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{ "count": 2044, "next": "?page=2", "previous": null, "list": [ { "name": "AMICI", "description": "AMICI provides a multi-language (Python, C++, Matlab) interface for the SUNDIALS solvers CVODES (for ordinary differential equations) and IDAS (for algebraic differential equations). AMICI allows the user to read differential equation models specified as SBML or PySB and automatically compiles such models into .mex simulation files (Matlab), C++ executables or Python modules.\n\nBeyond forward integration, the compiled simulation file also allows for forward sensitivity analysis, steady state sensitivity analysis and adjoint sensitivity analysis for likelihood-based output functions.\n\nThe interface was designed to provide routines for efficient gradient computation in parameter estimation of biochemical reaction models but it is also applicable to a wider range of differential equation constrained optimization problems.", "homepage": "https://github.com/AMICI-dev/AMICI", "biotoolsID": "AMICI", "biotoolsCURIE": "biotools:AMICI", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3562", "term": "Network simulation" }, { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" } ], "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_4015", "term": "PEtab" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3870", "term": "Trajectory data" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2259", "term": "Systems biology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "MATLAB", "C++", "Python" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/AMICI-dev/AMICI/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://amici.readthedocs.io/en/latest/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btab227", "pmid": "33821950", "pmcid": "PMC8545331", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "AMICI: high-performance sensitivity analysis for large ordinary differential equation models", "abstract": "Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.", "date": "2021-10-15T00:00:00Z", "citationCount": 37, "authors": [ { "name": "Frohlich F." }, { "name": "Weindl D." }, { "name": "Schalte Y." }, { "name": "Pathirana D." }, { "name": "Paszkowski L." }, { "name": "Lines G.T." }, { "name": "Stapor P." }, { "name": "Hasenauer J." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Fabian Fröhlich", "email": "fabian_froehlich@hms.harvard.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer", "Developer" ], "note": null }, { "name": "Jan Hasenauer", "email": "jan.hasenauer@uni-bonn.de", "url": null, "orcidid": "https://orcid.org/0000-0002-4935-3312", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Daniel Weindl", "email": "daniel.weindl@uni-bonn.de", "url": null, "orcidid": "https://orcid.org/0000-0001-9963-6057", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer", "Developer" ], "note": null } ], "owner": "dweindl", "additionDate": "2019-09-28T12:06:10Z", "lastUpdate": "2025-05-08T07:28:52.817175Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "StripePy", "description": "StripePy recognizes architectural stripes in 3C and Hi-C contact maps using geometric reasoning", "homepage": "https://github.com/paulsengroup/StripePy", "biotoolsID": "stripepy", "biotoolsCURIE": "biotools:stripepy", "version": [ "0.0.1", "0.0.2", "1.0.0", "1.1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/paulsengroup/StripePy/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/paulsengroup/StripePy/discussions", "type": [ "Discussion forum" ], "note": null }, { "url": "https://github.com/paulsengroup/StripePy.git", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/paulsengroup/StripePy/releases", "type": "Source code", "note": null, "version": null }, { "url": "https://hub.docker.com/r/paulsengroup/stripepy", "type": "Container file", "note": null, "version": null }, { "url": "https://doi.org/10.5281/zenodo.14283921", "type": "Test data", "note": null, "version": null }, { "url": "https://pypi.org/project/stripepy-hic/", "type": "Binaries", "note": null, "version": null }, { "url": "https://anaconda.org/bioconda/stripepy-hic", "type": "Binaries", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/paulsengroup/StripePy/blob/main/README.md", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.12.20.629789", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "robomics", "additionDate": "2024-12-21T15:24:55.662209Z", "lastUpdate": "2025-05-02T11:01:00.332596Z", "editPermission": { "type": "group", "authors": [ "rea1991" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "pyOpenMS", "description": "Python library for mass spectrometry, specifically for the analysis of proteomics and metabolomics data.", "homepage": "https://github.com/OpenMS/pyopenms-extra/blob/master/docs/source/index.rst", "biotoolsID": "pyOpenMS", "biotoolsCURIE": "biotools:pyOpenMS", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/OpenMS/pyopenms-extra", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/OpenMS/pyopenms-extra/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://pyopenms.readthedocs.io", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.7287/PEERJ.PREPRINTS.27736", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Hannes L. Rost", "email": "hannes.rost@utoronto.ca", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "samwein", "additionDate": "2019-08-09T13:16:33Z", "lastUpdate": "2025-04-30T11:39:29.549782Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Bracken", "description": "Statistical method that computes the abundance of species in DNA sequences from a metagenomics sample.", "homepage": "https://ccb.jhu.edu/software/bracken/", "biotoolsID": "bracken", "biotoolsCURIE": "biotools:bracken", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2238", "term": "Statistical calculation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" } ], "operatingSystem": [ "Linux" ], "language": [ "Perl", "Python" ], "license": "GPL-3.0", "collectionID": [ "Animal and Crop Genomics" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/jenniferlu717/Bracken", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/jenniferlu717/Bracken/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://ccb.jhu.edu/software/bracken/index.shtml?t=manual", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.7717/peerj-cs.104", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Bracken: Estimating species abundance in metagenomics data", "abstract": "Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomalRNAgene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.", "date": "2017-01-01T00:00:00Z", "citationCount": 900, "authors": [ { "name": "Lu J." }, { "name": "Breitwieser F.P." }, { "name": "Thielen P." }, { "name": "Salzberg S.L." } ], "journal": "PeerJ Computer Science" } } ], "credit": [ { "name": null, "email": "jlu26@jhmi.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "admin", "additionDate": "2017-08-20T15:17:20Z", "lastUpdate": "2025-04-23T07:43:18.805990Z", "editPermission": { "type": "group", "authors": [ "animalandcropgenomics", "ELIXIR-CZ", "bebatut", "vashokan" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Gemma", "description": "Gemma provides data, experimental design annotations, and differential expression analysis results for thousands of microarray and RNA-seq experiments. We re-analyze raw data from public sources (primarily NCBI GEO), annotate experimental conditions, conduct quality control and compute differential expression using standardized procedures. We have especially good coverage of experiments relevant to the nervous system.", "homepage": "https://gemma.msl.ubc.ca/", "biotoolsID": "gemma", "biotoolsCURIE": "biotools:gemma", "version": [ "1.31.13" ], "otherID": [], "relation": [ { "biotoolsID": "rsem", "type": "uses" }, { "biotoolsID": "multiqc", "type": "uses" }, { "biotoolsID": "cutadapt", "type": "uses" }, { "biotoolsID": "star", "type": "uses" }, { "biotoolsID": "ncbi_geo", "type": "uses" }, { "biotoolsID": "sradb", "type": "uses" }, { "biotoolsID": "ncbi_gene", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_3110", "term": "Raw microarray data" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1636", "term": "Heat map" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_3914", "term": "Quality control report" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Web API" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3053", "term": "Genetics" }, { "uri": "http://edamontology.org/topic_3518", "term": "Microarray experiment" }, { "uri": "http://edamontology.org/topic_0219", "term": "Data curation and archival" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Java", "JavaScript" ], "license": "CC-BY-NC-4.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/PavlidisLab/Gemma", "type": [ "Repository", "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/PavlidisLab/Gemma/releases", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://pavlidislab.github.io/Gemma/", "type": [ "General" ], "note": null }, { "url": "https://gemma.msl.ubc.ca/resources/restapidocs/", "type": [ "API documentation" ], "note": "Documentation for the Gemma REST API." } ], "publication": [ { "doi": "10.1093/bioinformatics/btp259", "pmid": "19376825", "pmcid": "PMC2687992", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Application and evaluation of automated semantic annotation of gene expression experiments", "abstract": "Motivation: Many microarray datasets are available online with formalized standards describing the probe sequences and expression values. Unfortunately, the description, conditions and parameters of the experiments are less commonly formalized and often occur as natural language text. This hinders searching, high-throughput analysis, organization and integration of the datasets. Results: We use the lexical resources and software tools from the Unified Medical Language System (UMLS) to extract concepts from text. We then link the UMLS concepts to classes in open biomedical ontologies. The result is accessible and clear semantic annotations of gene expression experiments. We applied the method to 595 expression experiments from Gemma, a resource for re-use and meta-analysis of gene expression profiling data. We evaluated and corrected all stages of the annotation process. The majority of missed annotations were due to a lack of cross-references. The most error-prone stage was the extraction of concepts from phrases. Final review of the annotations in context of the experiments revealed 89% precision. A naive system, lacking the phrase to concept corrections is 68% precise. We have integrated this annotation pipeline into Gemma. © 2009 The Author(s).", "date": "2009-06-09T00:00:00Z", "citationCount": 9, "authors": [ { "name": "French L." }, { "name": "Lane S." }, { "name": "Law T." }, { "name": "Xu L." }, { "name": "Pavlidis P." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/database/baab006", "pmid": "33599246", "pmcid": "PMC7904053", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Curation of over 10 000 transcriptomic studies to enable data reuse", "abstract": "Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html", "date": "2021-01-01T00:00:00Z", "citationCount": 21, "authors": [ { "name": "Lim N." }, { "name": "Tesar S." }, { "name": "Belmadani M." }, { "name": "Poirier-Morency G." }, { "name": "Mancarci B.O." }, { "name": "Sicherman J." }, { "name": "Jacobson M." }, { "name": "Leong J." }, { "name": "Tan P." }, { "name": "Pavlidis P." } ], "journal": "Database" } } ], "credit": [ { "name": "Pavlidis Lab Support", "email": "pavlab-support@msl.ubc.ca", "url": "https://pavlab.msl.ubc.ca/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null } ], "owner": "artrymix", "additionDate": "2017-04-22T17:35:18Z", "lastUpdate": "2025-04-22T22:09:03.811075Z", "editPermission": { "type": "group", "authors": [ "artrymix", "paulpavlidis" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "hictkpy", "description": "Python bindings for hictk: read and write .cool and .hic files directly from Python", "homepage": "https://github.com/paulsengroup/hictkpy", "biotoolsID": "hictkpy", "biotoolsCURIE": "biotools:hictkpy", "version": [ "0.0.1", "0.0.2", "0.0.3", "0.0.4", "0.0.5", "1.0.0", "1.1.0", "1.2.0" ], "otherID": [], "relation": [ { "biotoolsID": "hictk", "type": "uses" } ], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/paulsengroup/hictkpy", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/paulsengroup/hictkpy/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/paulsengroup/hictkpy/discussions", "type": [ "Helpdesk" ], "note": null } ], "download": [ { "url": "https://github.com/paulsengroup/hictkpy/releases", "type": "Downloads page", "note": null, "version": null }, { "url": "https://pypi.org/project/hictkpy/", "type": "Binaries", "note": null, "version": null }, { "url": "https://anaconda.org/bioconda/hictkpy", "type": "Binaries", "note": null, "version": null } ], "documentation": [ { "url": "https://hictkpy.readthedocs.io/en/stable/", "type": [ "General" ], "note": null }, { "url": "https://hictkpy.readthedocs.io/en/stable/installation.html", "type": [ "Installation instructions" ], "note": null }, { "url": "https://hictkpy.readthedocs.io/en/stable/quickstart.html", "type": [ "Quick start guide" ], "note": null }, { "url": "https://hictkpy.readthedocs.io/en/stable/api/index.html", "type": [ "API documentation" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btae408", "pmid": null, "pmcid": null, "type": [], "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": 0, "authors": [ { "name": "Rossini R." }, { "name": "Paulsen J." } ], "journal": "Bioinformatics" } } ], "credit": [], "owner": "robomics", "additionDate": "2024-04-18T08:31:46.459964Z", "lastUpdate": "2025-04-14T22:54:26.266145Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "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": [ "0.0.1", "0.0.2", "0.0.3", "0.0.4", "0.0.5", "0.0.6", "0.0.7", "0.0.8", "0.0.9", "0.0.10", "0.0.11", "0.0.12", "1.0.0", "2.0.0", "2.0.1", "2.0.2", "2.1.0", "2.1.1" ], "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/en/stable/index.html", "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": 0, "authors": [ { "name": "Rossini R." }, { "name": "Paulsen J." } ], "journal": "Bioinformatics" } } ], "credit": [], "owner": "robomics", "additionDate": "2024-02-02T14:42:46.030561Z", "lastUpdate": "2025-04-14T22:54:16.319235Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "The MINERVA Platform", "description": "The MINERVA (Molecular Interaction NEtwoRk VisuAlization) platform is a standalone webserver for visualization, exploration and management of molecular networks encoded in SBGN-compliant format, including files produced using CellDesigner or SBGN editors. Visualization of uploaded networks generated by the platform is accessible via a web browser to all viewers with the weblink to the resource.\n\nThe MINERVA Platform is a webservice using the Java Server Faces 2 technology. The server side, including data parsing, integration, annotation and verification, is implemented in Java. The platform uses the Postgres SQL database for data storage and the Hibernate framework as a middle layer between web server and database. The user web-interface is generated using React.js. The displayed content is visualized by OpenLayers API, dedicated JavaScript and CSS.", "homepage": "https://minerva.uni.lu", "biotoolsID": "MINERVA_Platform", "biotoolsCURIE": "biotools:MINERVA_Platform", "version": [ "13.1.3", "13.2.0", "14.0.13", "15.0.3", "16.4.0", "17.1.3", "18.1.1" ], "otherID": [], "relation": [ { "biotoolsID": "pathvisio", "type": "uses" }, { "biotoolsID": "sbgn", "type": "uses" }, { "biotoolsID": "libsbml", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3926", "term": "Pathway visualisation" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0571", "term": "Expression data visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_3391", "term": "Omics" }, { "uri": "http://edamontology.org/topic_3342", "term": "Translational medicine" } ], "operatingSystem": [], "language": [], "license": "AGPL-3.0", "collectionID": [ "ELIXIR-LU", "LCSB" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Luxembourg" ], "elixirCommunity": [], "link": [ { "url": "https://gitlab.lcsb.uni.lu/minerva/core/", "type": [ "Repository" ], "note": "GiLab repository for core functionalities (data and format handling, service stability, API access)" }, { "url": "https://gitlab.lcsb.uni.lu/minerva/core/-/issues", "type": [ "Issue tracker" ], "note": "Issue tracker for core functionalities (data and format handling, service stability, API access)" }, { "url": "https://gitlab.lcsb.uni.lu/minerva/frontend", "type": [ "Repository" ], "note": "GiLab repository for frontend functionalities" }, { "url": "https://gitlab.lcsb.uni.lu/minerva/frontend/-/issues", "type": [ "Issue tracker" ], "note": "Issue tracker for frontend functionalities" } ], "download": [ { "url": "https://minerva.pages.uni.lu/doc/install/", "type": "Other", "note": "Installation instructions, including debian package, virtual machine images and docker containers.", "version": "13.1.3 - 18.1.1" } ], "documentation": [ { "url": "https://minerva.uni.lu", "type": [ "Quick start guide", "Release notes", "User manual", "API documentation", "Citation instructions", "Terms of use" ], "note": null } ], "publication": [ { "doi": "10.1038/npjsba.2016.20", "pmid": "28725475", "pmcid": "PMC5516855", "type": [ "Primary" ], "version": "10.0", "note": null, "metadata": { "title": "MINERVA—A platform for visualization and curation of molecular interaction networks", "abstract": "Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.", "date": "2016-01-01T00:00:00Z", "citationCount": 60, "authors": [ { "name": "Gawron P." }, { "name": "Ostaszewski M." }, { "name": "Satagopam V." }, { "name": "Gebel S." }, { "name": "Mazein A." }, { "name": "Kuzma M." }, { "name": "Zorzan S." }, { "name": "McGee F." }, { "name": "Otjacques B." }, { "name": "Balling R." }, { "name": "Schneider R." } ], "journal": "npj Systems Biology and Applications" } }, { "doi": "10.1093/bioinformatics/btz286", "pmid": "31074494", "pmcid": "PMC6821317", "type": [ "Primary" ], "version": "12.2.3", "note": null, "metadata": { "title": "MINERVA API and plugins: Opening molecular network analysis and visualization to the community", "abstract": "Summary: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform's visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. Availability and implementation: Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.", "date": "2019-11-01T00:00:00Z", "citationCount": 24, "authors": [ { "name": "Hoksza D." }, { "name": "Gawron P." }, { "name": "Ostaszewski M." }, { "name": "Smula E." }, { "name": "Schneider R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/bib/bbz067", "pmid": "31273380", "pmcid": "PMC7373180", "type": [ "Primary" ], "version": "13.1.1", "note": null, "metadata": { "title": "Closing the gap between formats for storing layout information in systems biology", "abstract": "The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.", "date": "2019-07-10T00:00:00Z", "citationCount": 15, "authors": [ { "name": "Hoksza D." }, { "name": "Gawron P." }, { "name": "Ostaszewski M." }, { "name": "Hasenauer J." }, { "name": "Schneider R." } ], "journal": "Briefings in Bioinformatics" } }, { "doi": "10.1089/big.2015.0057", "pmid": "27441714", "pmcid": "PMC4932659", "type": [ "Usage" ], "version": "10.0", "note": null, "metadata": { "title": "Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases", "abstract": "Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services - tranSMART, a Galaxy Server, and a MINERVA platform - are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.", "date": "2016-06-01T00:00:00Z", "citationCount": 38, "authors": [ { "name": "Satagopam V." }, { "name": "Gu W." }, { "name": "Eifes S." }, { "name": "Gawron P." }, { "name": "Ostaszewski M." }, { "name": "Gebel S." }, { "name": "Barbosa-Silva A." }, { "name": "Balling R." }, { "name": "Schneider R." } ], "journal": "Big Data" } }, { "doi": "10.1016/j.envpol.2019.04.005", "pmid": "30991279", "pmcid": null, "type": [], "version": "13.1.1", "note": null, "metadata": { "title": "Genes associated with Parkinson's disease respond to increasing polychlorinated biphenyl levels in the blood of healthy females", "abstract": "Polychlorinated biphenyls (PCBs) are a class of widespread environmental pollutants, commonly found in human blood, that have been suggested to be linked to the occurrence of sporadic Parkinson's disease (PD). It has been reported that some non-coplanar PCBs accumulate in the brains of female PD patients. To improve our understanding of the association between PCB exposure and PD risk we have applied whole transcriptome gene expression analysis in blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly, we observe that in females, blood levels of non-coplanar PCBs appear to be associated with expression levels of PD-specific genes. However, no such association was detected in males. Among the 131 PD-specific genes affected, 39 have been shown to display similar changes in expression levels in the substantia nigra of deceased PD patients. Especially among the down-regulated genes, transcripts of genes involved in neurotransmitter vesicle-related functions were predominant. Capsule: Plasma PCB levels are associated with gene expression changes in females only, resulting in brain-related genes changing in blood cells of healthy individuals exposed to PCBs.", "date": "2019-07-01T00:00:00Z", "citationCount": 5, "authors": [ { "name": "Bohler S." }, { "name": "Krauskopf J." }, { "name": "Espin-Perez A." }, { "name": "Gebel S." }, { "name": "Palli D." }, { "name": "Rantakokko P." }, { "name": "Kiviranta H." }, { "name": "Kyrtopoulos S.A." }, { "name": "Balling R." }, { "name": "Kleinjans J." } ], "journal": "Environmental Pollution" } } ], "credit": [], "owner": "mjostaszewski", "additionDate": "2019-08-26T14:34:55Z", "lastUpdate": "2025-04-14T09:01:54.711218Z", "editPermission": { "type": "group", "authors": [ "sascha.herzinger" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Protists Ribosomal Reference Database", "description": "The PR2 reference sequence database was initiated in 2010 in the frame of the BioMarks project from work that had developed in the previous ten years in the Plankton Group of the Station Biologique of Roscoff. It aims to provide a reference database of carefully annotated 18S rRNA sequences using nine unique taxonomic fields (from domain to species). At present, it contains over 240,000 sequences. Although it focuses on protists, it also contains sequences from metazoa, fungi and plants as well a limited set of 16S sequences from plastids and bacteria. Several metadata fields are available for many sequences, including geo-localisation, whether it originates from a culture or a natural sample, host type etc… The annotation of PR2 is performed by experts from each taxonomic groups.", "homepage": "https://app.pr2-database.org/", "biotoolsID": "pr2-reference", "biotoolsCURIE": "biotools:pr2-reference", "version": [ "5.1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Database portal", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_4038", "term": "Metabarcoding" } ], "operatingSystem": [], "language": [ "R" ], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/pr2database/pr2database/releases", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/pr2database/pr2database/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://pr2-database.org/", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://github.com/pr2database/pr2database/releases", "type": "Downloads page", "note": null, "version": "5.1.0" } ], "documentation": [ { "url": "https://pr2database.github.io/pr2database/articles/pr2database.html", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gks1160", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy", "abstract": "The interrogation of genetic markers in environmental meta-barcoding studies is currently seriously hindered by the lack of taxonomically curated reference data sets for the targeted genes. The Protist Ribosomal Reference database (PR2, http://ssu-rrna.org/) provides a unique access to eukaryotic small sub-unit (SSU) ribosomal RNA and DNA sequences, with curated taxonomy. The database mainly consists of nuclear-encoded protistan sequences. However, metazoans, land plants, macrosporic fungi and eukaryotic organelles (mitochondrion, plastid and others) are also included because they are useful for the analysis of high-troughput sequencing data sets. Introns and putative chimeric sequences have been also carefully checked. Taxonomic assignation of sequences consists of eight unique taxonomic fields. In total, 136866 sequences are nuclear encoded, 45708 (36 501 mitochondrial and 9657 chloroplastic) are from organelles, the remaining being putative chimeric sequences. The website allows the users to download sequences from the entire and partial databases (including representative sequences after clustering at a given level of similarity). Different web tools also allow searches by sequence similarity. The presence of both rRNA and rDNA sequences, taking into account introns (crucial for eukaryotic sequences), a normalized eight terms ranked-taxonomy and updates of new GenBank releases were made possible by a long-term collaboration between experts in taxonomy and computer scientists. © The Author(s) 2012.", "date": "2013-01-01T00:00:00Z", "citationCount": 1456, "authors": [ { "name": "Guillou L." }, { "name": "Bachar D." }, { "name": "Audic S." }, { "name": "Bass D." }, { "name": "Berney C." }, { "name": "Bittner L." }, { "name": "Boutte C." }, { "name": "Burgaud G." }, { "name": "De Vargas C." }, { "name": "Decelle J." }, { "name": "Del Campo J." }, { "name": "Dolan J.R." }, { "name": "Dunthorn M." }, { "name": "Edvardsen B." }, { "name": "Holzmann M." }, { "name": "Kooistra W.H.C.F." }, { "name": "Lara E." }, { "name": "Le Bescot N." }, { "name": "Logares R." }, { "name": "Mahe F." }, { "name": "Massana R." }, { "name": "Montresor M." }, { "name": "Morard R." }, { "name": "Not F." }, { "name": "Pawlowski J." }, { "name": "Probert I." }, { "name": "Sauvadet A.-L." }, { "name": "Siano R." }, { "name": "Stoeck T." }, { "name": "Vaulot D." }, { "name": "Zimmermann P." }, { "name": "Christen R." } ], "journal": "Nucleic Acids Research" } } ], "credit": [], "owner": "vaulot", "additionDate": "2025-04-11T13:56:02.037873Z", "lastUpdate": "2025-04-11T14:01:09.249274Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "prolfquapp", "description": "A command-line tool for differential expression analysis in quantitative proteomics", "homepage": "https://github.com/prolfqua/prolfquapp", "biotoolsID": "prolfquapp", "biotoolsCURIE": "biotools:prolfquapp", "version": [ "0.1.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3741", "term": "Differential protein expression profiling" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2044", "term": "Sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": null, "cmd": "./prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis -s DIANN\n# and again you run the version within the docker container with\n# ./prolfquapp_docker.sh prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis -s DIANN" }, { "operation": [ { "uri": "http://edamontology.org/operation_2428", "term": "Validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3914", "term": "Quality control report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0571", "term": "Expression data visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3508", "term": "PDF" }, { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/prolfqua/prolfquapp", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/prolfqua/prolfquapp/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/prolfqua/prolfquapp/releases/tag/0.1.6", "type": "Downloads page", "note": null, "version": "0.1.6" } ], "documentation": [ { "url": "https://github.com/prolfqua/prolfquapp/blob/master/README.md", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1021/acs.jproteome.4c00911", "pmid": null, "pmcid": null, "type": [], "version": "0.0.6", "note": null, "metadata": { "title": "prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics", "abstract": "Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements or multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software, respectively. 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The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.", "date": "2020-12-01T00:00:00Z", "citationCount": 54, "authors": [ { "name": "Tarazona S." }, { "name": "Balzano-Nogueira L." }, { "name": "Gomez-Cabrero D." }, { "name": "Schmidt A." }, { "name": "Imhof A." }, { "name": "Hankemeier T." }, { "name": "Tegner J." }, { "name": "Westerhuis J.A." }, { "name": "Conesa A." } ], "journal": "Nature Communications" } } ], "credit": [ { "name": "Sonia Tarazona", "email": "sotacam@eio.upv.es", "url": null, "orcidid": "https://orcid.org/0000-0001-5346-1407", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer" ], "note": null } ], "owner": "biostatomics1", "additionDate": "2025-03-03T15:06:10.867996Z", "lastUpdate": "2025-03-04T15:16:47.048391Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MOSim", "description": "MOSim (Multi-Omics Simulation) package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.", "homepage": "https://bioconductor.org/packages/release/bioc/html/MOSim.html", "biotoolsID": "mosim", "biotoolsCURIE": "biotools:mosim", "version": [ "2.2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_4021", "term": "Multiomics" }, { "uri": "http://edamontology.org/topic_3524", "term": "Simulation experiment" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/ConesaLab/MOSim", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/ConesaLab/MOSim/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/421834", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": "This is a preprint accesible at bioRxiv. When the paper is published it will be updated", "metadata": null } ], "credit": [ { "name": "Sonia Tarazona", "email": "sotacam@eio.upv.es", "url": null, "orcidid": "https://orcid.org/0000-0001-5346-1407", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Maintainer" ], "note": null } ], "owner": "biostatomics1", "additionDate": "2025-03-03T13:46:56.613465Z", "lastUpdate": "2025-03-04T15:12:34.696650Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "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" } ], "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": 1960, "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": 168, "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": 75, "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": "2025-02-26T13:59:11.356816Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Bakta", "description": "Rapid & standardized annotation of bacterial genomes, MAGs & plasmids", "homepage": "https://github.com/oschwengers/bakta", "biotoolsID": "bakta", "biotoolsCURIE": "biotools:bakta", "version": [ "v1.10.3" ], "otherID": [], "relation": [ { "biotoolsID": "diamond", "type": "uses" }, { "biotoolsID": "hmmer3", "type": "uses" }, { "biotoolsID": "infernal", "type": "uses" }, { "biotoolsID": "trnascan-se", "type": "uses" }, { "biotoolsID": "blast", "type": "uses" }, { "biotoolsID": "aragorn", "type": "uses" }, { "biotoolsID": "pilercr", "type": "uses" }, { "biotoolsID": "deepsig", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0925", "term": "Sequence assembly" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_2914", "term": "Sequence features metadata" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2012", "term": "Sequence coordinates" }, "format": [ { "uri": "http://edamontology.org/format_1975", "term": "GFF3" } ] }, { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": 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"http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3604", "term": "SVG" } ] }, { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": "bakta --db <db-path> --prefix <prefix> --output <output-path> genome.fasta" } ], "toolType": [ "Command-line tool", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Germany" ], "elixirCommunity": [], "link": [ { "url": "https://github.com/oschwengers/bakta", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/oschwengers/bakta/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://bioconda.github.io/recipes/bakta/README.html", "type": [ "Other" ], "note": null }, { "url": "https://bakta.computational.bio", "type": [ "Service" ], "note": null } ], "download": [ { "url": "https://zenodo.org/records/7669534", "type": "Other", "note": "Mandatory annotation database", "version": "v5.1" } ], "documentation": [ { "url": "https://github.com/oschwengers/bakta/blob/main/README.md", "type": [ "General" ], "note": null }, { "url": "https://github.com/oschwengers/bakta/blob/main/CONTRIBUTION.md", "type": [ "Contributions policy" ], "note": null }, { "url": "https://github.com/oschwengers/bakta/blob/main/CODE_OF_CONDUCT.md", "type": [ "Code of conduct" ], "note": null }, { "url": "https://bakta.readthedocs.io/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1099/mgen.0.000685", "pmid": "34739369", "pmcid": "PMC8743544", "type": [ "Primary" ], "version": "1.1", "note": null, "metadata": { "title": "Bakta: Rapid and standardized annotation of bacterial genomes via alignment-free sequence identification", "abstract": "Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.", "date": "2021-01-01T00:00:00Z", "citationCount": 323, "authors": [ { "name": "Schwengers O." }, { "name": "Jelonek L." }, { "name": "Dieckmann M.A." }, { "name": "Beyvers S." }, { "name": "Blom J." }, { "name": "Goesmann A." } ], "journal": "Microbial Genomics" } } ], "credit": [ { "name": "Oliver Schwengers", "email": "oliver.schwengers@cb.jlug.de", "url": "https://github.com/oschwengers", "orcidid": "https://orcid.org/0000-0003-4216-2721", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Justus Liebig University Giessen", "email": null, "url": "https://www.uni-giessen.de", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null } ], "owner": "oschwengers", "additionDate": "2021-05-08T17:25:21Z", "lastUpdate": "2024-12-23T21:48:50.049892Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-CZ", "bebatut" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MALDIquant", "description": "MALDIquant is a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.", "homepage": "https://cran.r-project.org/package=MALDIquant", "biotoolsID": "maldi_quant", "biotoolsCURIE": "biotools:maldi_quant", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" }, { "uri": "http://edamontology.org/operation_3860", "term": "Spectrum calculation" }, { "uri": "http://edamontology.org/operation_3215", "term": "Peak detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3627", "term": "Mass spectra calibration" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library", "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/sgibb/MALDIquant/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/sgibb/MALDIquant/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://strimmerlab.org/software/maldiquant/", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [ { "url": "https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bts447", "pmid": "22796955", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Maldiquant: A versatile R package for the analysis of mass spectrometry data", "abstract": "MALDIquant is an R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution. © The Author 2012. Published by Oxford University Press. All rights reserved.", "date": "2012-09-01T00:00:00Z", "citationCount": 476, "authors": [ { "name": "Gibb S." }, { "name": "Strimmer K." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Sebastian Gibb", "email": "mail@sebastiangibb.de", "url": null, "orcidid": "https://orcid.org/0000-0001-7406-4443", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Korbinian Strimmer", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-7917-2056", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "Kigaard", "additionDate": "2021-05-26T20:42:18Z", "lastUpdate": "2024-12-15T19:31:00.871136Z", "editPermission": { "type": "group", "authors": [ "sizhengZhao" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "hictkR", "description": "R bindings for hictk: read .cool and .hic files directly from R", "homepage": "https://github.com/paulsengroup/hictkR", "biotoolsID": "hictkr", "biotoolsCURIE": "biotools:hictkr", "version": [ "v0.0.1" ], "otherID": [], "relation": [ { "biotoolsID": "hictk", "type": "uses" } ], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/paulsengroup/hictkR", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/paulsengroup/hictkR/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/paulsengroup/hictkR/releases", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://paulsengroup.github.io/hictkR/", "type": [ "General" ], "note": null }, { "url": "https://paulsengroup.github.io/hictkR/articles/hictkR-vignette.html", "type": [ "Quick start guide" ], "note": null }, { "url": "https://paulsengroup.github.io/hictkR/reference/index.html", "type": [ "API documentation" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btae408", "pmid": null, "pmcid": null, "type": [], "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": 0, "authors": [ { "name": "Rossini R." }, { "name": "Paulsen J." } ], "journal": "Bioinformatics" } } ], "credit": [], "owner": "robomics", "additionDate": "2024-04-18T08:54:15.428034Z", "lastUpdate": "2024-12-04T21:53:14.841716Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EnrichDO", "description": "A Global Weighted Model for Disease Ontology Enrichment Analysis. EnrichDO was based on the latest annotations of the human genome with DO terms, and double weighted the annotated genes. On one hand, to reinforce the saliency of direct gene-DO annotations, different initial weights were assigned to directly annotated genes and indirectly annotated genes, respectively. On the other hand, to detect locally most significant node between the parent and its children, less significant nodes were dynamically down-weighted.", "homepage": "https://github.com/liangcheng-hrbmu/EnrichDO", "biotoolsID": "enrichdo", "biotoolsCURIE": "biotools:enrichdo", "version": [ "1.1.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1027", "term": "Gene ID (NCBI)" }, "format": [ { "uri": "http://edamontology.org/format_3507", "term": "Document format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2353", "term": "Ontology data" }, "format": [ { "uri": "http://edamontology.org/format_3507", "term": "Document format" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [], "language": [ "R" ], "license": "MIT", "collectionID": [ "enrichdo" ], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/liangcheng-hrbmu/EnrichDO", "type": [ "Software catalogue" ], "note": "Please use the issue tracker for reporting bugs and making features requests." } ], "download": [ { "url": "https://github.com/liangcheng-hrbmu/EnrichDO", "type": "Source code", "note": null, "version": "1.1.1" }, { "url": "https://www.bioconductor.org/packages/devel/bioc/html/EnrichDO.html", "type": "Downloads page", "note": "R package download", "version": "1.1.1" } ], "documentation": [ { "url": "https://github.com/liangcheng-hrbmu/EnrichDO/blob/devel/vignettes/EnrichDO.Rmd", "type": [ "Quick start guide" ], "note": null }, { "url": "https://www.bioconductor.org/packages/devel/bioc/html/EnrichDO.html", "type": [ "Installation instructions" ], "note": null } ], "publication": [], "credit": [], "owner": "LiangCheng", "additionDate": "2024-09-06T08:11:29.614214Z", "lastUpdate": "2024-12-01T01:14:37.834760Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Jalview", "description": "Jalview is a free program for multiple sequence alignment editing, visualisation and analysis. Use it to view and edit sequence alignments, analyse them with phylogenetic trees and principal components analysis (PCA) plots and explore molecular structures and annotation.", "homepage": "https://www.jalview.org/", "biotoolsID": "Jalview", "biotoolsCURIE": "biotools:Jalview", "version": [ "2.11.4.1" ], "otherID": [], "relation": [ { "biotoolsID": "jabaws", "type": "uses" }, { "biotoolsID": "chimera", "type": "uses" }, { "biotoolsID": "chimerax", "type": "uses" }, { "biotoolsID": "pymol", "type": "uses" }, { "biotoolsID": "bioconda", "type": "includedIn" }, { "biotoolsID": "3d-beacons", "type": "uses" }, { "biotoolsID": "uniprot", "type": "uses" }, { "biotoolsID": "pfam", "type": "uses" }, { "biotoolsID": "ensembl", "type": "uses" }, { "biotoolsID": "pdb", "type": "uses" }, { "biotoolsID": "rfam", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0564", "term": "Sequence visualisation" }, { "uri": "http://edamontology.org/operation_0324", 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"note": "MMseqs2 Taxonomy", "metadata": null }, { "doi": "10.1101/2024.11.13.623350v1", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "MMseqs2-GPU", "metadata": null } ], "credit": [], "owner": "milot-mirdita", "additionDate": "2019-07-03T16:03:27Z", "lastUpdate": "2024-11-26T16:25:05.809769Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MDAnalysis", "description": "MDAnalysis is an object-oriented python toolkit to analyze molecular dynamics trajectories generated by CHARMM, Gromacs, NAMD, LAMMPS, Amber or DL_POLY; it also reads other formats (e.g. PDB files and XYZ format trajectories; see the supported coordinate formats for the full list). 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In addition, it implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.", "homepage": "http://bioconductor.org/packages/release/bioc/html/consensusOV.html", "biotoolsID": "consensusov", "biotoolsCURIE": "biotools:consensusov", "version": [ "1.2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2990", "term": "Classification" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3518", "term": "Microarray experiment" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "Artistic-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.pmgenomics.ca/bhklab/software/consensusOV", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/bhklab/consensusOV/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/consensusOV/", "type": [ "Mirror" ], "note": null }, { "url": "https://git.bioconductor.org/packages/consensusOV", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/consensusOV_1.2.0.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/consensusOV/man/consensusOV.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1158/1078-0432.ccr-18-0784", "pmid": "30084834", "pmcid": "PMC6207081", "type": [], "version": null, "note": null, "metadata": { "title": "Consensus on molecular subtypes of high-grade serous ovarian carcinoma", "abstract": "Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis across datasets (P < 10-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype.", "date": "2018-10-15T00:00:00Z", "citationCount": 85, "authors": [ { "name": "Chen G.M." }, { "name": "Kannan L." }, { "name": "Geistlinger L." }, { "name": "Kofia V." }, { "name": "Safikhani Z." }, { "name": "Gendoo D.M.A." }, { "name": "Parmigiani G." }, { "name": "Birrer M." }, { "name": "Haibe-Kains B." }, { "name": "Waldron L." } ], "journal": "Clinical Cancer Research" } } ], "credit": [ { "name": "Benjamin Haibe-Kains", "email": "benjamin.haibe.kains@utoronto.ca", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-17T14:46:40Z", "lastUpdate": "2024-11-25T14:35:19.593337Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "chromswitch", "description": "Flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data.", "homepage": "http://bioconductor.org/packages/release/bioc/html/chromswitch.html", "biotoolsID": "chromswitch", "biotoolsCURIE": "biotools:chromswitch", "version": [ "1.2.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" }, { "uri": "http://edamontology.org/topic_0085", "term": "Functional genomics" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "MIT", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/sjessa/chromswitch", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/sjessa/chromswitch/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/chromswitch/", "type": [ "Mirror" ], "note": null }, { "url": "https://git.bioconductor.org/packages/chromswitch", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/chromswitch_1.2.1.tar.gz", "type": "Software package", "note": null, "version": null }, { "url": "http://bioconductor.org/packages/release/bioc/vignettes/chromswitch/inst/doc/chromswitch_intro.R", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/chromswitch/man/chromswitch.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty075", "pmid": "29438498", "pmcid": "PMC6022667", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Chromswitch: A flexible method to detect chromatin state switches", "abstract": "Chromatin state plays amajor role in controlling gene expression, and comparative analysis of ChIP-seq data is key to understanding epigenetic regulation. We present chromswitch, an R/ Bioconductor package to integrate epigenomic data in a defined window of interest to detect an overall switch in chromatin state. Chromswitch accurately classifies a benchmarking dataset, and when applied genome-wide, the tool successfully detects chromatin changes that result in brain-specific expression.", "date": "2018-07-01T00:00:00Z", "citationCount": 9, "authors": [ { "name": "Jessa S." }, { "name": "Kleinman C.L." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Selin Jessa", "email": "selinjessa@gmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-17T13:51:36Z", "lastUpdate": "2024-11-25T14:35:18.381844Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "chimeraviz", "description": "Visualization tools for gene fusions.", "homepage": "https://bioconductor.org/packages/release/bioc/html/chimeraviz.html", "biotoolsID": "chimeraviz", "biotoolsCURIE": "biotools:chimeraviz", "version": [ "1.6.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0564", "term": "Sequence visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_3053", "term": "Genetics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "Artistic-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/stianlagstad/chimeraviz", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/stianlagstad/chimeraviz/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/chimeraviz/", "type": [ "Mirror" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/chimeraviz_1.6.0.tar.gz", "type": "Software package", "note": null, "version": null }, { "url": "http://bioconductor.org/packages/release/bioc/vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://bioconductor.org/packages/release/bioc/manuals/chimeraviz/man/chimeraviz.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btx329", "pmid": "28525538", "pmcid": "PMC5870674", "type": [], "version": null, "note": null, "metadata": { "title": "Chimeraviz: A tool for visualizing chimeric RNA", "abstract": "Summary: Advances in high-throughput RNA sequencing have enabled more efficient detection of fusion transcripts, but the technology and associated software used for fusion detection from sequencing data often yield a high false discovery rate. Good prioritization of the results is important, and this can be helped by a visualization framework that automatically integrates RNA data with known genomic features. Here we present chimeraviz, a Bioconductor package that automates the creation of chimeric RNA visualizations. The package supports input from nine different fusion-finder tools: deFuse, EricScript, InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION. Availability and implementation: chimeraviz is an R package available via Bioconductor (https://bio conductor.org/packages/release/bioc/html/chimeraviz.html) under Artistic-2.0. Source code and support is available at GitHub (https://github.com/stianlagstad/chimeraviz).", "date": "2017-09-15T00:00:00Z", "citationCount": 22, "authors": [ { "name": "Lagstad S." }, { "name": "Zhao S." }, { "name": "Hoff A.M." }, { "name": "Johannessen B." }, { "name": "Lingjaerde O.C." }, { "name": "Skotheim R.I." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Rolf I. Skotheim", "email": "rolf.i.skotheim@rr-research.no", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "d.gabrielaitis", "additionDate": "2018-06-11T17:34:32Z", "lastUpdate": "2024-11-25T14:35:17.124576Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "RcisTarget", "description": "It identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, it selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif.", "homepage": "http://bioconductor.org/packages/release/bioc/html/RcisTarget.html", "biotoolsID": "rcistarget", "biotoolsCURIE": "biotools:rcistarget", "version": [ "1.0.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2436", "term": "Gene-set enrichment analysis" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0445", "term": "Transcription factor binding site prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0204", "term": "Gene regulation" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://scenic.aertslab.org/", "type": [ "Mirror" ], "note": null }, { "url": "http://bioconductor.org/packages/RcisTarget/", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/aertslab/RcisTarget/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://git.bioconductor.org/packages/RcisTarget", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/RcisTarget_1.0.2.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/RcisTarget/man/RcisTarget.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/nmeth.4463", "pmid": "28991892", "pmcid": "PMC5937676", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "SCENIC: Single-cell regulatory network inference and clustering", "abstract": "We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.", "date": "2017-10-31T00:00:00Z", "citationCount": 2732, "authors": [ { "name": "Aibar S." }, { "name": "Gonzalez-Blas C.B." }, { "name": "Moerman T." }, { "name": "Huynh-Thu V.A." }, { "name": "Imrichova H." }, { "name": "Hulselmans G." }, { "name": "Rambow F." }, { "name": "Marine J.-C." }, { "name": "Geurts P." }, { "name": "Aerts J." }, { "name": "Van Den Oord J." }, { "name": "Atak Z.K." }, { "name": "Wouters J." }, { "name": "Aerts S." } ], "journal": "Nature Methods" } } ], "credit": [ { "name": "Sara Aibar", "email": "sara.aibar@kuleuven.vib.be", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-14T20:21:43Z", "lastUpdate": "2024-11-25T14:35:08.425464Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "RJMCMCNucleosomes", "description": "This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.", "homepage": "http://bioconductor.org/packages/release/bioc/html/RJMCMCNucleosomes.html", "biotoolsID": "rjmcmcnucleosomes", "biotoolsCURIE": "biotools:rjmcmcnucleosomes", "version": [ "1.4.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3169", "term": "ChIP-seq" }, { "uri": "http://edamontology.org/topic_3176", "term": "DNA packaging" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "Artistic-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/ArnaudDroitLab/RJMCMCNucleosomes", "type": [ "Mirror" ], "note": null }, { "url": "http://bioconductor.org/packages/RJMCMCNucleosomes/", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://git.bioconductor.org/packages/RJMCMCNucleosomes", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/RJMCMCNucleosomes_1.4.0.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/RJMCMCNucleosomes/man/RJMCMCNucleosomes.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1515/sagmb-2014-0098", "pmid": "26656614", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling", "abstract": "Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.", "date": "2015-12-01T00:00:00Z", "citationCount": 10, "authors": [ { "name": "Samb R." }, { "name": "Khadraoui K." }, { "name": "Belleau P." }, { "name": "Deschenes A." }, { "name": "Lakhal-Chaieb L." }, { "name": "Droit A." } ], "journal": "Statistical Applications in Genetics and Molecular Biology" } } ], "credit": [ { "name": "Astrid Deschênes", "email": "adeschen@hotmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-13T09:52:43Z", "lastUpdate": "2024-11-25T14:35:00.529400Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "REMP", "description": "Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE.", "homepage": "http://bioconductor.org/packages/release/bioc/html/REMP.html", "biotoolsID": "remp", "biotoolsCURIE": "biotools:remp", "version": [ "1.4.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2928", "term": "Alignment" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3809", "term": "Differentially-methylated region identification" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2454", "term": "Gene prediction" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2428", "term": "Validation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" }, { "uri": "http://edamontology.org/topic_3518", "term": "Microarray experiment" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://bioconductor.org/packages/REMP/", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/YinanZheng/REMP", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/YinanZheng/REMP/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://git.bioconductor.org/packages/REMP", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/REMP_1.4.0.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/REMP/man/REMP.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gkx587", "pmid": "28911103", "pmcid": "PMC5587781", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Prediction of genome-wide DNA methylation in repetitive elements", "abstract": "DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in genome is widely used to quantify globalmethylation. However, methylation may vary in specific RE and play diverse roles in disease development, thus averaging methylation across RE may lose significant biological information. The ambiguous mapping of short reads by and high cost of current bisulfite sequencing platforms make them impractical for quantifying locus-specific RE methylation. Although microarray-based approaches (particularly Illumina's Infinium methylation arrays) provide cost-effective and robust genome-wide methylation quantification, the number of interrogated CpGs in RE remains limited.We report a random forest-based algorithm (and corresponding R package, REMP) that can accurately predict genome-wide locus-specific RE methylation based on Infinium array profiling data. We validated its prediction performance using alternative sequencing and microarray data. Testing its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more comprehensively extended locus-specific RE methylation information that can be readily applied to large human studies in a cost-effective manner. Our work has the potential to improve our understanding of the role of global methylation in human diseases, especially cancer.", "date": "2017-09-01T00:00:00Z", "citationCount": 72, "authors": [ { "name": "Zheng Y." }, { "name": "Joyce B.T." }, { "name": "Liu L." }, { "name": "Zhang Z." }, { "name": "Kibbe W.A." }, { "name": "Zhang W." }, { "name": "Hou L." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Yinan Zheng", "email": "y-zheng@northwestern.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-13T08:49:55Z", "lastUpdate": "2024-11-25T14:34:59.156002Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BASiCS", "description": "Bayesian Analysis of Single-Cell Sequencing data is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments. It could perform built-in data normalisation and technical noise quantification. BASiCS provides an intuitive detection criterion for highly variable genes within a single group of cells. Also, it can compare gene expression patterns between two or more pre-specified groups of cells.", "homepage": "http://bioconductor.org/packages/release/bioc/html/BASiCS.html", "biotoolsID": "basics", "biotoolsCURIE": "biotools:basics", "version": [ "1.2.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/catavallejos/BASiCS", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/catavallejos/BASiCS/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/BASiCS/", "type": [ "Mirror" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/BASiCS_1.2.1.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/BASiCS/man/BASiCS.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pcbi.1004333", "pmid": "26107944", "pmcid": "PMC4480965", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data", "abstract": "Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.", "date": "2015-06-24T00:00:00Z", "citationCount": 203, "authors": [ { "name": "Vallejos C.A." }, { "name": "Marioni J.C." }, { "name": "Richardson S." } ], "journal": "PLoS Computational Biology" } }, { "doi": "10.1186/s13059-016-0930-3", "pmid": "27083558", "pmcid": "PMC4832562", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Beyond comparisons of means: Understanding changes in gene expression at the single-cell level", "abstract": "Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method's performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.", "date": "2016-04-15T00:00:00Z", "citationCount": 73, "authors": [ { "name": "Vallejos C.A." }, { "name": "Richardson S." }, { "name": "Marioni J.C." } ], "journal": "Genome Biology" } }, { "doi": "10.1101/237214", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Catalina A. Vallejos", "email": "cnvallej@uc.cl", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Nils Eling", "email": "eling@ebi.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-06-25T07:11:57Z", "lastUpdate": "2024-11-25T14:34:37.430997Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "splatter", "description": "Simulate single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.", "homepage": "http://bioconductor.org/packages/release/bioc/html/splatter.html", "biotoolsID": "splatter", "biotoolsCURIE": "biotools:splatter", "version": [ "1.4.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Oshlack/splatter", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/Oshlack/splatter/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/splatter_1.4.1.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/splatter/man/splatter.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1186/s13059-017-1305-0", "pmid": "28899397", "pmcid": "PMC5596896", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Splatter: Simulation of single-cell RNA sequencing data", "abstract": "As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.", "date": "2017-09-12T00:00:00Z", "citationCount": 493, "authors": [ { "name": "Zappia L." }, { "name": "Phipson B." }, { "name": "Oshlack A." } ], "journal": "Genome Biology" } } ], "credit": [ { "name": "Luke Zappia", "email": "luke.zappia@mcri.edu.au", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-26T17:19:05Z", "lastUpdate": "2024-11-25T14:34:32.146110Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "scDD", "description": "This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.", "homepage": "http://bioconductor.org/packages/release/bioc/html/scDD.html", "biotoolsID": "scdd", "biotoolsCURIE": "biotools:scdd", "version": [ "1.4.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression analysis" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/kdkorthauer/scDD", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/kdkorthauer/scDD/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/scDD/", "type": [ "Mirror" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/scDD_1.4.0.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/scDD/man/scDD.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1186/s13059-016-1077-y", "pmid": "27782827", "pmcid": "PMC5080738", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "A statistical approach for identifying differential distributions in single-cell RNA-seq experiments", "abstract": "The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.", "date": "2016-10-25T00:00:00Z", "citationCount": 154, "authors": [ { "name": "Korthauer K.D." }, { "name": "Chu L.-F." }, { "name": "Newton M.A." }, { "name": "Li Y." }, { "name": "Thomson J." }, { "name": "Stewart R." }, { "name": "Kendziorski C." } ], "journal": "Genome Biology" } } ], "credit": [ { "name": "Keegan Korthauer", "email": "keegan@jimmy.harvard.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-26T11:04:28Z", "lastUpdate": "2024-11-25T14:34:28.003998Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "runibic", "description": "This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data.", "homepage": "http://bioconductor.org/packages/release/bioc/html/runibic.html", "biotoolsID": "runibic", "biotoolsCURIE": "biotools:runibic", "version": [ "1.2.3" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3518", "term": "Microarray experiment" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "MIT", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://github.com/athril/runibic", "type": [ "Mirror" ], "note": null }, { "url": "http://github.com/athril/runibic/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/runibic_1.2.3.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/runibic/man/runibic.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/srep23466", "pmid": "27001340", "pmcid": "PMC4802312", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data", "abstract": "Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed.", "date": "2016-03-22T00:00:00Z", "citationCount": 51, "authors": [ { "name": "Wang Z." }, { "name": "Li G." }, { "name": "Robinson R.W." }, { "name": "Huang X." } ], "journal": "Scientific Reports" } }, { "doi": "10.1101/210682", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Patryk Orzechowski", "email": "patryk.orzechowski@gmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-25T15:56:51Z", "lastUpdate": "2024-11-25T14:34:25.941521Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "recount_Bioconductor", "description": "Using this package, you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis.", "homepage": "http://bioconductor.org/packages/release/bioc/html/recount.html", "biotoolsID": "recount_bioconductor", "biotoolsCURIE": "biotools:recount_bioconductor", "version": [ "1.6.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "BSD-2-Clause", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/leekgroup/recount", "type": [ "Mirror" ], "note": null }, { "url": "https://support.bioconductor.org/t/recount/", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/recount_1.6.2.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/recount/man/recount.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/nbt.3838", "pmid": "28398307", "pmcid": "PMC6742427", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Reproducible RNA-seq analysis using recount2", "abstract": "", "date": "2017-04-11T00:00:00Z", "citationCount": 232, "authors": [ { "name": "Collado-Torres L." }, { "name": "Nellore A." }, { "name": "Kammers K." }, { "name": "Ellis S.E." }, { "name": "Taub M.A." }, { "name": "Hansen K.D." }, { "name": "Jaffe A.E." }, { "name": "Langmead B." }, { "name": "Leek J.T." } ], "journal": "Nature Biotechnology" } }, { "doi": "10.12688/f1000research.12223.1", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor", "abstract": "The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene-level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data.", "date": "2017-01-01T00:00:00Z", "citationCount": 29, "authors": [ { "name": "Collado-Torres L." }, { "name": "Nellore A." }, { "name": "Jaffe A.E." } ], "journal": "F1000Research" } }, { "doi": "10.1093/nar/gky102", "pmid": "29514223", "pmcid": "PMC5961118", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Improving the value of public RNA-seq expression data by phenotype prediction", "abstract": "Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phe-notypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible.", "date": "2018-05-18T00:00:00Z", "citationCount": 27, "authors": [ { "name": "Ellis S.E." }, { "name": "Collado-Torres L." }, { "name": "Jaffe A." }, { "name": "Leek J.T." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Leonardo Collado-Torres", "email": "lcollado@jhu.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-25T15:03:49Z", "lastUpdate": "2024-11-25T14:34:20.043372Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "RaMWAS", "description": "A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well.", "homepage": "http://bioconductor.org/packages/release/bioc/html/ramwas.html", "biotoolsID": "ramwas", "biotoolsCURIE": "biotools:ramwas", "version": [ "1.4.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_3517", "term": "GWAS study" }, { "uri": "http://edamontology.org/topic_3295", "term": "DNA methylation" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "LGPL-3.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://bioconductor.org/packages/ramwas/", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/andreyshabalin/ramwas/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/ramwas_1.4.0.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/ramwas/man/ramwas.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty069", "pmid": "29447401", "pmcid": "PMC6022807", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "RaMWAS: Fast methylome-wide association study pipeline for enrichment platforms", "abstract": "Motivation: Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites. Results: We present a Bioconductor R package called RaMWAS with a full set of tools for largescale methylome-wide association studies. It is free, cross-platform, open source, memory efficient and fast.", "date": "2018-07-01T00:00:00Z", "citationCount": 35, "authors": [ { "name": "Shabalin A.A." }, { "name": "Hattab M.W." }, { "name": "Clark S.L." }, { "name": "Chan R.F." }, { "name": "Kumar G." }, { "name": "Aberg K.A." }, { "name": "Van Den Oord E.J.C.G." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Andrey A Shabalin", "email": "andrey.shabalin@gmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "d.gabrielaitis", "additionDate": "2018-06-30T17:49:04Z", "lastUpdate": "2024-11-25T14:34:18.705494Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "iterClust", "description": "A framework for performing clustering analysis iteratively.", "homepage": "http://bioconductor.org/packages/release/bioc/html/iterClust.html", "biotoolsID": "iterclust", "biotoolsCURIE": "biotools:iterclust", "version": [ "1.2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "Other", "collectionID": [ "BioConductor" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/hd2326/iterClust", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/hd2326/iterClust/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/iterClust/", "type": [ "Mirror" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/iterClust_1.2.0.tar.gzc", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/iterClust/man/iterClust.pdf", "type": [ "User manual" ], "note": null }, { "url": "http://bioconductor.org/packages/release/bioc/vignettes/iterClust/inst/doc/introduction.pdf", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty176", "pmid": "29579153", "pmcid": "PMC6084607", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "iterClust: a statistical framework for iterative clustering analysis", "abstract": "Motivation: In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. Results: Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory.", "date": "2018-08-15T00:00:00Z", "citationCount": 7, "authors": [ { "name": "Ding H." }, { "name": "Wang W." }, { "name": "Califano A." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Hongxu Ding", "email": "hd2326@columbia.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-20T19:33:53Z", "lastUpdate": "2024-11-25T14:33:47.574352Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ddPCRclust", "description": "Automated quantification of non-orthogonal, multiplexed ddPCR data.", "homepage": "https://github.com/bgbrink/ddPCRclust", "biotoolsID": "ddpcrclust", "biotoolsCURIE": "biotools:ddpcrclust", "version": [ "1.0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2495", "term": "Gene expression analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3519", "term": "PCR experiment" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_0632", "term": "Probes and primers" }, { "uri": "http://edamontology.org/topic_3360", "term": "Biomarkers" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "Artistic-2.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://bioconductor.org/packages/ddPCRclust/", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/bgbrink/ddPCRclust/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/ddPCRclust_1.0.1.tar.gz", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/bgbrink/ddPCRclust", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty136", "pmid": "29534153", "pmcid": "PMC6061851", "type": [], "version": null, "note": null, "metadata": { "title": "DdPCRclust: An R package and Shiny app for automated analysis of multiplexed ddPCR data", "abstract": "Motivation: Droplet digital PCR (ddPCR) is an emerging technology for quantifying DNA. By partitioning the target DNA into 20 000 droplets, each serving as its own PCR reaction compartment, a very high sensitivity of DNA quantification can be achieved. However, manual analysis of the data is time consuming and algorithms for automated analysis of non-orthogonal, multiplexed ddPCR data are unavailable, presenting a major bottleneck for the advancement of ddPCR transitioning from low-throughput to high-throughput. Results: ddPCRclust is an R package for automated analysis of data from Bio-Rad's droplet digital PCR systems (QX100 and QX200). It can automatically analyze and visualize multiplexed ddPCR experiments with up to four targets per reaction. Results are on par with manual analysis, but only take minutes to compute instead of hours. The accompanying Shiny app ddPCRvis provides easy access to the functionalities of ddPCRclust through a web-browser based GUI.", "date": "2018-08-01T00:00:00Z", "citationCount": 15, "authors": [ { "name": "Brink B.G." }, { "name": "Meskas J." }, { "name": "Brinkman R.R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Benedikt G Brink", "email": "bbrink@cebitec.uni-bielefeld.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "d.gabrielaitis", "additionDate": "2018-07-01T13:07:46Z", "lastUpdate": "2024-11-25T14:33:37.196482Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Cell Cycle Browser (CCB)", "description": "The Human Cell Cycle Browser (hCCB) is an interactive web tool for visualizing and simulating the human cell cycle to explore the timeline of molecular events during cell cycle progression.", "homepage": "https://cellcycle.renci.org/", "biotoolsID": "ccb", "biotoolsCURIE": "biotools:ccb", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3047", "term": "Molecular biology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "JavaScript" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/RENCI/CellCycleBrowser/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/RENCI/CellCycleBrowser/wiki", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.cels.2018.06.004", "pmid": "30077635", "pmcid": "PMC6214685", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The Cell Cycle Browser: An Interactive Tool for Visualizing, Simulating, and Perturbing Cell-Cycle Progression", "abstract": "The cell cycle is driven by precise temporal coordination among many molecular activities. To understand and explore this process, we developed the Cell Cycle Browser (CCB), an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, organizing, simulating, and predicting the outcomes of perturbing cell-cycle parameters. Time-series traces from individual cells can be combined to build a multi-layered timeline of molecular activities. Users can simulate the cell cycle using computational models that capture the dynamics of molecular activities and phase transitions. By adjusting individual expression levels and strengths of molecular relationships, users can predict effects on the cell cycle. Virtual assays, such as growth curves and flow cytometry, provide familiar outputs to compare cell-cycle behaviors for data and simulations. The CCB serves to unify our understanding of cell-cycle dynamics and provides a platform for generating hypotheses through virtual experiments. Borland, Yi, and colleagues develop an interactive website offering visualization, simulation, and virtual experiments to study cell-cycle progression at the molecular level.", "date": "2018-08-22T00:00:00Z", "citationCount": 4, "authors": [ { "name": "Borland D." }, { "name": "Yi H." }, { "name": "Grant G.D." }, { "name": "Kedziora K.M." }, { "name": "Chao H.X." }, { "name": "Haggerty R.A." }, { "name": "Kumar J." }, { "name": "Wolff S.C." }, { "name": "Cook J.G." }, { "name": "Purvis J.E." } ], "journal": "Cell Systems" } } ], "credit": [ { "name": "Jeremy E. Purvis", "email": "jeremy_purvis@med.unc.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "daniel_stefan", "additionDate": "2018-08-06T04:34:05Z", "lastUpdate": "2024-11-25T14:27:51.380681Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "INSPIIRED", "description": "INSPIIRED is a software suite designed to study viral integration sites and the longitudinal outcomes of gene therapy patients.", "homepage": "https://github.com/BushmanLab/INSPIIRED", "biotoolsID": "inspiired", "biotoolsCURIE": "biotools:inspiired", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3197", "term": "Genetic variation analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" }, { "uri": "http://edamontology.org/topic_3127", "term": "DNA replication and recombination" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/BushmanLab/INSPIIRED/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/BushmanLab/INSPIIRED/blob/master/README.md", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.omtm.2016.11.003", "pmid": "28344988", "pmcid": "PMC5363318", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "INSPIIRED: Quantification and Visualization Tools for Analyzing Integration Site Distributions", "abstract": "Analysis of sites of newly integrated DNA in cellular genomes is important to several fields, but methods for analyzing and visualizing these datasets are still under development. Here, we describe tools for data analysis and visualization that take as input integration site data from our INSPIIRED pipeline. Paired-end sequencing allows inference of the numbers of transduced cells as well as the distributions of integration sites in target genomes. We present interactive heatmaps that allow comparison of distributions of integration sites to genomic features and that support numerous user-defined statistical tests. To summarize integration site data from human gene therapy samples, we developed a reproducible report format that catalogs sample population structure, longitudinal dynamics, and integration frequency near cancer-associated genes. We also introduce a novel summary statistic, the UC50 (unique cell progenitors contributing the most expanded 50% of progeny cell clones), which provides a single number summarizing possible clonal expansion. Using these tools, we characterize ongoing longitudinal characterization of a patient from the first trial to treat severe combined immunodeficiency-X1 (SCID-X1), showing successful reconstitution for 15 years accompanied by persistence of a cell clone with an integration site near the cancer-associated gene CCND2. Software is available at https://github.com/BushmanLab/INSPIIRED.", "date": "2017-03-17T00:00:00Z", "citationCount": 57, "authors": [ { "name": "Berry C.C." }, { "name": "Nobles C." }, { "name": "Six E." }, { "name": "Wu Y." }, { "name": "Malani N." }, { "name": "Sherman E." }, { "name": "Dryga A." }, { "name": "Everett J.K." }, { "name": "Male F." }, { "name": "Bailey A." }, { "name": "Bittinger K." }, { "name": "Drake M.J." }, { "name": "Caccavelli L." }, { "name": "Bates P." }, { "name": "Hacein-Bey-Abina S." }, { "name": "Cavazzana M." }, { "name": "Bushman F.D." } ], "journal": "Molecular Therapy Methods and Clinical Development" } } ], "credit": [ { "name": "Frederic D. Bushman", "email": "bushman@mail.med.upenn.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "daniel_stefan", "additionDate": "2018-08-06T00:20:44Z", "lastUpdate": "2024-11-25T14:27:50.030219Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "TnT", "description": "A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data.", "homepage": "http://bioconductor.org/packages/release/bioc/html/TnT.html", "biotoolsID": "tnt", "biotoolsCURIE": "biotools:tnt", "version": [ "1.2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "AGPL-3.0", "collectionID": [ "BioConductor" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Marlin-Na/TnT", "type": [ "Mirror" ], "note": null }, { "url": "https://github.com/Marlin-Na/TnT/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://bioconductor.org/packages/TnT/", "type": [ "Mirror" ], "note": null }, { "url": "https://git.bioconductor.org/packages/TnT", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "http://bioconductor.org/packages/release/bioc/src/contrib/TnT_1.2.0.tar.gz", "type": "Software package", "note": null, "version": null }, { "url": "http://bioconductor.org/packages/release/bioc/vignettes/TnT/inst/doc/introduction.R", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "http://bioconductor.org/packages/release/bioc/manuals/TnT/man/TnT.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btw210", "pmid": "27153646", "pmcid": "PMC4978938", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "TnT: A set of libraries for visualizing trees and track-based annotations for the web", "abstract": "Summary: There is an increasing need for rich and dynamic biological data visualizations in bioinformatic web applications. New standards in web technologies, like SVG or Canvas, are now supported by most modern web browsers allowing the blossoming of powerful visualizations in biological data analysis. The exploration of different ways to visualize genomic data is still challenging due to the lack of flexible tools to develop them. Here, I present a set of libraries aimed at creating powerful tree- and track-based visualizations for the web. Its modularity and rich API facilitate the development of many different visualizations ranging from simple species trees to complex visualizations comprising per-node data annotations or even simple genome browsers.", "date": "2016-08-15T00:00:00Z", "citationCount": 4, "authors": [ { "name": "Pignatelli M." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Jialin Ma", "email": "marlin@gmx.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "shadi.m", "additionDate": "2018-07-15T21:52:58Z", "lastUpdate": "2024-11-25T14:27:16.454532Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "RAVEN Toolbox", "description": "The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology.", "homepage": "https://github.com/SysBioChalmers/RAVEN", "biotoolsID": "raven_toolbox", "biotoolsCURIE": "biotools:raven_toolbox", "version": [ "2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3439", "term": "Pathway or network prediction" }, { "uri": "http://edamontology.org/operation_3660", "term": "Metabolic network modelling" }, { "uri": "http://edamontology.org/operation_2497", "term": "Pathway or network analysis" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1208", "term": "protein" } ] }, { "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_3620", "term": "xlsx" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "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_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3750", "term": "YAML" } ] }, { "data": { "uri": "http://edamontology.org/data_3722", "term": "Physiology parameter" }, "format": [ { "uri": "http://edamontology.org/format_1957", "term": "raw" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Script" ], "topic": [ { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_2259", "term": "Systems biology" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_3398", "term": "Bioengineering" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "MATLAB" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://gitter.im/SysBioChalmers/RAVEN", "type": [ "Mailing list" ], "note": "Gitter chat room" }, { "url": "https://github.com/SysBioChalmers/RAVEN/issues", "type": [ "Issue tracker" ], "note": "GitHub Issues" }, { "url": "https://github.com/SysBioChalmers/RAVEN", "type": [ "Repository" ], "note": "GitHub Repository" }, { "url": "https://github.com/SysBioChalmers/RAVEN/wiki", "type": [ "Helpdesk" ], "note": "GitHub Wiki" } ], "download": [ { "url": "https://github.com/SysBioChalmers/RAVEN/releases", "type": "Binaries", "note": "Stabile releases", "version": null }, { "url": "https://github.com/SysBioChalmers/RAVEN", "type": "Source code", "note": "Source code", "version": null } ], "documentation": [ { "url": "https://github.com/SysBioChalmers/RAVEN/wiki", "type": [ "General" ], "note": "GitHub Wiki" } ], "publication": [ { "doi": "10.1101/321067", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": null, "email": null, "url": "https://gitter.im/SysBioChalmers/RAVEN?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Hao Wang", "email": "hao.wang@chalmers.se", "url": null, "orcidid": "http://orcid.org/0000-0001-7475-0136", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "eduardk", "additionDate": "2018-07-18T09:31:39Z", "lastUpdate": "2024-11-25T14:23:23.622737Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "gbm.auto", "description": "Process and predict mapping of species abundance data. The package automates and greatly simplifies Boosted Regression Tree spatial modelling and makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities.", "homepage": "https://github.com/SimonDedman/gbm.auto", "biotoolsID": "gbm.auto", "biotoolsCURIE": "biotools:gbm.auto", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0573", "term": "Map drawing" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" }, { "uri": "http://edamontology.org/topic_3387", "term": "Marine biology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/SimonDedman/gbm.auto/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/SimonDedman/gbm.auto/blob/master/README.md", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pone.0192520", "pmid": "29394292", "pmcid": "PMC5796701", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Correction: Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning (PLoS ONE (2017) 12:12 (e0188955) DOI: 10.1371/journal.pone.0188955)", "abstract": "The subheading “Worked examplePre-run parameter scoping with gbm.bfcheck” under the Guide to software functions section is incorrect. The subheadings should be “Worked example” and “Pre-run parameter scoping with gbm.bfcheck”. The following information is missing from the eighth paragraph of the Guide to software functions section under the subheading “Worked example”: To demonstrate gbm.auto we will run a simple example in the sections below; here we load the required functions and data. Please see Supplementary Material D1 for all details of each function and more fully worked examples. install.packages(\"devtools\") library(\"devtools\") install_github(’SimonDedman/gbm.auto’) library(\"gbm.auto\") mygrids <- gbm.auto::grids # load grids mysamples <- gbm.auto::samples # load samples", "date": "2018-02-01T00:00:00Z", "citationCount": 0, "authors": [], "journal": "PLoS ONE" } } ], "credit": [ { "name": "Simon Dedman", "email": "simondedman@gmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "migle_sur", "additionDate": "2018-06-02T07:25:33Z", "lastUpdate": "2024-11-25T14:22:47.664052Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "GUIdock-VNC", "description": "Using a graphical desktop sharing system to provide a browser-based interface for containerized software.", "homepage": "https://github.com/BioDepot/GUIdock-VNC", "biotoolsID": "guidock-vnc", "biotoolsCURIE": "biotools:guidock-vnc", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3762", "term": "Service composition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_3372", "term": "Software engineering" }, { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_3053", "term": "Genetics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "JavaScript", "Java", "R", "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/BioDepot/GUIdock-VNC/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/BioDepot/GUIdock-VNC", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/gigascience/giw013", "pmid": "28327936", "pmcid": "PMC5530313", "type": [], "version": null, "note": null, "metadata": { "title": "GUIdock-VNC: Using a graphical desktop sharing system to provide a browser-based interface for containerized software", "abstract": "Background: Software container technology such as Docker can be used to package and distribute bioinformatics workflows consisting of multiple software implementations and dependencies. However, Docker is a command line-based tool, and many bioinformatics pipelines consist of components that require a graphical user interface. Results: We present a container tool called GUIdock-VNC that uses a graphical desktop sharing system to provide a browser-based interface for containerized software. GUIdock-VNC uses the Virtual Network Computing protocol to render the graphics within most commonly used browsers. We also present a minimal image builder that can add our proposed graphical desktop sharing system to any Docker packages, with the end result that any Docker packages can be run using a graphical desktop within a browser. In addition, GUIdock-VNC uses the Oauth2 authentication protocols when deployed on the cloud. Conclusions: As a proof-of-concept, we demonstrated the utility of GUIdock-noVNC in gene network inference. We benchmarked our container implementation on various operating systems and showed that our solution creates minimal overhead.", "date": "2017-04-01T00:00:00Z", "citationCount": 6, "authors": [ { "name": "Mittal V." }, { "name": "Hung L.-H." }, { "name": "Keswani J." }, { "name": "Kristiyanto D." }, { "name": "Lee S.B." }, { "name": "Yeung K.Y." } ], "journal": "GigaScience" } } ], "credit": [ { "name": "Ka Yee Yeung", "email": "kayee@uw.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "d.gabrielaitis", "additionDate": "2018-07-15T10:12:10Z", "lastUpdate": "2024-11-25T14:22:37.744584Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "LEVER", "description": "Collection of software tools for analyzing the development of proliferating (dividing) cells in time-lapse microscopy image sequences. It includes algorithms for segmentation, tracking and lineaging. It also includes a user interface that allows the segmentation, tracking and lineaging results to be validated, with any errors in the automated processing easily identified and quickly corrected.", "homepage": "https://bioimage.coe.drexel.edu/mp/lever/", "biotoolsID": "lever", "biotoolsCURIE": "biotools:lever", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3443", "term": "Image analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3385", "term": "Light microscopy" } ], "operatingSystem": [ "Windows" ], "language": [ "MATLAB" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/lever-stem-cell-segmentation-tracking-and-lineaging.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://git-bioimage.coe.drexel.edu/opensource/lever/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://bioimage.coe.drexel.edu/mp/lever/index.php/sample-movies/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btw406", "pmid": "27423896", "pmcid": "PMC5181556", "type": [], "version": null, "note": null, "metadata": { "title": "LEVER: Software tools for segmentation, tracking and lineaging of proliferating cells", "abstract": "The analysis of time-lapse images showing cells dividing to produce clones of related cells is an important application in biological microscopy. Imaging at the temporal resolution required to establish accurate tracking for vertebrate stem or cancer cells often requires the use of transmitted light or phase-contrast microscopy. Processing these images requires automated segmentation, tracking and lineaging algorithms. There is also a need for any errors in the automated processing to be easily identified and quickly corrected. We have developed LEVER, an open source software tool that combines the automated image analysis for phase-contrast microscopy movies with an easy-to-use interface for validating the results and correcting any errors.", "date": "2016-11-15T00:00:00Z", "citationCount": 39, "authors": [ { "name": "Winter M." }, { "name": "Mankowski W." }, { "name": "Wait E." }, { "name": "Temple S." }, { "name": "Cohen A.R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Andrew R. Cohen", "email": "andrew.r.cohen@drexel.edu", "url": "https://bioimage.coe.drexel.edu/mp/contact/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Mark R. Winter", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0003-1180-1957", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "mbs_import", "additionDate": "2017-08-03T18:47:58Z", "lastUpdate": "2024-11-25T14:22:31.353359Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "TRANSIT", "description": "A tool for the analysis of Tn-Seq data. It provides an easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality in a single condition as well as between conditions.", "homepage": "http://saclab.tamu.edu/essentiality/transit/", "biotoolsID": "transit", "biotoolsCURIE": "biotools:transit", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0427", "term": "Transposon prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0798", "term": "Mobile genetic elements" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/transit-analysis-of-tn-seq-data.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://github.com/mad-lab/transit", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/mad-lab/transit/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "http://transit.readthedocs.io/en/latest/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pcbi.1004401", "pmid": "26447887", "pmcid": "PMC4598096", "type": [], "version": null, "note": null, "metadata": { "title": "TRANSIT - A Software Tool for Himar1 TnSeq Analysis", "abstract": "TnSeq has become a popular technique for determining the essentiality of genomic regions in bacterial organisms. Several methods have been developed to analyze the wealth of data that has been obtained through TnSeq experiments. We developed a tool for analyzing Himar1 TnSeq data called TRANSIT. TRANSIT provides a graphical interface to three different statistical methods for analyzing TnSeq data. These methods cover a variety of approaches capable of identifying essential genes in individual datasets as well as comparative analysis between conditions. We demonstrate the utility of this software by analyzing TnSeq datasets of M. tuberculosis grown on glycerol and cholesterol. We show that TRANSIT can be used to discover genes which have been previously implicated for growth on these carbon sources. TRANSIT is written in Python, and thus can be run on Windows, OSX and Linux platforms. The source code is distributed under the GNU GPL v3 license and can be obtained from the following GitHub repository: https://github.com/mad-lab/transit", "date": "2015-01-01T00:00:00Z", "citationCount": 119, "authors": [ { "name": "DeJesus M.A." }, { "name": "Ambadipudi C." }, { "name": "Baker R." }, { "name": "Sassetti C." }, { "name": "Ioerger T.R." } ], "journal": "PLoS Computational Biology" } } ], "credit": [ { "name": "Michael A. DeJesus", "email": "michael.dejesus@tamu.edu", "url": "http://students.cs.tamu.edu/mad/", "orcidid": "http://orcid.org/0000-0003-3867-0299", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "mbs_import", "additionDate": "2017-08-03T19:02:53Z", "lastUpdate": "2024-11-25T14:22:27.846591Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SINA", "description": "Aligns and optionally taxonomically classifies your rRNA gene sequences.\n\nReference based multiple sequence alignment", "homepage": "https://sina.readthedocs.io", "biotoolsID": "sina", "biotoolsCURIE": "biotools:sina", "version": [ "1.3.1", "1.5.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0258", "term": "Sequence alignment analysis" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0492", "term": "Multiple sequence alignment" }, { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" }, { "uri": "http://edamontology.org/operation_0294", "term": "Structure-based sequence alignment" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0099", "term": "RNA" }, { "uri": "http://edamontology.org/topic_0097", "term": "Nucleic acid structure analysis" }, { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C++" ], "license": "GPL-3.0", "collectionID": [ "SINA" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/sina-high-throughput-multiple-sequence-alignment-of-ribosomal-rna-genes.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://github.com/epruesse/SINA/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://github.com/epruesse/SINA/releases", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/epruesse/SINA", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bts252", "pmid": "22556368", "pmcid": "PMC3389763", "type": [], "version": null, "note": null, "metadata": { "title": "SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes", "abstract": "Motivation: In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements.Results: In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands. SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks. © The Author(s) 2012. Published by Oxford University Press.", "date": "2012-07-01T00:00:00Z", "citationCount": 2327, "authors": [ { "name": "Pruesse E." }, { "name": "Peplies J." }, { "name": "Glockner F.O." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": null, "email": "contact@arb-silva.de", "url": "http://www.arb-silva.de/contact/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "epruesse", "additionDate": "2017-08-03T18:59:43Z", "lastUpdate": "2024-11-25T14:22:22.697934Z", "editPermission": { "type": "group", "authors": [ "epruesse" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ARB", "description": "The ARB software is a graphically oriented package comprising various tools for sequence database handling and data analysis. A central database of processed (aligned) sequences and any type of additional data linked to the respective sequence entries is structured according to phylogeny or other user defined criteria", "homepage": "http://arb-home.de", "biotoolsID": "arb", "biotoolsCURIE": "biotools:arb", "version": [ "6.0.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0346", "term": "Sequence similarity search" }, { "uri": "http://edamontology.org/operation_0287", "term": "Base position variability plotting" }, { "uri": "http://edamontology.org/operation_0260", "term": "Sequence alignment conversion" }, { "uri": "http://edamontology.org/operation_0294", "term": "Structure-based sequence alignment" }, { "uri": "http://edamontology.org/operation_0567", "term": "Phylogenetic tree visualisation" }, { "uri": "http://edamontology.org/operation_3081", "term": "Sequence alignment editing" }, { "uri": "http://edamontology.org/operation_0326", "term": "Phylogenetic tree editing" }, { "uri": "http://edamontology.org/operation_0289", "term": "Sequence distance matrix generation" }, { "uri": "http://edamontology.org/operation_0278", "term": "RNA secondary structure prediction" }, { "uri": "http://edamontology.org/operation_0492", "term": "Multiple sequence alignment" }, { "uri": "http://edamontology.org/operation_0259", "term": "Sequence alignment comparison" }, { "uri": "http://edamontology.org/operation_0368", "term": "Sequence masking" }, { "uri": "http://edamontology.org/operation_0291", "term": "Sequence clustering" }, { "uri": "http://edamontology.org/operation_0349", "term": "Sequence database search (by property)" }, { "uri": "http://edamontology.org/operation_0496", "term": "Global alignment" }, { "uri": "http://edamontology.org/operation_0558", "term": "Phylogenetic tree annotation" }, { "uri": "http://edamontology.org/operation_3080", "term": "Structure editing" }, { "uri": "http://edamontology.org/operation_0371", "term": "DNA translation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Shell", "C++", "Perl" ], "license": "Proprietary", "collectionID": [ "ARB" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://groups.yahoo.com/neo/groups/arb_users/info", "type": [ "Mailing list" ], "note": null }, { "url": "http://bugs.arb-home.de/", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://download.arb-home.de/release/latest/", "type": "Binaries", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1093/nar/gkh293", "pmid": "14985472", "pmcid": "PMC390282", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "ARB: A software environment for sequence data", "abstract": "The ARB (from Latin arbor, tree) project was initiated almost 10 years ago. The ARB program package comprises a variety of directly interacting software tools for sequence database maintenance and analysis which are controlled by a common graphical user interface. Although it was initially designed for ribosomal RNA data, it can be used for any nucleic and amino acid sequence data as well. A central database contains processed (aligned) primary structure data. Any additional descriptive data can be stored in database fields assigned to the individual sequences or linked via local or worldwide networks. A phylogenetic tree visualized in the main window can be used for data access and visualization. The package comprises additional tools for data import and export, sequence alignment, primary and secondary structure editing, profile and filter calculation, phylogenetic analyses, specific hybridization probe design and evaluation and other components for data analysis. Currently, the package is used by numerous working groups worldwide. © Oxford University Press 2004; all rights reserved.", "date": "2004-07-14T00:00:00Z", "citationCount": 5442, "authors": [ { "name": "Ludwig W." }, { "name": "Strunk O." }, { "name": "Westram R." }, { "name": "Richter L." }, { "name": "Meier H." }, { "name": "Yadhukumar A." }, { "name": "Buchner A." }, { "name": "Lai T." }, { "name": "Steppi S." }, { "name": "Jacob G." }, { "name": "Forster W." }, { "name": "Brettske I." }, { "name": "Gerber S." }, { "name": "Ginhart A.W." }, { "name": "Gross O." }, { "name": "Grumann S." }, { "name": "Hermann S." }, { "name": "Jost R." }, { "name": "Konig A." }, { "name": "Liss T." }, { "name": "Lussbmann R." }, { "name": "May M." }, { "name": "Nonhoff B." }, { "name": "Reichel B." }, { "name": "Strehlow R." }, { "name": "Stamatakis A." }, { "name": "Stuckmann N." }, { "name": "Vilbig A." }, { "name": "Lenke M." }, { "name": "Ludwig T." }, { "name": "Bode A." }, { "name": "Schleifer K.-H." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1002/9781118010518.ch46", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "ARB: A Software Environment for Sequence Data", "abstract": "", "date": "2011-05-03T00:00:00Z", "citationCount": 79, "authors": [ { "name": "Westram R." }, { "name": "Bader K." }, { "name": "Prusse E." }, { "name": "Kumar Y." }, { "name": "Meier H." }, { "name": "Glockner F.O." }, { "name": "Ludwig W." } ], "journal": "Handbook of Molecular Microbial Ecology I: Metagenomics and Complementary Approaches" } } ], "credit": [ { "name": "ARB Users Mailing List", "email": "arb@arb-home.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "epruesse", "additionDate": "2018-02-22T19:13:08Z", "lastUpdate": "2024-11-25T14:21:30.842496Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Curtain", "description": "Curtain is a Java wrapper around next-generation assemblers such as Velvet which allows the incremental introduction of read-pair information into the assembly process. This enables the assembly of larger genomes than would otherwise be possible within existing memory constraints.", "homepage": "http://code.google.com/p/curtain/", "biotoolsID": "curtain", "biotoolsCURIE": "biotools:curtain", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" } ], "operatingSystem": [], "language": [ "Java" ], "license": null, "collectionID": [], "maturity": "Emerging", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/curtain-0-2-3-beta-assembling-large-genomes-short-read-sequences.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://groups.google.com/forum/#!forum/curtain-assembler", "type": [ "Issue tracker" ], "note": "Discussion forum" } ], "download": [], "documentation": [ { "url": "https://code.google.com/archive/p/curtain/wikis", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/2047-217x-2-10", "pmid": "23870653", "pmcid": "PMC3844414", "type": [], "version": null, "note": null, "metadata": { "title": "Assemblathon 2: Evaluating de novo methods of genome assembly in three vertebrate species", "abstract": "Background: The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results: In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions: Many current genome assemblers produced useful assemblies, containing a significant representation of their genes and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another.", "date": "2013-01-01T00:00:00Z", "citationCount": 481, "authors": [ { "name": "Bradnam K.R." }, { "name": "Fass J.N." }, { "name": "Alexandrov A." }, { "name": "Baranay P." }, { "name": "Bechner M." }, { "name": "Birol I." }, { "name": "Boisvert S." }, { "name": "Chapman J.A." }, { "name": "Chapuis G." }, { "name": "Chikhi R." }, { "name": "Chitsaz H." }, { "name": "Chou W.-C." }, { "name": "Corbeil J." }, { "name": "Fabbro C.D." }, { "name": "Docking T.R." }, { "name": "Durbin R." }, { "name": "Earl D." }, { "name": "Emrich S." }, { "name": "Fedotov P." }, { "name": "Fonseca N.A." }, { "name": "Ganapathy G." }, { "name": "Gibbs R.A." }, { "name": "Gnerre S." }, { "name": "Godzaridis E." }, { "name": "Goldstein S." }, { "name": "Haimel M." }, { "name": "Hall G." }, { "name": "Haussler D." }, { "name": "Hiatt J.B." }, { "name": "Ho I.Y." }, { "name": "Howard J." }, { "name": "Hunt M." }, { "name": "Jackman S.D." }, { "name": "Jaffe D.B." }, { "name": "Jarvis E.D." }, { "name": "Jiang H." }, { "name": "Kazakov S." }, { "name": "Kersey P.J." }, { "name": "Kitzman J.O." }, { "name": "Knight J.R." }, { "name": "Koren S." }, { "name": "Lam T.-W." }, { "name": "Lavenier D." }, { "name": "Laviolette F." }, { "name": "Li Y." }, { "name": "Li Z." }, { "name": "Liu B." }, { "name": "Liu Y." }, { "name": "Luo R." }, { "name": "MacCallum I." }, { "name": "MacManes M.D." }, { "name": "Maillet N." }, { "name": "Melnikov S." }, { "name": "Naquin D." }, { "name": "Ning Z." }, { "name": "Otto T.D." }, { "name": "Paten B." }, { "name": "Paulo O.S." }, { "name": "Phillippy A.M." }, { "name": "Pina-Martins F." }, { "name": "Place M." }, { "name": "Przybylski D." }, { "name": "Qin X." }, { "name": "Qu C." }, { "name": "Ribeiro F.J." }, { "name": "Richards S." }, { "name": "Rokhsar D.S." }, { "name": "Ruby J.G." }, { "name": "Scalabrin S." }, { "name": "Schatz M.C." }, { "name": "Schwartz D.C." }, { "name": "Sergushichev A." }, { "name": "Sharpe T." }, { "name": "Shaw T.I." }, { "name": "Shendure J." }, { "name": "Shi Y." }, { "name": "Simpson J.T." }, { "name": "Song H." }, { "name": "Tsarev F." }, { "name": "Vezzi F." }, { "name": "Vicedomini R." }, { "name": "Vieira B.M." }, { "name": "Wang J." }, { "name": "Worley K.C." }, { "name": "Yin S." }, { "name": "Yiu S.-M." }, { "name": "Yuan J." }, { "name": "Zhang G." }, { "name": "Zhang H." }, { "name": "Zhou S." }, { "name": "Korf I.F." } ], "journal": "GigaScience" } } ], "credit": [ { "name": null, "email": "mhaimel.ebi@googlemail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "seqwiki_import", "additionDate": "2017-01-13T13:14:15Z", "lastUpdate": "2024-11-25T14:20:16.624148Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }
