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{ "count": 6393, "next": "?page=2", "previous": null, "list": [ { "name": "compareMS2", "description": "compareMS2 is a tool for comparing sets of (tandem) mass spectra for clustering samples, molecular phylogenetics, identification of biological species or tissues, and quality control. compareMS2 currently consumes Mascot Generic Format, or MGF, and produces output in a variety of common image and distance matrix formats.", "homepage": "https://github.com/524D/compareMS2", "biotoolsID": "comparems2", "biotoolsCURIE": "biotools:comparems2", "version": [ "1.0", "2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" }, { "uri": "http://edamontology.org/operation_0567", "term": "Phylogenetic tree visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3651", "term": "MGF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3272", "term": "Species tree" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3604", "term": "SVG" } ] }, { "data": { "uri": "http://edamontology.org/data_2855", "term": "Distance matrix" }, "format": [ { "uri": "http://edamontology.org/format_1991", "term": "mega" }, { "uri": "http://edamontology.org/format_1912", "term": "Nexus format" }, { "uri": "http://edamontology.org/format_1910", "term": "newick" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C", "JavaScript" ], "license": "MIT", "collectionID": [ "ms-utils", "Proteomics" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [ "Netherlands" ], "elixirCommunity": [ "Proteomics" ], "link": [ { "url": "https://github.com/524D/compareMS2", "type": [ "Repository" ], "note": null }, { "url": "https://www.ms-utils.org/compareMS2.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://research-software-directory.org/software/comparems2", "type": [ "Software catalogue" ], "note": null } ], "download": [ { "url": "http://www.ms-utils.org/compareMS2.c", "type": "Source code", "note": null, "version": "1.0" }, { "url": "http://www.ms-utils.org/compareMS2.html", "type": "Binaries", "note": null, "version": "1.0" }, { "url": "http://www.ms-utils.org/compareMS2.c", "type": "Source code", "note": null, "version": "1.0" }, { "url": "https://github.com/524D/compareMS2/tree/main/src", "type": "Source code", "note": null, "version": "2.0" }, { "url": "https://github.com/524D/compareMS2/tree/main", "type": "Binaries", "note": null, "version": "2.0" } ], "documentation": [ { "url": "http://www.ms-utils.org/compareMS2.html", "type": [ "General", "Command-line options" ], "note": null }, { "url": "https://github.com/524D/compareMS2", "type": [ "General", "User manual", "Command-line options", "Installation instructions" ], "note": null } ], "publication": [ { "doi": "10.1002/rcm.6162", "pmid": "22368051", "pmcid": null, "type": [ "Primary" ], "version": "1.0", "note": null, "metadata": { "title": "Molecular phylogenetics by direct comparison of tandem mass spectra", "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.", "date": "2012-04-15T00:00:00Z", "citationCount": 30, "authors": [ { "name": "Palmblad M." }, { "name": "Deelder A.M." } ], "journal": "Rapid Communications in Mass Spectrometry" } }, { "doi": "10.1021/acs.jproteome.2c00457", "pmid": "36173614", "pmcid": "PMC9903320", "type": [ "Primary" ], "version": "2.0", "note": null, "metadata": { "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets", "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.", "date": "2023-02-03T00:00:00Z", "citationCount": 7, "authors": [ { "name": "Marissen R." }, { "name": "Varunjikar M.S." }, { "name": "Laros J.F.J." }, { "name": "Rasinger J.D." }, { "name": "Neely B.A." }, { "name": "Palmblad M." } ], "journal": "Journal of Proteome Research" } }, { "doi": "10.1021/acs.jproteome.1c00528", "pmid": "34523928", "pmcid": "PMC8491155", "type": [ "Review" ], "version": "2.0", "note": null, "metadata": { "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics", "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.", "date": "2021-10-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Neely B.A." }, { "name": "Palmblad M." } ], "journal": "Journal of Proteome Research" } } ], "credit": [ { "name": "lumc.nl", "email": null, "url": "https://www.lumc.nl", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Support" ], "note": null }, { "name": "Magnus Palmblad", "email": "magnus.palmblad@gmail.com", "url": "https://github.com/magnuspalmblad", "orcidid": "http://orcid.org/0000-0002-5865-8994", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Primary contact", "Documentor" ], "note": null }, { "name": "Rob Marissen", "email": null, "url": "https://github.com/524D", "orcidid": "https://orcid.org/0000-0002-1220-9173", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "n.m.palmblad@lumc.nl", "additionDate": "2016-04-15T11:52:42Z", "lastUpdate": "2025-04-16T14:22:04.957317Z", "editPermission": { "type": "group", "authors": [ "proteomics.bio.tools" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "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": "MarineMetagenomeDB", "description": "MarineMetagenomeDB provides standardized and manually curated metadata for 11,449 marine metagenomes from SRA and MG-RAST. It enables users to search, filter, visualize, and download metadata through a web application equipped with quick and advanced search options, interactive map selection, and export tools. The database enhances data findability and reuse for comparative and large-scale meta-analyses in marine microbiome research.", "homepage": "https://webapp.ufz.de/marmdb/", "biotoolsID": "marinemetagenomedb", "biotoolsCURIE": "biotools:marinemetagenomedb", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_3837", "term": "Metagenomic sequencing" }, { "uri": "http://edamontology.org/topic_3277", "term": "Sample collections" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s40793-022-00449-7", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "andersonavilasantos", "additionDate": "2025-04-08T12:40:26.806570Z", "lastUpdate": "2025-04-08T12:44:37.167081Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "AnimalAssociatedMetagenomeDB", "description": "The AnimalAssociatedMetagenomeDB (AAMDB) provides standardized and manually curated metadata for 10,885 non-human, animal-associated metagenomes collected from public repositories such as SRA and MG-RAST. It allows users to search, filter, visualize, and export metadata through an interactive web application featuring quick and advanced search options, geographic map-based selection, and support for raw data download. The tool facilitates the discovery and reuse of metagenomic data in biodiversity and microbiome studies.", "homepage": "https://webapp.ufz.de/aamdb/", "biotoolsID": "animalassociatedmetagenomedb", "biotoolsCURIE": "biotools:animalassociatedmetagenomedb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3837", "term": "Metagenomic sequencing" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_3277", "term": "Sample collections" } ], "operatingSystem": [], "language": [ "R" ], "license": null, "collectionID": [ "NFDI4Microbiota" ], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s42523-023-00267-3", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "andersonavilasantos", "additionDate": "2025-04-08T12:16:36.396210Z", "lastUpdate": "2025-04-08T12:38:13.124276Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "CheckM2", "description": "Rapid assessment of genome bin quality using machine learning.\n\nCheckM2 uses two distinct machine learning models to predict genome completeness. The 'general' gradient boost model is able to generalize well and is intended to be used on organisms not well represented in GenBank or RefSeq (roughly, when an organism is novel at the level of order, class or phylum). The 'specific' neural network model is more accurate when predicting completeness of organisms more closely related to the reference training set (roughly, when an organism belongs to a known species, genus or family). CheckM2 uses a cosine similarity calculation to automatically determine the appropriate completeness model for each input genome, but you can also force the use of a particular completeness model, or get the prediction outputs for both. There is only one contamination model (based on gradient boost) which is applied regardless of taxonomic novelty and works well across all cases.", "homepage": "https://github.com/chklovski/CheckM2", "biotoolsID": "checkm2", "biotoolsCURIE": "biotools:checkm2", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_0194", "term": "Phylogenomics" }, { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" } ], "operatingSystem": [ "Linux" ], "language": [], "license": "GPL-3.0", "collectionID": [ "NFDI4Microbiota" ], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.nature.com/articles/s41592-023-01940-w", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://doi.org/10.5281/zenodo.14897628", "type": "Biological data", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/chklovski/CheckM2", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1038/s41592-023-01940-w", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "Kasmanas", "additionDate": "2025-04-08T11:23:25.060891Z", "lastUpdate": "2025-04-08T11:23:25.063938Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Coreprofiler", "description": "CoreProfiler is a cgMLST (core genome multilocus sequence typing) software that identifies alleles in bacterial genome assemblies by comparing them to a reference allele scheme. It detects both exact matches to known alleles and potential novel alleles using a two-step BLAST-based approach, enabling robust and reproducible strain genotyping.", "homepage": "https://gitlab.com/ifb-elixirfr/abromics/coreprofiler", "biotoolsID": "coreprofiler", "biotoolsCURIE": "biotools:coreprofiler", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3840", "term": "Multilocus sequence typing" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://gitlab.com/ifb-elixirfr/abromics/coreprofiler/-/blob/main/README.md?ref_type=heads", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.5281/ZENODO.8282656", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.1016/S0022-2836(05)80360-2", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Basic local alignment search tool", "abstract": "A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straight-forward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity. © 1990, Academic Press Limited. All rights reserved.", "date": "1990-01-01T00:00:00Z", "citationCount": 79275, "authors": [ { "name": "Altschul S.F." }, { "name": "Gish W." }, { "name": "Miller W." }, { "name": "Myers E.W." }, { "name": "Lipman D.J." } ], "journal": "Journal of Molecular Biology" } } ], "credit": [], "owner": "clsiguret", "additionDate": "2025-04-08T08:17:22.895529Z", "lastUpdate": "2025-04-08T08:22:57.038047Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "csdR", "description": "Differential gene coexpression analysis based on the Conserved, Specific, and Differentiated (CSD) method", "homepage": "https://almaaslab.github.io/csdR/", "biotoolsID": "csdr", "biotoolsCURIE": "biotools:csdr", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0204", "term": "Gene regulation" }, { "uri": "http://edamontology.org/topic_0085", "term": "Functional genomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.bioconductor.org/packages/release/bioc/html/csdR.html", "type": [ "Software catalogue" ], "note": null }, { "url": "https://github.com/AlmaasLab/csdR", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s12859-022-04605-1", "pmid": "35183100", "pmcid": "PMC8858518", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "csdR, an R package for differential co-expression analysis", "abstract": "Background: Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation. Results: We created the R-package csdR aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. csdR was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. csdR was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than ∼ 2.7 parallel speedup with saturation reached at about 10 cores. Conclusion: The results suggest that csdR is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.", "date": "2022-12-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Pettersen J.P." }, { "name": "Almaas E." } ], "journal": "BMC Bioinformatics" } } ], "credit": [], "owner": "japet", "additionDate": "2025-03-31T08:50:20.800385Z", "lastUpdate": "2025-03-31T09:00:57.202555Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "PPAI", "description": "PPAI is a web server for predicting protein-aptamer interactions.\n\nThe interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is very important and feasible to construct an accurate computational model to predict protein-aptamer interactions, which is benefit for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies.\n\nPPAI is a novel web-server to predict aptamers and protein-aptamer interactions with sequence features of proteins/aptamers and machine learning framework which integrated Adaboost and Random Forest. PPAI not only provides more accurate prediction functions, but also provides the protein aptamer information to the user for query.", "homepage": "http://39.96.85.9/PPAI", "biotoolsID": "ppai", "biotoolsCURIE": "biotools:ppai", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2492", "term": "Protein interaction prediction" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0253", "term": "Sequence feature detection" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0224", "term": "Query and retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0128", "term": "Protein interactions" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0659", "term": "Functional, regulatory and non-coding RNA" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-020-03574-7", "pmid": "32517696", "pmcid": "PMC7285591", "type": [], "version": null, "note": null, "metadata": { "title": "PPAI: A web server for predicting protein-aptamer interactions", "abstract": "Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. Conclusion: These results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.", "date": "2020-06-09T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Li J." }, { "name": "Ma X." }, { "name": "Li X." }, { "name": "Gu J." } ], "journal": "BMC Bioinformatics" } }, { "doi": "10.21203/RS.3.RS-27174/V2", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Jianwei Li", "email": "lijianwei@hebut.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-9795-2635", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "zsmag19", "additionDate": "2021-01-18T09:26:31Z", "lastUpdate": "2025-03-20T07:27:32.204279Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "European Health Research Data and Sample Catalogue", "description": "A collaborative effort to integrate the catalogues of diverse EU research projects and networks to accelerate reuse and improve citizens health.", "homepage": "https://data-catalogue.molgeniscloud.org/", "biotoolsID": "molgenis_european_health_research_data_and_sample_catalogue", "biotoolsCURIE": "biotools:molgenis_european_health_research_data_and_sample_catalogue", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Database portal" ], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1055/s-0042-1742522", "pmid": "36463884", "pmcid": "PMC9719789", "type": [], "version": null, "note": null, "metadata": { "title": "Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies", "abstract": "Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.", "date": "2022-12-04T00:00:00Z", "citationCount": 9, "authors": [ { "name": "Swertz M." }, { "name": "Van Enckevort E." }, { "name": "Oliveira J.L." }, { "name": "Fortier I." }, { "name": "Bergeron J." }, { "name": "Thurin N.H." }, { "name": "Hyde E." }, { "name": "Kellmann A." }, { "name": "Pahoueshnja R." }, { "name": "Sturkenboom M." }, { "name": "Cunnington M." }, { "name": "Nybo Andersen A.-M." }, { "name": "Marcon Y." }, { "name": "Goncalves G." }, { "name": "Gini R." } ], "journal": "Yearbook of Medical Informatics" } } ], "credit": [], "owner": "EleanorHyde", "additionDate": "2025-03-11T14:55:53.492167Z", "lastUpdate": "2025-03-19T15:08:24.036904Z", "editPermission": { "type": "group", "authors": [ "mswertz", "EleanorHyde" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "GTestimate", "description": "GTestimate is a scRNA-seq normalization method. In contrast to other methods it uses the Simple Good-Turing estimator for the per cell relative gene expression estimation.", "homepage": "https://github.com/Martin-Fahrenberger/GTestimate", "biotoolsID": "gtestimate", "biotoolsCURIE": "biotools:gtestimate", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3917", "term": "Count matrix" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [] } ], "note": "GTestimate is provided as an R-package containing the GTestimate() function.", "cmd": "GTestimate()" } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Martin-Fahrenberger/GTestimate", "type": [ "Issue tracker", "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/Martin-Fahrenberger/GTestimate", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.07.02.601501", "pmid": null, "pmcid": null, "type": [ "Method" ], "version": "2", "note": "preprint version 2", "metadata": null } ], "credit": [], "owner": "martin_fahrenberger", "additionDate": "2025-03-12T15:38:32.218141Z", "lastUpdate": "2025-03-12T15:38:32.221353Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Arabidopsis Co-expression Tool (ACT)", "description": "The Arabidopsis Coexpression Tool (ACT) is based on the coexpression analysis of 21273 Arabidopsis thaliana genes from gene pair correlation data of 3500 Affymetrix Arabidopsis ATH1 Genome Array Chip microarray samples.", "homepage": "https://www.michalopoulos.net/act/", "biotoolsID": "act", "biotoolsCURIE": "biotools:act", "version": [ "2.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2938", "term": "Dendrogram visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1025", "term": "Gene identifier" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3271", "term": "Gene tree" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Greece" ], "elixirCommunity": [ "Plant Sciences" ], "link": [], "download": [], "documentation": [ { "url": "https://www.michalopoulos.net/act2.6/help.php", "type": [ "FAQ" ], "note": null } ], "publication": [ { "doi": "10.3390/genes16030258", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null }, { "doi": "10.1016/j.xpro.2022.101208", "pmid": "35243384", "pmcid": "PMC8885756", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "Gene coexpression analysis in Arabidopsis thaliana based on public microarray data", "abstract": "Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach. For complete details on the use and execution of this protocol, please refer to Zogopoulos et al. (2021).", "date": "2022-03-18T00:00:00Z", "citationCount": 5, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Malatras A." }, { "name": "Michalopoulos I." } ], "journal": "STAR Protocols" } }, { "doi": "10.3390/biology11071019", "pmid": "36101400", "pmcid": "PMC9312353", "type": [ "Review" ], "version": null, "note": null, "metadata": { "title": "Approaches in Gene Coexpression Analysis in Eukaryotes", "abstract": "Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied ex-tensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble account of the steps required for performing a complete gene coexpression analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.", "date": "2022-07-01T00:00:00Z", "citationCount": 9, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Saxami G." }, { "name": "Malatras A." }, { "name": "Papadopoulos K." }, { "name": "Tsotra I." }, { "name": "Iconomidou V.A." }, { "name": "Michalopoulos I." } ], "journal": "Biology" } }, { "doi": "10.1093/nar/gkl204", "pmid": "16845059", "pmcid": "PMC1538833", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based gene expression analysis", "abstract": "The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for ∼21 800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT. © The Author 2006. Published by Oxford University Press. All rights reserved.", "date": "2006-07-01T00:00:00Z", "citationCount": 130, "authors": [ { "name": "Manfield I.W." }, { "name": "Jen C.-H." }, { "name": "Pinney J.W." }, { "name": "Michalopoulos I." }, { "name": "Bradford J.R." }, { "name": "Gilmartin P.M." }, { "name": "Westhead D.R." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1111/j.1365-313x.2006.02681.x", "pmid": "16623895", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The Arabidopsis co-expression tool (ACT): A WWW-based tool and database for microarray-based gene expression analysis", "abstract": "We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel CLIQUE FINDER tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/. © 2006 The Authors.", "date": "2006-04-01T00:00:00Z", "citationCount": 65, "authors": [ { "name": "Jen C.-H." }, { "name": "Manfield I.W." }, { "name": "Michalopoulos I." }, { "name": "Pinney J.W." }, { "name": "Willats W.G.T." }, { "name": "Gilmartin P.M." }, { "name": "Westhead D.R." } ], "journal": "Plant Journal" } }, { "doi": "10.1016/j.isci.2021.102848", "pmid": "34381973", "pmcid": "PMC8334378", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana", "abstract": "Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.", "date": "2021-08-20T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Saxami G." }, { "name": "Malatras A." }, { "name": "Angelopoulou A." }, { "name": "Jen C.-H." }, { "name": "Duddy W.J." }, { "name": "Daras G." }, { "name": "Hatzopoulos P." }, { "name": "Westhead D.R." }, { "name": "Michalopoulos I." } ], "journal": "iScience" } } ], "credit": [ { "name": "David R Westhead", "email": "D.R.Westhead@leeds.ac.uk", "url": "https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/154/professor-david-r-westhead", "orcidid": "https://orcid.org/0000-0002-0519-3820", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Ioannis Michalopoulos", "email": "imichalop@bioacademy.gr", "url": "https://www.michalopoulos.net/", "orcidid": "https://orcid.org/0000-0001-8991-8712", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "imichalop@bioacademy.gr", "additionDate": "2017-02-10T14:14:47Z", "lastUpdate": "2025-03-10T11:30:02.403040Z", "editPermission": { "type": "private", "authors": [ "imichalop@bioacademy.gr" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MultiPower", "description": "The MultiPower R method performs statistical power studies for multi-omics experiments, and is designed to assist users in experimental design as well as in the evaluation of already-generated multi-omics datasets.", "homepage": "https://github.com/ConesaLab/MultiPower", "biotoolsID": "multipower", "biotoolsCURIE": "biotools:multipower", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_4031", "term": "Power test" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_4021", "term": "Multiomics" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/ConesaLab/MultiPower", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/ConesaLab/MultiPower/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/ConesaLab/MultiPower/blob/master/MultiPowerUsersGuide_v2.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/s41467-020-16937-8", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Harmonization of quality metrics and power calculation in multi-omic studies", "abstract": "Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. 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": "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": "CODARFE", "description": "COmpositional Data Analysis with Recursive Feature Elimination.", "homepage": "https://github.com/alerpaschoal/CODARFE", "biotoolsID": "codarfe", "biotoolsCURIE": "biotools:codarfe", "version": [ "1" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Script", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Linux", "Windows" ], "language": [ "Python" ], "license": "CC-BY-4.0", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/alerpaschoal/CODARFE", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/alerpaschoal/CODARFE", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.07.18.604052", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": "1", "note": "This doi will take you to the BioRxiv publication", "metadata": null } ], "credit": [], "owner": "MBarbosa", "additionDate": "2025-02-14T13:45:26.612737Z", "lastUpdate": "2025-02-14T13:45:48.480129Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EnzymeDetector", "description": "The EnzymeDetector database offers a comparative and integrative approach to find enzymatic annotations. The most comprehensive databases for protein and genome annotation, namely manually annotated data and text mining data from BRENDA, UniProt, KEGG, PATRIC, and NCBI's RefSeq, are integrated to have a vast view on the organism of interest. The data are complemented with self-performed annotation methods, i.e. BLAST vs. all enzyme annotations from Swiss-Prot and BrEPS enzyme pattern recognition.", "homepage": "https://ed.brenda-enzymes.org/help.php", "biotoolsID": "enzymedetector", "biotoolsCURIE": "biotools:enzymedetector", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0821", "term": "Enzymes" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": "CC-BY-4.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Data", "Tools" ], "elixirNode": [ "Germany" ], "elixirCommunity": [], "link": [ { "url": "https://www.brenda-enzymes.org/", "type": [ "Repository" ], "note": null }, { "url": "https://hub.dsmz.de/#/", "type": [ "Service" ], "note": null } ], "download": [], "documentation": [ { "url": "https://www.brenda-enzymes.org/information/tutorial_training/BRENDA_Tutorial_EnzymeDetector.pdf", "type": [ "Training material" ], "note": null } ], "publication": [ { "doi": "10.1186/1471-2105-12-376", "pmid": "21943292", "pmcid": "PMC3224133", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "EnzymeDetector: An integrated enzyme function prediction tool and database", "abstract": "Background: The ability to accurately predict enzymatic functions is an essential prerequisite for the interpretation of cellular functions, and the reconstruction and analysis of metabolic models. Several biological databases exist that provide such information. However, in many cases these databases provide partly different and inconsistent genome annotations.Description: We analysed nine prokaryotic genomes and found about 70% inconsistencies in the enzyme predictions of the main annotation resources. Therefore, we implemented the annotation pipeline EnzymeDetector. This tool automatically compares and evaluates the assigned enzyme functions from the main annotation databases and supplements them with its own function prediction. This is based on a sequence similarity analysis, on manually created organism-specific enzyme information from BRENDA (Braunschweig Enzyme Database), and on sequence pattern searches.Conclusions: EnzymeDetector provides a fast and comprehensive overview of the available enzyme function annotations for a genome of interest. The web interface allows the user to work with customisable weighting schemes and cut-offs for the different prediction methods. These customised quality criteria can easily be applied, and the resulting annotation can be downloaded. The summarised view of all used annotation sources provides up-to-date information. Annotation errors that occur in only one of the databases can be recognised (because of their low relevance score). The results are stored in a database and can be accessed at http://enzymedetector.tu-bs.de. © 2011 Quester and Schomburg; licensee BioMed Central Ltd.", "date": "2011-09-23T00:00:00Z", "citationCount": 49, "authors": [ { "name": "Quester S." }, { "name": "Schomburg D." } ], "journal": "BMC Bioinformatics" } }, { "doi": "10.1093/nar/gkaa1025", "pmid": "33211880", "pmcid": "PMC7779020", "type": [ "Review" ], "version": null, "note": null, "metadata": { "title": "BRENDA, the ELIXIR core data resource in 2021: New developments and updates", "abstract": "The BRENDA enzyme database (https://www.brendaenzymes.org), established in 1987, has evolved into the main collection of functional enzyme and metabolism data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted from ∼157 000 primary literature references, combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, lig- and information, taxonomic, bibliographic, and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.", "date": "2021-01-08T00:00:00Z", "citationCount": 392, "authors": [ { "name": "Chang A." }, { "name": "Jeske L." }, { "name": "Ulbrich S." }, { "name": "Hofmann J." }, { "name": "Koblitz J." }, { "name": "Schomburg I." }, { "name": "Neumann-Schaal M." }, { "name": "Jahn D." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } } ], "credit": [], "owner": "lje", "additionDate": "2025-01-22T09:30:44.090073Z", "lastUpdate": "2025-01-30T16:15:27.318809Z", "editPermission": { "type": "group", "authors": [ "matkrull", "juh22" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ARTEMIS", "description": "a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment", "homepage": "https://github.com/david-bogdan-r/ARTEMIS", "biotoolsID": "artemis3D", "biotoolsCURIE": "biotools:artemis3D", "version": [ "version 1.51" ], "otherID": [], "relation": [ { "biotoolsID": "artem", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0503", "term": "Pairwise structure alignment" }, { "uri": "http://edamontology.org/operation_2518", "term": "Nucleic acid structure comparison" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1459", "term": "Nucleic acid structure" }, "format": [ { "uri": "http://edamontology.org/format_1477", "term": "mmCIF" }, { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] }, { "data": { "uri": "http://edamontology.org/data_1459", "term": "Nucleic acid structure" }, "format": [ { "uri": "http://edamontology.org/format_1477", "term": "mmCIF" }, { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1482", "term": "Nucleic acid structure alignment" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] }, { "data": { "uri": "http://edamontology.org/data_0888", "term": "Structure similarity score" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] }, { "data": { "uri": "http://edamontology.org/data_1459", "term": "Nucleic acid structure" }, "format": [ { "uri": "http://edamontology.org/format_1477", "term": "mmCIF" }, { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "note": null, "cmd": "python3 artemis.py r=FILENAME q=FILENAME [OPTIONS]" } ], "toolType": [ "Command-line tool", "Script" ], "topic": [ { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_0077", "term": "Nucleic acids" }, { "uri": "http://edamontology.org/topic_0097", "term": "Nucleic acid structure analysis" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" }, { "uri": "http://edamontology.org/topic_3511", "term": "Nucleic acid sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_0099", "term": "RNA" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [ "3D-BioInfo-Structure-Function" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/david-bogdan-r/ARTEMIS", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/david-bogdan-r/ARTEMIS", "type": "Source code", "note": null, "version": "version 1.5" } ], "documentation": [ { "url": "https://github.com/david-bogdan-r/ARTEMIS/blob/main/README.md", "type": [ "Quick start guide" ], "note": "README file" }, { "url": "https://github.com/david-bogdan-r/ARTEMIS/blob/main/ARTEMIS_TUTORIAL_v1.51.pdf", "type": [ "User manual" ], "note": "Tutorial in pdf" } ], "publication": [ { "doi": "10.1093/nar/gkae758", "pmid": "39258540", "pmcid": "PMC11472068", "type": [ "Primary" ], "version": "version 1.5", "note": null, "metadata": { "title": "ARTEMIS: a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment", "abstract": "Non-coding RNAs play a major role in diverse processes in living cells with their sequence and spatial structure serving as the principal determinants of their function. Superposition of RNA 3D structures is the most accurate method for comparative analysis of RNA molecules and for inferring structure-based sequence alignments. Topology-independent superposition is particularly relevant, as evidenced by structurally similar RNAs with sequence permutations such as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure superposition rely on intricate heuristics, and the potential for topology-independent superposition has not been exhausted. Recently, we introduced the ARTEM method for unrestrained pairwise superposition of RNA 3D modules and now we developed it further to solve the global RNA 3D structure alignment problem. Our new tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition. Using ARTEMIS we discovered a helical packing motif to be preserved within different backbone topology contexts across various non-coding RNAs, including multiple ribozymes and riboswitches. We anticipate that ARTEMIS will be essential for elucidating the landscape of RNA 3D folds and motifs featuring sequence permutations that thus far remained unexplored due to limitations in previous computational approaches.", "date": "2024-10-14T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Bohdan D.R." }, { "name": "Bujnicki J.M." }, { "name": "Baulin E.F." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1101/2024.04.06.588371", "pmid": null, "pmcid": null, "type": [], "version": "version 1.0", "note": null, "metadata": null } ], "credit": [ { "name": "Davyd Bohdan", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-6456-6658", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Janusz Bujnicki", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-6633-165X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Eugene Baulin", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-4694-9783", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "febos", "additionDate": "2024-09-09T07:49:35.385723Z", "lastUpdate": "2025-01-30T09:07:50.841042Z", "editPermission": { "type": "private", "authors": [ "febos" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BKMS-react", "description": "BKMS-react is an integrated and non-redundant biochemical reaction database containing known enzyme-catalyzed and spontaneous reactions. Biochemical reactions collected from BRENDA, KEGG, MetaCyc and SABIO-RK were matched and integrated by aligning substrates and products.", "homepage": "https://bkms.brenda-enzymes.org/", "biotoolsID": "bkmsreact", "biotoolsCURIE": "biotools:bkmsreact", "version": [], "otherID": [], "relation": [ { "biotoolsID": "brenda", "type": "includedIn" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3561", "term": "Database comparison" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2978", "term": "Reaction data" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0842", "term": "Identifier" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2105", "term": "Compound ID (BioCyc)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0990", "term": "Compound name" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2605", "term": "Compound ID (KEGG)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1011", "term": "EC number" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1012", "term": "Enzyme name" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2342", "term": "Pathway or network name" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2978", "term": "Reaction data" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0842", "term": "Identifier" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2600", "term": "Pathway or network" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1011", "term": "EC number" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2106", "term": "Reaction ID (BioCyc)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2608", "term": "Reaction ID (KEGG)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2309", "term": "Reaction ID (SABIO-RK)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1012", "term": "Enzyme name" }, "format": [] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3282", "term": "ID mapping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_3345", "term": "Data identity and mapping" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0821", "term": "Enzymes" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": "CC-BY-4.0", "collectionID": [ "DSMZ Digital Diversity", "de.NBI" ], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Germany" ], "elixirCommunity": [], "link": [ { "url": "https://www.dsmz.de/", "type": [ "Other" ], "note": null }, { "url": "https://hub.dsmz.de/#/", "type": [ "Service" ], "note": null }, { "url": "https://www.brenda-enzymes.org/index.php", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://bkms.brenda-enzymes.org/download.php", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://bkms.brenda-enzymes.org/readme.php", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/1471-2091-12-42", "pmid": "21824409", "pmcid": "PMC3167764", "type": [], "version": null, "note": null, "metadata": { "title": "BKM-react, an integrated biochemical reaction database", "abstract": "Background: The systematic, complete and correct reconstruction of genome-scale metabolic networks or metabolic pathways is one of the most challenging tasks in systems biology research. An essential requirement is the access to the complete biochemical knowledge - especially on the biochemical reactions. This knowledge is extracted from the scientific literature and collected in biological databases. Since the available databases differ in the number of biochemical reactions and the annotation of the reactions, an integrated knowledge resource would be of great value. Results: We developed a comprehensive non-redundant reaction database containing known enzyme-catalyzed and spontaneous reactions. Currently, it comprises 18,172 unique biochemical reactions. As source databases the biochemical databases BRENDA, KEGG, and MetaCyc were used. Reactions of these databases were matched and integrated by aligning substrates and products. For the latter a two-step comparison using their structures (via InChIs) and names was performed. Each biochemical reaction given as a reaction equation occurring in at least one of the databases was included. Conclusions: An integrated non-redundant reaction database has been developed and is made available to users. The database can significantly facilitate and accelerate the construction of accurate biochemical models. © 2011 Lang et al; licensee BioMed Central Ltd.", "date": "2011-08-10T00:00:00Z", "citationCount": 56, "authors": [ { "name": "Lang M." }, { "name": "Stelzer M." }, { "name": "Schomburg D." } ], "journal": "BMC Biochemistry" } }, { "doi": "10.1093/nar/gky1048", "pmid": "30395242", "pmcid": "PMC6323942", "type": [], "version": null, "note": null, "metadata": { "title": "BRENDA in 2019: A European ELIXIR core data resource", "abstract": "The BRENDA enzyme database (www.brenda-enzymes.org), recently appointed ELIXIR Core Data Resource, is the main enzyme and enzyme-ligand information system. The core database provides a comprehensive overview on enzymes. A collection of 4.3 million data for ∼84 000 enzymes manually evaluated and extracted from ∼140 000 primary literature references is combined with information obtained by text and data mining, data integration and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, predicted enzyme locations and genome annotations. Major developments are a revised ligand summary page and the structure search now including a similarity and isomer search. BKMS-react, an integrated database containing known enzyme-catalyzed reactions, is supplemented with further reactions and improved access to pathway connections. In addition to existing enzyme word maps with graphical information of enzyme specific terms, plant word maps have been developed. They show a graphical overview of terms, e.g. enzyme or plant pathogen information, connected to specific plants. An organism summary page showing all relevant information, e.g. taxonomy and synonyms linked to enzyme data, was implemented. Based on a decision by the IUBMB enzyme task force the enzyme class EC 7 has been established for 'translocases-, enzymes that catalyze a transport of ions or metabolites across cellular membranes.", "date": "2019-01-08T00:00:00Z", "citationCount": 297, "authors": [ { "name": "Jeske L." }, { "name": "Placzek S." }, { "name": "Schomburg I." }, { "name": "Chang A." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures", "email": null, "url": "https://www.dsmz.de/", "orcidid": null, "gridid": null, "rorid": "02tyer376", "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null }, { "name": null, "email": "contact@brenda-enzymes.org", "url": "https://brenda-enzymes.org/support.php", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "juh22", "additionDate": "2025-01-22T10:04:16.284330Z", "lastUpdate": "2025-01-28T11:34:26.685306Z", "editPermission": { "type": "group", "authors": [ "juh22", "matkrull", "AJaede", "lje" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BRENDA", "description": "Internationally leading information system on all aspects of enzymes, including function, structure, involvement in diseases, application, engineering, and molecular properties. 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In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted from ∼157 000 primary literature references, combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, lig- and information, taxonomic, bibliographic, and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.", "date": "2021-01-08T00:00:00Z", "citationCount": 393, "authors": [ { "name": "Chang A." }, { "name": "Jeske L." }, { "name": "Ulbrich S." }, { "name": "Hofmann J." }, { "name": "Koblitz J." }, { "name": "Schomburg I." }, { "name": "Neumann-Schaal M." }, { "name": "Jahn D." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gky1048", "pmid": "30395242", "pmcid": "PMC6323942", "type": [], "version": null, "note": null, "metadata": { "title": "BRENDA in 2019: A European ELIXIR core data resource", "abstract": "The BRENDA enzyme database (www.brenda-enzymes.org), recently appointed ELIXIR Core Data Resource, is the main enzyme and enzyme-ligand information system. The core database provides a comprehensive overview on enzymes. A collection of 4.3 million data for ∼84 000 enzymes manually evaluated and extracted from ∼140 000 primary literature references is combined with information obtained by text and data mining, data integration and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, predicted enzyme locations and genome annotations. Major developments are a revised ligand summary page and the structure search now including a similarity and isomer search. BKMS-react, an integrated database containing known enzyme-catalyzed reactions, is supplemented with further reactions and improved access to pathway connections. In addition to existing enzyme word maps with graphical information of enzyme specific terms, plant word maps have been developed. They show a graphical overview of terms, e.g. enzyme or plant pathogen information, connected to specific plants. An organism summary page showing all relevant information, e.g. taxonomy and synonyms linked to enzyme data, was implemented. Based on a decision by the IUBMB enzyme task force the enzyme class EC 7 has been established for 'translocases-, enzymes that catalyze a transport of ions or metabolites across cellular membranes.", "date": "2019-01-08T00:00:00Z", "citationCount": 296, "authors": [ { "name": "Jeske L." }, { "name": "Placzek S." }, { "name": "Schomburg I." }, { "name": "Chang A." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1016/j.jbiotec.2017.04.020", "pmid": "28438579", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "The BRENDA enzyme information system–From a database to an expert system", "abstract": "Enzymes, representing the largest and by far most complex group of proteins, play an essential role in all processes of life, including metabolism, gene expression, cell division, the immune system, and others. Their function, also connected to most diseases or stress control makes them interesting targets for research and applications in biotechnology, medical treatments, or diagnosis. Their functional parameters and other properties are collected, integrated, and made available to the scientific community in the BRaunschweig ENzyme DAtabase (BRENDA). In the last 30 years BRENDA has developed into one of the most highly used biological databases worldwide. The data contents, the process of data acquisition, data integration and control, the ways to access the data, and visualizations provided by the website are described and discussed.", "date": "2017-11-10T00:00:00Z", "citationCount": 133, "authors": [ { "name": "Schomburg I." }, { "name": "Jeske L." }, { "name": "Ulbrich M." }, { "name": "Placzek S." }, { "name": "Chang A." }, { "name": "Schomburg D." } ], "journal": "Journal of Biotechnology" } }, { "doi": "10.1093/nar/gku1068", "pmid": "25378310", "pmcid": "PMC4383907", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA in 2015: Exciting developments in its 25th year of existence", "abstract": "The BRENDA enzyme information system (http://www.brenda-enzymes.org/) has developed into an elaborate system of enzyme and enzyme-ligand information obtained from different sources, combined with flexible query systems and evaluation tools. The information is obtained by manual extraction from primary literature, text and data mining, data integration, and prediction algorithms. Approximately 300 million data include enzyme function and molecular data from more than 30 000 organisms. The manually derived core contains 3 million data from 77 000 enzymes annotated from 135 000 literature references. Each entry is connected to the literature reference and the source organism. They are complemented by information on occurrence, enzyme/disease relationships from text mining, sequences and 3D structures from other databases, and predicted enzyme location and genome annotation. Functional and structural data of more than 190 000 enzyme ligands are stored in BRENDA. New features improving the functionality and analysis tools were implemented. The human anatomy atlas CAVEman is linked to the BRENDA Tissue Ontology terms providing a connection between anatomical and functional enzyme data. Word Maps for enzymes obtained from PubMed.", "date": "2015-01-28T00:00:00Z", "citationCount": 161, "authors": [ { "name": "Chang A." }, { "name": "Schomburg I." }, { "name": "Placzek S." }, { "name": "Jeske L." }, { "name": "Ulbrich M." }, { "name": "Xiao M." }, { "name": "Sensen C.W." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkn820", "pmid": "18984617", "pmcid": "PMC2686525", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA, AMENDA and FRENDA the enzyme information system: New content and tools in 2009", "abstract": "The BRENDA (BRaunschweig ENzyme DAtabase) (http://www.brenda-enzymes.org) represents the largest freely available information system containing a huge amount of biochemical and molecular information on all classified enzymes as well as software tools for querying the database and calculating molecular properties. The database covers information on classification and nomenclature, reaction and specificity, functional parameters, occurrence, enzyme structure and stability, mutants and enzyme engineering, preparation and isolation, the application of enzymes, and ligand-related data. The data in BRENDA are manually curated from more than 79 000 primary literature references. Each entry is clearly linked to a literature reference, the origin organism and, where available, to the protein sequence of the enzyme protein. A new search option provides the access to protein-specific data. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are additional databases created by continuously improved text-mining procedures. These databases ought to provide a complete survey on enzyme data of the literature collection of PubMed. The web service via a SOAP (Simple Object Access Protocol) interface for access to the BRENDA data has been further enhanced. © 2008 The Author(s).", "date": "2009-01-09T00:00:00Z", "citationCount": 314, "authors": [ { "name": "Chang A." }, { "name": "Scheer M." }, { "name": "Grote A." }, { "name": "Schomburg I." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkq1089", "pmid": "21062828", "pmcid": "PMC3013686", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA, the enzyme information system in 2011", "abstract": "The BRENDA (BRaunschweig ENzyme Database, http://www.brenda-enzymes.org) enzyme information system is the main collection of enzyme functional and property data for the scientific community. The majority of the data are manually extracted from the primary literature. The content covers information on function, structure, occurrence, preparation and application of enzymes as well as properties of mutants and engineered variants. The number of manually annotated references increased by 30% to more than 100 000, the number of ligand structures by 45% to almost 100 000. New query, analysis and data management tools were implemented to improve data processing, data presentation, data input and data access. BRENDA now provides new viewing options such as the display of the statistics of functional parameters and the 3D view of protein sequence and structure features. Furthermore a ligand summary shows comprehensive information on the BRENDA ligands. The enzymes are linked to their respective pathways and can be viewed in pathway maps. The disease text mining part is strongly enhanced. It is possible to submit new, not yet classified enzymes to BRENDA, which then are reviewed and classified by the International Union of Biochemistry and Molecular Biology. A new SBML output format of BRENDA kinetic data allows the construction of organism-specific metabolic models. © The Author(s) 2010.", "date": "2011-01-01T00:00:00Z", "citationCount": 353, "authors": [ { "name": "Scheer M." }, { "name": "Grote A." }, { "name": "Chang A." }, { "name": "Schomburg I." }, { "name": "Munaretto C." }, { "name": "Rother M." }, { "name": "Sohngen C." }, { "name": "Stelzer M." }, { "name": "Thiele J." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1186/1471-2105-12-329", "pmid": "21827651", "pmcid": "PMC3166944", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Development of a classification scheme for disease-related enzyme information", "abstract": "Background: BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods.Results: Currently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall.Conclusions: The DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA. © 2011 Söhngen et al; licensee BioMed Central Ltd.", "date": "2011-08-09T00:00:00Z", "citationCount": 16, "authors": [ { "name": "Sohngen C." }, { "name": "Chang A." }, { "name": "Schomburg D." } ], "journal": "BMC Bioinformatics" } }, { "doi": "10.1093/nar/gkh081", "pmid": "14681450", "pmcid": "PMC308815", "type": [ "Other" ], "version": null, "note": null, "metadata": null }, { "doi": "10.1093/nar/gks1049", "pmid": "23203881", "pmcid": "PMC3531171", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA in 2013: Integrated reactions, kinetic data, enzyme function data, improved disease classification: New options and contents in BRENDA", "abstract": "The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org) is the main information system of functional biochemical and molecular enzyme data and provides access to seven interconnected databases. BRENDA contains 2.7 million manually annotated data on enzyme occurrence, function, kinetics and molecular properties. Each entry is connected to a reference and the source organism. Enzyme ligands are stored with their structures and can be accessed via their names, synonyms or via a structure search. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text mining methods and represent a complete survey of PubMed abstracts with information on enzymes in different organisms, tissues or organelles. The supplemental database DRENDA provides more than 910000 new EC number-disease relations in more than 510 000 references from automatic search and a classification of enzyme-disease-related information. KENDA (Kinetic ENzyme DAta), a new amendment extracts and displays kinetic values from PubMed abstracts. The integration of the EnzymeDetector offers an automatic comparison, evaluation and prediction of enzyme function annotations for prokaryotic genomes. The biochemical reaction database BKM-react contains non-redundant enzyme-catalysed and spontaneous reactions and was developed to facilitate and accelerate the construction of biochemical models. © The Author(s) 2012.", "date": "2013-01-01T00:00:00Z", "citationCount": 347, "authors": [ { "name": "Schomburg I." }, { "name": "Chang A." }, { "name": "Placzek S." }, { "name": "Sohngen C." }, { "name": "Rother M." }, { "name": "Lang M." }, { "name": "Munaretto C." }, { "name": "Ulas S." }, { "name": "Stelzer M." }, { "name": "Grote A." }, { "name": "Scheer M." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1016/s0968-0004(01)02027-8", "pmid": "11796225", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA: A resource for enzyme data and metabolic information", "abstract": "BRENDA (BRaunschweig ENzyme DAtabase), founded in 1987 by Dietmar Schomburg, is a comprehensive protein function database, containing enzymatic and metabolic information extracted from the primary literature. Presently, the database holds data on more than 40 000 enzymes and 4460 different organisms, and includes information about enzyme -ligand relationships with numerous chemical compounds. The collection of molecular and biochemical information in BRENDA provides a fundamental resource for research in biotechnology, pharmacology, medicinal diagnostics, enzyme mechanics, and metabolism. BRENDA is accessible free of charge to the academic community at http://www.brenda.uni-koeln.de/; commercial users need a license available from http://www.science-factory.com/.", "date": "2002-01-01T00:00:00Z", "citationCount": 159, "authors": [ { "name": "Schomburg I." }, { "name": "Chang A." }, { "name": "Hofmann O." }, { "name": "Ebeling C." }, { "name": "Ehrentreich F." }, { "name": "Schomburg D." } ], "journal": "Trends in Biochemical Sciences" } }, { "doi": "10.1093/nar/gkl972", "pmid": "17202167", "pmcid": "PMC1899097", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA, AMENDA and FRENDA: The enzyme information system in 2007", "abstract": "The BRENDA (BRaunschweig ENzyme DAtabase) enzyme information system (http://www.brenda.uni-koeln.de) is the largest publicly available enzyme information system worldwide. The major parts of its contents are manually extracted from primary literature. It is not restricted to specific groups of enzymes, but includes information on all identified enzymes irrespective of the enzyme's source. The range of data encompasses functional, structural, sequence, localisation, disease-related, isolation, stability information on enzyme and ligand-related data. Each single entry is linked to the enzyme source and to a literature reference. Recently the data repository was complemented by text-mining data in AMENDA (Automatic Mining of ENzyme DAta) and FRENDA (Full Reference ENzyme DAta). A genome browser, membrane protein prediction and full-text search capacities were added. The newly implemented web service provides instant access to the data for programmers via a SOAP (Simple Object Access Protocol) interface. The BRENDA data can be downloaded in the form of a text file from the beginning of 2007. © 2007 Oxford University Press.", "date": "2007-01-01T00:00:00Z", "citationCount": 137, "authors": [ { "name": "Barthelmes J." }, { "name": "Ebeling C." }, { "name": "Chang A." }, { "name": "Schomburg I." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/30.1.47", "pmid": "11752250", "pmcid": "PMC99121", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "BRENDA, enzyme data and metabolic information", "abstract": "BRENDA is a comprehensive relational database on functional and molecular information of enzymes, based on primary literature. The database contains information extracted and evaluated from approximately 46 000 references, holding data of at least 40 000 different enzymes from more than 6900 different organisms, classified in approximately 3900 EC numbers. BRENDA is an important tool for biochemical and medical research covering information on properties of all classified enzymes, including data on the occurrence, catalyzed reaction, kinetics, substrates/products, inhibitors, cofactors, activators, structure and stability. All data are connected to literature references which in turn are linked to PubMed. The data and information provide a fundamental tool for research of enzyme mechanisms, metabolic pathways, the evolution of metabolism and, furthermore, for medicinal diagnostics and pharmaceutical research. The database is a resource for data of enzymes, classified according to the EC system of the IUBMB Enzyme Nomenclature Committee, and the entries are cross-referenced to other databases, i.e. organism classification, protein sequence, protein structure and literature references. BRENDA provides an academic web access at http://www.brenda.uni-koeln.de.", "date": "2002-01-01T00:00:00Z", "citationCount": 384, "authors": [ { "name": "Schomburg I." }, { "name": "Chang A." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkq968", "pmid": "21030441", "pmcid": "PMC3013802", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The BRENDA Tissue Ontology (BTO): The first all-integrating ontology of all organisms for enzyme sources", "abstract": "BTO, the BRENDA Tissue Ontology (http://www.BTO.brenda-enzymes.org) represents a comprehensive structured encyclopedia of tissue terms. The project started in 2003 to create a connection between the enzyme data collection of the BRENDA enzyme database and a structured network of source tissues and cell types. Currently, BTO contains more than 4600 different anatomical structures, tissues, cell types and cell lines, classified under generic categories corresponding to the rules and formats of the Gene Ontology Consortium and organized as a directed acyclic graph (DAG). Most of the terms are endowed with comments on their derivation or definitions. The content of the ontology is constantly curated with ̃1000 new terms each year. Four different types of relationships between the terms are implemented. A versatile web interface with several search and navigation functionalities allows convenient online access to the BTO and to the enzymes isolated from the tissues. Important areas of applications of the BTO terms are the detection of enzymes in tissues and the provision of a solid basis for text-mining approaches in this field. It is widely used by lab scientists, curators of genomic and biochemical databases and bioinformaticians. The BTO is freely available at http://www.obofoundry.org. © The Author(s) 2010.", "date": "2011-01-01T00:00:00Z", "citationCount": 141, "authors": [ { "name": "Gremse M." }, { "name": "Chang A." }, { "name": "Schomburg I." }, { "name": "Grote A." }, { "name": "Scheer M." }, { "name": "Ebeling C." }, { "name": "Schomburg D." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "BMBF", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Funding agency", "typeRole": [ "Contributor" ], "note": null }, { "name": null, "email": "contact@brenda-enzymes.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures", "email": null, "url": "https://www.dsmz.de/", "orcidid": null, "gridid": "grid.420081.f", "rorid": "02tyer376", "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "matkrull", "additionDate": "2016-02-03T14:20:28Z", "lastUpdate": "2025-01-27T14:31:05.585156Z", "editPermission": { "type": "group", "authors": [ "AJaede", "juh22", "lje" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "TYGS", "description": "Automated high-throughput platform for state-of-the-art genome-based taxonomy.", "homepage": "https://tygs.dsmz.de", "biotoolsID": "TYGS", "biotoolsCURIE": "biotools:TYGS", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" }, { "uri": "http://edamontology.org/operation_0544", "term": "Phylogenetic species tree construction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" }, { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "Unlicense", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://tygs.dsmz.de/faqs", "type": [ "FAQ" ], "note": null } ], "publication": [ { "doi": "10.1038/S41467-019-10210-3", "pmid": "31097708", "pmcid": "PMC6522516", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy", "abstract": "© 2019, The Author(s).Microbial taxonomy is increasingly influenced by genome-based computational methods. Yet such analyses can be complex and require expert knowledge. Here we introduce TYGS, the Type (Strain) Genome Server, a user-friendly high-throughput web server for genome-based prokaryote taxonomy, connected to a large, continuously growing database of genomic, taxonomic and nomenclatural information. It infers genome-scale phylogenies and state-of-the-art estimates for species and subspecies boundaries from user-defined and automatically determined closest type genome sequences. TYGS also provides comprehensive access to nomenclature, synonymy and associated taxonomic literature. Clinically important examples demonstrate how TYGS can yield new insights into microbial classification, such as evidence for a species-level separation of previously proposed subspecies of Salmonella enterica. TYGS is an integrated approach for the classification of microbes that unlocks novel scientific approaches to microbiologists worldwide and is particularly helpful for the rapidly expanding field of genome-based taxonomic descriptions of new genera, species or subspecies.", "date": "2019-12-01T00:00:00Z", "citationCount": 409, "authors": [ { "name": "Meier-Kolthoff J.P." }, { "name": "Goker M." } ], "journal": "Nature Communications" } } ], "credit": [ { "name": "Jan P. Meier-Kolthoff", "email": "jan.meier-kolthoff@dsmz.de", "url": null, "orcidid": "https://orcid.org/0000-0001-9105-9814", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "mgoeker", "additionDate": "2019-08-09T13:13:30Z", "lastUpdate": "2025-01-23T15:00:45.502376Z", "editPermission": { "type": "group", "authors": [ "mgoeker" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MetaboMAPS", "description": "Pathway sharing and multi-omics data visualization in metabolic context.\n\nMetaboMAPS is a web project for manipulating metabolic pathways in SVG format. MetaboMAPS consists of two main parts: the visualization tool, where users can plot their own data set, and the plot box editor, where users can add or change the areas for visualization (plot boxes) and assign identifiers to them.", "homepage": "https://metabomaps.brenda-enzymes.org", "biotoolsID": "metabomaps", "biotoolsCURIE": "biotools:metabomaps", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0533", "term": "Expression profile pathway mapping" }, { "uri": "http://edamontology.org/operation_3929", "term": "Metabolic pathway prediction" }, { "uri": "http://edamontology.org/operation_3926", "term": "Pathway visualisation" }, { "uri": "http://edamontology.org/operation_2943", "term": "Box-Whisker plot plotting" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [], "language": [ "JavaScript" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/JuliaHelmecke/MetaboMAPS", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.12688/F1000RESEARCH.23427.1", "pmid": "32765840", "pmcid": "PMC7383707", "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Meina Neumann-Schaal", "email": "meina.neumann-schaal@dsmz.de", "url": null, "orcidid": "https://orcid.org/0000-0002-1641-019X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Niclaskn", "additionDate": "2021-01-18T10:23:20Z", "lastUpdate": "2025-01-23T15:00:32.268669Z", "editPermission": { "type": "group", "authors": [ "JKoblitz" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "COMET", "description": "A toolkit for composing customizable genetic programs in mammalian cells.\nCOmposable Mammalian Elements of Transcription (COMET) is an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic", "homepage": "https://github.com/leonardlab/COMET", "biotoolsID": "comet-cell", "biotoolsCURIE": "biotools:comet-cell", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" }, { "uri": "http://edamontology.org/operation_0420", "term": "Nucleic acids-binding site prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" } ], "operatingSystem": [], "language": [ "MATLAB" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/769794", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.1038/s41467-019-14147-5", "pmid": "32034124", "pmcid": "PMC7005830", "type": [], "version": null, "note": null, "metadata": { "title": "The COMET toolkit for composing customizable genetic programs in mammalian cells", "abstract": "Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we develop the COmposable Mammalian Elements of Transcription (COMET)—an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not, to the best of our knowledge, previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells.", "date": "2020-12-01T00:00:00Z", "citationCount": 49, "authors": [ { "name": "Donahue P.S." }, { "name": "Draut J.W." }, { "name": "Muldoon J.J." }, { "name": "Edelstein H.I." }, { "name": "Bagheri N." }, { "name": "Leonard J.N." } ], "journal": "Nature Communications" } } ], "credit": [], "owner": "Kigaard", "additionDate": "2019-11-14T20:04:03Z", "lastUpdate": "2025-01-23T14:42:57.607598Z", "editPermission": { "type": "public", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "DSMZCellDive", "description": "Diving into high-throughput DSMZ cell line data.", "homepage": "http://celldive.dsmz.de", "biotoolsID": "dsmzcelldive", "biotoolsCURIE": "biotools:dsmzcelldive", "version": [], "otherID": [], "relation": [ { "biotoolsID": "d3hub", "type": "includedIn" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3200", "term": "DNA barcoding" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" } ], "input": [], "output": [], "note": null, "cmd": "No" } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3500", "term": "Zoology" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "PHP", "JavaScript" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/JKoblitz/DSMZCellDive", "type": [ "Repository" ], "note": "Webpages and contents" }, { "url": "https://doi.org/10.5281/zenodo.6401600", "type": [ "Repository" ], "note": "RNA-seq gene expression analysis" }, { "url": "https://doi.org/10.5281/zenodo.6401594", "type": [ "Repository" ], "note": "HLA anlaysis (human leukocyte antigen)" } ], "download": [], "documentation": [ { "url": "https://celldive.dsmz.de/documentation", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.12688/f1000research.111175.2", "pmid": "35949917", "pmcid": "PMC9334839", "type": [], "version": null, "note": null, "metadata": { "title": "DSMZCellDive: Diving into high-throughput cell line data", "abstract": "Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data.", "date": "2022-01-01T00:00:00Z", "citationCount": 2, "authors": [ { "name": "Koblitz J." }, { "name": "Steenpass L." }, { "name": "Dirks W.G." }, { "name": "Eberth S." }, { "name": "Nagel S." }, { "name": "Pommerenke C." } ], "journal": "F1000Research" } } ], "credit": [ { "name": "Julia Koblitz", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-7260-2129", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Laura Steenpass", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Claudia Pommerenke", "email": "claudia.pommerenke@dsmz.de", "url": null, "orcidid": "https://orcid.org/0000-0002-9448-416X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Sonja Eberth", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-5796-2089", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "puffin", "additionDate": "2023-01-19T00:01:13.848337Z", "lastUpdate": "2025-01-23T07:50:00.213259Z", "editPermission": { "type": "group", "authors": [ "puffin" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SILVA rRNA database", "description": "SILVA provides comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukaryota).", "homepage": "http://www.arb-silva.de", "biotoolsID": "silva", "biotoolsCURIE": "biotools:silva", "version": [], "otherID": [ { "value": "RRID:SCR_006423", "type": "rrid", "version": null } ], "relation": [ { "biotoolsID": "silvangs", "type": "usedBy" }, { "biotoolsID": "d3hub", "type": "includedIn" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0224", "term": "Query and retrieval" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1097", "term": "Sequence accession (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] }, { "data": { "uri": "http://edamontology.org/data_1868", "term": "Taxon" }, "format": [ { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] }, { "data": { "uri": "http://edamontology.org/data_1046", "term": "Strain name" }, "format": [ { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] }, { "data": { "uri": "http://edamontology.org/data_1088", "term": "Article ID" }, "format": [ { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] }, { "data": { "uri": "http://edamontology.org/data_2909", "term": "Organism name" }, "format": [ { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2955", "term": "Sequence report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_1383", "term": "Sequence alignment (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_2333", "term": "Binary format" }, { "uri": "http://edamontology.org/format_1984", "term": "FASTA-aln" } ] } ], "note": "The search and retrieval functions of the SILVA website can be used to build custom subsets of sequences. In addition to simple searches e.g. for accession numbers, organism names, taxonomic entities, or publication DOI/PubMed IDs, complex queries over several database fields using constraints such as sequence length or quality values are possible. The results can be sorted according to accession numbers, organism names, sequence length, sequence and alignment quality and Pintail values. The search results show accession number, organism name, sequence length, sequence quality values, taxonomic classifications, and links to view the full sequence record on SILVA and on ENA. Sequences found via search and added to download cart can be downloaded as FASTA and ARB files.", "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0292", "term": "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_1383", "term": "Sequence alignment (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_2333", "term": "Binary format" }, { "uri": "http://edamontology.org/format_1984", "term": "FASTA-aln" } ] } ], "note": "SILVA Incremental Aligner (SINA) is used to align the rRNA gene databases provided by SILVA, as well as user submitted sequences. 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. Aligned sequences can be dowloaded as FASTA or ARB files", "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "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_1383", "term": "Sequence alignment (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_2333", "term": "Binary format" }, { "uri": "http://edamontology.org/format_1984", "term": "FASTA-aln" } ] }, { "data": { "uri": "http://edamontology.org/data_1872", "term": "Taxonomic classification" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": "SINA can also operate as a taxonomic classification tool. It uses the search result to derive a classification with the LCA (lowest common ancestor) method. Each query sequence is assigned the shared part of the classifications of the search results. Aligned sequences can be dowloaded as FASTA or ARB files Taxonomic classification results can be downloaded as csv files.", "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2419", "term": "Primer and probe design" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1207", "term": "nucleotide" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": "The SILVA Probe Match and Evaluation Tool detects and displays all occurrences of a given probe or primer sequence in the SILVA datasets. TestPrime allows you to evaluate the performance of primer pairs by running an in silico PCR on the SILVA databases. From the results of the PCR, TestPrime computes coverages for each taxonomic group in all of the taxonomies offered by SILVA.", "cmd": null } ], "toolType": [ "Web application", "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_0659", "term": "Functional, regulatory and non-coding RNA" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" }, { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_3050", "term": "Biodiversity" }, { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" }, { "uri": "http://edamontology.org/topic_0632", "term": "Probes and primers" } ], "operatingSystem": [], "language": [], "license": "CC-BY-4.0", "collectionID": [ "de.NBI", "de.NBI-biodata", "DSMZ Digital Diversity" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Data" ], "elixirNode": [ "Germany" ], "elixirCommunity": [], "link": [ { "url": "https://www.arb-silva.de/browser/", "type": [ "Service" ], "note": "SILVA Taxonomy Browser" }, { "url": "https://www.arb-silva.de/search/", "type": [ "Service" ], "note": "SILVA metadata search" }, { "url": "https://www.arb-silva.de/aligner/", "type": [ "Service" ], "note": "ACT: Alignment, Classification and Tree Service" }, { "url": "https://www.arb-silva.de/search/testprobe/", "type": [ "Service" ], "note": "SILVA Probe Match and Evaluation Tool" }, { "url": "https://www.arb-silva.de/search/testprime/", "type": [ "Service" ], "note": "SILVA Primer Evaluation Tool" }, { "url": "https://treeviewer.arb-silva.de/", "type": [ "Service" ], "note": "Web-based viewer for the SILVA guide trees" } ], "download": [ { "url": "https://www.arb-silva.de/download/archive/", "type": "Downloads page", "note": "SILVA dataset archive", "version": null } ], "documentation": [ { "url": "https://www.arb-silva.de/silva-license-information/", "type": [ "Terms of use" ], "note": null }, { "url": "https://www.arb-silva.de/contact/", "type": [ "Citation instructions" ], "note": null }, { "url": "http://www.arb-silva.de/documentation/", "type": [ "General" ], "note": null }, { "url": "https://www.arb-silva.de/documentation/faqs/", "type": [ "FAQ" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gks1219", "pmid": "23193283", "pmcid": "PMC3531112", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools", "abstract": "SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3194 778 small subunit and 288717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. © The Author(s) 2012.", "date": "2013-01-01T00:00:00Z", "citationCount": 20831, "authors": [ { "name": "Quast C." }, { "name": "Pruesse E." }, { "name": "Yilmaz P." }, { "name": "Gerken J." }, { "name": "Schweer T." }, { "name": "Yarza P." }, { "name": "Peplies J." }, { "name": "Glockner F.O." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkt1209", "pmid": "24293649", "pmcid": "PMC3965112", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The SILVA and \"all-species Living Tree Project (LTP)\" taxonomic frameworks", "abstract": "SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive resource for up-to-date quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. SILVA provides a manually curated taxonomy for all three domains of life, based on representative phylogenetic trees for the small- and large-subunit rRNA genes. This article describes the improvements the SILVA taxonomy has undergone in the last 3 years. Specifically we are focusing on the curation process, the various resources used for curation and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies. Our comparisons not only revealed a reasonable overlap between the taxa names, but also points to significant differences in both names and numbers of taxa between the three resources. © 2013 The Author(s). Published by Oxford University Press.", "date": "2014-01-01T00:00:00Z", "citationCount": 2372, "authors": [ { "name": "Yilmaz P." }, { "name": "Parfrey L.W." }, { "name": "Yarza P." }, { "name": "Gerken J." }, { "name": "Pruesse E." }, { "name": "Quast C." }, { "name": "Schweer T." }, { "name": "Peplies J." }, { "name": "Ludwig W." }, { "name": "Glockner F.O." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkm864", "pmid": "17947321", "pmcid": "PMC2175337", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB", "abstract": "Sequencing ribosomal RNA (rRNA) genes is currently the method of choice for phylogenetic reconstruction, nucleic acid based detection and quantification of microbial diversity. The ARB software suite with its corresponding rRNA datasets has been accepted by researchers worldwide as a standard tool for large scale rRNA analysis. However, the rapid increase of publicly available rRNA sequence data has recently hampered the maintenance of comprehensive and curated rRNA knowledge databases. A new system, SILVA (from Latin silva, forest), was implemented to provide a central comprehensive web resource for up to date, quality controlled databases of aligned rRNA sequences from the Bacteria, Archaea and Eukarya domains. All sequences are checked for anomalies, carry a rich set of sequence associated contextual information, have multiple taxonomic classifications, and the latest validly described nomenclature. Furthermore, two precompiled sequence datasets compatible with ARB are offered for download on the SILVA website: (i) the reference (Ref) datasets, comprising only high quality, nearly full length sequences suitable for in-depth phylogenetic analysis and probe design and (ii) the comprehensive Parc datasets with all publicly available rRNA sequences longer than 300 nucleotides suitable for biodiversity analyses. The latest publicly available database release 91 (August 2007) hosts 547 521 sequences split into 461 823 small subunit and 85 689 large subunit rRNAs. © 2007 The Author(s).", "date": "2007-12-01T00:00:00Z", "citationCount": 5145, "authors": [ { "name": "Pruesse E." }, { "name": "Quast C." }, { "name": "Knittel K." }, { "name": "Fuchs B.M." }, { "name": "Ludwig W." }, { "name": "Peplies J." }, { "name": "Glockner F.O." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/bioinformatics/bts252", "pmid": "22556368", "pmcid": "PMC3389763", "type": [ "Other" ], "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": 2352, "authors": [ { "name": "Pruesse E." }, { "name": "Peplies J." }, { "name": "Glockner F.O." } ], "journal": "Bioinformatics" } }, { "doi": "10.1016/j.jbiotec.2017.06.1198", "pmid": "28648396", "pmcid": null, "type": [ "Review" ], "version": null, "note": null, "metadata": { "title": "25 years of serving the community with ribosomal RNA gene reference databases and tools", "abstract": "SILVA (lat. forest) is a comprehensive web resource, providing services around up to date, high-quality datasets of aligned ribosomal RNA gene (rDNA) sequences from the Bacteria, Archaea, and Eukaryota domains. SILVA dates back to the year 1991 when Dr. Wolfgang Ludwig from the Technical University Munich started the integrated software workbench ARB (lat. tree) to support high-quality phylogenetic inference and taxonomy based on the SSU and LSU rDNA marker genes. At that time, the ARB project maintained both, the sequence reference datasets and the software package for data analysis. In 2005, with the massive increase of DNA sequence data, the maintenance of the software system ARB and the corresponding rRNA databases SILVA was split between Munich and the Microbial Genomics and Bioinformatics Research Group in Bremen. ARB has been continuously developed to include new features and improve the usability of the workbench. Thousands of users worldwide appreciate the seamless integration of common analysis tools under a central graphical user interface, in combination with its versatility. The first SILVA release was deployed in February 2007 based on the EMBL-EBI/ENA release 89. Since then, full SILVA releases offering the database content in various flavours are published at least annually, complemented by intermediate web-releases where only the SILVA web dataset is updated. SILVA is the only rDNA database project worldwide where special emphasis is given to the consistent naming of clades of uncultivated (environmental) sequences, where no validly described cultivated representatives are available. Also exclusive for SILVA is the maintenance of both comprehensive aligned 16S/18S rDNA and 23S/28S rDNA sequence datasets. Furthermore, the SILVA alignments and trees were designed to include Eukaryota, another unique feature among rDNA databases. With the termination of the European Ribosomal RNA Database Project in 2007, the SILVA database has become the authoritative rDNA database project for Europe. The application spectrum of ARB and SILVA ranges from biodiversity analysis, medical diagnostics, to biotechnology and quality control for academia and industry.", "date": "2017-11-10T00:00:00Z", "citationCount": 578, "authors": [ { "name": "Glockner F.O." }, { "name": "Yilmaz P." }, { "name": "Quast C." }, { "name": "Gerken J." }, { "name": "Beccati A." }, { "name": "Ciuprina A." }, { "name": "Bruns G." }, { "name": "Yarza P." }, { "name": "Peplies J." }, { "name": "Westram R." }, { "name": "Ludwig W." } ], "journal": "Journal of Biotechnology" } }, { "doi": "10.1186/s12859-017-1841-3", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "SILVA tree viewer: Interactive web browsing of the SILVA phylogenetic guide trees", "abstract": "Background: Phylogenetic trees are an important tool to study the evolutionary relationships among organisms. The huge amount of available taxa poses difficulties in their interactive visualization. This hampers the interaction with the users to provide feedback for the further improvement of the taxonomic framework. Results: The SILVA Tree Viewer is a web application designed for visualizing large phylogenetic trees without requiring the download of any software tool or data files. The SILVA Tree Viewer is based on Web Geographic Information Systems (Web-GIS) technology with a PostgreSQL backend. It enables zoom and pan functionalities similar to Google Maps. The SILVA Tree Viewer enables access to two phylogenetic (guide) trees provided by the SILVA database: the SSU Ref NR99 inferred from high-quality, full-length small subunit sequences, clustered at 99% sequence identity and the LSU Ref inferred from high-quality, full-length large subunit sequences. Conclusions: The Tree Viewer provides tree navigation, search and browse tools as well as an interactive feedback system to collect any kinds of requests ranging from taxonomy to data curation and improving the tool itself.", "date": "2017-09-30T00:00:00Z", "citationCount": 28, "authors": [ { "name": "Beccati A." }, { "name": "Gerken J." }, { "name": "Quast C." }, { "name": "Yilmaz P." }, { "name": "Glockner F.O." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures", "email": "hub@dsmz.de", "url": "https://www.dsmz.de", "orcidid": null, "gridid": "grid.420081.f", "rorid": "02tyer376", "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": null, "email": "contact@arb-silva.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "silva", "additionDate": "2016-09-30T15:59:05Z", "lastUpdate": "2025-01-22T09:59:03.064970Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SCANPY", "description": "Scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.", "homepage": "https://github.com/theislab/Scanpy", "biotoolsID": "scanpy", "biotoolsCURIE": "biotools:scanpy", "version": [], "otherID": [], "relation": [ { "biotoolsID": "anndata", "type": "uses" }, { "biotoolsID": "muon", "type": "usedBy" } ], "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_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3053", "term": "Genetics" }, { "uri": "http://edamontology.org/topic_4028", "term": "Single-cell sequencing" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Python" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/theislab/scanpy", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://scanpy.readthedocs.io/en/latest/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/s13059-017-1382-0", "pmid": "29409532", "pmcid": "PMC5802054", "type": [], "version": null, "note": null, "metadata": { "title": "SCANPY: Large-scale single-cell gene expression data analysis", "abstract": "Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).", "date": "2018-02-06T00:00:00Z", "citationCount": 3414, "authors": [ { "name": "Wolf F.A." }, { "name": "Angerer P." }, { "name": "Theis F.J." } ], "journal": "Genome Biology" } } ], "credit": [ { "name": "F. Alexander Wolf", "email": "alex.wolf@helmholtz-muenchen.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "d.gabrielaitis", "additionDate": "2018-08-13T12:41:27Z", "lastUpdate": "2025-01-13T10:00:07.404738Z", "editPermission": { "type": "group", "authors": [ "pavanvidem" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "muon", "description": "muon is a Python framework for multimodal omics analysis.", "homepage": "https://muon.scverse.org/", "biotoolsID": "muon", "biotoolsCURIE": "biotools:muon", "version": [], "otherID": [], "relation": [ { "biotoolsID": "anndata", "type": "uses" }, { "biotoolsID": "scanpy", "type": "uses" } ], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_4028", "term": "Single-cell sequencing" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" }, { "uri": "http://edamontology.org/topic_4021", "term": "Multiomics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/scverse/muon", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://muon.readthedocs.io/en/latest/index.html", "type": [ "General" ], "note": null }, { "url": "https://muon-tutorials.readthedocs.io/en/latest/", "type": [ "Training material" ], "note": null } ], "publication": [ { "doi": "10.1186/s13059-021-02577-8", "pmid": "35105358", "pmcid": "PMC8805324", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "MUON: multimodal omics analysis framework", "abstract": "Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analyses, from data preprocessing to flexible multi-omics alignment.", "date": "2022-12-01T00:00:00Z", "citationCount": 51, "authors": [ { "name": "Bredikhin D." }, { "name": "Kats I." }, { "name": "Stegle O." } ], "journal": "Genome Biology" } } ], "credit": [], "owner": "pavanvidem", "additionDate": "2025-01-09T13:20:38.336969Z", "lastUpdate": "2025-01-09T13:20:38.339496Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SDM", "description": "A server for predicting effects of mutations on protein stability", "homepage": "https://compbio.medschl.cam.ac.uk/sdm2/", "biotoolsID": "sdm2", "biotoolsCURIE": "biotools:sdm2", "version": [ "2.0" ], "otherID": [], "relation": [ { "biotoolsID": "sdm", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0331", "term": "Variant effect prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [], "operatingSystem": [ "Linux" ], "language": [], "license": null, "collectionID": [ "3D-BioInfo" ], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [ "Tools" ], "elixirNode": [ "UK" ], "elixirCommunity": [ "3D-BioInfo" ], "link": [], "download": [], "documentation": [ { "url": "https://compbio.medschl.cam.ac.uk/sdm2/help", "type": [ "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gkx439", "pmid": "28525590", "pmcid": "PMC5793720", "type": [ "Method" ], "version": "2.0", "note": "SDM: a server for predicting effects of mutations on protein stability", "metadata": { "title": "SDM: A server for predicting effects of mutations on protein stability", "abstract": "Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein.", "date": "2017-07-03T00:00:00Z", "citationCount": 374, "authors": [ { "name": "Pandurangan A.P." }, { "name": "Ochoa-Montano B." }, { "name": "Ascher D.B." }, { "name": "Blundell T.L." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "ARUN PRASAD PANDURANGAN", "email": "app41@cam.ac.uk", "url": "https://www.infectiousdisease.cam.ac.uk/directory/apandura%40mrc-lmb.cam.ac.uk", "orcidid": "https://orcid.org/0000-0001-7168-7143", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Primary contact", "Maintainer" ], "note": "Computational Biologist at the University of Cambridge" } ], "owner": "apandura", "additionDate": "2023-08-29T21:12:45.977710Z", "lastUpdate": "2025-01-08T14:32:09.070558Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "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": "http://edamontology.org/data_1364", "term": "Hidden Markov model" }, "format": [ { "uri": "http://edamontology.org/format_3329", "term": "HMMER3" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1270", "term": "Feature table" }, "format": [ { "uri": "http://edamontology.org/format_1936", "term": "GenBank format" }, { "uri": "http://edamontology.org/format_1927", "term": "EMBL format" }, { "uri": "http://edamontology.org/format_1975", "term": "GFF3" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_2887", "term": "Nucleic acid sequence record" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_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": "Multi-regional prostate segmentation tool", "description": "The tool performs an automatic multi-regional segmentation of the prostate into central-transition zone (CZ+TZ), peripheral zone (PZ), and seminal vesicle (SV) using a T2-weighted MRI image. A heterogeneous database of 243 T2-weighted prostate studies was used to train a U-Net based model with deep supervision.", "homepage": "https://quibim.com/es/qp-prostate/", "biotoolsID": "multi-regional_prostate_segmentation_tool", "biotoolsCURIE": "biotools:multi-regional_prostate_segmentation_tool", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [], "note": "-i → input directory to the T2w MRI sequence containing .dcm files. \n-o → output directory to store the results.", "cmd": "python -m t2_prostate_segmentation -i [INPUT] -o [OUTPUT]" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Commercial", "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://pubmed.ncbi.nlm.nih.gov/36690774/", "type": [ "Other" ], "note": "Publication" }, { "url": "https://quibim.com/es/qp-prostate/", "type": [ "Other" ], "note": "Product website." } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1007/s00330-023-09410-9", "pmid": "36690774", "pmcid": null, "type": [ "Primary" ], "version": "1.0.0", "note": null, "metadata": { "title": "Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks", "abstract": "Objective: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI. Methods: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate. Results: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents. Conclusion: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population. Key Points: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.", "date": "2023-07-01T00:00:00Z", "citationCount": 9, "authors": [ { "name": "Jimenez-Pastor A." }, { "name": "Lopez-Gonzalez R." }, { "name": "Fos-Guarinos B." }, { "name": "Garcia-Castro F." }, { "name": "Wittenberg M." }, { "name": "Torregrosa-Andres A." }, { "name": "Marti-Bonmati L." }, { "name": "Garcia-Fontes M." }, { "name": "Duarte P." }, { "name": "Gambini J.P." }, { "name": "Bittencourt L.K." }, { "name": "Kitamura F.C." }, { "name": "Venugopal V.K." }, { "name": "Mahajan V." }, { "name": "Ros P." }, { "name": "Soria-Olivas E." }, { "name": "Alberich-Bayarri A." } ], "journal": "European Radiology" } } ], "credit": [ { "name": "Alejandro Vergara Richart", "email": "alejandrovergara@quibim.com", "url": "https://quibim.com/es/", "orcidid": "https://orcid.org/0009-0003-5434-7499", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null } ], "owner": "alejandrovergara", "additionDate": "2024-12-19T08:54:13.149951Z", "lastUpdate": "2024-12-19T08:54:27.878162Z", "editPermission": { "type": "private", "authors": [ "alejandrovergara" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "metaMDBG", "description": "MetaMDBG is a fast and low-memory assembler for long and accurate metagenomics reads (e.g. PacBio HiFi, Nanopore r10.4). It is based on the minimizer de-Brujin graph (MDBG), which have been reimplemetend specifically for metagenomics assembly. MetaMDBG combines an efficient multi-k approach in minimizer-space for dealing with uneven species coverages, and a novel abundance-based filtering method for simplifying strain complexity.", "homepage": "https://github.com/GaetanBenoitDev/metaMDBG", "biotoolsID": "metamdbg", "biotoolsCURIE": "biotools:metamdbg", "version": [ "1.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "C++" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/GaetanBenoitDev/metaMDBG", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/GaetanBenoitDev/metaMDBG", "type": "Source code", "note": null, "version": "1.1" } ], "documentation": [ { "url": "https://github.com/GaetanBenoitDev/metaMDBG", "type": [ "Installation instructions" ], "note": null } ], "publication": [ { "doi": "10.1038/s41587-023-01983-6", "pmid": "38168989", "pmcid": "PMC11392814", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "High-quality metagenome assembly from long accurate reads with metaMDBG", "abstract": "We introduce metaMDBG, a metagenomics assembler for PacBio HiFi reads. MetaMDBG combines a de Bruijn graph assembly in a minimizer space with an iterative assembly over sequences of minimizers to address variations in genome coverage depth and an abundance-based filtering strategy to simplify strain complexity. For complex communities, we obtained up to twice as many high-quality circularized prokaryotic metagenome-assembled genomes as existing methods and had better recovery of viruses and plasmids.", "date": "2024-09-01T00:00:00Z", "citationCount": 13, "authors": [ { "name": "Benoit G." }, { "name": "Raguideau S." }, { "name": "James R." }, { "name": "Phillippy A.M." }, { "name": "Chikhi R." }, { "name": "Quince C." } ], "journal": "Nature Biotechnology" } } ], "credit": [ { "name": "Gaetan Benoit", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "tcollins", "additionDate": "2024-12-17T20:42:26.939752Z", "lastUpdate": "2024-12-17T20:42:26.942889Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ppiPre", "description": "Predicting protein-protein interactions by combining heterogeneous features.", "homepage": "https://cran.r-project.org/src/contrib/Archive/ppiPre/", "biotoolsID": "ppipre", "biotoolsCURIE": "biotools:ppipre", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2492", "term": "Protein interaction prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2259", "term": "Systems biology" }, { "uri": "http://edamontology.org/topic_0128", "term": "Protein interactions" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://cran.r-project.org/src/contrib/Archive/ppiPre/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/1752-0509-7-s2-s8", "pmid": "24565177", "pmcid": "PMC3851814", "type": [], "version": null, "note": null, "metadata": { "title": "ppiPre: Predicting protein-protein interactions by combining heterogeneous features", "abstract": "Background: Protein-protein interactions (PPIs) are crucial in cellular processes. Since the current biological experimental techniques are time-consuming and expensive, and the results suffer from the problems of incompleteness and noise, developing computational methods and software tools to predict PPIs is necessary. Although several approaches have been proposed, the species supported are often limited and additional data like homologous interactions in other species, protein sequence and protein expression are often required. And predictive abilities of different features for different kinds of PPI data have not been studied. Results: In this paper, we propose ppiPre, an open-source framework for PPI analysis and prediction using a combination of heterogeneous features including three GO-based semantic similarities, one KEGG-based co-pathway similarity and three topology-based similarities. It supports up to twenty species. Only the original PPI data and gold-standard PPI data are required from users. The experiments on binary and co-complex gold-standard yeast PPI data sets show that there exist big differences among the predictive abilities of different features on different kinds of PPI data sets. And the prediction performance on the two data sets shows that ppiPre is capable of handling PPI data in different kinds and sizes. ppiPre is implemented in the R language and is freely available on the CRAN ( http://cran.r-project.org/web/packages/ppiPre/ ). Conclusions: We applied our framework to both binary and co-complex gold-standard PPI data sets. The detailed analysis on three GO aspects suggests that different GO aspects should be used on different kinds of data sets, and that combining all the three aspects of GO often gets the best result. The analysis also shows that using only features based solely on the topology of the PPI network can get a very good result when predicting the co-complex PPI data. ppiPre provides useful functions for analysing PPI data and can be used to predict PPIs for multiple species.", "date": "2013-10-14T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Deng Y." }, { "name": "Gao L." }, { "name": "Wang B." } ], "journal": "BMC Systems Biology" } } ], "credit": [ { "name": "Lin Gao", "email": "lgao@mail.xidian.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "aquib806", "additionDate": "2018-08-20T14:19:10Z", "lastUpdate": "2024-12-16T10:12:36.110260Z", "editPermission": { "type": "group", "authors": [ "aquib806" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Vireo", "description": "Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference.", "homepage": "https://github.com/single-cell-genetics/vireo", "biotoolsID": "Vireo", "biotoolsCURIE": "biotools:Vireo", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3226", "term": "Variant prioritisation" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_3200", "term": "Community profiling" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/single-cell-genetics/vireo", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://vireosnp.readthedocs.io/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/S13059-019-1865-2", "pmid": "31836005", "pmcid": "PMC6909514", "type": [], "version": null, "note": null, "metadata": { "title": "Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference", "abstract": "Multiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising experimental design, offering increased throughput while allowing to overcome batch variation. To reconstruct the sample identify of each cell, genetic variants that segregate between the samples in the pool have been proposed as natural barcode for cell demultiplexing. Existing demultiplexing strategies rely on availability of complete genotype data from the pooled samples, which limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using pools based on synthetic mixtures and results on real data, we demonstrate the robustness of Vireo and illustrate the utility of multiplexed experimental designs for common expression analyses.", "date": "2019-12-13T00:00:00Z", "citationCount": 123, "authors": [ { "name": "Huang Y." }, { "name": "McCarthy D.J." }, { "name": "Stegle O." } ], "journal": "Genome Biology" } } ], "credit": [ { "name": "Yuanhua Huang", "email": "yuanhua@ebi.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Davis J. 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The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. 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