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{ "count": 30539, "next": "?page=2", "previous": null, "list": [ { "name": "Atomic Charge Calculator II", "description": "Atomic Charge Calculator II (ACC II) is a web application for fast calculation of partial atomic charges.", "homepage": "https://acc2.ncbr.muni.cz", "biotoolsID": "atomic-charge-calculator-ii", "biotoolsCURIE": "biotools:atomic-charge-calculator-ii", "version": [ "2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3438", "term": "Calculation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1463", "term": "Small molecule structure" }, "format": [ { "uri": "http://edamontology.org/format_3814", "term": "SDF" }, { "uri": "http://edamontology.org/format_1477", "term": "mmCIF" } ] }, { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" }, { "uri": "http://edamontology.org/format_1477", 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"pmcid": "PMC7319571", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Atomic charge calculator II: Web-based tool for the calculation of partial atomic charges", "abstract": "", "date": "2021-01-01T00:00:00Z", "citationCount": 46, "authors": [], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Radka Svobodová", "email": "radka.svobodova@mail.muni.cz", "url": null, "orcidid": "https://orcid.org/0000-0002-3840-8760", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Provider" ], "note": null }, { "name": "Tomáš Raček", "email": "tomas.racek@muni.cz", "url": null, "orcidid": "https://orcid.org/0000-0002-0296-2452", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": null } ], "owner": "ELIXIR-CZ", "additionDate": "2021-01-18T09:10:00Z", "lastUpdate": "2025-09-11T08:26:42.258113Z", "editPermission": { "type": "private", 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"citationCount": 279, "authors": [], "journal": "PLoS Computational Biology" } }, { "doi": "10.1007/s00239-003-0007-2", "pmid": "15008403", "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "Detection of New Transposable Element Families in Drosophila melanogaster and Anopheles gambiae Genomes", "abstract": "", "date": "2003-12-29T00:00:00Z", "citationCount": 65, "authors": [], "journal": "Journal of Molecular Evolution" } }, { "doi": "10.1109/JPROC.2016.2590833", "pmid": null, "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "De Novo Annotation of Transposable Elements: Tackling the Fat Genome Issue", "abstract": "", "date": "2017-03-01T00:00:00Z", "citationCount": 18, "authors": [], "journal": "Proceedings of the IEEE" } } ], "credit": [ { "name": "URGI", "email": "urgi-repet@versailles.inrae.fr", "url": "http://urgi.versailles.inrae.fr/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Developer", "Contributor", "Maintainer", "Provider", "Support", "Documentor" ], "note": "This tool has a \"Numero de depot APP\": FR 001 480007 000 R P 2008 000 31 235" } ], "owner": "Institut Francais de Bioinform", "additionDate": "2016-03-24T16:22:28Z", "lastUpdate": "2025-09-09T08:10:41.737859Z", "editPermission": { "type": "group", "authors": [ "vjamilloux", "johann_confais", "raphael.flores", "cmichotey" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PASTEClassifier", "description": "The PASTEClassifier (Pseudo Agent System for Transposable Elements Classification) is a transposable element (TE) classifier searching for structural features. 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Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation and detection of strains of non-human origin. Results: Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome sequencing samples with a mean of > 99:8% identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. 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Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Results: Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Availability and implementation: Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q.", "date": "2024-07-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Mendes A." }, { "name": "Havelund J.F." }, { "name": "Lemvig J." }, { "name": "Schwammle V." }, { "name": "Faergeman N.J." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Ana Mendes", "email": "anamendesml@outlook.com", "url": null, "orcidid": "https://orcid.org/0009-0008-5170-0927", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Nils J Færgeman", "email": "nils.f@bmb.sdu.dk", "url": null, "orcidid": "https://orcid.org/0000-0002-9281-5287", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "Pub2Tools", "additionDate": "2025-07-21T09:43:28.406602Z", "lastUpdate": "2025-08-28T13:24:34.167825Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ASAP", "description": "ASAP (Assemble Species by Automatic Partitioning) is a method to build species partitions from single locus sequence alignments.\nASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. 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ASAP is efficient enough to split data sets as large 104 sequences into putative species in several minutes. Although grounded in evolutionary theory, ASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. Importantly, ASAP proposes species partitions ranked by a new scoring system that uses no biological prior insight of intraspecific diversity. ASAP is a stand-alone program that can be used either through a graphical web-interface or that can be downloaded and compiled for local usage. We have assessed its power along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode data sets representing various degrees of challenge (from small and easy cases to large and complicated data sets). We also used Monte-Carlo simulations of a multispecies coalescent framework to assess the strengths and weaknesses of ASAP and the other programs. Through these analyses, we demonstrate that ASAP has the potential to become a major tool for taxonomists as it proposes rapidly in a full graphical exploratory interface relevant species hypothesis as a first step of the integrative taxonomy process.", "date": "2021-02-01T00:00:00Z", "citationCount": 15, "authors": [ { "name": "Puillandre N." }, { "name": "Brouillet S." }, { "name": "Achaz G." } ], "journal": "Molecular Ecology Resources" } } ], "credit": [ { "name": "Nicolas Puillandre", "email": "puillandre@mnhn.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Kigaard", "additionDate": "2021-01-18T11:19:37Z", "lastUpdate": "2025-08-28T07:33:58.127682Z", "editPermission": { "type": "group", "authors": [ "arkamukherjee" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ArrayAnalysis", "description": "ArrayAnalysis is a web-based application for transcriptomic data analysis. It supports the analysis of both microarray and RNA-seq data. The tool may also be installed locally as a desktop app, Docker image, or R package.", "homepage": "https://www.arrayanalysis.org", "biotoolsID": "arrayanalysis.org", "biotoolsCURIE": "biotools:arrayanalysis.org", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "CC-BY-4.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://www.arrayanalysis.org", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gkt293", "pmid": "23620278", "pmcid": "PMC3692049", "type": [], "version": null, "note": null, "metadata": { "title": "User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org.", "abstract": "Quality control (QC) is crucial for any scientific method producing data. Applying adequate QC introduces new challenges in the genomics field where large amounts of data are produced with complex technologies. For DNA microarrays, specific algorithms for QC and pre-processing including normalization have been developed by the scientific community, especially for expression chips of the Affymetrix platform. Many of these have been implemented in the statistical scripting language R and are available from the Bioconductor repository. However, application is hampered by lack of integrative tools that can be used by users of any experience level. To fill this gap, we developed a freely available tool for QC and pre-processing of Affymetrix gene expression results, extending, integrating and harmonizing functionality of Bioconductor packages. The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org or downloaded for local use in R. The portal provides extensive documentation, including user guides, interpretation help with real output illustrations and detailed technical documentation. It assists newcomers to the field in performing state-of-the-art QC and pre-processing while offering data analysts an integral open-source package. Providing the scientific community with this easily accessible tool will allow improving data quality and reuse and adoption of standards.", "date": "2013-01-01T00:00:00Z", "citationCount": 107, "authors": [ { "name": "Eijssen L.M." }, { "name": "Jaillard M." }, { "name": "Adriaens M.E." }, { "name": "Gaj S." }, { "name": "de Groot P.J." }, { "name": "Muller M." }, { "name": "Evelo C.T." } ], "journal": "Nucleic acids research" } }, { "doi": "10.1186/s12864-015-1689-8", "pmid": "26122086", "pmcid": "PMC4486126", "type": [], "version": null, "note": null, "metadata": { "title": "A user-friendly workflow for analysis of Illumina gene expression bead array data available at the arrayanalysis.org portal", "abstract": "Background: Illumina whole-genome expression bead arrays are a widely used platform for transcriptomics. Most of the tools available for the analysis of the resulting data are not easily applicable by less experienced users. ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows. Results: To enable data analysis of Illumina bead arrays for a broad user community, we have developed a module for ArrayAnalysis.org that provides a free and user-friendly web interface for quality control and pre-processing for these arrays. This module can be used together with existing modules for statistical and pathway analysis to provide a full workflow for Illumina gene expression data analysis. Conclusions: The Illumina bead arrays analysis module is available at http://www.arrayanalysis.org. A user guide, a tutorial demonstrating the analysis of an example dataset, and R scripts are available. The module can be used as a starting point for statistical evaluation and pathway analysis provided on the website or to generate processed input data for a broad range of applications in life sciences research.", "date": "2015-06-30T00:00:00Z", "citationCount": 12, "authors": [ { "name": "Eijssen L.M.T." }, { "name": "Goelela V.S." }, { "name": "Kelder T." }, { "name": "Adriaens M.E." }, { "name": "Evelo C.T." }, { "name": "Radonjic M." } ], "journal": "BMC Genomics" } } ], "credit": [ { "name": "Support Forum", "email": null, "url": "https://www.arrayanalysis.org/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "jarnokoetsier", "additionDate": "2017-05-16T12:28:07Z", "lastUpdate": "2025-08-28T07:09:05.106319Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "UCPH COVID19 DASHBOARD", "description": "The dashboard provides tools for visualizing the COVID-19 spread in individual countries over time, as well as comparing different countries on a similar time scale. We provide time series of Danish test data. 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Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. 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Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. 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Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.", "date": "2021-01-01T00:00:00Z", "citationCount": 32, "authors": [ { "name": "Lee J." }, { "name": "Shah M." }, { "name": "Ballouz S." }, { "name": "Crow M." }, { "name": "Gillis J." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Jesse Gillis", "email": "jgillis@cshl.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Kigaard", "additionDate": "2021-01-18T08:31:27Z", "lastUpdate": "2025-08-27T10:44:27.458203Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "datmat", "description": "‘datmat’ is a Python package aimed to easily gather data from a variety of sources and materialize it into a standardized format. 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In this study, we aimed to increase the diagnostic yield of CE using a custom reanalysis algorithm. Sequencing data were available for three cohorts using two commercial protocols applied as part of the diagnostic process. Using these cohorts, we compared the performance of general and clinically relevant variant calling and the efficacy of an in-house bioinformatic protocol (FJD-pipeline) in detecting causal variants as compared to commercial protocols. On the whole, the FJD-pipeline detected 99.74% of the causal variants identified by the commercial protocol in previously solved cases. In the unsolved cases, FJD-pipeline detects more INDELs and non-exonic variants, and is able to increase the diagnostic yield in 2.5% and 3.2% in the re-analysis of 78 cancer and 62 cardiovascular cases. These results were considered to design a reanalysis, filtering and prioritization algorithm that was tested by reassessing 68 inconclusive cases of monoallelic autosomal recessive retinal dystrophies increasing the diagnosis by 4.4%. 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Macromolecular System Finder (MacSyFinder) is a program that uses these properties to model and then annotate cellular functions in microbial genomes. This is done by integrating the identification of each individual gene at the level of the molecular system. We hereby present a major release of MacSyFinder (version 2) coded in Python 3. The code was improved and rationalized to facilitate future maintainability. Several new features were added to allow more flexible modelling of the systems. We introduce a more intuitive and comprehensive search engine to identify all the best candidate systems and sub-optimal ones that respect the models’ constraints. We also introduce the novel macsydata companion tool that enables the easy installation and broad distribution of the models developed for MacSyFinder (macsy-models) from GitHub repositories. Finally, we have updated and improved MacSyFinder popular mod-els: TXSScan to identify protein secretion systems, TFFscan to identify type IV filaments, CONJscan to identify conjugative systems, and CasFinder to identify CRISPR associated proteins. 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