-evolutionary-genomics/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Division", "typeRole": [], "note": null }, { "name": "Bioinformatics and Biostatistics HUB", "email": null, "url": "https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Division", "typeRole": [], "note": null }, { "name": "Eduardo P.C. Rocha", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-7704-822X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Sophie Abby", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-5231-3346", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Bertrand Néron", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-0220-0482", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "bneron", "additionDate": "2025-08-07T10:25:22.118390Z", "lastUpdate": "2025-08-11T12:14:42.322020Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EukCC", "description": "EukCC is a completeness and contamination estimator for metagenomic assembled microbial eukaryotic genomes.", "homepage": "https://github.com/EBI-Metagenomics/EukCC", "biotoolsID": "eukcc", "biotoolsCURIE": "biotools:eukcc", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3180", "term": "Sequence assembly validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2955", "term": "Sequence report" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s13059-020-02155-4", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC", "abstract": "Microbial eukaryotes constitute a significant fraction of biodiversity and have recently gained more attention, but the recovery of high-quality metagenomic assembled eukaryotic genomes is limited by the current availability of tools. To help address this, we have developed EukCC, a tool for estimating the quality of eukaryotic genomes based on the automated dynamic selection of single copy marker gene sets. 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"format": [ { "uri": "http://edamontology.org/format_3508", "term": "PDF" } ] }, { "data": { "uri": "http://edamontology.org/data_0906", "term": "Protein interaction data" }, "format": [] } ], "note": "Run ETENLNC on paired-end raw RNA-Seq (.fastq) data", "cmd": "bash ETENLNC_docker.sh" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0659", "term": "Functional, regulatory and non-coding RNA" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [], "license": "GPL-3.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/EvolOMICS-TU/ETENLNC", "type": [ "Repository" ], "note": "GitHub Repo" } ], "download": [ { "url": "https://zenodo.org/records/14325721?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImVmYTllNWJkLWE4ODUtNGM3OC05YTAxLWM4ZDk5YTljZDZjMCIsImRhdGEiOnt9LCJyYW5kb20iOiI4M2I1YjBlZDQ4MmUyZTIxNDg2YmI0YTFkMWE5MTI1OCJ9.HeB3WrsPduNzMyXjH4x5HfCgmIp4NzYv0P_11XU9lcXC_ZxEaVndP-kD0LDkxwufHVNlroeAhdK33PR51F6jnw", "type": "Biological data", "note": "Sample/demo data for ETENLNC. A detailed guide on running ETENLNC using the demo data can be found in the ETENLNC manual (supplementary to our publication).", "version": "1.0" } ], "documentation": [ { "url": "https://github.com/EvolOMICS-TU/ETENLNC", "type": [ "Command-line options" ], "note": "A detailed guide on running ETENLNC using the demo data can be found in the ETENLNC manual (supplementary to our publication)" } ], "publication": [ { "doi": "10.1016/j.compbiolchem.2024.108140", "pmid": "38996755", "pmcid": null, "type": [ "Primary" ], "version": ".0", "note": null, "metadata": { "title": "ETENLNC: An end to end lncRNA identification and analysis framework to facilitate construction of known and novel lncRNA regulatory networks", "abstract": "Long non-coding RNAs (lncRNAs) play crucial roles in the regulation of gene expression and maintenance of genomic integrity through various interactions with DNA, RNA, and proteins. The availability of large-scale sequence data from various high-throughput platforms has opened possibilities to identify, predict, and functionally annotate lncRNAs. As a result, there is a growing demand for an integrative computational framework capable of identifying known lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding), a user-friendly, integrative, open-source, scalable, and modular computational framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC employs six stringent filtration steps to identify novel lncRNAs, performs differential expression analysis of mRNA and lncRNA transcripts, and predicts regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked ETENLNC against six existing tools and optimized it for desktop workstations and high-performance computing environments using data from three different species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.", "date": "2024-10-01T00:00:00Z", "citationCount": 2, "authors": [ { "name": "Nath P." }, { "name": "Bhuyan K." }, { "name": "Bhattacharyya D.K." }, { "name": "Barah P." } ], "journal": "Computational Biology and Chemistry" } } ], "credit": [ { "name": "Pankaj Barah", "email": "barah@tezu.ernet.in", "url": "https://www.tezu.ernet.in/dmbbt/profile/34", "orcidid": "https://orcid.org/0000-0001-7039-7996", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer" ], "note": "Assistant Professor at Department of Molecular Biology and Biotechnology, Tezpur University." }, { "name": "Prangan Nath", "email": "prangannathofficial@gmai.com", "url": null, "orcidid": "https://orcid.org/0000-0002-9451-7822", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Maintainer" ], "note": null } ], "owner": "prangannath", "additionDate": "2025-07-23T10:27:57.572501Z", "lastUpdate": "2025-07-23T10:27:57.574879Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "InMoose", "description": "InMoose, the INtegrated Multi-Omic Open-Source environment, ports to Python state-of-the-art R tools for the analysis of transcriptomic data.", "homepage": "https://github.com/epigenelabs/inmoose", "biotoolsID": "inmoose", "biotoolsCURIE": "biotools:inmoose", "version": [ "0.8.0" ], "otherID": [], "relation": [ { "biotoolsID": "deseq2", "type": "isNewVersionOf" }, { "biotoolsID": "limma", "type": "isNewVersionOf" }, { "biotoolsID": "edger", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3917", "term": "Count matrix" }, "format": [] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/epigenelabs/inmoose", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/epigenelabs/inmoose/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://inmoose.readthedocs.io/en/stable/", "type": [ "General" ], "note": null }, { "url": "https://colab.research.google.com/drive/1bzmNDZeaw1A6tSgfx61O6PFtaQDve6SV?usp=sharing", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1038/s41598-025-03376-y", "pmid": null, "pmcid": null, "type": [ "Primary", "Usage" ], "version": null, "note": null, "metadata": { "title": "Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose", "abstract": "We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. InMoose focuses on providing drop-in replacements for R tools, to ensure consistency and reproducibility between R-based and Python-based pipelines. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.", "date": "2025-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Colange M." }, { "name": "Appe G." }, { "name": "Meunier L." }, { "name": "Weill S." }, { "name": "Johnson W.E." }, { "name": "Nordor A." }, { "name": "Behdenna A." } ], "journal": "Scientific Reports" } }, { "doi": "10.1186/s12859-023-05578-5", "pmid": null, "pmcid": null, "type": [ "Benchmarking study", "Method" ], "version": null, "note": null, "metadata": { "title": "pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods", "abstract": "Background: Variability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data. Results: In this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose . Conclusions: We present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost.", "date": "2023-12-01T00:00:00Z", "citationCount": 30, "authors": [ { "name": "Behdenna A." }, { "name": "Colange M." }, { "name": "Haziza J." }, { "name": "Gema A." }, { "name": "Appe G." }, { "name": "Azencott C.-A." }, { "name": "Nordor A." } ], "journal": "BMC Bioinformatics" } }, { "doi": "10.1186/s12859-025-06180-7", "pmid": null, "pmcid": null, "type": [ "Benchmarking study", "Method" ], "version": null, "note": null, "metadata": { "title": "Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python", "abstract": "Background: Differential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma—for microarray data –, and edgeR and DESeq2—for RNA-Seq data–, are the most widely used tools for differential gene expression analysis of bulk transcriptomic data. Results: We present the differential expression features of InMoose, a Python implementation of R tools limma, edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io. Conclusions: We present a new Python implementation of state-of-the-art tools limma, edgeR, and DESeq2, to perform differential gene expression analysis of bulk transcriptomic data. This new implementation exhibits results nearly identical to the original tools, improving interoperability and reproducibility between Python and R bioinformatics pipelines.", "date": "2025-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Colange M." }, { "name": "Appe G." }, { "name": "Meunier L." }, { "name": "Weill S." }, { "name": "Nordor A." }, { "name": "Behdenna A." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Maximilien Colange", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-4769-3302", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Epigene Labs", "email": null, "url": "https://www.epigenelabs.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "maximilien.colange", "additionDate": "2025-07-10T12:32:00.838316Z", "lastUpdate": "2025-07-10T13:00:37.310588Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SQANTI3", "description": "SQANTI3 constitutes the first module of the Functional IsoTranscriptomics (FIT) pipeline, which is an end-to-end strategy to perform isoform-level bioinformatics analyses. \n\nThe SQANTI3 tool is designed to enable quality control and filtering of long read-defined transcriptomes, which are often rich in artifacts and false-positive isoforms. \n\nTherefore, a good curation of the transcriptome is indispensable to proceed with FIT analysis and produce valid, biologically sound conclusions/hypothesis.", "homepage": "https://github.com/ConesaLab/SQANTI3", "biotoolsID": "sqanti3", "biotoolsCURIE": "biotools:sqanti3", "version": [ "5.5" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [ "Spain" ], "elixirCommunity": [], "link": [ { "url": "https://github.com/ConesaLab/SQANTI3", "type": [ "Repository" ], "note": "Github repository for source code access and download" } ], "download": [ { "url": "https://github.com/ConesaLab/SQANTI3/releases", "type": "Downloads page", "note": null, "version": null }, { "url": "https://github.com/ConesaLab/SQANTI3/releases/download/v5.5/SQANTI3_v5.5.zip", "type": "Binaries", "note": "Version 5.5 download link from github", "version": "5.5" } ], "documentation": [ { "url": "https://github.com/ConesaLab/SQANTI3/wiki", "type": [ "General" ], "note": "SQANTI3 main documentation resource" }, { "url": "https://github.com/ConesaLab/SQANTI3/blob/master/CODE_OF_CONDUCT.md", "type": [ "Code of conduct" ], "note": null }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-Quality-Control", "type": [ "Command-line options" ], "note": "Command-line options and instructions for the QC submodule" }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-filter", "type": [ "Command-line options" ], "note": "Command-line options and instructions for the filter submodule" }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-rescue", "type": [ "Command-line options" ], "note": "Command-line options and instructions for the rescue submodule" }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/Dependencies-and-installation", "type": [ "Installation instructions" ], "note": "Instructions to install and use sqanti, either in docker or apptainer containers or in a linux system" }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/Tutorial:-running-SQANTI3-on-an-example-dataset", "type": [ "Quick start guide" ], "note": "Basic tutorial with examples to start using SQANTI3" }, { "url": "https://github.com/ConesaLab/SQANTI3/releases", "type": [ "Release notes" ], "note": "Changelog and release notes for every version" }, { "url": "https://github.com/ConesaLab/SQANTI3?tab=readme-ov-file#how-to-cite-sqanti3", "type": [ "Citation instructions" ], "note": "Citation instructions are on the end of the Github repository's main page" }, { "url": "https://github.com/ConesaLab/SQANTI3/wiki/SQANTI3-memory-requeriments-and-paralellization", "type": [ "Other" ], "note": "Benchmarking about the resources needed to run sqanti3 with multiple cores" } ], "publication": [ { "doi": "10.1038/s41592-024-02229-2", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": "5.1", "note": "Publication of SQANTI3", "metadata": { "title": "SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms", "abstract": "SQANTI3 offers a flexible tool for quality control, curation and annotation of long-read RNA sequencing data. SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.", "date": "2024-05-01T00:00:00Z", "citationCount": 41, "authors": [ { "name": "Pardo-Palacios F.J." }, { "name": "Arzalluz-Luque A." }, { "name": "Kondratova L." }, { "name": "Salguero P." }, { "name": "Mestre-Tomas J." }, { "name": "Amorin R." }, { "name": "Estevan-Morio E." }, { "name": "Liu T." }, { "name": "Nanni A." }, { "name": "McIntyre L." }, { "name": "Tseng E." }, { "name": "Conesa A." } ], "journal": "Nature Methods" } }, { "doi": "10.1101/gr.222976.117", "pmid": null, "pmcid": null, "type": [ "Method" ], "version": null, "note": "Original SQANTI publication", "metadata": { "title": "SQANTI: Extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification", "abstract": "High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.", "date": "2018-03-01T00:00:00Z", "citationCount": 256, "authors": [ { "name": "Tardaguila M." }, { "name": "De La Fuente L." }, { "name": "Marti C." }, { "name": "Pereira C." }, { "name": "Pardo-Palacios F.J." }, { "name": "Del Risco H." }, { "name": "Ferrell M." }, { "name": "Mellado M." }, { "name": "Macchietto M." }, { "name": "Verheggen K." }, { "name": "Edelmann M." }, { "name": "Ezkurdia I." }, { "name": "Vazquez J." }, { "name": "Tress M." }, { "name": "Mortazavi A." }, { "name": "Martens L." }, { "name": "Rodriguez-Navarro S." }, { "name": "Moreno-Manzano V." }, { "name": "Conesa A." } ], "journal": "Genome Research" } } ], "credit": [ { "name": "Ana Conesa", "email": "ana.conesa@csic.es", "url": null, "orcidid": "https://orcid.org/0000-0001-9597-311X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Pablo Atienza", "email": "pablo.atienza@csic.es", "url": null, "orcidid": "https://orcid.org/0009-0002-1093-693X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Maintainer" ], "note": "Maintainer of SQANTI3" }, { "name": "Fabián Robledo", "email": "fabian.robledo@csic.es", "url": null, "orcidid": "https://orcid.org/0009-0005-9047-3315", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Maintainer" ], "note": "Maintainer of SQANTI3 and ELIXIR-related contact" } ], "owner": "Fabian-RY", "additionDate": "2025-07-07T09:04:03.787249Z", "lastUpdate": "2025-07-07T09:35:33.280160Z", "editPermission": { "type": "group", "authors": [ "Fabian-RY" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "16SMicrobiomeMLFS", "description": "Code and supporting data for the article: \"Exploring the role of normalization and feature selection in microbiome disease classification pipelines.\"", "homepage": "https://github.com/nach00gar/16SMicrobiomeMLFS", "biotoolsID": "16smicrobiomemlfs", "biotoolsCURIE": "biotools:16smicrobiomemlfs", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Script" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Windows", "Linux", "Mac" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/nach00gar/16SMicrobiomeMLFS", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "Ignacio Garach Vélez", "email": "igarachv@ugr.es", "url": "https://github.com/nach00gar/16SMicrobiomeMLFS", "orcidid": "https://orcid.org/0009-0006-6618-7949", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "igarachv", "additionDate": "2025-07-04T16:10:32.821423Z", "lastUpdate": "2025-07-04T16:10:32.823947Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DeepSig", "description": "Prediction of secretory signal peptides in protein sequences", "homepage": "https://busca.biocomp.unibo.it/deepsig/", "biotoolsID": "deepsig", "biotoolsCURIE": "biotools:deepsig", "version": [ "1.2.5" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0418", "term": "Protein signal peptide detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0123", "term": "Protein properties" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python", "C++" ], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/BolognaBiocomp/deepsig", "type": "Source code", "note": null, "version": "1.2.5" }, { "url": "https://hub.docker.com/r/bolognabiocomp/deepsig", "type": "Container file", "note": null, "version": "1.2.5" } ], "documentation": [ { "url": "https://github.com/BolognaBiocomp/deepsig", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btx818", "pmid": "29280997", "pmcid": "PMC5946842", "type": [ "Primary" ], "version": "1.0", "note": null, "metadata": { "title": "DeepSig: Deep learning improves signal peptide detection in proteins", "abstract": "Motivation The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.", "date": "2018-05-15T00:00:00Z", "citationCount": 96, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": "http://biocomp.unibo.it", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Primary contact" ], "note": null }, { "name": "Pier Luigi Martelli", "email": "pierluigi.martelli@unibo.it", "url": "http://biocomp.unibo.it", "orcidid": "https://orcid.org/0000-0002-0274-5669", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2018-05-28T14:50:09Z", "lastUpdate": "2025-06-19T11:55:09.017105Z", "editPermission": { "type": "group", "authors": [ "savo", "ELIXIR-ITA-BOLOGNA" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "NanoPlot", "description": "NanoPlot is a tool with various visualizations of sequencing data in bam, cram, fastq, fasta or platform-specific TSV summaries, mainly intended for long-read sequencing from Oxford Nanopore Technologies and Pacific Biosciences", "homepage": "https://github.com/wdecoster/NanoPlot", "biotoolsID": "nanoplot", "biotoolsCURIE": "biotools:nanoplot", "version": [ "v.1.42.0" ], "otherID": [], "relation": [ { "biotoolsID": "nanopack", "type": "includedIn" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2940", "term": "Scatter plot plotting" }, { "uri": "http://edamontology.org/operation_2943", "term": "Box-Whisker plot plotting" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2546", "term": "FASTA-like" }, { "uri": "http://edamontology.org/format_1207", "term": "nucleotide" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [ "ONTeater" ], "maturity": "Mature", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/wdecoster/NanoPlot", "type": [ "Repository" ], "note": "Issue tracker and most up to date software version" }, { "url": "http://nanoplot.bioinf.be/", "type": [ "Service" ], "note": "Web service with more limited options compared to the command line tool" } ], "download": [ { "url": "https://anaconda.org/bioconda/nanoplot", "type": "Command-line specification", "note": null, "version": null }, { "url": "https://pypi.org/project/NanoPlot/", "type": "Command-line specification", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/wdecoster/NanoPlot", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty149", "pmid": "29547981", "pmcid": "PMC6061794", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "NanoPack: Visualizing and processing long-read sequencing data", "abstract": "Summary: Here we describe NanoPack, a set of tools developed for visualization and processing of long-read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences. Availability and implementation: The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.", "date": "2018-08-01T00:00:00Z", "citationCount": 1840, "authors": [ { "name": "De Coster W." }, { "name": "D'Hert S." }, { "name": "Schultz D.T." }, { "name": "Cruts M." }, { "name": "Van Broeckhoven C." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Wouter De Coster", "email": null, "url": "https://gigabaseorgigabyte.wordpress.com/", "orcidid": "https://orcid.org/0000-0002-5248-8197", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "wdecoster", "additionDate": "2021-07-06T20:27:27Z", "lastUpdate": "2025-06-18T12:31:48.762608Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "NextDenovo", "description": "NextDenovo is a string graph-based de novo assembler for long reads (CLR, HiFi and ONT). It uses a \"correct-then-assemble\" strategy similar to canu (no correction step for PacBio Hifi reads), but requires significantly less computing resources and storages.", "homepage": "https://github.com/Nextomics/NextDenovo", "biotoolsID": "nextdenovo", "biotoolsCURIE": "biotools:nextdenovo", "version": [ "v.2.5.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0924", "term": "Sequence trace" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0925", "term": "Sequence assembly" }, "format": [ { "uri": "http://edamontology.org/format_2561", "term": "Sequence assembly format (text)" }, { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" } ], "operatingSystem": [], "language": [ "Python", "C" ], "license": "GPL-3.0", "collectionID": [ "ONTeater" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Nextomics/NextDenovo/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/Nextomics/NextDenovo/releases/tag/2.5.2", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://nextdenovo.readthedocs.io/en/latest/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1101/2023.03.09.531669.", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Nextomics", "email": "support@nextomics.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "Kigaard", "additionDate": "2021-05-26T21:13:32Z", "lastUpdate": "2025-06-18T12:20:23.607241Z", "editPermission": { "type": "group", "authors": [ "jw", "ELIXIR-CZ", "Keiler_Collier" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "quickmerge", "description": "Quickmerge is a program that uses complementary information from genomes assembled with long reads in order to improve contiguity, and works with assemblies derived from both Pacific Biosciences or Oxford Nanopore. Quickmerge will even work with hybrid assemblies made by combining long reads and Illumina short reads.", "homepage": "https://github.com/mahulchak/quickmerge", "biotoolsID": "quickmerge", "biotoolsCURIE": "biotools:quickmerge", "version": [ "v.0.3" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" }, { "uri": "http://edamontology.org/operation_3216", "term": "Scaffolding" }, { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "note": "Runs whole merge process on an input assembly.\nAssembly 2 will be used to fill gaps in assembly 1.", "cmd": "merge_wrapper.py -pre output_prefix assembly_1.fa assembly_2.fa" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3175", "term": "Structural variation" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_2885", "term": "DNA polymorphism" }, { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "C++", "C" ], "license": "GPL-3.0", "collectionID": [ "ONTeater" ], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/mahulchak/quickmerge/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/mahulchak/quickmerge", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1534/g3.118.200162", "pmid": "30018084", "pmcid": "PMC6169397", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Rapid low-cost assembly of the drosophila melanogaster reference genome using low-coverage, long-read sequencing", "abstract": "Accurate and comprehensive characterization of genetic variation is essential for deciphering the genetic basis of diseases and other phenotypes. A vast amount of genetic variation stems from largescale sequence changes arising from the duplication, deletion, inversion, and translocation of sequences. In the past 10 years, high-throughput short reads have greatly expanded our ability to assay sequence variation due to single nucleotide polymorphisms. However, a recent de novo assembly of a second Drosophila melanogaster reference genome has revealed that short read genotyping methods miss hundreds of structural variants, including those affecting phenotypes. While genomes assembled using highcoverage long reads can achieve high levels of contiguity and completeness, concerns about cost, errors, and low yield have limited widespread adoption of such sequencing approaches. Here we resequenced the reference strain of D. melanogaster (ISO1) on a single Oxford Nanopore MinION flow cell run for 24 hr. Using only reads longer than 1 kb or with at least 30x coverage, we assembled a highly contiguous de novo genome. The addition of inexpensive paired reads and subsequent scaffolding using an optical map technology achieved an assembly with completeness and contiguity comparable to the D. melanogaster reference assembly. Comparison of our assembly to the reference assembly of ISO1 uncovered a number of structural variants (SVs), including novel LTR transposable element insertions and duplications affecting genes with developmental, behavioral, and metabolic functions. Collectively, these SVs provide a snapshot of the dynamics of genome evolution. Furthermore, our assembly and comparison to the D. melanogaster reference genome demonstrates that high-quality de novo assembly of reference genomes and comprehensive variant discovery using such assemblies are now possible by a single lab for under $1,000 (USD).", "date": "2018-10-01T00:00:00Z", "citationCount": 70, "authors": [ { "name": "Solares E.A." }, { "name": "Chakraborty M." }, { "name": "Miller D.E." }, { "name": "Kalsow S." }, { "name": "Hall K." }, { "name": "Perera A.G." }, { "name": "Emerson J.J." }, { "name": "Scott Hawley R." } ], "journal": "G3: Genes, Genomes, Genetics" } } ], "credit": [ { "name": "Mahul Chakraborty", "email": null, "url": "https://mahulchakraborty.wordpress.com/", "orcidid": "https://orcid.org/0000-0003-2414-9187", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "Kigaard", "additionDate": "2021-05-27T09:04:45Z", "lastUpdate": "2025-06-18T12:17:20.983962Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-CZ", "Keiler_Collier" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Pavian", "description": "Web application for exploring metagenomics classification results, with a special focus on infectious disease diagnosis. Pinpointing pathogens in metagenomics classification results is often complicated by host and laboratory contaminants as well as many non-pathogenic microbiota. Researchers can analyze, display and transform results from the Kraken and Centrifuge classifiers using interactive tables, heatmaps and flow diagrams.", "homepage": "https://ccb.jhu.edu/software/pavian/", "biotoolsID": "pavian", "biotoolsCURIE": "biotools:pavian", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0226", "term": "Annotation" }, { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [ "Animal and Crop Genomics", "Biodiversity", "microbiome" ], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/fbreitwieser/pavian/tree/master", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://ccb.jhu.edu/software/pavian/index.shtml?t=manual", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1101/084715", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": null, "email": "fbreitw1@jhmi.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "admin", "additionDate": "2017-08-20T16:34:27Z", "lastUpdate": "2025-06-17T15:12:54.095225Z", "editPermission": { "type": "public", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "wtv", "description": "A CLI/python library that selects characteristic qualitative ions based on \nhttps://github.com/yuanhonglun/WTV_2.0", "homepage": "https://recetox.github.io/wtv/", "biotoolsID": "wtv", "biotoolsCURIE": "biotools:wtv", "version": [ "0.0.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3936", "term": "Feature selection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0943", "term": "Mass spectrum" }, "format": [ { "uri": "http://edamontology.org/format_4039", "term": "MSP" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0943", "term": "Mass spectrum" }, "format": [ { "uri": "http://edamontology.org/format_4039", "term": "MSP" } ] } ], "note": null, "cmd": "wtv-cli --msp_path input.msp --outpath output --mz_min 35 --mz_max 400 ..." } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Emerging", "cost": null, "accessibility": null, "elixirPlatform": [ "Tools" ], "elixirNode": [ "Czech Republic" ], "elixirCommunity": [ "Metabolomics" ], "link": [ { "url": "https://github.com/RECETOX/wtv", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/RECETOX/wtv", "type": "Source code", "note": null, "version": null }, { "url": "https://pypi.org/project/wtv/", "type": "Software package", "note": null, "version": null }, { "url": "https://github.com/RECETOX/galaxytools/tree/master/tools/wtv", "type": "Tool wrapper (Galaxy)", "note": null, "version": null } ], "documentation": [ { "url": "https://recetox.github.io/wtv/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1016/j.molp.2024.04.012", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode", "abstract": "Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.", "date": "2024-06-03T00:00:00Z", "citationCount": 12, "authors": [ { "name": "Yuan H." }, { "name": "Jiangfang Y." }, { "name": "Liu Z." }, { "name": "Su R." }, { "name": "Li Q." }, { "name": "Fang C." }, { "name": "Huang S." }, { "name": "Liu X." }, { "name": "Fernie A.R." }, { "name": "Luo J." } ], "journal": "Molecular Plant" } } ], "credit": [], "owner": "acquayefrank", "additionDate": "2025-06-02T12:24:30.535794Z", "lastUpdate": "2025-06-12T09:26:29.793603Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Bracken", "description": "Bracken is a companion program to Kraken 1, KrakenUniq, or Kraken 2 While Kraken classifies reads to multiple levels in the taxonomic tree, Bracken allows estimation of abundance at a single level using those classifications (e.g. Bracken can estimate abundance of species within a sample).", "homepage": "https://ccb.jhu.edu/software/bracken/", "biotoolsID": "bracken", "biotoolsCURIE": "biotools:bracken", "version": [], "otherID": [], "relation": [ { "biotoolsID": "kraken2", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2238", "term": "Statistical calculation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" } ], "operatingSystem": [ "Linux" ], "language": [ "Perl", "Python" ], "license": "GPL-3.0", "collectionID": [ "Animal and Crop Genomics" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/jenniferlu717/Bracken", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/jenniferlu717/Bracken/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://ccb.jhu.edu/software/bracken/index.shtml?t=manual", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.7717/peerj-cs.104", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Bracken: Estimating species abundance in metagenomics data", "abstract": "Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomalRNAgene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.", "date": "2017-01-01T00:00:00Z", "citationCount": 1084, "authors": [ { "name": "Lu J." }, { "name": "Breitwieser F.P." }, { "name": "Thielen P." }, { "name": "Salzberg S.L." } ], "journal": "PeerJ Computer Science" } } ], "credit": [ { "name": null, "email": "jlu26@jhmi.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "admin", "additionDate": "2017-08-20T15:17:20Z", "lastUpdate": "2025-06-11T12:25:29.118910Z", "editPermission": { "type": "group", "authors": [ "animalandcropgenomics", "ELIXIR-CZ", "bebatut", "vashokan" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MALDIquant", "description": "MALDIquant is a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.", "homepage": "https://cran.r-project.org/package=MALDIquant", "biotoolsID": "maldi_quant", "biotoolsCURIE": "biotools:maldi_quant", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" }, { "uri": "http://edamontology.org/operation_3860", "term": "Spectrum calculation" }, { "uri": "http://edamontology.org/operation_3215", "term": "Peak detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3627", "term": "Mass spectra calibration" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library", "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/sgibb/MALDIquant/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/sgibb/MALDIquant/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://strimmerlab.org/software/maldiquant/", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [ { "url": "https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bts447", "pmid": "22796955", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Maldiquant: A versatile R package for the analysis of mass spectrometry data", "abstract": "MALDIquant is an R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution. © The Author 2012. Published by Oxford University Press. All rights reserved.", "date": "2012-09-01T00:00:00Z", "citationCount": 499, "authors": [ { "name": "Gibb S." }, { "name": "Strimmer K." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Sebastian Gibb", "email": "mail@sebastiangibb.de", "url": null, "orcidid": "https://orcid.org/0000-0001-7406-4443", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Korbinian Strimmer", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-7917-2056", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "Kigaard", "additionDate": "2021-05-26T20:42:18Z", "lastUpdate": "2025-06-05T11:28:57.497366Z", "editPermission": { "type": "group", "authors": [ "sizhengZhao" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM tags extractor", "description": "The tool scans the imaging DICOM files of a defined directory at the series level, and produces as output one single JSON file containing all the DICOM tags for each detected series.\nIf a list of selected DICOM tags is provided, the tool can also produce one single csv file containing the listed DICOM tags.\nTo use this tool, the user can download an executable file (.exe). Two execution modes are available : manual execution and command line execution.\nFor the tool to scan data successfully, it is important that : 1/ the extension “.dcm” is visible, as the tool only scans those files, and discard any other; 2/ each DICOM series has its own folder, as the tool gets a single .dcm file from each folder and extracts the tags.", "homepage": "https://www.bcplatforms.com/", "biotoolsID": "dicom_tags_extractor", "biotoolsCURIE": "biotools:dicom_tags_extractor", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0335", "term": "Data formatting" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [], "language": [], "license": "GPL-3.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.bcplatforms.com/", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories", "type": "Software package", "note": "1- Install ORAS tool (https://oras.land/docs/installation).\n2- Login in the registry using the command oras login harbor.eucaim.cancerimage.eu, and provide a username and a token\n3- Download the file by using the command oras pull harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_tag_extraction:1.0", "version": "1.0" } ], "documentation": [], "publication": [], "credit": [ { "name": "BC|Platforms", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "lsaint-aubert", "additionDate": "2025-04-18T15:04:18.411979Z", "lastUpdate": "2025-06-04T11:29:09.107990Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "bdct", "description": "Estimator of epidemiological parameters under the Birth-Death with Contact Tracing (BD-CT) model from phylogenetic trees and a non-parametric CT detection test.", "homepage": "https://github.com/evolbioinfo/bdct", "biotoolsID": "bdpn", "biotoolsCURIE": "biotools:bdpn", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3658", "term": "Statistical inference" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0872", "term": "Phylogenetic tree" }, "format": [ { "uri": "http://edamontology.org/format_1910", "term": "newick" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3305", "term": "Public health and epidemiology" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/evolbioinfo/bdct", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/evolbioinfo/bdct", "type": "Source code", "note": null, "version": null }, { "url": "https://pypi.org/project/bdct", "type": "Downloads page", "note": null, "version": null }, { "url": "https://hub.docker.com/r/evolbioinfo/bdct", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://pypi.org/project/bdct", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pcbi.1012461", "pmid": "40440423", "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Anna Zhukova", "email": "anna.zhukova@pasteur.fr", "url": "https://research.pasteur.fr/en/member/anna-zhukova/", "orcidid": "https://orcid.org/0000-0003-2200-7935", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "azhukova", "additionDate": "2024-09-06T14:19:58.602565Z", "lastUpdate": "2025-06-02T14:34:59.109392Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MuDoGeR", "description": "The Multi-Domain Genome Recovery v1.0 (MuDoGeR v1.0) framework is a tool developed to help users to recover Metagenome-Assembled Genomes and Uncultivated Viral Genomes from whole-genome sequence (WGS) samples simultaneously. The MuDoGeR v1.0 framework acts as a wrapper for several tools. It was designed to be an easy-to-use tool that outputs ready-to-use comprehensive files.", "homepage": "https://github.com/mdsufz/MuDoGeR", "biotoolsID": "mudoger", "biotoolsCURIE": "biotools:mudoger", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" }, { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Shell", "R", "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/mdsufz/MuDoGeR", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1111/1755-0998.13904", "pmid": "37994269", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy", "abstract": "Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.", "date": "2024-02-01T00:00:00Z", "citationCount": 5, "authors": [ { "name": "Rocha U." }, { "name": "CoelhoKasmanas J." }, { "name": "Kallies R." }, { "name": "Saraiva J.P." }, { "name": "Toscan R.B." }, { "name": "Stefanic P." }, { "name": "Bicalho M.F." }, { "name": "BorimCorrea F." }, { "name": "Basturk M.N." }, { "name": "Fousekis E." }, { "name": "VianaBarbosa L.M." }, { "name": "Plewka J." }, { "name": "Probst A.J." }, { "name": "Baldrian P." }, { "name": "Stadler P.F." } ], "journal": "Molecular Ecology Resources" } } ], "credit": [ { "name": "Ulisses Rocha", "email": "ulisses.rocha@ufz.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Pub2Tools", "additionDate": "2024-04-16T14:14:05.166974Z", "lastUpdate": "2025-04-09T07:23:51.614291Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "CheckM2", "description": "Rapid assessment of genome bin quality using machine learning.\n\nCheckM2 uses two distinct machine learning models to predict genome completeness. The 'general' gradient boost model is able to generalize well and is intended to be used on organisms not well represented in GenBank or RefSeq (roughly, when an organism is novel at the level of order, class or phylum). The 'specific' neural network model is more accurate when predicting completeness of organisms more closely related to the reference training set (roughly, when an organism belongs to a known species, genus or family). CheckM2 uses a cosine similarity calculation to automatically determine the appropriate completeness model for each input genome, but you can also force the use of a particular completeness model, or get the prediction outputs for both. There is only one contamination model (based on gradient boost) which is applied regardless of taxonomic novelty and works well across all cases.", "homepage": "https://github.com/chklovski/CheckM2", "biotoolsID": "checkm2", "biotoolsCURIE": "biotools:checkm2", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_0194", "term": "Phylogenomics" }, { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" } ], "operatingSystem": [ "Linux" ], "language": [], "license": "GPL-3.0", "collectionID": [ "NFDI4Microbiota" ], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.nature.com/articles/s41592-023-01940-w", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://doi.org/10.5281/zenodo.14897628", "type": "Biological data", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/chklovski/CheckM2", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1038/s41592-023-01940-w", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "Kasmanas", "additionDate": "2025-04-08T11:23:25.060891Z", "lastUpdate": "2025-04-08T11:23:25.063938Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "csdR", "description": "Differential gene coexpression analysis based on the Conserved, Specific, and Differentiated (CSD) method", "homepage": "https://almaaslab.github.io/csdR/", "biotoolsID": "csdr", "biotoolsCURIE": "biotools:csdr", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0204", "term": "Gene regulation" }, { "uri": "http://edamontology.org/topic_0085", "term": "Functional genomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.bioconductor.org/packages/release/bioc/html/csdR.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://github.com/AlmaasLab/csdR", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s12859-022-04605-1", "pmid": "35183100", "pmcid": "PMC8858518", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "csdR, an R package for differential co-expression analysis", "abstract": "Background: Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation. Results: We created the R-package csdR aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. csdR was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. csdR was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than ∼ 2.7 parallel speedup with saturation reached at about 10 cores. Conclusion: The results suggest that csdR is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.", "date": "2022-12-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Pettersen J.P." }, { "name": "Almaas E." } ], "journal": "BMC Bioinformatics" } } ], "credit": [], "owner": "japet", "additionDate": "2025-03-31T08:50:20.800385Z", "lastUpdate": "2025-03-31T09:00:57.202555Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DeepAnnotation", "description": "The python package 'DeepAnnotation' can be used to perform genomic selection (GS), which is a promising breeding strategy for agricultural breeding. 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Spacedust adapts the fast and sensitive structure comparisons of Foldseek and homology search capabilities of MMseqs2. 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However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers. The chewBBACA software uses Python 3.4 or higher and can run on a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. 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When the paper is published it will be updated", "metadata": null } ], "credit": [ { "name": "Sonia Tarazona", "email": "sotacam@eio.upv.es", "url": null, "orcidid": "https://orcid.org/0000-0001-5346-1407", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer" ], "note": null } ], "owner": "biostatomics1", "additionDate": "2025-03-03T13:46:56.613465Z", "lastUpdate": "2025-03-04T15:12:34.696650Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MetIDfyR", "description": "Open-Source R Package to Decipher Small-Molecule Drugs Metabolism Through High Resolution Mass Spectrometry.\n\nMetIDfyR is an open-source, cross-platform and versatile R script to predict and detect metabolites in mass spectrometry data (mzML) based on the raw formula of the drug of interest.", "homepage": "https://github.com/agnesbrnb/MetIDfyR", "biotoolsID": "metidfyr", "biotoolsCURIE": "biotools:metidfyr", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3803", "term": "Natural product identification" }, { "uri": "http://edamontology.org/operation_3454", "term": "Phasing" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3244", "term": "mzML" } ] }, { "data": { "uri": "http://edamontology.org/data_0846", "term": "Chemical formula" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3604", "term": "SVG" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Script" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3375", "term": "Drug metabolism" }, { "uri": "http://edamontology.org/topic_3370", "term": "Analytical chemistry" } ], "operatingSystem": [], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://github.com/agnesbrnb/MetIDfyR", "type": [ "Quick start guide", "Installation instructions", "Citation instructions" ], "note": null } ], "publication": [ { "doi": "10.1021/acs.analchem.0c02281", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "MetIDfyR: An Open-Source R Package to Decipher Small-Molecule Drug Metabolism through High-Resolution Mass Spectrometry", "abstract": "With recent advances in analytical chemistry, liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) has become an essential tool for metabolite discovery and detection. Even if most of the common drug transformations have already been extensively described, manual search of drug metabolites in LC-HRMS/MS datasets is still a common practice in toxicology laboratories, complicating metabolite discovery. Furthermore, the availability of free open-source software for metabolite discovery is still limited. In this article, we present MetIDfyR, an open-source and cross-platform R package for in silico drug phase I/II biotransformation prediction and mass-spectrometric data mining. MetIDfyR has proven its efficacy for advanced metabolite identification in semi-complex and complex mixtures in in vitro or in vivo drug studies and is freely available at github.com/agnesblch/MetIDfyR.", "date": "2020-10-06T00:00:00Z", "citationCount": 10, "authors": [ { "name": "Delcourt V." }, { "name": "Barnabe A." }, { "name": "Loup B." }, { "name": "Garcia P." }, { "name": "Andre F." }, { "name": "Chabot B." }, { "name": "Trevisiol S." }, { "name": "Moulard Y." }, { "name": "Popot M.-A." }, { "name": "Bailly-Chouriberry L." } ], "journal": "Analytical Chemistry" } } ], "credit": [], "owner": "agnesbarnabe", "additionDate": "2021-01-18T09:08:27Z", "lastUpdate": "2025-02-27T15:46:01.807140Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MAGNETO", "description": "An automated workflow for genome-resolved metagenomics", "homepage": "https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto", "biotoolsID": "magneto", "biotoolsCURIE": "biotools:magneto", "version": [ "1.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" }, { "uri": "http://edamontology.org/operation_3219", "term": "Read pre-processing" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" }, { "uri": "http://edamontology.org/operation_2454", "term": "Gene prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" }, { "uri": "http://edamontology.org/format_3989", "term": "GZIP format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1872", "term": "Taxonomic classification" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_0916", "term": "Gene report" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": "magneto run all --profile config/slurm/ --config target=single_assembly --rerun-incomplete" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3050", "term": "Biodiversity" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto/-/releases/1.2", "type": "Source code", "note": null, "version": "1.2" }, { "url": "https://anaconda.org/bioconda/magneto", "type": "Software package", "note": null, "version": "1.2" } ], "documentation": [ { "url": "https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto/-/wikis/home", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1128/msystems.00432-22", "pmid": "35703559", "pmcid": "PMC9426564", "type": [], "version": null, "note": null, "metadata": { "title": "MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics", "abstract": "Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAG reconstruction but also emphasized several limitations, such as the nonbinning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used; however, it still remains unclear which assembly strategy, in combination with which binning approach, offers the best performance for MAG recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to coassemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets. We propose a novel approach to automate coassembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous coassembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing coassembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.", "date": "2022-08-01T00:00:00Z", "citationCount": 8, "authors": [ { "name": "Churcheward B." }, { "name": "Millet M." }, { "name": "Bihouee A." }, { "name": "Fertin G." }, { "name": "Chaffron S." } ], "journal": "mSystems" } } ], "credit": [ { "name": "Samuel Chaffron", "email": "samuel.chaffron@ls2n.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Benjamin Churcheward", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Audrey Bihouée", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Guillaume Fertin", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Hugo Lefeuvre", "email": "hugo.lefeuvre@univ-nantes.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "Jennifer", "additionDate": "2022-08-28T00:23:42.459646Z", "lastUpdate": "2025-02-24T16:29:20.503951Z", "editPermission": { "type": "group", "authors": [ "hugo-lefeuvre" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ENQUIRE", "description": "ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers an alternative to manual literature curation and database mining to study complex biomedical phenomena. ENQUIRE generates a co-occurrence network of genes and biomedical ontologies (MeSH) using a corpus of publications as input. 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Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based \"eigen-molecules\" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. 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Although ESI spectra of multiply charged protein molecules also can be measured on this type of devices, only average spectra are produced for the majority of naturally occurring proteins. Evaluating such ESI protein spectra would provide valuable information about the native state of investigated proteins. However, no suitable and freely available software could be found which allows the charge state determination and molecular weight calculation of single proteins from average ESI-MS data. Therefore, an algorithm based on standard deviation optimization (scatter minimization) was implemented for the analysis of protein ESI-MS data. The resulting software ESIprot was tested with ESI-MS data of six intact reference proteins between 12.4 and 66.7kDa. In all cases, the correct charge states could be determined. The obtained absolute mass errors were in a range between -0.2 and 1.2Da, the relative errors below 30ppm. The possible mass accuracy allows for valid conclusions about the actual condition of proteins. Moreover, the ESIprot algorithm demonstrates an extraordinary robustness and allows spectral interpretation from as little as two peaks, given sufficient quality of the provided m/z data, without the necessity for peak intensity data. ESIprot is independent from the raw data format and the computer platform, making it a versatile tool for mass spectrometrists. 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