count: 726
list:
- accessibility: Open access
  additionDate: '2016-12-19T14:26:53Z'
  biotoolsCURIE: biotools:rnaspades
  biotoolsID: rnaspades
  collectionID:
  - galaxyPasteur
  - SPAdes
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: galaxy@pasteur.fr
    fundrefid: null
    gridid: null
    name: Galaxy Support Team
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Pipeline for de novo transcriptome assembly from RNA-Seq.
  documentation:
  - note: null
    type:
    - User manual
    url: http://spades.bioinf.spbau.ru/release3.11.1/rnaspades_manual.html
  download: []
  editPermission:
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    type: public
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  function:
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    input:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Transcriptome assembly
      uri: http://edamontology.org/operation_3258
    output:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://cab.spbu.ru/software/spades/
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-08T15:29:01.478112Z'
  license: null
  link:
  - note: null
    type:
    - Galaxy service
    url: https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/fmareuil/rnaspades/rnaspades/3.9.0
  maturity: Mature
  name: rnaspades
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_016992
    version: null
  owner: hmenager
  publication:
  - doi: 10.1089/cmb.2012.0021
    metadata:
      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.
      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.
      - name: Pevzner P.A.
      citationCount: 15962
      date: '2012-05-01T00:00:00Z'
      journal: Journal of Computational Biology
      title: 'SPAdes: A new genome assembly algorithm and its applications to single-cell
        sequencing'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkw343
    metadata:
      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.
      authors:
      - name: Afgan E.
      - name: Baker D.
      - 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.
      citationCount: 1348
      date: '2016-07-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The Galaxy platform for accessible, reproducible and collaborative biomedical
        analyses: 2016 update'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.7490/f1000research.1114334.1
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 1
  version:
  - 3.9.0
- accessibility: Open access
  additionDate: '2021-06-22T12:35:44Z'
  biotoolsCURIE: biotools:m2aia
  biotoolsID: m2aia
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: M²aia (MSI applications for interactive analysis in MITK) is a software
    tool enabling interactive signal processing and visualisation of mass spectrometry
    imaging (MSI) datasets. M²aia extends the open source Medical Imaging and Interaction
    Toolkit (MITK; https://www.mitk.org)  and provides powerful methods that the MSI
    community can adopt, exploit and improve further. In it’s current state, it is
    designed to enable multi-modal 2D registration and 3D MSI reconstruction.
  documentation:
  - 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"'
    type:
    - Training material
    url: https://www.protocols.io/view/supporting-protocol-for-use-case-1-dimensionality-brw4m7gw
  - 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"'
    type:
    - Training material
    url: https://www.protocols.io/view/supporting-protocol-for-use-case-2-multi-modal-3d-bvq8n5zw
  - 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"'
    type:
    - Training material
    url: https://www.protocols.io/view/supporting-protocol-for-use-case-1-n-linked-glycan-bvq5n5y6
  - note: 'Supporting capsule for use-case 1: R-based processing in "M²aia - Interactive,
      fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry
      imaging data"'
    type:
    - Training material
    url: https://doi.org/10.24433/CO.2384502.v1
  - 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"'
    type:
    - Training material
    url: https://doi.org/10.24433/CO.7662658.v1
  download:
  - note: null
    type: Downloads page
    url: https://m2aia.de
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
      - term: ibd
        uri: http://edamontology.org/format_3839
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nrrd
        uri: http://edamontology.org/format_3551
      - term: OME-TIFF
        uri: http://edamontology.org/format_3727
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
      - term: ibd
        uri: http://edamontology.org/format_3839
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nrrd
        uri: http://edamontology.org/format_3551
  homepage: https://m2aia.github.io/m2aia
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-02-26T14:30:06.751229Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/m2aia/m2aia
  - note: null
    type:
    - Other
    url: https://www.mitk.org
  maturity: Emerging
  name: M2aia
  operatingSystem:
  - Linux
  - Windows
  otherID:
  - type: rrid
    value: RRID:SCR_019324
    version: null
  owner: jtfc
  publication: []
  relation: []
  toolType:
  - Desktop application
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2016-03-24T16:39:49Z'
  biotoolsCURIE: biotools:bowtie2
  biotoolsID: bowtie2
  collectionID:
  - galaxyPasteur
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Institut Français de Bioinformatique
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ben Langmead
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
  - email: null
    fundrefid: null
    gridid: null
    name: Ben Langmead
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
  description: 'Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing
    reads to long reference sequences. It is particularly good at aligning reads of
    about 50 up to 100s or 1,000s of characters, and particularly good at aligning
    to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with
    an FM Index to keep its memory footprint small: for the human genome, its memory
    footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end
    alignment modes.'
  documentation:
  - note: null
    type:
    - User manual
    url: http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml
  - note: null
    type:
    - General
    url: https://github.com/BenLangmead/bowtie2
  download:
  - note: null
    type: Tool wrapper (CWL)
    url: https://gitlab.com/sibyllewohlgemuth/cwl_files/raw/master/bowtie2.cwl
    version: null
  - note: null
    type: Tool wrapper (CWL)
    url: https://gitlab.com/sibyllewohlgemuth/cwl_files/raw/master/bowtie2_build.cwl
    version: null
  - note: null
    type: Container file
    url: https://docker-ui.genouest.org/app/#/container/bioconda/bowtie2
    version: null
  - note: null
    type: Container file
    url: https://anaconda.org/bioconda/bowtie2
    version: null
  - note: Link to the Debian package information. This form of the link is recommended
      by the Debian devs & Med team, and in the future all this information, including
      versions, will be pushed to GitHub automatically (matuskalas)
    type: Software package
    url: https://tracker.debian.org/pkg/bowtie2
    version: 2.2.4-1, 2.3.0-2, 2.3.4.3-1, 2.3.5.1-1, 2.3.5.1-1
  - note: Link to the Bioconda package information. This form of the link is recommended
      by the Bioconda team, and in the future all this information, including versions
      (limit 100 chars), will be pushed to GitHub automatically (matuskalas)
    type: Software package
    url: https://bioconda.github.io/recipes/bowtie2/README.html
    version: 2.3.5-0, 2.3.4.3-1 - 2.3.0-0, 2.2.8-2 - 2.2.1-0
  editPermission:
    authors:
    - swohlgemuth
    - rioualen
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Read mapping
      uri: http://edamontology.org/operation_3198
    output:
    - data:
        term: Sequence alignment (nucleic acid)
        uri: http://edamontology.org/data_1383
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
  homepage: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-02-16T10:45:32.677126Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Galaxy service
    url: https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/fmareuil/bowtie2/bowtie2/2.2.6.2
  maturity: Mature
  name: Bowtie 2
  operatingSystem:
  - Linux
  otherID:
  - type: rrid
    value: RRID:SCR_016368
    version: null
  owner: swohlgemuth
  publication:
  - doi: 10.1038/nmeth.1923
    metadata:
      abstract: As the rate of sequencing increases, greater throughput is demanded
        from read aligners. The full-text minute index is often used to make alignment
        very fast and memory-efficient, but the approach is ill-suited to finding
        longer, gapped alignments. Bowtie 2 combines the strengths of the full-text
        minute index with the flexibility and speed of hardware-accelerated dynamic
        programming algorithms to achieve a combination of high speed, sensitivity
        and accuracy. © 2012 Nature America, Inc. All rights reserved.
      authors:
      - name: Langmead B.
      - name: Salzberg S.L.
      citationCount: 18223
      date: '2012-04-01T00:00:00Z'
      journal: Nature Methods
      title: Fast gapped-read alignment with Bowtie 2
    note: null
    pmcid: PMC3322381
    pmid: '22388286'
    type:
    - Primary
    version: null
  - doi: 10.1186/1471-2105-14-184
    metadata:
      abstract: 'Background: The development of next-generation sequencing instruments
        has led to the generation of millions of short sequences in a single run.
        The process of aligning these reads to a reference genome is time consuming
        and demands the development of fast and accurate alignment tools. However,
        the current proposed tools make different compromises between the accuracy
        and the speed of mapping. Moreover, many important aspects are overlooked
        while comparing the performance of a newly developed tool to the state of
        the art. Therefore, there is a need for an objective evaluation method that
        covers all the aspects. In this work, we introduce a benchmarking suite to
        extensively analyze sequencing tools with respect to various aspects and provide
        an objective comparison.Results: We applied our benchmarking tests on 9 well
        known mapping tools, namely, Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP,
        Novoalign, and mrsFAST (mrFAST) using synthetic data and real RNA-Seq data.
        MAQ and RMAP are based on building hash tables for the reads, whereas the
        remaining tools are based on indexing the reference genome. The benchmarking
        tests reveal the strengths and weaknesses of each tool. The results show that
        no single tool outperforms all others in all metrics. However, Bowtie maintained
        the best throughput for most of the tests while BWA performed better for longer
        read lengths. The benchmarking tests are not restricted to the mentioned tools
        and can be further applied to others.Conclusion: The mapping process is still
        a hard problem that is affected by many factors. In this work, we provided
        a benchmarking suite that reveals and evaluates the different factors affecting
        the mapping process. Still, there is no tool that outperforms all of the others
        in all the tests. Therefore, the end user should clearly specify his needs
        in order to choose the tool that provides the best results. © 2013 Hatem et
        al.; licensee BioMed Central Ltd.'
      authors:
      - name: Hatem A.
      - name: Bozdag D.
      - name: Toland A.E.
      - name: Catalyurek U.V.
      citationCount: 121
      date: '2013-06-07T00:00:00Z'
      journal: BMC Bioinformatics
      title: Benchmarking short sequence mapping tools
    note: null
    pmcid: PMC3694458
    pmid: '23758764'
    type:
    - Benchmarking study
    version: null
  - doi: 10.1016/j.ygeno.2017.03.001
    metadata:
      abstract: © 2017 Elsevier Inc.Massive data produced due to the advent of next-generation
        sequencing (NGS) technology is widely used for biological researches and medical
        diagnosis. The crucial step in NGS analysis is read alignment or mapping which
        is computationally intensive and complex. The mapping bias tends to affect
        the downstream analysis, including detection of polymorphisms. In order to
        provide guidelines to the biologist for suitable selection of aligners; we
        have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign,
        Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial
        genomes. Two million simulated read pairs of various sizes (36 bp, 50 bp,
        72 bp, 100 bp, 125 bp, 150 bp, 200 bp, 250 bp and 300 bp) were aligned. Specific
        alignment features such as sensitivity of mapping, percentage of properly
        paired reads, alignment time and effect of tandem repeats on incorrectly mapped
        reads were evaluated. BWA showed faster alignment followed by Bowtie2 and
        Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners
        showed high sensitivity towards long reads (> 100 bp) mapping. On the other
        hand NovoAlign showed higher sensitivity towards both short reads (36 bp,
        50 bp, 72 bp) and long reads (> 100 bp) mappings; It also showed higher sensitivity
        towards mapping a complex genome like Plasmodium falciparum. The percentage
        of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly
        higher. None of the aligners outperforms the others in the benchmark, however
        the aligners perform differently with genome characteristics. We expect that
        the results from this study will be useful for the end user to choose aligner,
        thus enhance the accuracy of read mapping.
      authors:
      - name: Thankaswamy-Kosalai S.
      - name: Sen P.
      - name: Nookaew I.
      citationCount: 30
      date: '2017-07-01T00:00:00Z'
      journal: Genomics
      title: Evaluation and assessment of read-mapping by multiple next-generation
        sequencing aligners based on genome-wide characteristics
    note: null
    pmcid: null
    pmid: '28286147'
    type:
    - Benchmarking study
    version: null
  - doi: 10.1186/1471-2164-15-264
    metadata:
      abstract: 'Background: The rapid evolution in high-throughput sequencing (HTS)
        technologies has opened up new perspectives in several research fields and
        led to the production of large volumes of sequence data. A fundamental step
        in HTS data analysis is the mapping of reads onto reference sequences. Choosing
        a suitable mapper for a given technology and a given application is a subtle
        task because of the difficulty of evaluating mapping algorithms.Results: In
        this paper, we present a benchmark procedure to compare mapping algorithms
        used in HTS using both real and simulated datasets and considering four evaluation
        criteria: computational resource and time requirements, robustness of mapping,
        ability to report positions for reads in repetitive regions, and ability to
        retrieve true genetic variation positions. To measure robustness, we introduced
        a new definition for a correctly mapped read taking into account not only
        the expected start position of the read but also the end position and the
        number of indels and substitutions. We developed CuReSim, a new read simulator,
        that is able to generate customized benchmark data for any kind of HTS technology
        by adjusting parameters to the error types. CuReSim and CuReSimEval, a tool
        to evaluate the mapping quality of the CuReSim simulated reads, are freely
        available. We applied our benchmark procedure to evaluate 14 mappers in the
        context of whole genome sequencing of small genomes with Ion Torrent data
        for which such a comparison has not yet been established.Conclusions: A benchmark
        procedure to compare HTS data mappers is introduced with a new definition
        for the mapping correctness as well as tools to generate simulated reads and
        evaluate mapping quality. The application of this procedure to Ion Torrent
        data from the whole genome sequencing of small genomes has allowed us to validate
        our benchmark procedure and demonstrate that it is helpful for selecting a
        mapper based on the intended application, questions to be addressed, and the
        technology used. This benchmark procedure can be used to evaluate existing
        or in-development mappers as well as to optimize parameters of a chosen mapper
        for any application and any sequencing platform. © 2014 Caboche et al.; licensee
        BioMed Central Ltd.'
      authors:
      - name: Caboche S.
      - name: Audebert C.
      - name: Lemoine Y.
      - name: Hot D.
      citationCount: 55
      date: '2014-04-05T00:00:00Z'
      journal: BMC Genomics
      title: 'Comparison of mapping algorithms used in high-throughput sequencing:
        Application to Ion Torrent data'
    note: null
    pmcid: PMC4051166
    pmid: '24708189'
    type:
    - Benchmarking study
    version: null
  - doi: 10.1093/bioinformatics/btu146
    metadata:
      abstract: 'Motivation: Next-generation sequencing has become an important tool
        in molecular biology. Various protocols to investigate genomic, transcriptomic
        and epigenomic features across virtually all species and tissues have been
        devised. For most of these experiments, one of the first crucial steps of
        bioinformatic analysis is the mapping of reads to reference genomes. Results:
        Here, we present thorough benchmarks of our read aligner segemehl in comparison
        with other state-of-the-art methods. Furthermore, we introduce the tool lack
        to rescue unmapped RNA-seq reads which works in conjunction with segemehl
        and many other frequently used split-read aligners. © The Author 2014.'
      authors:
      - name: Otto C.
      - name: Stadler P.F.
      - name: Hoffmann S.
      citationCount: 46
      date: '2014-07-01T00:00:00Z'
      journal: Bioinformatics
      title: Lacking alignments? The next-generation sequencing mapper segemehl revisited
    note: null
    pmcid: null
    pmid: '24626854'
    type:
    - Benchmarking study
    version: null
  - doi: 10.7490/f1000research.1114334.1
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Mapping
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  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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  publication:
  - doi: 10.1126/science.1260419
    metadata:
      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.
      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.
      citationCount: 8758
      date: '2015-01-23T00:00:00Z'
      journal: Science
      title: Tissue-based map of the human proteome
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
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    uri: http://edamontology.org/topic_0203
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Humans
    uri: http://edamontology.org/topic_2815
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-03-04T22:39:18Z'
  biotoolsCURIE: biotools:clinvar
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  collectionID:
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  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Donna R. Maglott
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: A freely accessible, public archive of reports of relationships among
    medically important variants and phenotypes hosted by the NCBI. It integrates
    and cross-references data from multiple databases at NCBI. In addition to dbSNP
    and dbVar, it depends on MedGen to represent phenotype, Gene to represent genes,
    and on human RefSeqs to represent the location of sequence variation. Each record
    represents the submitter, the variation and the phenotype.
  documentation:
  - note: null
    type:
    - General
    url: https://www.ncbi.nlm.nih.gov/clinvar/intro/
  download: []
  editPermission:
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    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://www.ncbi.nlm.nih.gov/clinvar/
  homepage_status: 0
  language: []
  lastUpdate: '2024-02-14T13:12:50.053374Z'
  license: null
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  maturity: Mature
  name: ClinVar
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_006169
    version: null
  owner: ELIXIR-EE
  publication:
  - doi: 10.1093/nar/gkt1113
    metadata:
      abstract: ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available
        archive of reports of relationships among medically important variants and
        phenotypes. ClinVar accessions submissions reporting human variation, interpretations
        of the relationship of that variation to human health and the evidence supporting
        each interpretation. The database is tightly coupled with dbSNP and dbVar,
        which maintain information about the location of variation on human assemblies.
        ClinVar is also based on the phenotypic descriptions maintained in MedGen
        (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter,
        the variation and the phenotype, i.e. the unit that is assigned an accession
        of the format SCV000000000.0. The submitter can update the submission at any
        time, in which case a new version is assigned. To facilitate evaluation of
        the medical importance of each variant, ClinVar aggregates submissions with
        the same variation/phenotype combination, adds value from other NCBI databases,
        assigns a distinct accession of the format RCV000000000.0 and reports if there
        are conflicting clinical interpretations. Data in ClinVar are available in
        multiple formats, including html, download as XML, VCF or tab-delimited subsets.
        Data from ClinVar are provided as annotation tracks on genomic RefSeqs and
        are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter),
        which reports what is known about variation based on user-supplied locations.
        © 2013 The Author(s). Published by Oxford University Press.
      authors:
      - name: Landrum M.J.
      - name: Lee J.M.
      - name: Riley G.R.
      - name: Jang W.
      - name: Rubinstein W.S.
      - name: Church D.M.
      - name: Maglott D.R.
      citationCount: 1487
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ClinVar: Public archive of relationships among sequence variation and
        human phenotype'
    note: null
    pmcid: PMC3965032
    pmid: '24234437'
    type:
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    version: null
  - doi: 10.1093/nar/gkx1153
    metadata:
      abstract: © Published by Oxford University Press on behalf of Nucleic Acids
        Research 2017.ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) is a freely
        available, public archive of human genetic variants and interpretations of
        their significance to disease, maintained at the National Institutes of Health.
        Interpretations of the clinical significance of variants are submitted by
        clinical testing laboratories, research laboratories, expert panels and other
        groups. ClinVar aggregates data by variant-disease pairs, and by variant (or
        set of variants). Data aggregated by variant are accessible on the website,
        in an improved set of variant call format files and as a new comprehensive
        XML report. ClinVar recently started accepting submissions that are focused
        primarily on providing phenotypic information for individuals who have had
        genetic testing. Submissions may come from clinical providers providing their
        own interpretation of the variant ('provider interpretation') or from groups
        such as patient registries that primarily provide phenotypic information from
        patients ('phenotyping only'). ClinVar continues to make improvements to its
        search and retrieval functions. Several new fields are now indexed for more
        precise searching, and filters allow the user to narrow down a large set of
        search results.
      authors:
      - name: Landrum M.J.
      - name: Lee J.M.
      - name: Benson M.
      - name: Brown G.R.
      - name: Chao C.
      - name: Chitipiralla S.
      - name: Gu B.
      - name: Hart J.
      - name: Hoffman D.
      - name: Jang W.
      - name: Karapetyan K.
      - name: Katz K.
      - name: Liu C.
      - name: Maddipatla Z.
      - name: Malheiro A.
      - name: McDaniel K.
      - name: Ovetsky M.
      - name: Riley G.
      - name: Zhou G.
      - name: Holmes J.B.
      - name: Kattman B.L.
      - name: Maglott D.R.
      citationCount: 1190
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ClinVar: Improving access to variant interpretations and supporting
        evidence'
    note: null
    pmcid: PMC5753237
    pmid: '29165669'
    type: []
    version: null
  relation: []
  toolType:
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  - Database portal
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Restricted access
  additionDate: '2017-03-04T22:58:49Z'
  biotoolsCURIE: biotools:hgmd
  biotoolsID: hgmd
  collectionID:
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  cost: Free of charge (with restrictions)
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    name: Contact Form
    note: null
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    typeEntity: Person
    typeRole:
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    url: http://www.hgmd.cf.ac.uk/docs/comment_form.html
  description: The Human Gene Mutation Database (HGMD) - comprehensive collection
    of germline mutations in nuclear genes that underlie, or are associated with,
    human inherited disease. It is a flexible resource that can be adapted to a broad
    range of applications including searching for an overview of known mutations associated
    with a particular disease, interpreting clinical test results or looking for the
    likely causal mutation in a list of variants.
  documentation:
  - note: null
    type:
    - General
    url: http://www.hgmd.cf.ac.uk/docs/new_help.html
  download: []
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      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://www.hgmd.org
  homepage_status: 0
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  lastUpdate: '2024-02-14T13:12:49.217756Z'
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  maturity: Mature
  name: HGMD
  operatingSystem:
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  - Mac
  otherID:
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    version: null
  owner: ELIXIR-EE
  publication:
  - doi: 10.1007/s00439-013-1358-4
    metadata:
      abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive collection
        of germline mutations in nuclear genes that underlie, or are associated with,
        human inherited disease. By June 2013, the database contained over 141,000
        different lesions detected in over 5,700 different genes, with new mutation
        entries currently accumulating at a rate exceeding 10,000 per annum. HGMD
        was originally established in 1996 for the scientific study of mutational
        mechanisms in human genes. However, it has since acquired a much broader utility
        as a central unified disease-oriented mutation repository utilized by human
        molecular geneticists, genome scientists, molecular biologists, clinicians
        and genetic counsellors as well as by those specializing in biopharmaceuticals,
        bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org)
        is freely available to registered users from academic institutions/non-profit
        organizations whilst the subscription version (HGMD Professional) is available
        to academic, clinical and commercial users under license via BIOBASE GmbH.
        © 2013 The Author(s).
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Shaw K.
      - name: Phillips A.D.
      - name: Cooper D.N.
      citationCount: 904
      date: '2014-01-01T00:00:00Z'
      journal: Human Genetics
      title: 'The Human Gene Mutation Database: Building a comprehensive mutation
        repository for clinical and molecular genetics, diagnostic testing and personalized
        genomic medicine'
    note: null
    pmcid: PMC3898141
    pmid: '24077912'
    type:
    - Primary
    version: null
  - doi: 10.1136/jmg.2007.055210
    metadata:
      abstract: ''
      authors:
      - name: Stenson P.D.
      - name: Ball E.
      - name: Howells K.
      - name: Phillips A.
      - name: Mort M.
      - name: Cooper D.N.
      citationCount: 87
      date: '2008-02-01T00:00:00Z'
      journal: Journal of Medical Genetics
      title: 'Human Gene Mutation Database: Towards a comprehensive central mutation
        database'
    note: null
    pmcid: null
    pmid: '18245393'
    type: []
    version: null
  - doi: 10.1002/humu.10212
    metadata:
      abstract: 'The Human Gene Mutation Database (HGMD) constitutes a comprehensive
        core collection of data on germ-line mutations in nuclear genes underlying
        or associated with human inherited disease (www.hgmd.org). Data catalogued
        includes: single base-pair substitutions in coding, regulatory and splicing-relevant
        regions; micro-deletions and micro-insertions; indels; triplet repeat expansions
        as well as gross deletions; insertions; duplications; and complex rearrangements.
        Each mutation is entered into HGMD only once in order to avoid confusion between
        recurrent and identical-by-descent lesions. By March 2003, the database contained
        in excess of 39,415 different lesions detected in 1,516 different nuclear
        genes, with new entries currently accumulating at a rate exceeding 5,000 per
        annum. Since its inception, HGMD has been expanded to include cDNA reference
        sequences for more than 87% of listed genes, splice junction sequences, disease-associated
        and functional polymorphisms, as well as links to data present in publicly
        available online locus-specific mutation databases. Although HGMD has recently
        entered into a licensing agreement with Celera Genomics (Rockville, MD), mutation
        data will continue to be made freely available via the Internet. © 2003 Wiley-Liss,
        Inc.'
      authors:
      - name: Stenson P.D.
      - name: Ball E.V.
      - name: Mort M.
      - name: Phillips A.D.
      - name: Shiel J.A.
      - name: Thomas N.S.T.
      - name: Abeysinghe S.
      - name: Krawczak M.
      - name: Cooper D.N.
      citationCount: 1269
      date: '2003-06-16T00:00:00Z'
      journal: Human Mutation
      title: 'Human Gene Mutation Database (HGMD®): 2003 Update'
    note: null
    pmcid: null
    pmid: '12754702'
    type: []
    version: null
  - doi: 10.1186/gm13
    metadata:
      abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive core collection
        of germlinemutations in nuclear genes that underlie or are associated with
        human inherited disease. Here we summarize the history of the database and
        its current resources. By December 2008, thedatabase contained over 85,000
        different lesions detected in 3,253 ifferent genes, with new entries currently
        accumulating at a rate exceeding 9,000 per annum. Although originally established
        for the scientific study of mutational mechanisms in human genes, HGMD has
        since acquired a much broader utility for researchers, physicians, clinicians
        and genetic counselors as well as for companies specializing in biopharmaceuticals,
        bioinformatics and personalized genomics. HGMD was first made publicly available
        in April 1996, and a collaboration was initiated in 2006 between HGMD and
        BIOBASE GmbH. This cooperative agreement covers the exclusive worldwide marketing
        of the most up-to-date (subscription) version of HGMD, HGMD Professional,
        to academic, clinical and commercial users. © 2009 BioMed Central Ltd.
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Howells K.
      - name: Phillips P.D.
      - name: Cooper D.N.
      - name: Thomas N.S.T.
      citationCount: 665
      date: '2009-01-22T00:00:00Z'
      journal: Genome Medicine
      title: 'The human gene mutation database: 2008 update'
    note: null
    pmcid: null
    pmid: '19348700'
    type: []
    version: null
  - doi: 10.1007/s00439-017-1779-6
    metadata:
      abstract: © 2017, The Author(s).The Human Gene Mutation Database (HGMD®) constitutes
        a comprehensive collection of published germline mutations in nuclear genes
        that underlie, or are closely associated with human inherited disease. At
        the time of writing (March 2017), the database contained in excess of 203,000
        different gene lesions identified in over 8000 genes manually curated from
        over 2600 journals. With new mutation entries currently accumulating at a
        rate exceeding 17,000 per annum, HGMD represents de facto the central unified
        gene/disease-oriented repository of heritable mutations causing human genetic
        disease used worldwide by researchers, clinicians, diagnostic laboratories
        and genetic counsellors, and is an essential tool for the annotation of next-generation
        sequencing data. The public version of HGMD (http://www.hgmd.org) is freely
        available to registered users from academic institutions and non-profit organisations
        whilst the subscription version (HGMD Professional) is available to academic,
        clinical and commercial users under license via QIAGEN Inc.
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Evans K.
      - name: Hayden M.
      - name: Heywood S.
      - name: Hussain M.
      - name: Phillips A.D.
      - name: Cooper D.N.
      citationCount: 724
      date: '2017-06-01T00:00:00Z'
      journal: Human Genetics
      title: 'The Human Gene Mutation Database: towards a comprehensive repository
        of inherited mutation data for medical research, genetic diagnosis and next-generation
        sequencing studies'
    note: null
    pmcid: null
    pmid: '28349240'
    type: []
    version: null
  relation: []
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  topic:
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    uri: http://edamontology.org/topic_0199
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2015-10-09T16:38:04Z'
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    name: SIB Swiss Institute of Bioinformatics
    note: null
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  description: Data on proteins identified on various 2-D PAGE and SDS-PAGE reference
    maps.
  documentation:
  - note: null
    type:
    - General
    url: http://world-2dpage.expasy.org/swiss-2dpage/docs/manch2d.html
  download: []
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        term: Experiment report
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      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
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    note: null
    operation:
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    output:
    - data:
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        uri: http://edamontology.org/data_2531
      format:
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        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
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        term: Reference map name (SWISS-2DPAGE)
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      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
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        uri: http://edamontology.org/data_2531
      format:
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  - cmd: null
    input:
    - data:
        term: Reference map name (SWISS-2DPAGE)
        uri: http://edamontology.org/data_2791
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Experiment report
        uri: http://edamontology.org/data_2531
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Spot serial number
        uri: http://edamontology.org/data_2374
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Experiment report
        uri: http://edamontology.org/data_2531
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://www.expasy.org/ch2d/
  homepage_status: 0
  language: []
  lastUpdate: '2024-02-14T13:12:44.876955Z'
  license: null
  link: []
  maturity: null
  name: SWISS-2DPAGE
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_006946
    version: null
  owner: SIB
  publication:
  - doi: 10.1002/pmic.200300830
    metadata:
      abstract: The SWISS-2DPAGE database was established in 1993 and is maintained
        collaboratively by the Swiss Institute of Bioinformatics (SIB) and the Biomedical
        Proteomics Research Group (BPRG) of the Geneva University Hospital. During
        these years, SWISS-2DPAGE underwent constant modification and improvement.
        Current content includes about 4000 identified spots corresponding to 1200
        different protein entries in 36 reference maps from human, mouse, Arabidopsis
        thaliana, Dictyostelium discoideum, Escherichia coli, Saccharomyces cerevisiae
        and Staphylococcus aureus origins. With a high level of annotation and integration
        with other relevant databases, SWISS-2DPAGE is a reference source in the proteomics
        world. Queries to SWISS-2DPAGE database currently reach 1000 hits per day.
      authors:
      - name: Hoogland C.
      - name: Mostaguir K.
      - name: Sanchez J.-C.
      - name: Hochstrasser D.F.
      - name: Appel R.D.
      citationCount: 88
      date: '2004-08-01T00:00:00Z'
      journal: Proteomics
      title: SWISS-2DPAGE, ten years later
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 1
  version: []
- accessibility: null
  additionDate: '2015-02-05T13:24:16Z'
  biotoolsCURIE: biotools:reditools
  biotoolsID: reditools
  collectionID:
  - ELIXIR-ITA-CNR
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-CNR
    note: null
    orcidid: https://orcid.org/0000-0002-6549-0114
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: ernesto.picardi@uniba.it
    fundrefid: null
    gridid: null
    name: Ernesto Picardi
    note: null
    orcidid: https://orcid.org/0000-0002-6549-0114
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.uniba.it/docenti/picardi-ernesto/en
  description: Python scripts to detect RNA editing in deep transcriptome sequencing
    data (RNAseq)
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BioinfoUNIBA/REDItools
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BARI
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: REDItoolDnaRna.py -i rnaseq.bam -f myreference.fa -o myoutputfolder
    input:
    - data:
        term: Alignment
        uri: http://edamontology.org/data_1916
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
    note: Run REDItools on RNA sequences
    operation:
    - term: Analysis
      uri: http://edamontology.org/operation_2945
    output:
    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/BioinfoUNIBA/REDItools
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-02-09T14:59:52.330758Z'
  license: MIT
  link: []
  maturity: Mature
  name: REDItools
  operatingSystem:
  - Linux
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_012133
    version: null
  owner: ELIXIR-ITA-CNR
  publication:
  - doi: 10.1093/bioinformatics/btt287
    metadata:
      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.'
      authors:
      - name: Picardi E.
      - name: Pesole G.
      citationCount: 201
      date: '2013-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'REDItools: High-throughput RNA editing detection made easy'
    note: null
    pmcid: null
    pmid: '23742983'
    type:
    - Primary
    version: 1.0-1.3
  - doi: 10.1186/s12859-020-03562-x
    metadata:
      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.'
      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.
      citationCount: 21
      date: '2020-08-21T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'HPC-REDItools: A novel HPC-aware tool for improved large scale RNA-editing
        analysis'
    note: null
    pmcid: PMC7446188
    pmid: '32838738'
    type: []
    version: '2.0'
  - doi: 10.1038/s41596-019-0279-7
    metadata:
      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.
      authors:
      - name: Lo Giudice C.
      - name: Tangaro M.A.
      - name: Pesole G.
      - name: Picardi E.
      citationCount: 69
      date: '2020-03-01T00:00:00Z'
      journal: Nature Protocols
      title: Investigating RNA editing in deep transcriptome datasets with REDItools
        and REDIportal
    note: null
    pmcid: null
    pmid: '31996844'
    type: []
    version: null
  - doi: 10.1007/978-1-0716-1307-8_14
    metadata:
      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.
      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.
      citationCount: 1
      date: '2021-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: RNA Editing Detection in HPC Infrastructures
    note: null
    pmcid: null
    pmid: '33835447'
    type:
    - Usage
    version: null
  - doi: 10.1002/0471250953.bi1212s49
    metadata:
      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.
      authors:
      - name: Picardi E.
      - name: D'Erchia A.M.
      - name: A. Antonio
      - name: G. Graziano
      citationCount: 25
      date: '2015-01-01T00:00:00Z'
      journal: Current Protocols in Bioinformatics
      title: Using REDItools to detect RNA editing events in NGS datasets
    note: null
    pmcid: null
    pmid: '25754992'
    type:
    - Usage
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - '1.3'
  - '2.0'
- accessibility: null
  additionDate: '2015-01-29T15:47:31Z'
  biotoolsCURIE: biotools:muscle_ebi
  biotoolsID: muscle_ebi
  collectionID:
  - MUSCLE
  - EBI Tools
  - Job Dispatcher Tools
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Robert Edgar
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: EMBL-EBI
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Job Dispatcher
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Primary contact
    url: https://www.ebi.ac.uk/jdispatcher
  description: Sequence alignment using the Multiple Sequence Comparison by Log-Expectation
    (MUSCLE) method
  documentation:
  - note: null
    type:
    - Terms of use
    url: http://www.ebi.ac.uk/about/terms-of-use
  - note: null
    type:
    - General
    url: https://www.ebi.ac.uk/jdispatcher/help
  - note: null
    type:
    - General
    url: https://www.drive5.com/muscle/
  download:
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    type: Downloads page
    url: https://www.drive5.com/muscle/
    version: null
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    - nandana
    type: group
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  function:
  - cmd: null
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format: []
    note: null
    operation:
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    output:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format: []
  homepage: https://www.ebi.ac.uk/jdispatcher/msa/muscle
  homepage_status: 0
  language: []
  lastUpdate: '2024-02-05T19:06:27.801304Z'
  license: null
  link:
  - note: null
    type:
    - Helpdesk
    url: https://www.ebi.ac.uk/about/contact/support/job-dispatcher-services
  maturity: null
  name: MUSCLE (EBI)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_011812
    version: null
  owner: jdispatcher
  publication:
  - doi: 10.1093/nar/gkh340
    metadata:
      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.'
      authors:
      - name: Edgar R.C.
      citationCount: 32864
      date: '2004-07-09T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MUSCLE: Multiple sequence alignment with high accuracy and high throughput'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkac240
    metadata:
      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.
      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.
      citationCount: 696
      date: '2022-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: Search and sequence analysis tools services from EMBL-EBI in 2022
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation:
  - biotoolsID: muscle
    type: uses
  toolType:
  - Web application
  - Web service
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - '1'
- accessibility: Open access
  additionDate: '2023-11-23T10:53:51.802150Z'
  biotoolsCURIE: biotools:x-omics_action_demonstrator_multi-omics_analysis_workflow
  biotoolsID: x-omics_action_demonstrator_multi-omics_analysis_workflow
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit: []
  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.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/Xomics/ACTIONdemonstrator_workflow/blob/main/Documentation.md
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://workflowhub.eu/workflows/402
  homepage_status: 0
  language:
  - Python
  - R
  lastUpdate: '2024-01-24T06:19:18.681462Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Xomics/ACTIONdemonstrator_workflow
  - note: null
    type:
    - Software catalogue
    url: https://doi.org/10.48546/workflowhub.workflow.402.8
  maturity: Emerging
  name: X-omics ACTION demonstrator multi-omics analysis workflow
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_024719
    version: null
  owner: aniehues
  publication:
  - doi: 10.1093/gigascience/giad115
    metadata: null
    note: Journal article (technical note)
    pmcid: PMC10787363
    pmid: '38217405'
    type:
    - Other
    version: null
  relation:
  - biotoolsID: nextflow
    type: uses
  toolType:
  - Workflow
  topic:
  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Epigenomics
    uri: http://edamontology.org/topic_3173
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  validated: 0
  version:
  - '2.2'
  - '2.3'
next: ?page=2
previous: null
