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GET /api/t/?toolType=%22Command-line+tool%22
{ "count": 9469, "next": "?page=2", "previous": null, "list": [ { "name": "SERtool", "description": "A tool for find repetitive sequences or sequence enrichment regions", "homepage": "https://github.com/rennmeng/SERtool", "biotoolsID": "sertool", "biotoolsCURIE": "biotools:sertool", "version": [ "1.1" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Bioinformatics portal", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0157", "term": "Sequence composition, complexity and repeats" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "Rust" ], "license": "MIT", "collectionID": [], "maturity": "Legacy", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/rennmeng/SERtool", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "rennmeng", "additionDate": "2025-09-25T00:18:49.762990Z", "lastUpdate": "2025-09-25T00:29:07.274216Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Constava", "description": "Constava calculates conformational states probability and conformational state variability from protein structure ensembles.", "homepage": "https://pypi.org/project/constava/", "biotoolsID": "constava", "biotoolsCURIE": "biotools:constava", "version": [ "1.1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0249", "term": "Protein geometry calculation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2991", "term": "Protein geometry data" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2016", "term": "Amino acid property" }, "format": [] } ], "note": "This command analyzes the provided backbone dihedral angles and infers the propensities for each residue to reside in a given conformational state.", "cmd": "constava analyze" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0130", "term": "Protein folding, stability and design" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/bio2byte/constava", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/Bio2Byte/constava/releases/tag/v1.1.0", "type": "Source code", "note": "Source code in tar.gz or zip formats.", "version": "1.1.0" } ], "documentation": [ { "url": "https://pypi.org/project/constava/", "type": [ "Command-line options" ], "note": "Table of content: \nDescription\nInstallation\nPrerequisites\nInstallation through PyPI\nInstallation from the source\nUsage\nExecution from the command line\nExtracting backbone dihedrals from a trajectory\nAnalyzing a conformational ensemble\nGenerating custom conformational state models\nExecution as a python library\nExtracting backbone dihedrals as a DataFrame\nSetting parameters and analyzing a conformational ensemble\nGenerating and loading conformational state models\nConstava-class parameters vs. command line arguments\nLicense\nAuthors\nAcknowledgements\nContact" } ], "publication": [ { "doi": "10.1093/nargab/lqae082", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Data-driven probabilistic definition of the low energy conformational states of protein residues", "abstract": "", "date": "2024-09-01T00:00:00Z", "citationCount": 2, "authors": [], "journal": "NAR Genomics and Bioinformatics" } }, { "doi": "10.1016/j.jmb.2024.168900", "pmid": null, "pmcid": null, "type": [ "Usage" ], "version": null, "note": null, "metadata": { "title": "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics", "abstract": "", "date": "2025-01-15T00:00:00Z", "citationCount": 3, "authors": [], "journal": "Journal of Molecular Biology" } } ], "credit": [ { "name": "Wim Vranken", "email": "wim.vranken@vub.be", "url": "https://bio2byte.be/people/31", "orcidid": "https://orcid.org/0000-0001-7470-4324", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "W.V., D.R. and G.O. conceptualised the study. W.V., D.R., G.O. and D.B. provided supervision. W.V. provided the NMR data and directed the project." }, { "name": "José Gavalda-Garcia", "email": null, "url": "https://bio2byte.be/people/38", "orcidid": "https://orcid.org/0000-0001-6431-3442", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "J.G.-G. and D.B. developed, implemented and validated the method." }, { "name": "David Bickel", "email": null, "url": "https://bio2byte.be/people/47", "orcidid": "https://orcid.org/0000-0003-0332-8338", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "W.V., D.R., G.O. and D.B. provided supervision. J.G.-G. and D.B. developed, implemented and validated the method." }, { "name": "Adrián Díaz", "email": "adrian.diaz@vub.be", "url": "https://bio2byte.be/people/48", "orcidid": "https://orcid.org/0000-0003-0165-1318", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Maintainer" ], "note": "A.D. contributed in the distribution of this software." }, { "name": "Daniele Raimondi", "email": null, "url": "https://orcid.org/0000-0003-1157-1899", "orcidid": "https://orcid.org/0000-0003-1157-1899", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": "W.V., D.R. and G.O. conceptualised the study. W.V., D.R., G.O. and D.B. provided supervision." }, { "name": "Gabriele Orlando", "email": null, "url": "https://orcid.org/0000-0002-5935-5258", "orcidid": "https://orcid.org/0000-0002-5935-5258", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": "W.V., D.R. and G.O. conceptualised the study. G.O. developed the initial methodology. W.V., D.R., G.O. and D.B. provided supervision." }, { "name": "Bio2Byte", "email": "bio2byte@vub.be", "url": "https://bio2byte.be", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null }, { "name": "Joel Roca-Martinez", "email": null, "url": "https://orcid.org/0000-0002-4313-3845", "orcidid": "https://orcid.org/0000-0002-4313-3845", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": "J.R.-M. performed the MD simulations." } ], "owner": "adrian.diaz", "additionDate": "2025-09-23T08:54:29.614497Z", "lastUpdate": "2025-09-23T08:54:29.626822Z", "editPermission": { "type": "group", "authors": [ "wvranken@vub.ac.be", "adrian.diaz" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "picnic-bio", "description": "PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based model that predicts proteins involved in biomolecular condensates. 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Overall, we recommend using PICNIC that is an unbiased predictor, and using PICNIC-GO for specific cases, for example for experimental hypothesis generation.", "homepage": "https://git.mpi-cbg.de/tothpetroczylab/picnic", "biotoolsID": "picnic-bio", "biotoolsCURIE": "biotools:picnic-bio", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "Python" ], "license": "CC-BY-SA-4.0", "collectionID": [ "biomolecular-condensates" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://picnic-bio.org/", "type": [ "Service" ], "note": "Web server implementation of the CLI tool." } ], "download": [ { "url": "https://git.mpi-cbg.de/tothpetroczylab/picnic/-/releases", "type": "Downloads page", "note": null, "version": "1.0.0" } ], "documentation": [ { "url": "https://git.mpi-cbg.de/tothpetroczylab/picnic/-/blob/main/README.md", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1038/s41467-024-55089-x", "pmid": "39663388", "pmcid": "PMC11634905", "type": [ "Method" ], "version": "1.0.0", "note": null, "metadata": { "title": "PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms", "abstract": "", "date": "2024-12-01T00:00:00Z", "citationCount": 8, "authors": [], "journal": "Nature Communications" } } ], "credit": [ { "name": "Anna Hadarovich", "email": "hadarovi@mpi-cbg.de", "url": null, "orcidid": "https://orcid.org/0000-0002-5139-4308", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact" ], "note": null } ], "owner": "atplab-admin", "additionDate": "2025-09-23T08:51:10.252083Z", "lastUpdate": "2025-09-23T08:51:32.053381Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "TwistMethylFlow", "description": "This Nextflow pipeline is designed for the analysis of Twist NGS Methylation data, including quality control, alignment, methylation calling, differential methylation analysis, and post-processing. 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GFFx introduces a compact, model-aware indexing system inspired by binning strategies and leverages Rust’s strengths in execution speed, memory safety, and multithreading. 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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": "img2catalog", "description": "This tool, 'img2catalog', is built to extract metadata from imaging data repositories, map it to a model class, and export it to a catalog. 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This tool is no longer maintained. See the new version: PARROT-FJD.", "homepage": "https://github.com/TBLabFJD/VariantCallingFJD", "biotoolsID": "fjd-pipeline", "biotoolsCURIE": "biotools:fjd-pipeline", "version": [], "otherID": [], "relation": [ { "biotoolsID": "priorr", "type": "usedBy" }, { "biotoolsID": "parrot-fjd", "type": "hasNewVersion" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" }, { "uri": "http://edamontology.org/operation_3961", "term": "Copy number variation detection" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1193", "term": "Tool name (FASTA)" }, "format": [ { "uri": "http://edamontology.org/format_2545", "term": "FASTQ-like format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3498", "term": "Sequence variations" }, "format": [ { "uri": "http://edamontology.org/format_3016", "term": "VCF" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "R", "Bash" ], "license": "CC-BY-NC-SA-4.0", "collectionID": [ "IMPaCT-Data" ], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/TBLabFJD/VariantCallingFJD", "type": [ "Repository" ], "note": "GitHub repository" } ], "download": [], "documentation": [ { "url": "https://github.com/TBLabFJD/VariantCallingFJD", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/s41525-021-00278-6", "pmid": "35087072", "pmcid": "PMC8795168", "type": [ "Usage" ], "version": null, "note": null, "metadata": { "title": "An evaluation of pipelines for DNA variant detection can guide a reanalysis protocol to increase the diagnostic ratio of genetic diseases", "abstract": "Clinical exome (CE) sequencing has become a first-tier diagnostic test for hereditary diseases; however, its diagnostic rate is around 30–50%. In this study, we aimed to increase the diagnostic yield of CE using a custom reanalysis algorithm. Sequencing data were available for three cohorts using two commercial protocols applied as part of the diagnostic process. Using these cohorts, we compared the performance of general and clinically relevant variant calling and the efficacy of an in-house bioinformatic protocol (FJD-pipeline) in detecting causal variants as compared to commercial protocols. On the whole, the FJD-pipeline detected 99.74% of the causal variants identified by the commercial protocol in previously solved cases. In the unsolved cases, FJD-pipeline detects more INDELs and non-exonic variants, and is able to increase the diagnostic yield in 2.5% and 3.2% in the re-analysis of 78 cancer and 62 cardiovascular cases. These results were considered to design a reanalysis, filtering and prioritization algorithm that was tested by reassessing 68 inconclusive cases of monoallelic autosomal recessive retinal dystrophies increasing the diagnosis by 4.4%. In conclusion, a guided NGS reanalysis of unsolved cases increases the diagnostic yield in genetic disorders, making it a useful diagnostic tool in medical genetics.", "date": "2022-12-01T00:00:00Z", "citationCount": 12, "authors": [ { "name": "Romero R." }, { "name": "de la Fuente L." }, { "name": "Del Pozo-Valero M." }, { "name": "Riveiro-Alvarez R." }, { "name": "Trujillo-Tiebas M.J." }, { "name": "Martin-Merida I." }, { "name": "Avila-Fernandez A." }, { "name": "Iancu I.-F." }, { "name": "Perea-Romero I." }, { "name": "Nunez-Moreno G." }, { "name": "Damian A." }, { "name": "Rodilla C." }, { "name": "Almoguera B." }, { "name": "Corton M." }, { "name": "Ayuso C." }, { "name": "Minguez P." } ], "journal": "npj Genomic Medicine" } } ], "credit": [ { "name": "Bioinformatics Unit IIS-FJD", "email": "pablo.minguez@quironsalud.es", "url": "https://www.translationalbioinformaticslab.es/", "orcidid": "https://orcid.org/0000-0003-4099-9421", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "iiancu", "additionDate": "2022-03-21T17:29:40.559556Z", "lastUpdate": "2025-08-12T08:31:29.574795Z", "editPermission": { "type": "group", "authors": [ "gonumo", "pminguez", "yolandabq" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "GENEX workflow", "description": "This is a computational workflow for detecting coordinates of microbial-like or human-like sequences in eukaryotic and prokaryotic reference genomes. The workflow accepts a reference genome in FASTA-format and outputs coordinates of microbial-like (human-like) regions in BED-format. The workflow builds a Bowtie2 index of the reference genome and aligns pre-computed microbial (GTDB v.214 or NCBI RefSeq release 213) or human (hg38) pseudo-reads to the reference, then custom scripts are used for detection of the positions of covered regions and quantification of most abundant microbial species, the latter is only when screening for microbial-like sequences in eukaryotic references.", "homepage": "https://github.com/NikolayOskolkov/MCWorkflow", "biotoolsID": "genex_workflow", "biotoolsCURIE": "biotools:genex_workflow", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" } ], "operatingSystem": [], "language": [], "license": "CC0-1.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge (with restrictions)", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/NikolayOskolkov/MCWorkflow", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/2025.03.19.644176", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "Nikolay_Oskolkov", "additionDate": "2025-08-10T14:15:14.443314Z", "lastUpdate": "2025-08-10T14:20:33.908626Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "hictk", "description": "Blazing fast toolkit to work with .hic and .cool files", "homepage": "https://github.com/paulsengroup/hictk", "biotoolsID": "hictk", "biotoolsCURIE": "biotools:hictk", "version": [ "1.0.0", "2.0.0", "2.0.1", "2.0.2", "2.1.0", "2.1.1", "2.1.2", "2.1.3", "2.1.4" ], "otherID": [], "relation": [ { "biotoolsID": "hictkpy", "type": "usedBy" }, { "biotoolsID": "hictkr", "type": "usedBy" } ], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [], "license": "MIT", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/paulsengroup/hictk", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/paulsengroup/hictk/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/paulsengroup/hictk/discussions", "type": [ "Helpdesk" ], "note": null } ], "download": [ { "url": "https://github.com/paulsengroup/hictk/releases", "type": "Downloads page", "note": null, "version": null }, { "url": "https://anaconda.org/bioconda/hictk", "type": "Binaries", "note": null, "version": null }, { "url": "https://github.com/paulsengroup/hictk/pkgs/container/hictk", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://hictk.readthedocs.io", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btae408", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "hictk: blazing fast toolkit to work with. hic and. cool files", "abstract": "Motivation: Hi-C is gaining prominence as a method for mapping genome organization. With declining sequencing costs and a growing demand for higher-resolution data, efficient tools for processing Hi-C datasets at different resolutions are crucial. Over the past decade, the. hic and Cooler file formats have become the de-facto standard to store interaction matrices produced by Hi-C experiments in binary format. Interoperability issues make it unnecessarily difficult to convert between the two formats and to develop applications that can process each format natively. Results: We developed hictk, a toolkit that can transparently operate on. hic and. cool files with excellent performance. The toolkit is written in C++ and consists of a C++ library with Python and R bindings as well as CLI tools to perform common operations directly from the shell, including converting between. hic and. mcool formats. We benchmark the performance of hictk and compare it with other popular tools and libraries. We conclude that hictk significantly outperforms existing tools while providing the flexibility of natively working with both file formats without code duplication.", "date": "2024-07-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Rossini R." }, { "name": "Paulsen J." } ], "journal": "Bioinformatics" } } ], "credit": [], "owner": "robomics", "additionDate": "2024-02-02T14:42:46.030561Z", "lastUpdate": "2025-08-04T21:43:36.678557Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EukRep", "description": "Classification of Eukaryotic and Prokaryotic sequences from metagenomic datasets", "homepage": "https://github.com/patrickwest/EukRep", "biotoolsID": "eukrep", "biotoolsCURIE": "biotools:eukrep", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2478", "term": "Nucleic acid sequence analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/gr.228429.117", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Genome-reconstruction for eukaryotes from complex natural microbial communities", "abstract": "Microbial eukaryotes are integral components of natural microbial communities, and their inclusion is critical for many ecosystem studies, yet the majority of published metagenome analyses ignore eukaryotes. In order to include eukaryotes in environmental studies, we propose a method to recover eukaryotic genomes from complex metagenomic samples. A key step for genome recovery is separation of eukaryotic and prokaryotic fragments. We developed a k-mer-based strategy, EukRep, for eukaryotic sequence identification and applied it to environmental samples to show that it enables genome recovery, genome completeness evaluation, and prediction of metabolic potential. We used this approach to test the effect of addition of organic carbon on a geyser-associated microbial community and detected a substantial change of the community metabolism, with selection against almost all candidate phyla bacteria and archaea and for eukaryotes. Near complete genomes were reconstructed for three fungi placed within the Eurotiomycetes and an arthropod. While carbon fixation and sulfur oxidation were important functions in the geyser community prior to carbon addition, the organic carbon-impacted community showed enrichment for secreted proteases, secreted lipases, cellulose targeting CAZymes, and methanol oxidation. We demonstrate the broader utility of EukRep by reconstructing and evaluating relatively high-quality fungal, protist, and rotifer genomes from complex environmental samples. This approach opens the way for cultivation-independent analyses of whole microbial communities.", "date": "2018-04-01T00:00:00Z", "citationCount": 143, "authors": [ { "name": "West P.T." }, { "name": "Probst A.J." }, { "name": "Grigoriev I.V." }, { "name": "Thomas B.C." }, { "name": "Banfield J.F." } ], "journal": "Genome Research" } } ], "credit": [], "owner": "m.bernt", "additionDate": "2025-07-30T10:37:08.226825Z", "lastUpdate": "2025-07-30T10:37:08.229304Z", "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. We demonstrate that our method outperforms current genome quality estimators, particularly for estimating contamination, and have applied EukCC to datasets derived from two different environments to enable the identification of novel eukaryote genomes, including one from the human skin.", "date": "2020-09-10T00:00:00Z", "citationCount": 70, "authors": [ { "name": "Saary P." }, { "name": "Mitchell A.L." }, { "name": "Finn R.D." } ], "journal": "Genome Biology" } } ], "credit": [], "owner": "m.bernt", "additionDate": "2025-07-24T07:14:51.692628Z", "lastUpdate": "2025-07-24T07:14:51.694965Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ETENLNC", "description": "ETENLNC (End-To-End-Novel-NonCoding) is a lncRNA identification and analysis framework", "homepage": "https://github.com/EvolOMICS-TU/ETENLNC", "biotoolsID": "etenlnc", "biotoolsCURIE": "biotools:etenlnc", "version": [ "1.0" ], "otherID": [], "relation": [ { "biotoolsID": "hisat2", "type": "uses" }, { "biotoolsID": "salmon", "type": "uses" }, { "biotoolsID": "fastqc", "type": "uses" }, { "biotoolsID": "fastp", "type": "uses" }, { "biotoolsID": "stringtie", "type": "uses" }, { "biotoolsID": "gffcompare", "type": "uses" }, { "biotoolsID": "cpc2", "type": "uses" }, { "biotoolsID": "ncbi_blast_plus", "type": "uses" }, { "biotoolsID": "tximport", "type": "uses" }, { "biotoolsID": "deseq2", "type": "uses" }, { "biotoolsID": "lnctar", "type": "uses" }, { "biotoolsID": "capsule-lpi", "type": "uses" }, { "biotoolsID": "seekr", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3219", "term": "Read pre-processing" }, { "uri": "http://edamontology.org/operation_3218", "term": "Sequencing quality control" }, { "uri": "http://edamontology.org/operation_3258", "term": "Transcriptome assembly" }, { "uri": "http://edamontology.org/operation_0415", "term": "Nucleic acid feature 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"http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_1288", "term": "Genome map" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3002", "term": "Annotation track" }, "format": [ { "uri": "http://edamontology.org/format_2306", "term": "GTF" }, { "uri": "http://edamontology.org/format_2305", "term": "GFF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2887", "term": "Nucleic acid sequence record" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_1636", "term": "Heat map" }, "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": 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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": "nnUNet", "description": "nnU-Net is a self-configuring method for deep learning-based biomedical image segmentation, developed by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). It is designed to automatically adapt to a given dataset, analyzing the provided training cases to configure a matching U-Net-based segmentation pipeline without requiring expertise from the user.", "homepage": "https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1", "biotoolsID": "nnunet", "biotoolsCURIE": "biotools:nnunet", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/s41592-020-01008-z", "pmid": null, "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation", "abstract": "Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.", "date": "2021-02-01T00:00:00Z", "citationCount": 5362, "authors": [ { "name": "Isensee F." }, { "name": "Jaeger P.F." }, { "name": "Kohl S.A.A." }, { "name": "Petersen J." }, { "name": "Maier-Hein K.H." } ], "journal": "Nature Methods" } } ], "credit": [], "owner": "ASHISR", "additionDate": "2025-07-21T09:36:40.711284Z", "lastUpdate": "2025-07-21T15:06:33.111687Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "PhysiBoSS", "description": "A sustainable integration of stochastic Boolean and agent-based modelling frameworks.", "homepage": "https://github.com/PhysiBoSS/PhysiBoSS", "biotoolsID": "physiboss", "biotoolsCURIE": "biotools:physiboss", "version": [ 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Published by Oxford University Press.MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging - especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format.", "date": "2016-06-15T00:00:00Z", "citationCount": 7, "authors": [ { "name": "Gomez H.F." }, { "name": "Hucka M." }, { "name": "Keating S.M." }, { "name": "Nudelman G." }, { "name": "Iber D." }, { "name": "Sealfon S.C." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Michael Hucka,", "email": "mhucka@caltech.edu", "url": "https://github.com/sbmlteam/moccasin", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "frankbergmann", "additionDate": "2017-08-03T18:49:51Z", "lastUpdate": "2025-07-08T14:13:52.296025Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "COPASI", "description": "Open-source software application for creating and solving mathematical models of biological processes such as metabolic networks, cell-signaling pathways, regulatory networks, infectious diseases, and many others. It includes features to define models of biological processes, simulate and analyze these models, generate analysis reports, and import/export models in SBML format.", "homepage": "http://copasi.org/", "biotoolsID": "copasi", "biotoolsCURIE": "biotools:copasi", "version": [], "otherID": [], "relation": [ { "biotoolsID": "corc", "type": "usedBy" }, { "biotoolsID": "pycotools", "type": "usedBy" }, { "biotoolsID": "biosimulations", "type": "includedIn" }, { "biotoolsID": "sbmlwebapp", "type": "usedBy" }, { "biotoolsID": "libsbml", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3562", "term": "Network simulation" }, { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" }, { "uri": "http://edamontology.org/operation_3660", "term": "Metabolic network modelling" }, { "uri": "http://edamontology.org/operation_3926", "term": "Pathway visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2600", "term": "Pathway or network" }, "format": [ { "uri": "http://edamontology.org/format_2585", "term": "SBML" }, { "uri": "http://edamontology.org/format_3239", "term": "CopasiML" }, { "uri": "http://edamontology.org/format_3685", "term": "SED-ML" }, { "uri": "http://edamontology.org/format_3686", "term": "COMBINE OMEX" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2600", "term": "Pathway or network" }, "format": [ { "uri": "http://edamontology.org/format_2585", "term": "SBML" }, { "uri": "http://edamontology.org/format_3239", "term": "CopasiML" }, { "uri": "http://edamontology.org/format_3685", "term": "SED-ML" }, { "uri": "http://edamontology.org/format_3686", "term": "COMBINE OMEX" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Library", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_2259", "term": "Systems biology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C++" ], "license": "Artistic-2.0", "collectionID": [ "de.NBI", "EBI Training Tools" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Germany" ], "elixirCommunity": [], "link": [ { "url": "https://groups.google.com/g/copasi-user-forum", "type": [ "Discussion forum" ], "note": "User Forum" }, { "url": "http://tracker.copasi.org/", "type": [ "Issue tracker" ], "note": "Issue tracker" }, { "url": "https://github.com/copasi/COPASI", "type": [ "Repository" ], "note": "Github Repo" }, { "url": "https://fosstodon.org/@copasi", "type": [ "Social media" ], "note": null } ], "download": [ { "url": "http://copasi.org/Download/", "type": "Binaries", "note": "Source and binary packages are available for download.", "version": null } ], "documentation": [ { "url": "http://copasi.org/Support/User_Manual/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btl485", "pmid": "17032683", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "COPASI - A COmplex PAthway SImulator", "abstract": "Motivation: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Results: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. © 2006 Oxford University Press.", "date": "2006-12-15T00:00:00Z", "citationCount": 2004, "authors": [ { "name": "Hoops S." }, { "name": "Gauges R." }, { "name": "Lee C." }, { "name": "Pahle J." }, { "name": "Simus N." }, { "name": "Singhal M." }, { "name": "Xu L." }, { "name": "Mendes P." }, { "name": "Kummer U." } ], "journal": "Bioinformatics" } }, { "doi": "10.1007/978-1-59745-525-1_2", "pmid": "19399433", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Computational modeling of biochemical networks using COPASI", "abstract": "Computational modeling and simulation of biochemical networks is at the core of systems biology and this includes many types of analyses that can aid understanding of how these systems work. COPASI is a generic software package for modeling and simulation of biochemical networks which provides many of these analyses in convenient ways that do not require the user to program or to have deep knowledge of the numerical algorithms. Here we provide a description of how these modeling techniques can be applied to biochemical models using COPASI. The focus is both on practical aspects of software usage as well as on the utility of these analyses in aiding biological understanding. Practical examples are described for steady-state and time-course simulations, stoichiometric analyses, parameter scanning, sensitivity analysis (including metabolic control analysis), global optimization, parameter estimation, and stochastic simulation. The examples used are all published models that are available in the BioModels database in SBML format. © 2009 Humana Press.", "date": "2009-12-01T00:00:00Z", "citationCount": 171, "authors": [ { "name": "Mendes P." }, { "name": "Hoops S." }, { "name": "Sahle S." }, { "name": "Gauges R." }, { "name": "Dada J." }, { "name": "Kummer U." } ], "journal": "Methods in Molecular Biology" } }, { "doi": "10.1016/j.jbiotec.2017.06.1200", "pmid": "28655634", "pmcid": "PMC5623632", "type": [], "version": null, "note": null, "metadata": { "title": "COPASI and its applications in biotechnology", "abstract": "COPASI is software used for the creation, modification, simulation and computational analysis of kinetic models in various fields. It is open-source, available for all major platforms and provides a user-friendly graphical user interface, but is also controllable via the command line and scripting languages. These are likely reasons for its wide acceptance. We begin this review with a short introduction describing the general approaches and techniques used in computational modeling in the biosciences. Next we introduce the COPASI package, and its capabilities, before looking at typical applications of COPASI in biotechnology.", "date": "2017-11-10T00:00:00Z", "citationCount": 81, "authors": [ { "name": "Bergmann F.T." }, { "name": "Hoops S." }, { "name": "Klahn B." }, { "name": "Kummer U." }, { "name": "Mendes P." }, { "name": "Pahle J." }, { "name": "Sahle S." } ], "journal": "Journal of Biotechnology" } } ], "credit": [ { "name": null, "email": null, "url": "http://copasi.org/About/Team/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Frank T. Bergmann", "email": "frank.bergmann@bioquant.uni-heidelberg.de", "url": null, "orcidid": "https://orcid.org/0000-0001-5553-4702", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "frankbergmann", "additionDate": "2017-01-17T15:07:47Z", "lastUpdate": "2025-07-07T11:17:37.638447Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "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": "RepeatModeler2", "description": "RepeatModeler is a de novo transposable element (TE) family identification and modeling package. At the heart of RepeatModeler are three de-novo repeat finding programs ( RECON, RepeatScout and LtrHarvest/Ltr_retriever ) which employ complementary computational methods for identifying repeat element boundaries and family relationships from sequence data.", "homepage": "https://github.com/Dfam-consortium/RepeatModeler", "biotoolsID": "RepeatModeler2", "biotoolsCURIE": "biotools:RepeatModeler2", "version": [ "2.0.7" ], "otherID": [], "relation": [ { "biotoolsID": "repeatmodeler", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" }, { "uri": "http://edamontology.org/operation_3644", "term": "de Novo sequencing" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0157", "term": "Sequence composition, complexity and repeats" }, { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_0798", "term": "Mobile genetic elements" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "Perl" ], "license": "OSL-2.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Dfam-consortium/RepeatModeler/blob/master/LICENSE", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/856591", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "Pub2Tools", "additionDate": "2020-01-14T08:51:14Z", "lastUpdate": "2025-07-01T04:28:03.302129Z", "editPermission": { "type": "public", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Chopper", "description": "Rust implementation of NanoFilt+NanoLyse, both originally written in Python. This tool, intended for long read sequencing such as PacBio or ONT, filters and trims a fastq file.", "homepage": "https://github.com/wdecoster/chopper", "biotoolsID": "chopper", "biotoolsCURIE": "biotools:chopper", "version": [ "v0.10.0b" ], "otherID": [], "relation": [ { "biotoolsID": "nanopack", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3695", "term": "Data filtering" }, { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "note": null, "cmd": "gunzip -c reads.fastq.gz | chopper -q 10 -l 500 | gzip > filtered_reads.fastq.gz\nchopper -q 10 -l 500 -i reads.fastq > filtered_reads.fastq\nchopper -q 10 -l 500 -i reads.fastq.gz | gzip > filtered_reads.fastq.gz" }, { "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_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3579", "term": "JPG" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [], "license": "MIT", "collectionID": [ "ONTeater" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/wdecoster/chopper", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/bioinformatics/btad311", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "NanoPack2: population-scale evaluation of long-read sequencing data", "abstract": "Increases in the cohort size in long-read sequencing projects necessitate more efficient software for quality assessment and processing of sequencing data from Oxford Nanopore Technologies and Pacific Biosciences. Here, we describe novel tools for summarizing experiments, filtering datasets, visualizing phased alignments results, and updates to the NanoPack software suite. Availability and implementation: The cramino, chopper, kyber, and phasius tools are written in Rust and available as executable binaries without requiring installation or managing dependencies. Binaries build on musl are available for broad compatibility. NanoPlot and NanoComp are written in Python3. Links to the separate tools and their documentation can be found at https://github.com/wdecoster/nanopack. All tools are compatible with Linux, Mac OS, and the MS Windows Subsystem for Linux and are released under the MIT license. The repositories include test data, and the tools are continuously tested using GitHub Actions and can be installed with the conda dependency manager.", "date": "2023-05-01T00:00:00Z", "citationCount": 211, "authors": [ { "name": "De Coster W." }, { "name": "Rademakers R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Wouter de Coster", "email": "decosterwouter@gmail.com", "url": "https://gigabaseorgigabyte.wordpress.com", "orcidid": "https://orcid.org/0000-0002-5248-8197", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "Keiler_Collier", "additionDate": "2025-06-18T09:20:19.874306Z", "lastUpdate": "2025-06-30T15:46:01.095401Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Compleasm", "description": "Compleasm: a faster and more accurate reimplementation of BUSCO.\nIt provides measures for quantitative assessment of genome assembly, gene set, and transcriptome completeness based on evolutionarily informed expectations of gene content from near-universal single-copy orthologs.", "homepage": "https://github.com/huangnengCSU/compleasm", "biotoolsID": "compleasm", "biotoolsCURIE": "biotools:compleasm", "version": [ "v.0.2.5" ], "otherID": [], "relation": [ { "biotoolsID": "busco", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3180", "term": "Sequence assembly validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_2546", "term": "FASTA-like" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2955", "term": "Sequence report" }, "format": [] } ], "note": "Runs compleasm using the BUSCO set corresponding to the lineage given.", "cmd": "compleasm run -l \"$lineage\" -a assembly.fa -o output_prefix" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [ "ONTeater" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/huangnengCSU/compleasm", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/huangnengCSU/compleasm/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://busco.ezlab.org/list_of_lineages.html", "type": [ "Other" ], "note": "List of accepted lineages (taxonomic groups with curated BUSCO sets)" } ], "download": [], "documentation": [ { "url": "https://github.com/huangnengCSU/compleasm/blob/0.2.6/README.md", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btad595", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "compleasm: a faster and more accurate reimplementation of BUSCO", "abstract": "Motivation: Evaluating the gene completeness is critical to measuring the quality of a genome assembly. An incomplete assembly can lead to errors in gene predictions, annotation, and other downstream analyses. Benchmarking Universal Single-Copy Orthologs (BUSCO) is a widely used tool for assessing the completeness of genome assembly by testing the presence of a set of single-copy orthologs conserved across a wide range of taxa. However, BUSCO is slow particularly for large genome assemblies. It is cumbersome to apply BUSCO to a large number of assemblies. Results: Here, we present compleasm, an efficient tool for assessing the completeness of genome assemblies. Compleasm utilizes the miniprot protein-to-genome aligner and the conserved orthologous genes from BUSCO. It is 14 times faster than BUSCO for human assemblies and reports a more accurate completeness of 99.6% than BUSCO's 95.7%, which is in close agreement with the annotation completeness of 99.5% for T2T-CHM13.", "date": "2023-10-01T00:00:00Z", "citationCount": 95, "authors": [ { "name": "Huang N." }, { "name": "Li H." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Neng Huang", "email": "neng@ds.dfci.harvard.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7187-0749", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "rlibouban", "additionDate": "2024-03-18T14:51:49.667412Z", "lastUpdate": "2025-06-30T15:30:41.266812Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "LexicMap", "description": "LexicMap is a nucleotide sequence alignment tool for efficiently querying gene, plasmid, viral, or long-read sequences (>100 bp) against up to millions of prokaryotic genomes.", "homepage": "https://bioinf.shenwei.me/LexicMap/", "biotoolsID": "lexicmap", "biotoolsCURIE": "biotools:lexicmap", "version": [ "v0.7.0", "v0.6.1", "v0.6.0", "v0.5.0", "v0.4.0", "v0.3.0", "v0.2.0", "v0.1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [], "operatingSystem": [], "language": [ "Other" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/shenwei356/LexicMap", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://bioinf.shenwei.me/LexicMap/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.08.30.610459", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Wei Shen", "email": "shenwei356@gmail.com", "url": "http://shenwei.me", "orcidid": "https://orcid.org/0000-0002-8099-8258", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "smeds", "additionDate": "2025-06-30T15:03:36.801398Z", "lastUpdate": "2025-06-30T15:05:12.697639Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "FANTASIA", "description": "FANTASIA (Functional ANnoTAtion based on embedding space SImilArity) is a pipeline for annotating GO terms in protein sequence files using GOPredSim with the protein language model ProtT5", "homepage": "https://github.com/MetazoaPhylogenomicsLab/FANTASIA", "biotoolsID": "fantasia", "biotoolsCURIE": "biotools:fantasia", "version": [ "2.1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0324", "term": "Phylogenetic analysis" }, { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1481", "term": "Protein structure alignment" }, "format": [] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/2024.02.28.582465", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.1101/2024.02.14.580341", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "arojas", "additionDate": "2024-07-12T13:51:31.192159Z", "lastUpdate": "2025-06-28T15:45:24.390300Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "FANTASIAV2", "description": "FANTASIA (Functional ANnoTAtion based on embedding space SImilArity) is a pipeline for protein annotating via GO term transference using the embedding space. FANTASIA’s latest developments include additional protein language models and provide enhanced functionalities.", "homepage": "https://github.com/CBBIO/FANTASIA", "biotoolsID": "fantasiav2", "biotoolsCURIE": "biotools:fantasiav2", "version": [ "2.8.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3945", "term": "Molecular evolution" }, { "uri": "http://edamontology.org/topic_0218", "term": "Natural language processing" }, { "uri": "http://edamontology.org/topic_0085", "term": "Functional genomics" }, { "uri": "http://edamontology.org/topic_4010", "term": "Open science" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "SQL" ], "license": "MIT", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Spain" ], "elixirCommunity": [ "3D-BioInfo", "Proteomics" ], "link": [ { "url": "https://github.com/CBBIO/FANTASIA", "type": [ "Repository" ], "note": "Main repository contains documentation from latest version" }, { "url": "https://github.com/MetazoaPhylogenomicsLab/FANTASIA", "type": [ "Repository" ], "note": "Main repository contains documentation from linitial version based on Bioembeddings implementation." }, { "url": "https://www.earthbiogenome.org/report-on-annotation-recommended-tools", "type": [ "Software catalogue" ], "note": "Recommended tool for the Earth Biogenome Project" } ], "download": [], "documentation": [ { "url": "https://fantasia.readthedocs.io/en/latest/", "type": [ "General" ], "note": "Full documetnation with user cases, benchmarking, and cluster implementations" } ], "publication": [ { "doi": "10.1093/nargab/lqae078", "pmid": null, "pmcid": null, "type": [ "Usage" ], "version": null, "note": null, "metadata": { "title": "Decoding functional proteome information in model organisms using protein language models", "abstract": "Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning-based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein language models are more precise and informative than deep learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomic experiments. The results obtained indicate that these language models are likely to be suitable for large-scale annotation and downstream analyses, and we recommend a guide for their use.", "date": "2024-09-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Barrios-Nunez I." }, { "name": "Martinez-Redondo G.I." }, { "name": "Medina-Burgos P." }, { "name": "Cases I." }, { "name": "Fernandez R." }, { "name": "Rojas A.M." } ], "journal": "NAR Genomics and Bioinformatics" } }, { "doi": "10.1101/2024.02.28.582465", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Francisco Miguel Pérez Canales", "email": "fmpercan@upo.es", "url": "http://www.bioinfocb.es/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Maintainer", "Support", "Provider" ], "note": "Programmer" }, { "name": "Ana M Rojas Mendoza", "email": "a.rojas.m@csic.es", "url": "http://www.bioinfocb.es/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Contributor", "Documentor", "Support" ], "note": "Scientific concept and functionalities" }, { "name": "Rosa Fernandez", "email": "rosa.fernandez@ibe.upf-csic.es", "url": "https://www.metazomics.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Contributor", "Documentor" ], "note": "Scientific concept and functionalities" }, { "name": "Francisco J. Ruiz Mota", "email": "fraruimot@alum.us.es", "url": "http://www.bioinfocb.es/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": "Junior developer" }, { "name": "Gemma Martinez Redondo", "email": "gemma.martinez@ibe.upf-csic.es", "url": "https://www.metazomics.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor", "Developer" ], "note": "Contributed as developer of the first version of FANTASIA V1" } ], "owner": "arojas", "additionDate": "2025-06-25T13:31:29.384393Z", "lastUpdate": "2025-06-25T14:34:50.629045Z", "editPermission": { "type": "group", "authors": [ "frapercan" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ONCOCNV", "description": "Package to detect copy number changes in Deep Sequencing data developed by OncoDNA.", "homepage": "https://oncocnv.curie.fr/", "biotoolsID": "oncocnv", "biotoolsCURIE": "biotools:oncocnv", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R", "Perl" ], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/oncocnv-5-4-detection-of-copy-number-changes-in-deep-sequencing-data.html", "type": [ "Software catalogue" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/BoevaLab/ONCOCNV/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btu436", "pmid": "25016581", "pmcid": "PMC4253825", "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": null, "email": "infos@oncodna.com", "url": "http://www.oncodna.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Valentina Boeva", "email": "valentina.boeva@curie.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "aminoacid", "additionDate": "2017-08-03T18:53:02Z", "lastUpdate": "2025-06-25T13:52:15.437225Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DeepSig", "description": 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[ { "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": "Breast Dense Tissue Segmentation", "description": "The tool takes a digital mammogram and performs an automatic segmentation of the breast area and the dense tissue. After the mammogram segmentation, the tool returns a DICOM-SEG image with both the dense tissue and the breast tissue mask combined.", "homepage": "https://pubmed.ncbi.nlm.nih.gov/36010173/", "biotoolsID": "breast_dense_tissue_segmentation", "biotoolsCURIE": "biotools:breast_dense_tissue_segmentation", "version": [ "v1.0.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [], "language": [], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/diagnostics12081822", "pmid": "36010173", "pmcid": "PMC9406546", "type": [], "version": null, "note": null, "metadata": { "title": "Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach", "abstract": "Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82 ± 0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of (0.76 ± 0.17). An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.", "date": "2022-08-01T00:00:00Z", "citationCount": 5, "authors": [ { "name": "Larroza A." }, { "name": "Perez-Benito F.J." }, { "name": "Perez-Cortes J.-C." }, { "name": "Roman M." }, { "name": "Pollan M." }, { "name": "Perez-Gomez B." }, { "name": "Salas-Trejo D." }, { "name": "Casals M." }, { "name": "Llobet R." } ], "journal": "Diagnostics" } } ], "credit": [], "owner": "dsilveira", "additionDate": "2025-05-22T12:06:46.866961Z", "lastUpdate": "2025-06-19T06:23:14.022250Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EAPP - Extended a Priori Probability tool", "description": "The tool provides a semi-supervised metric (EAPP) for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset, as well as other features that could provide a relatively trivial separability of the target classes. Therefore, it allows for evaluating the ease or complexity of the task or bias of the data beyond the well-established baseline for any binary classification.", "homepage": "https://ieeexplore.ieee.org/document/9950248", "biotoolsID": "extended_a_priori_probability_tool", "biotoolsCURIE": "biotools:extended_a_priori_probability_tool", "version": [ "v1.0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2945", "term": "Data analysis" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3443", "term": "Image analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" } ], "operatingSystem": [], "language": [], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1109/ACCESS.2022.3221936", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks", "abstract": "The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.", "date": "2022-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Castello V.O." }, { "name": "Perez-Benito F.J." }, { "name": "Catala O.D.T." }, { "name": "Igual I.S." }, { "name": "Llobet R." }, { "name": "Perez-Cortes J.-C." } ], "journal": "IEEE Access" } } ], "credit": [], "owner": "dsilveira", "additionDate": "2025-05-22T14:58:20.772120Z", "lastUpdate": "2025-06-19T06:22:40.072300Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "QIIME 2", "description": "QIIME 2 is an AI-ready microbiome multi-omics data science platform that is trusted, free, open source, extensible, and community developed and supported bioinformatics.", "homepage": "https://qiime2.org", "biotoolsID": "qiime2", "biotoolsCURIE": "biotools:qiime2", "version": [ "2.0" 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"Caraballo-Rodriguez A.M." }, { "name": "Chase J." }, { "name": "Cope E.K." }, { "name": "Da Silva R." }, { "name": "Diener C." }, { "name": "Dorrestein P.C." }, { "name": "Douglas G.M." }, { "name": "Durall D.M." }, { "name": "Duvallet C." }, { "name": "Edwardson C.F." }, { "name": "Ernst M." }, { "name": "Estaki M." }, { "name": "Fouquier J." }, { "name": "Gauglitz J.M." }, { "name": "Gibbons S.M." }, { "name": "Gibson D.L." }, { "name": "Gonzalez A." }, { "name": "Gorlick K." }, { "name": "Guo J." }, { "name": "Hillmann B." }, { "name": "Holmes S." }, { "name": "Holste H." }, { "name": "Huttenhower C." }, { "name": "Huttley G.A." }, { "name": "Janssen S." }, { "name": "Jarmusch A.K." }, { "name": "Jiang L." }, { "name": "Kaehler B.D." }, { "name": "Kang K.B." }, { "name": "Keefe C.R." }, { "name": "Keim P." }, { "name": "Kelley S.T." }, { "name": "Knights D." }, { "name": "Koester I." }, { "name": "Kosciolek T." }, { "name": "Kreps J." }, { "name": "Langille M.G.I." }, { "name": "Lee J." }, { "name": "Ley R." }, { "name": "Liu Y.-X." }, { "name": "Loftfield E." }, { "name": "Lozupone C." }, { "name": "Maher M." }, { "name": "Marotz C." }, { "name": "Martin B.D." }, { "name": "McDonald D." }, { "name": "McIver L.J." }, { "name": "Melnik A.V." }, { "name": "Metcalf J.L." }, { "name": "Morgan S.C." }, { "name": "Morton J.T." }, { "name": "Naimey A.T." }, { "name": "Navas-Molina J.A." }, { "name": "Nothias L.F." }, { "name": "Orchanian S.B." }, { "name": "Pearson T." }, { "name": "Peoples S.L." }, { "name": "Petras D." }, { "name": "Preuss M.L." }, { "name": "Pruesse E." }, { "name": "Rasmussen L.B." }, { "name": "Rivers A." }, { "name": "Robeson M.S." }, { "name": "Rosenthal P." }, { "name": "Segata N." }, { "name": "Shaffer M." }, { "name": "Shiffer A." }, { "name": "Sinha R." }, { "name": "Song S.J." }, { "name": "Spear J.R." }, { "name": "Swafford A.D." }, { "name": "Thompson L.R." }, { "name": "Torres P.J." }, { "name": "Trinh P." }, { "name": "Tripathi A." }, { "name": "Turnbaugh P.J." }, { "name": "Ul-Hasan S." }, { "name": "van der Hooft J.J.J." }, { "name": "Vargas F." }, { "name": "Vazquez-Baeza Y." }, { "name": "Vogtmann E." }, { "name": "von Hippel M." }, { "name": "Walters W." }, { "name": "Wan Y." }, { "name": "Wang M." }, { "name": "Warren J." }, { "name": "Weber K.C." }, { "name": "Williamson C.H.D." }, { "name": "Willis A.D." }, { "name": "Xu Z.Z." }, { "name": "Zaneveld J.R." }, { "name": "Zhang Y." }, { "name": "Zhu Q." }, { "name": "Knight R." }, { "name": "Caporaso J.G." } ], "journal": "Nature Biotechnology" } } ], "credit": [ { "name": "Greg Caporaso", "email": "greg.caporaso@nau.edu", "url": "https://cap-lab.bio", "orcidid": "https://orcid.org/0000-0002-8865-1670", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact" ], "note": null } ], "owner": "gregcaporaso", "additionDate": "2021-05-27T09:02:10Z", "lastUpdate": "2025-06-18T21:00:46.185472Z", "editPermission": { 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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": "kraken2", "description": "Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm.\nAny assumption that Kraken’s raw read assignments can be directly translated into species or strain-level abundance estimates is flawed. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN), estimates species abundances in metagenomics samples by probabilistically re-distributing reads in the taxonomic tree. (Lu, Jennifer et al. “Bracken: estimating species abundance in metagenomics data.”)", "homepage": "https://ccb.jhu.edu/software/kraken2/", "biotoolsID": "kraken2", "biotoolsCURIE": "biotools:kraken2", "version": [ "2.0.8-beta" ], "otherID": [], "relation": [ { "biotoolsID": "kraken", "type": "isNewVersionOf" }, { "biotoolsID": "bracken", "type": "usedBy" } ], "function": [ { "operation": [ { "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" }, { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": "`kraken2 --db <kraken2_database> <input.fastq>`" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" } ], "operatingSystem": [], "language": [ "C++", "Perl" ], "license": "MIT", "collectionID": [ "ONTeater" ], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/DerrickWood/kraken2", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/DerrickWood/kraken2/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/DerrickWood/kraken2/archive/v2.0.8-beta.tar.gz", "type": "Source code", "note": null, "version": "2.0.8-beta" } ], "documentation": [ { "url": "https://github.com/DerrickWood/kraken2/wiki/Manual", "type": [ "User manual" ], "note": null }, { "url": "https://benlangmead.github.io/aws-indexes/k2", "type": [ "User manual" ], "note": "Links to multiple Kraken 2 and bracken databases and indexes" } ], "publication": [ { "doi": "10.1101/762302", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Derrick E. Wood", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0002-7429-1854", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jennifer Lu", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0001-9167-2002", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ben Langmead", "email": "langmea@cs.jhu.edu", "url": null, "orcidid": "http://orcid.org/0000-0003-2437-1976", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "anand-anshu", "additionDate": "2019-09-13T12:51:16Z", "lastUpdate": "2025-06-18T12:24:26.657325Z", "editPermission": { "type": "group", "authors": [ "vashokan", "Keiler_Collier" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }
