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            "name": "rnaspades",
            "description": "Pipeline for de novo transcriptome assembly from RNA-Seq.",
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                            "term": "De-novo assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3258",
                            "term": "Transcriptome assembly"
                        }
                    ],
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                                "term": "Data"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
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                    "term": "RNA-Seq"
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                {
                    "url": "http://spades.bioinf.spbau.ru/release3.11.1/rnaspades_manual.html",
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                {
                    "doi": "10.1089/cmb.2012.0021",
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                    "metadata": {
                        "title": "SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing",
                        "abstract": "The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software. © Copyright 2012, Mary Ann Liebert, Inc.",
                        "date": "2012-05-01T00:00:00Z",
                        "citationCount": 15962,
                        "authors": [
                            {
                                "name": "Bankevich A."
                            },
                            {
                                "name": "Nurk S."
                            },
                            {
                                "name": "Antipov D."
                            },
                            {
                                "name": "Gurevich A.A."
                            },
                            {
                                "name": "Dvorkin M."
                            },
                            {
                                "name": "Kulikov A.S."
                            },
                            {
                                "name": "Lesin V.M."
                            },
                            {
                                "name": "Nikolenko S.I."
                            },
                            {
                                "name": "Pham S."
                            },
                            {
                                "name": "Prjibelski A.D."
                            },
                            {
                                "name": "Pyshkin A.V."
                            },
                            {
                                "name": "Sirotkin A.V."
                            },
                            {
                                "name": "Vyahhi N."
                            },
                            {
                                "name": "Tesler G."
                            },
                            {
                                "name": "Alekseyev M.A."
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                            {
                                "name": "Pevzner P.A."
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                        "journal": "Journal of Computational Biology"
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                    "metadata": {
                        "title": "The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update",
                        "abstract": "High-throughput data production technologies, particularly ‘next-generation’ DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. 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. In this report we highlight recently added features enabling biomedical analyses on a large scale.",
                        "date": "2016-07-08T00:00:00Z",
                        "citationCount": 1348,
                        "authors": [
                            {
                                "name": "Afgan E."
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                            {
                                "name": "Baker D."
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                            {
                                "name": "van den Beek M."
                            },
                            {
                                "name": "Blankenberg D."
                            },
                            {
                                "name": "Bouvier D."
                            },
                            {
                                "name": "Cech M."
                            },
                            {
                                "name": "Chilton J."
                            },
                            {
                                "name": "Clements D."
                            },
                            {
                                "name": "Coraor N."
                            },
                            {
                                "name": "Eberhard C."
                            },
                            {
                                "name": "Gruning B."
                            },
                            {
                                "name": "Guerler A."
                            },
                            {
                                "name": "Hillman-Jackson J."
                            },
                            {
                                "name": "Kuster G.V."
                            },
                            {
                                "name": "Rasche E."
                            },
                            {
                                "name": "Soranzo N."
                            },
                            {
                                "name": "Turaga N."
                            },
                            {
                                "name": "Taylor J."
                            },
                            {
                                "name": "Nekrutenko A."
                            },
                            {
                                "name": "Goecks J."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.7490/f1000research.1114334.1",
                    "pmid": null,
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                    "type": [
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                    "note": null,
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                    "note": "Supporting protocol for use-case 1: Dimensionality reduction in \"M2aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data\""
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                    "note": "Supporting protocol for use-case 2: Multi-modal 3D image reconstruction in \"M2aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data\""
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                    "note": "Supporting protocol for use-case 1: N-linked glycan m/z candidate detection in \"M2aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data\""
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                },
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                    "note": "Supporting capsule for use-case 1: Command-line application based pre-processing in \"M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data\""
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        {
            "name": "The Human Protein Atlas",
            "description": "Database with millions of high-resolution images. Expression and localization of proteins in a large variety of normal human tissues, cancer cells and cell lines with the aid of immunohistochemistry (IHC) images and immunofluorescence (IF) confocal microscopy images.",
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                    "term": "Gene and protein families"
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                    "term": "Oncology"
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                    "term": "Structural biology"
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                    "term": "Protein targeting and localisation"
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                    "term": "Model organisms"
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                    "metadata": {
                        "title": "Tissue-based map of the human proteome",
                        "abstract": "Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes.We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.",
                        "date": "2015-01-23T00:00:00Z",
                        "citationCount": 8758,
                        "authors": [
                            {
                                "name": "Uhlen M."
                            },
                            {
                                "name": "Fagerberg L."
                            },
                            {
                                "name": "Hallstrom B.M."
                            },
                            {
                                "name": "Lindskog C."
                            },
                            {
                                "name": "Oksvold P."
                            },
                            {
                                "name": "Mardinoglu A."
                            },
                            {
                                "name": "Sivertsson A."
                            },
                            {
                                "name": "Kampf C."
                            },
                            {
                                "name": "Sjostedt E."
                            },
                            {
                                "name": "Asplund A."
                            },
                            {
                                "name": "Olsson I."
                            },
                            {
                                "name": "Edlund K."
                            },
                            {
                                "name": "Lundberg E."
                            },
                            {
                                "name": "Navani S."
                            },
                            {
                                "name": "Szigyarto C.A.-K."
                            },
                            {
                                "name": "Odeberg J."
                            },
                            {
                                "name": "Djureinovic D."
                            },
                            {
                                "name": "Takanen J.O."
                            },
                            {
                                "name": "Hober S."
                            },
                            {
                                "name": "Alm T."
                            },
                            {
                                "name": "Edqvist P.-H."
                            },
                            {
                                "name": "Berling H."
                            },
                            {
                                "name": "Tegel H."
                            },
                            {
                                "name": "Mulder J."
                            },
                            {
                                "name": "Rockberg J."
                            },
                            {
                                "name": "Nilsson P."
                            },
                            {
                                "name": "Schwenk J.M."
                            },
                            {
                                "name": "Hamsten M."
                            },
                            {
                                "name": "Von Feilitzen K."
                            },
                            {
                                "name": "Forsberg M."
                            },
                            {
                                "name": "Persson L."
                            },
                            {
                                "name": "Johansson F."
                            },
                            {
                                "name": "Zwahlen M."
                            },
                            {
                                "name": "Von Heijne G."
                            },
                            {
                                "name": "Nielsen J."
                            },
                            {
                                "name": "Ponten F."
                            }
                        ],
                        "journal": "Science"
                    }
                }
            ],
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                {
                    "name": "bils.se",
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                {
                    "name": "Support",
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        },
        {
            "name": "REDItools",
            "description": "Python scripts to detect RNA editing in deep transcriptome sequencing data (RNAseq)",
            "homepage": "https://github.com/BioinfoUNIBA/REDItools",
            "biotoolsID": "reditools",
            "biotoolsCURIE": "biotools:reditools",
            "version": [
                "1.3",
                "2.0"
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                    "type": "rrid",
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                        {
                            "uri": "http://edamontology.org/operation_2945",
                            "term": "Analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1916",
                                "term": "Alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2572",
                                    "term": "BAM"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
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                                "term": "Text data"
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                                    "term": "Textual format"
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                        }
                    ],
                    "note": "Run REDItools on RNA sequences",
                    "cmd": "REDItoolDnaRna.py -i rnaseq.bam -f myreference.fa -o myoutputfolder"
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                }
            ],
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                "Linux",
                "Mac"
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                "Python"
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            "license": "MIT",
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            "cost": "Free of charge",
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            "documentation": [
                {
                    "url": "https://github.com/BioinfoUNIBA/REDItools",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btt287",
                    "pmid": "23742983",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0-1.3",
                    "note": null,
                    "metadata": {
                        "title": "REDItools: High-throughput RNA editing detection made easy",
                        "abstract": "The reliable detection of RNA editing sites from massive sequencing data remains challenging and, although several methodologies have been proposed, no computational tools have been released to date. Here, we introduce REDItools a suite of python scripts to perform high-throughput investigation of RNA editing using next-generation sequencing data.Availability and implementation: REDItools are in python programming language and freely available at http://code.google. com/p/reditools/.Contact: or graziano.pesole@uniba.itSupplementary information: Supplementary data are available at Bioinformatics online. © 2013 The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.",
                        "date": "2013-07-15T00:00:00Z",
                        "citationCount": 201,
                        "authors": [
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "Pesole G."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/s12859-020-03562-x",
                    "pmid": "32838738",
                    "pmcid": "PMC7446188",
                    "type": [],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "HPC-REDItools: A novel HPC-aware tool for improved large scale RNA-editing analysis",
                        "abstract": "Background: RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. Results: In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2. Conclusions: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.",
                        "date": "2020-08-21T00:00:00Z",
                        "citationCount": 21,
                        "authors": [
                            {
                                "name": "Flati T."
                            },
                            {
                                "name": "Gioiosa S."
                            },
                            {
                                "name": "Spallanzani N."
                            },
                            {
                                "name": "Tagliaferri I."
                            },
                            {
                                "name": "Diroma M.A."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Chillemi G."
                            },
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "Castrignano T."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1038/s41596-019-0279-7",
                    "pmid": "31996844",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal",
                        "abstract": "RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task. To this aim, we developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in our specialized REDIportal database, containing more than 4.5 million events. Here we describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, we outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, we show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads. Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.",
                        "date": "2020-03-01T00:00:00Z",
                        "citationCount": 69,
                        "authors": [
                            {
                                "name": "Lo Giudice C."
                            },
                            {
                                "name": "Tangaro M.A."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Picardi E."
                            }
                        ],
                        "journal": "Nature Protocols"
                    }
                },
                {
                    "doi": "10.1007/978-1-0716-1307-8_14",
                    "pmid": "33835447",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RNA Editing Detection in HPC Infrastructures",
                        "abstract": "RNA editing by A-to-I deamination is a relevant co/posttranscriptional modification carried out by ADAR enzymes. In humans, it has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, the detection of RNA editing variants in large transcriptome sequencing experiments (RNAseq) is yet a challenging computational task. To drastically reduce computing times we have developed a novel REDItools version able to identify A-to-I events in huge amount of RNAseq data employing High Performance Computing (HPC) infrastructures. Here we show how to use REDItools v2 in HPC systems.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Giudice C.L."
                            },
                            {
                                "name": "Mansi L."
                            },
                            {
                                "name": "Flati T."
                            },
                            {
                                "name": "Gioiosa S."
                            },
                            {
                                "name": "Chillemi G."
                            },
                            {
                                "name": "Libro P."
                            },
                            {
                                "name": "Castrignano T."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Picardi E."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
                    }
                },
                {
                    "doi": "10.1002/0471250953.bi1212s49",
                    "pmid": "25754992",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Using REDItools to detect RNA editing events in NGS datasets",
                        "abstract": "RNAediting is a post-transcriptional/co-transcriptional molecular phenomenon whereby a genetic message is modified from the corresponding DNA template by means of substitutions, insertions, and/or deletions. It occurs in a variety of organisms and different cellular locations through evolutionally and biochemically unrelated proteins. RNA editing has a plethora of biological effects including the modulation of alternative splicing and fine-tuning of gene expression. RNA editing events by base substitutions can be detected on a genomic scale by NGS technologies through the REDItools package, an ad hoc suite of Python scripts to study RNA editing using RNA-Seq and DNA-Seq data or RNA-Seq data alone. REDItools implement effective filters to minimize biases due to sequencing errors, mapping errors, and SNPs. The package is freely available at Google Code repository (http://code.google.com/p/reditools/) and released under the MIT license. In the present unit we show three basic protocols corresponding to three main REDItools scripts.",
                        "date": "2015-01-01T00:00:00Z",
                        "citationCount": 25,
                        "authors": [
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "D'Erchia A.M."
                            },
                            {
                                "name": "A. Antonio"
                            },
                            {
                                "name": "G. Graziano"
                            }
                        ],
                        "journal": "Current Protocols in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-CNR",
                    "email": null,
                    "url": null,
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                    "rorid": null,
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                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Ernesto Picardi",
                    "email": "ernesto.picardi@uniba.it",
                    "url": "https://www.uniba.it/docenti/picardi-ernesto/en",
                    "orcidid": "https://orcid.org/0000-0002-6549-0114",
                    "gridid": null,
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                    ],
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                }
            ],
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        },
        {
            "name": "MUSCLE (EBI)",
            "description": "Sequence alignment using the Multiple Sequence Comparison by Log-Expectation (MUSCLE) method",
            "homepage": "https://www.ebi.ac.uk/jdispatcher/msa/muscle",
            "biotoolsID": "muscle_ebi",
            "biotoolsCURIE": "biotools:muscle_ebi",
            "version": [
                "1"
            ],
            "otherID": [
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                    "value": "RRID:SCR_011812",
                    "type": "rrid",
                    "version": null
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                {
                    "biotoolsID": "muscle",
                    "type": "uses"
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0492",
                            "term": "Multiple sequence alignment"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
                            },
                            "format": []
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application",
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
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            "link": [
                {
                    "url": "https://www.ebi.ac.uk/about/contact/support/job-dispatcher-services",
                    "type": [
                        "Helpdesk"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://www.drive5.com/muscle/",
                    "type": "Downloads page",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "http://www.ebi.ac.uk/about/terms-of-use",
                    "type": [
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                    "note": null
                },
                {
                    "url": "https://www.ebi.ac.uk/jdispatcher/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://www.drive5.com/muscle/",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkh340",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MUSCLE: Multiple sequence alignment with high accuracy and high throughput",
                        "abstract": "We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5.com/muscle. © Oxford University Press 20004; all rights reserved.",
                        "date": "2004-07-09T00:00:00Z",
                        "citationCount": 32864,
                        "authors": [
                            {
                                "name": "Edgar R.C."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkac240",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 696,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Edbali O."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Robert Edgar",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "EMBL-EBI",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Job Dispatcher",
                    "email": null,
                    "url": "https://www.ebi.ac.uk/jdispatcher",
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                    "typeEntity": "Project",
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                }
            ],
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            "owner": "jdispatcher",
            "additionDate": "2015-01-29T15:47:31Z",
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        },
        {
            "name": "X-omics ACTION demonstrator multi-omics analysis workflow",
            "description": "This multi-omics data analysis workflow was developed as part of a case study investigating shared patterns between multi-omics data and childhood externalizing behaviour. The modular Nextflow workflow was implemented using FAIR practices. It comprises genome-wide DNA methylation, targeted metabolomics and behaviour data pre-processing, and integrative analysis methods. Individuals of a cohort are clustered using Similarity Network Fusion (SNF), latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) and Multiple Correspondence Analysis (MCA), and correlations between -omics and phenotype dimensions are determined in downstream analyses including results visualisation.",
            "homepage": "https://workflowhub.eu/workflows/402",
            "biotoolsID": "x-omics_action_demonstrator_multi-omics_analysis_workflow",
            "biotoolsCURIE": "biotools:x-omics_action_demonstrator_multi-omics_analysis_workflow",
            "version": [
                "2.2",
                "2.3"
            ],
            "otherID": [
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                    "biotoolsID": "nextflow",
                    "type": "uses"
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            ],
            "function": [],
            "toolType": [
                "Workflow"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3391",
                    "term": "Omics"
                },
                {
                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3173",
                    "term": "Epigenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_0092",
                    "term": "Data visualisation"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Python",
                "R"
            ],
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            "collectionID": [],
            "maturity": "Emerging",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/Xomics/ACTIONdemonstrator_workflow",
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                        "Repository"
                    ],
                    "note": null
                },
                {
                    "url": "https://doi.org/10.48546/workflowhub.workflow.402.8",
                    "type": [
                        "Software catalogue"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://github.com/Xomics/ACTIONdemonstrator_workflow/blob/main/Documentation.md",
                    "type": [
                        "User manual"
                    ],
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                }
            ],
            "publication": [
                {
                    "doi": "10.1093/gigascience/giad115",
                    "pmid": "38217405",
                    "pmcid": "PMC10787363",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": "Journal article (technical note)",
                    "metadata": null
                }
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                        "authors": [
                            {
                                "name": "Sievers F."
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                            {
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                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
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                            {
                                "name": "Madeira F."
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                            {
                                "name": "Pearce M."
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                            {
                                "name": "Tivey A.R.N."
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                            {
                                "name": "Basutkar P."
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                            {
                                "name": "Lee J."
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                            {
                                "name": "Edbali O."
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                            {
                                "name": "Madhusoodanan N."
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                            {
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                            {
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