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    note: 'Other Input formats:

      AMSA (.amsa);

      JnetFile (.concise, .jnet);

      PFAM (.pfam);

      Substitution matrix (.matrix);

      Jalview Project File (.jvp);

      Jalview Feature File (.features, .jvfeatures);

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      Other Output formats:

      PFAM (.pfam);

      BioJS (.biojs) (interactive HTML/Javascript);

      Jalview Project File (.jvp);'
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      - term: SVG
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  - note: YouTube training videos
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      date: '2009-05-07T00:00:00Z'
      journal: Bioinformatics
      title: Jalview Version 2-A multiple sequence alignment editor and analysis workbench
    note: null
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    note: null
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    - data:
        term: Nucleic acid sequence record
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      - term: FASTA
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    - data:
        term: Plot
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      - term: SVG
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    - data:
        term: Score
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        uri: http://edamontology.org/format_3475
  homepage: https://github.com/oschwengers/bakta
  homepage_status: 0
  language:
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    type:
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    url: https://github.com/oschwengers/bakta
  - note: null
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  description: Metabolic Atlas is a web platform integrating open-source genome scale
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  - note: null
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  - note: null
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  toolType:
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  validated: 0
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  additionDate: '2025-06-17T20:53:54.551980Z'
  biotoolsCURIE: biotools:metasbt
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  cost: Free of charge
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    note: null
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    rorid: null
    typeEntity: null
    typeRole:
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  description: MetaSBT is a scalable framework designed to automatically index microbial
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  - note: Official MetaSBT Wiki
    type:
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    url: https://github.com/cumbof/MetaSBT/wiki
  download:
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    type:
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  operatingSystem:
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  cost: null
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    name: Admin
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: webmaster@ms-utils.org
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    name: null
    note: null
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    rorid: null
    typeEntity: Person
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  description: A suite of protein identification tools.
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  download: []
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  function:
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    input: []
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    version: null
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  publication:
  - doi: 10.1074/mcp.d500002-mcp200
    metadata:
      abstract: A thorough analysis of the protein interaction partners of the yeast
        GTPase Gsp1p was carried out by a multidimensional chromatography strategy
        of strong cation exchange fractionation of peptides followed by reverse phase
        LC-ESI-MSMS using a QSTAR instrument. This dataset was then analyzed using
        the latest developmental version of Protein Prospector. The Prospector search
        results were also compared with results from the search engine "Mascot" using
        a new results comparison program within Prospector named "SearchCompare."
        The results from this study demonstrate that the high quality data produced
        on a quadrupole selecting, quadrupole collision cell, time-of-flight (QqTOF)
        geometry instrument allows for confident assignment of the vast majority of
        interpretable spectra by current search engines. © 2005 by The American Society
        for Biochemistry and Molecular Biology, Inc.
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      - name: Baker P.R.
      - name: Huang L.
      - name: Hansen K.C.
      - name: Allen N.P.
      - name: Rexach M.
      - name: Burlingame A.L.
      citationCount: 165
      date: '2005-08-01T00:00:00Z'
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        cells, time-of-flight mass spectrometer: II New developments in protein prospector
        allow for reliable and comprehensive automatic analysis of large datasets'
    note: null
    pmcid: null
    pmid: '15937296'
    type:
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    version: null
  relation: []
  toolType:
  - Web application
  topic:
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    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Database management
    uri: http://edamontology.org/topic_3489
  validated: 1
  version: []
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  additionDate: '2020-01-14T09:34:01Z'
  biotoolsCURIE: biotools:vapor
  biotoolsID: vapor
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: VAPOR is a tool for classification of Influenza samples from raw short
    read sequence data for downstream bioinformatics analysis. VAPOR is provided with
    a fasta file of full-length sequences (> 20,000) for a given segment, a set of
    reads, and attempts to retrieve a reference that is closest to the sample strain.
  documentation: []
  download:
  - note: null
    type: Software package
    url: https://pypi.org/project/vaporup/#files
    version: null
  - note: A curated collection of reference sequences for each influenza A genome
      segment that can be used with the tool for classification of sequences from
      this virus
    type: Biological data
    url: https://doi.org/10.5281/zenodo.15364147
    version: null
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  function:
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    input: []
    note: null
    operation:
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    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Read mapping
      uri: http://edamontology.org/operation_3198
    output: []
  homepage: https://github.com/wm75/vaporup
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-08-28T14:32:58.093703Z'
  license: GPL-3.0
  link:
  - note: Original VAPOR repository from which versions prior to 1.0.3 were released
    type:
    - Other
    url: https://github.com/connor-lab/vapor
  maturity: null
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  owner: Kigaard
  publication:
  - doi: 10.1093/bioinformatics/btz814
    metadata:
      abstract: 'Motivation: Influenza viruses represent a global public health burden
        due to annual epidemics and pandemic potential. Due to a rapidly evolving
        RNA genome, inter-species transmission, intra-host variation, and noise in
        short-read data, reads can be lost during mapping, and de novo assembly can
        be time consuming and result in misassembly. We assessed read loss during
        mapping and designed a graph-based classifier, VAPOR, for selecting mapping
        references, assembly validation and detection of strains of non-human origin.
        Results: Standard human reference viruses were insufficient for mapping diverse
        influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome
        sequencing samples with a mean of > 99:8% identity to assemblies, and increased
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      - name: Bull M.J.
      - name: Brown C.M.
      - name: Watkins J.
      - name: Corden S.
      - name: Southgate B.
      - name: Moore C.
      - name: Connor T.R.
      citationCount: 7
      date: '2020-01-01T00:00:00Z'
      journal: Bioinformatics
      title: Influenza classification from short reads with VAPOR facilitates robust
        mapping pipelines and zoonotic strain detection for routine surveillance applications
    note: null
    pmcid: PMC7703727
    pmid: '31693070'
    type: []
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  topic:
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  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version:
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- accessibility: null
  additionDate: '2017-12-06T18:14:40Z'
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    name: ExPASy helpdesk
    note: null
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  description: Make2D-DB II is an environment to create, convert, publish, interconnect
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    update data related to numerous external data resources. It is also possible to
    dynamically interconnect several remote databases or projects to form a virtual
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    used to build up 2D-PAGE repositories, accessible by any one from the Web.
  documentation:
  - note: null
    type:
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  function:
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    input: []
    note: null
    operation:
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    - term: Protein quantification
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    output: []
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  homepage_status: 0
  language:
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  license: null
  link: []
  maturity: null
  name: Make2D-DB II
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    metadata:
      abstract: The Make 2D-DB tool has been previously developed to help build federated
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        to fulfill the different needs and requirements arising from both the growing
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      authors:
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      - name: Hoogland C.
      - name: Binz P.-A.
      - name: Appel R.D.
      citationCount: 27
      date: '2003-08-01T00:00:00Z'
      journal: Proteomics
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        distributed databases'
    note: null
    pmcid: null
    pmid: '12923769'
    type:
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  topic:
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  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-12-06T18:14:54Z'
  biotoolsCURIE: biotools:swiss_mass_abacus
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  credit:
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    name: ExPASy helpdesk
    note: null
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    url: https://www.expasy.org/support
  description: Calculator of peptide and glycopeptide masses. It is purposefully kept
    as simple as a basic calculator executing arithmetic operations.
  documentation:
  - note: null
    type:
    - General
    url: https://glycoproteome.expasy.org/swiss-mass-abacus/
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  function:
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    input: []
    note: null
    operation:
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    output: []
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  homepage_status: 0
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  lastUpdate: '2025-08-28T13:43:28.119620Z'
  license: null
  link: []
  maturity: null
  name: Swiss Mass Abacus
  operatingSystem:
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  - Windows
  - Mac
  otherID: []
  owner: admin
  publication: []
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Structural genomics
    uri: http://edamontology.org/topic_0122
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-12-06T18:14:30Z'
  biotoolsCURIE: biotools:glydin
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    name: ExPASy helpdesk
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    orcidid: null
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  description: Glydin' compiles and maps information relative to glycoepitopes (glycan
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  - note: null
    type:
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    operation:
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  - Windows
  - Mac
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  version: []
- accessibility: null
  additionDate: '2017-12-06T18:14:10Z'
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    url: https://www.expasy.org/support
  description: A package to identify insertion sequences and similar transposable
    elements, their inverted repeats, and the direct target repeats they generate
    in entire genomes.
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  - note: null
    type:
    - General
    url: http://www.ieu.uzh.ch/wagner/software/IScan/index.html
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    gridid: null
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    note: null
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    url: null
  - email: nils.f@bmb.sdu.dk
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    gridid: null
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    note: null
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    url: null
  description: A web application for Streamlined Processing and Analysis of Large-Scale
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  license: MIT
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    metadata:
      abstract: 'Motivation: The post-processing and analysis of large-scale untargeted
        metabolomics data face significant challenges due to the intricate nature
        of correction, filtration, imputation, and normalization steps. Manual execution
        across various applications often leads to inefficiencies, human-induced errors,
        and inconsistencies within the workflow. Results: Addressing these issues,
        we introduce MetaboLink, a novel web application designed to process LC-MS
        metabolomics datasets combining established methodologies and offering flexibility
        and ease of implementation. It offers visualization options for data interpretation,
        an interface for statistical testing, and integration with PolySTest for further
        tests and with VSClust for clustering analysis. Availability and implementation:
        Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/.
        The source code is available at https://github.com/anitamnd/MetaboLink and
        a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki.
        A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q.'
      authors:
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      - name: Havelund J.F.
      - name: Lemvig J.
      - name: Schwammle V.
      - name: Faergeman N.J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'MetaboLink: a web application for streamlined processing and analysis
        of large-scale untargeted metabolomics data'
    note: null
    pmcid: PMC11269424
    pmid: '39018180'
    type:
    - Primary
    version: null
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  toolType:
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  topic:
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  - term: Lipidomics
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  - term: Workflows
    uri: http://edamontology.org/topic_0769
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  version: []
- accessibility: null
  additionDate: '2021-01-18T11:19:37Z'
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    name: Nicolas Puillandre
    note: null
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  description: 'ASAP (Assemble Species by Automatic Partitioning) is a method to build
    species partitions from single locus sequence alignments.

    ASAP is the implementation of a hierarchical clustering algorithm that only uses
    pairwise genetic distances, avoiding the computational burden of phylogenetic
    reconstruction. Importantly, ASAP proposes species partitions ranked by a new
    scoring system that uses no biological prior insight of intraspecific diversity.'
  documentation:
  - note: null
    type:
    - General
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  - note: null
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    - term: DNA barcoding
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    - term: Sequence assembly
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    - term: Splitting
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    output: []
  homepage: https://bioinfo.mnhn.fr/abi/public/asap
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    metadata:
      abstract: © 2020 John Wiley & Sons LtdHere, we describe Assemble Species by
        Automatic Partitioning (ASAP), a new method to build species partitions from
        single locus sequence alignments (i.e., barcode data sets). ASAP is efficient
        enough to split data sets as large 104 sequences into putative species in
        several minutes. Although grounded in evolutionary theory, ASAP is the implementation
        of a hierarchical clustering algorithm that only uses pairwise genetic distances,
        avoiding the computational burden of phylogenetic reconstruction. Importantly,
        ASAP proposes species partitions ranked by a new scoring system that uses
        no biological prior insight of intraspecific diversity. ASAP is a stand-alone
        program that can be used either through a graphical web-interface or that
        can be downloaded and compiled for local usage. We have assessed its power
        along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode
        data sets representing various degrees of challenge (from small and easy cases
        to large and complicated data sets). We also used Monte-Carlo simulations
        of a multispecies coalescent framework to assess the strengths and weaknesses
        of ASAP and the other programs. Through these analyses, we demonstrate that
        ASAP has the potential to become a major tool for taxonomists as it proposes
        rapidly in a full graphical exploratory interface relevant species hypothesis
        as a first step of the integrative taxonomy process.
      authors:
      - name: Puillandre N.
      - name: Brouillet S.
      - name: Achaz G.
      citationCount: 15
      date: '2021-02-01T00:00:00Z'
      journal: Molecular Ecology Resources
      title: 'ASAP: assemble species by automatic partitioning'
    note: null
    pmcid: null
    pmid: '33058550'
    type: []
    version: null
  relation: []
  toolType:
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  - Desktop application
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2017-05-16T12:28:07Z'
  biotoolsCURIE: biotools:arrayanalysis.org
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  confidence_flag: null
  cost: Free of charge
  credit:
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    gridid: null
    name: Support Forum
    note: null
    orcidid: null
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    typeEntity: Person
    typeRole:
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    url: https://www.arrayanalysis.org/
  description: ArrayAnalysis is a web-based application for transcriptomic data analysis.
    It supports the analysis of both microarray and RNA-seq data. The tool may also
    be installed locally as a desktop app, Docker image, or R package.
  documentation:
  - note: null
    type:
    - General
    url: https://www.arrayanalysis.org
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  homepage: https://www.arrayanalysis.org
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  license: CC-BY-4.0
  link: []
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  name: ArrayAnalysis
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  owner: jarnokoetsier
  publication:
  - doi: 10.1093/nar/gkt293
    metadata:
      abstract: Quality control (QC) is crucial for any scientific method producing
        data. Applying adequate QC introduces new challenges in the genomics field
        where large amounts of data are produced with complex technologies. For DNA
        microarrays, specific algorithms for QC and pre-processing including normalization
        have been developed by the scientific community, especially for expression
        chips of the Affymetrix platform. Many of these have been implemented in the
        statistical scripting language R and are available from the Bioconductor repository.
        However, application is hampered by lack of integrative tools that can be
        used by users of any experience level. To fill this gap, we developed a freely
        available tool for QC and pre-processing of Affymetrix gene expression results,
        extending, integrating and harmonizing functionality of Bioconductor packages.
        The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org
        or downloaded for local use in R. The portal provides extensive documentation,
        including user guides, interpretation help with real output illustrations
        and detailed technical documentation. It assists newcomers to the field in
        performing state-of-the-art QC and pre-processing while offering data analysts
        an integral open-source package. Providing the scientific community with this
        easily accessible tool will allow improving data quality and reuse and adoption
        of standards.
      authors:
      - name: Eijssen L.M.
      - name: Jaillard M.
      - name: Adriaens M.E.
      - name: Gaj S.
      - name: de Groot P.J.
      - name: Muller M.
      - name: Evelo C.T.
      citationCount: 107
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic acids research
      title: User-friendly solutions for microarray quality control and pre-processing
        on ArrayAnalysis.org.
    note: null
    pmcid: PMC3692049
    pmid: '23620278'
    type: []
    version: null
  - doi: 10.1186/s12864-015-1689-8
    metadata:
      abstract: 'Background: Illumina whole-genome expression bead arrays are a widely
        used platform for transcriptomics. Most of the tools available for the analysis
        of the resulting data are not easily applicable by less experienced users.
        ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive
        interface to the functionality of R and Bioconductor packages for microarray
        data analysis. As a modular open source project, it allows developers to contribute
        modules that provide support for additional types of data or extend workflows.
        Results: To enable data analysis of Illumina bead arrays for a broad user
        community, we have developed a module for ArrayAnalysis.org that provides
        a free and user-friendly web interface for quality control and pre-processing
        for these arrays. This module can be used together with existing modules for
        statistical and pathway analysis to provide a full workflow for Illumina gene
        expression data analysis. Conclusions: The Illumina bead arrays analysis module
        is available at http://www.arrayanalysis.org. A user guide, a tutorial demonstrating
        the analysis of an example dataset, and R scripts are available. The module
        can be used as a starting point for statistical evaluation and pathway analysis
        provided on the website or to generate processed input data for a broad range
        of applications in life sciences research.'
      authors:
      - name: Eijssen L.M.T.
      - name: Goelela V.S.
      - name: Kelder T.
      - name: Adriaens M.E.
      - name: Evelo C.T.
      - name: Radonjic M.
      citationCount: 12
      date: '2015-06-30T00:00:00Z'
      journal: BMC Genomics
      title: A user-friendly workflow for analysis of Illumina gene expression bead
        array data available at the arrayanalysis.org portal
    note: null
    pmcid: PMC4486126
    pmid: '26122086'
    type: []
    version: null
  relation: []
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_3308
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2020-03-27T14:31:47Z'
  biotoolsCURIE: biotools:ucph_covid19_dashboard
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  confidence_flag: null
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  credit:
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    fundrefid: null
    gridid: null
    name: Nikolaj Theodor Thams
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
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    - Developer
    - Maintainer
    url: null
  - email: m.jakobsen@math.ku.dk
    fundrefid: null
    gridid: null
    name: Martin Emil Jakobsen
    note: null
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    url: null
  - email: pbm@math.ku.dk
    fundrefid: null
    gridid: null
    name: Phillip Bredahl Mogensen
    note: null
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    rorid: null
    typeEntity: Person
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    - Maintainer
    url: null
  - email: null
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    gridid: null
    name: Copenhagen Causality Lab (CoCaLa)
    note: null
    orcidid: null
    rorid: null
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    url: https://www.math.ku.dk/english/research/spt/cocala/
  - email: null
    fundrefid: null
    gridid: null
    name: Johns Hopkins University
    note: null
    orcidid: null
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    typeEntity: Institute
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    url: https://www.jhu.edu/
  - email: null
    fundrefid: null
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    name: Danish State Serum Institute (SSI)
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    url: https://www.ssi.dk/
  description: The dashboard provides tools for visualizing the COVID-19 spread in
    individual countries over time, as well as comparing different countries on a
    similar time scale. We provide time series of Danish test data. Furthermore, we
    have implemented an estimator for the actual cumulative number of COVID19 cases
    for every country -- a.k.a. the dark numbers.
  documentation:
  - note: null
    type:
    - Terms of use
    url: https://github.com/CSSEGISandData/COVID-19
  - note: Analysis white paper
    type:
    - Other
    url: https://github.com/nikolajthams/COVID-19/blob/master/wirvsvirus/draft/concept_online_version.pdf
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    note: null
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    - term: Modelling and simulation
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  homepage: https://shiny.science.ku.dk/pbm/COVID19/
  homepage_status: 0
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  license: null
  link:
  - note: COVID19 data
    type:
    - Repository
    url: https://github.com/CSSEGISandData/COVID-19
  - note: SSI data reports
    type:
    - Other
    url: https://www.ssi.dk/aktuelt/sygdomsudbrud/coronavirus/covid-19-i-danmark-epidemiologisk-overvaagningsrapport
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    - Other
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  name: UCPH COVID19 DASHBOARD
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  topic:
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  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Virology
    uri: http://edamontology.org/topic_0781
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 0
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- accessibility: Open access
  additionDate: '2016-12-19T14:28:48Z'
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    note: null
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    url: null
  description: List names of sequence pairs from two FASTQ formatted files.
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      - term: FASTQ-illumina
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      - term: FASTQ
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    - data:
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    note: null
    operation:
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    output:
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      format:
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  homepage: http://ftp.pasteur.fr/pub/gensoft/projects/fqtools/README
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  lastUpdate: '2025-08-27T10:52:16.324100Z'
  license: null
  link:
  - note: null
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  maturity: Mature
  name: fqseqpair
  operatingSystem: []
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  owner: hmenager
  publication:
  - 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: 1477
      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: []
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  - term: Data management
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  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2016-12-19T14:29:17Z'
  biotoolsCURIE: biotools:fqseqname
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  confidence_flag: null
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    fundrefid: null
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    note: null
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    metadata:
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      - name: Baker D.
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      - name: Blankenberg D.
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  description: Output reads with low quality bases trimmed (at the extremities) or
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      - name: Soranzo N.
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    note: null
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  - doi: 10.7490/f1000research.1114334.1
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  relation: []
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  - term: Data quality management
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  additionDate: '2016-12-19T14:23:06Z'
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  description: Extract a subset of fastq reads.
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      - name: Rasche E.
      - name: Soranzo N.
      - name: Turaga N.
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      - name: Nekrutenko A.
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      citationCount: 1477
      date: '2016-07-08T00:00:00Z'
      journal: Nucleic Acids Research
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    note: null
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  - doi: 10.7490/f1000research.1114334.1
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  relation: []
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  topic:
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  validated: 1
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  additionDate: '2016-12-19T14:22:09Z'
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  description: Detect duplicate reads. Outputs a list of read names corresponding
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    operation:
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  homepage: http://ftp.pasteur.fr/pub/gensoft/projects/fqtools/README
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      - name: Cech M.
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      - name: Clements D.
      - name: Coraor N.
      - name: Eberhard C.
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      - 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: 1477
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  - doi: 10.7490/f1000research.1114334.1
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  relation: []
  toolType:
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  topic:
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  - term: Nucleic acids
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  validated: 1
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  description: Convert quality scoring to Sanger in FASTQ files.
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    note: null
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  lastUpdate: '2025-08-27T10:48:08.675794Z'
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  operatingSystem:
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  otherID: []
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    metadata:
      abstract: High-throughput data production technologies, particularly ‘next-generation’
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        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
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      - 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.
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      - name: Kuster G.V.
      - name: Rasche E.
      - name: Soranzo N.
      - name: Turaga N.
      - name: Taylor J.
      - name: Nekrutenko A.
      - name: Goecks J.
      citationCount: 1477
      date: '2016-07-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The Galaxy platform for accessible, reproducible and collaborative biomedical
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    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  - doi: 10.7490/f1000research.1114334.1
    metadata: null
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    pmcid: null
    pmid: null
    type:
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    version: null
  relation: []
  toolType:
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  topic:
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  validated: 1
  version:
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- accessibility: Open access
  additionDate: '2016-12-19T14:27:26Z'
  biotoolsCURIE: biotools:fqcleaner
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    note: null
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    url: null
  description: Mini workflow for preprocessing fastq files, it includes quality trimming,
    duplicate and artefacts filters for FASTQ read files.
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  function:
  - cmd: null
    input:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ
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    - 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: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ-sanger
        uri: http://edamontology.org/format_1932
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ-sanger
        uri: http://edamontology.org/format_1932
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: FASTQ-sanger
        uri: http://edamontology.org/format_1932
  homepage: https://gensoft.pasteur.fr/docs/fqCleanER/21.10/
  homepage_status: 0
  language: []
  lastUpdate: '2025-08-27T10:47:14.412802Z'
  license: null
  link:
  - note: null
    type:
    - Galaxy service
    url: https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/odoppelt/fqcleaner/fqCleaner/0.4.1.1
  maturity: Mature
  name: fqcleaner
  operatingSystem: []
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  owner: hmenager
  publication:
  - 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: 1477
      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: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Data quality management
    uri: http://edamontology.org/topic_3572
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - 0.4.1.1
- accessibility: null
  additionDate: '2021-01-18T08:31:27Z'
  biotoolsCURIE: biotools:cococonet
  biotoolsID: cococonet
  collectionID: []
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  credit:
  - email: jgillis@cshl.edu
    fundrefid: null
    gridid: null
    name: Jesse Gillis
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: CoCoCoNet iis a tool for identifying conserved gene modules and co
    mparing co -expression networks. CoCoCoNet is a resource for both data and methods,
    providing gold-standard networks and sophisticated tools for on-the-fly comparative
    analyses across 14 species.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/johnlee4/CoCoCoNet
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    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_3463
    - term: Network analysis
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    - term: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    output: []
  homepage: https://milton.cshl.edu/CoCoCoNet/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-08-27T10:44:27.458203Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/johnlee4/CoCoCoNet
  maturity: null
  name: CoCoCoNet
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1093/nar/gkaa348
    metadata:
      abstract: Co-expression analysis has provided insight into gene function in
        organisms from Arabidopsis to zebrafish. Comparison across species has the
        potential to enrich these results, for example by prioritizing among candidate
        human disease genes based on their network properties or by finding alternative
        model systems where their co-expression is conserved. Here, we present CoCoCoNet
        as a tool for identifying conserved gene modules and comparing co-expression
        networks. CoCoCoNet is a resource for both data and methods, providing gold
        standard networks and sophisticated tools for on-the-fly comparative analyses
        across 14 species. We show how CoCoCoNet can be used in two use cases. In
        the first, we demonstrate deep conservation of a nucleolus gene module across
        very divergent organisms, and in the second, we show how the heterogeneity
        of autism mechanisms in humans can be broken down by functional groups and
        translated to model organisms. CoCoCoNet is free to use and available to all
        at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at
        ftp://milton.cshl.edu/data.
      authors:
      - name: Lee J.
      - name: Shah M.
      - name: Ballouz S.
      - name: Crow M.
      - name: Gillis J.
      citationCount: 32
      date: '2021-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'CoCoCoNet: Conserved and comparative co-expression across a diverse
        set of species'
    note: null
    pmcid: PMC7319556
    pmid: '32392296'
    type: []
    version: null
  relation: []
  toolType:
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  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-08-25T14:36:13.568250Z'
  biotoolsCURIE: biotools:datmat
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  collectionID: []
  confidence_flag: null
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    fundrefid: null
    gridid: null
    name: Alexander Harms
    note: null
    orcidid: https://orcid.org/0009-0001-5727-6732
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: h.achterberg@erasmusmc.nl
    fundrefid: null
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    name: Hakim Achterberg
    note: null
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    rorid: null
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    - Developer
    url: null
  - email: null
    fundrefid: null
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    name: Erasmus MC
    note: null
    orcidid: null
    rorid: 018906e22
    typeEntity: Institute
    typeRole: []
    url: null
  description: ‘datmat’ is a Python package aimed to easily gather data from a variety
    of sources and materialize it into a standardized format. It can download projects
    of imaging data from XNAT into the EUCAIM folder structure, and is designed to
    be extendible to support other inputs and outputs.
  documentation:
  - note: null
    type:
    - General
    url: https://datmat.readthedocs.io/latest/
  download:
  - note: null
    type: Software package
    url: https://pypi.org/project/datmat/
    version: null
  - note: null
    type: Source code
    url: https://gitlab.com/radiology/infrastructure/data-materialisation
    version: null
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data sorting
      uri: http://edamontology.org/operation_3802
    output: []
  homepage: https://gitlab.com/radiology/infrastructure/data-materialisation/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-08-26T09:43:49.092198Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://pypi.org/project/datmat/
  maturity: Emerging
  name: datmat
  operatingSystem:
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  - Windows
  - Mac
  otherID: []
  owner: agjharms
  publication: []
  relation:
  - biotoolsID: xnatpy
    type: uses
  toolType:
  - Library
  topic: []
  validated: 0
  version:
  - 0.2.0
- accessibility: null
  additionDate: '2025-08-25T21:19:45.632393Z'
  biotoolsCURIE: biotools:single-cell_multi-scale_clustering_msc
  biotoolsID: single-cell_multi-scale_clustering_msc
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Single-cell multi-scale clustering framework published in GigaScience.
  documentation: []
  download: []
  editPermission:
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  elixir_badge: 0
  function: []
  homepage: https://github.com/songlabcodes/MSC
  homepage_status: 0
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  lastUpdate: '2025-08-25T21:19:45.634805Z'
  license: null
  link: []
  maturity: null
  name: Single-cell multi-scale clustering (MSC)
  operatingSystem: []
  otherID: []
  owner: songw01
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-08-25T14:46:01.217831Z'
  biotoolsCURIE: biotools:img2catalog
  biotoolsID: img2catalog
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: alexander.harms@health-ri.nl
    fundrefid: null
    gridid: null
    name: Alexander Harms
    note: null
    orcidid: https://orcid.org/0009-0001-5727-6732
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Health-RI
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.health-ri.nl/
  description: This tool, 'img2catalog', is built to extract metadata from imaging
    data repositories, map it to a model class, and export it to a catalog. The tool
    is set up to be modular, but it currently supports extracting metadata from an
    XNAT server, converting it to the Health-RI Core v2 metadata model (https://github.com/Health-RI/health-ri-metadata/tree/v2.0.0),
    based on DCAT-AP v3 and Health-DCAT AP, as defined in Pydantic classes using SeMPyRO
    (https://github.com/Health-RI/SeMPyRO), and either writing it to an RDF file,
    or pushing it to a FAIR Data Point (FDP).
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/Health-RI/img2catalog/blob/main/README.md
  download:
  - note: null
    type: Source code
    url: https://github.com/Health-RI/img2catalog
    version: null
  - note: null
    type: Software package
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    version: null
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_0224
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data parsing
      uri: http://edamontology.org/operation_1812
    output: []
  homepage: https://github.com/Health-RI/img2catalog
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-08-25T14:46:01.219964Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Health-RI/img2catalog
  maturity: null
  name: img2catalog
  operatingSystem:
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  - Mac
  - Windows
  otherID: []
  owner: agjharms
  publication: []
  relation:
  - biotoolsID: xnatpy
    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: FAIR data
    uri: http://edamontology.org/topic_4012
  validated: 0
  version:
  - 1.0.1
- accessibility: null
  additionDate: '2025-08-22T12:42:23.841729Z'
  biotoolsCURIE: biotools:viromexplore
  biotoolsID: viromexplore
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: rhernandvel@gmail.com
    fundrefid: null
    gridid: null
    name: Rodrigo Hernández Velázquez
    note: null
    orcidid: https://orcid.org/0009-0009-3048-8573
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: 'ViromeXplore conatains a set of bioinformatic workflows that can be
    used reproducibly, sequentially and interoperably between them and with other
    workflows. It allows multiple input types mand starting points that include raw
    reads, contigs, genomes, abbundance tables, phylogenies and taxonomies. ViromeXplore
    contains the most varied and comprehensive workflows to explore viral genes and
    genomes from viromes and metagenomes to date. The future aim for ViromeXplore
    is to continue expanding the workflows and including the most recent and efficient
    tools.


    The workflows are implemented in Nextflow, and additionally only Docker or Singularity
    are needed to run ViromeXplore.'
  documentation:
  - note: All the updated documentation can be found in readthedocs.
    type:
    - User manual
    url: https://viromexplore.readthedocs.io
  download:
  - note: Download via github
    type: Other
    url: https://github.com/rhernandvel/ViromeXplore
    version: 1.0.0
  editPermission:
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    type: group
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  elixir_badge: 0
  function: []
  homepage: https://viromexplore.readthedocs.io
  homepage_status: 0
  language: []
  lastUpdate: '2025-08-22T12:53:03.396154Z'
  license: BSD-3-Clause
  link:
  - note: Github repo
    type:
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    url: https://github.com/rhernandvel/ViromeXplore
  maturity: null
  name: ViromeXplore
  operatingSystem: []
  otherID: []
  owner: rhernandvel
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 1.0.0
- accessibility: Open access
  additionDate: '2025-08-20T17:28:51.309591Z'
  biotoolsCURIE: biotools:esssubgraph
  biotoolsID: esssubgraph
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hudie.luoluo@gmail.com
    fundrefid: null
    gridid: null
    name: haimei wen
    note: null
    orcidid: https://orcid.org/0009-0006-1600-1210
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Susan Carpenter
    note: null
    orcidid: https://orcid.org/0000-0002-5600-5404
    rorid: null
    typeEntity: null
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    url: null
  - email: null
    fundrefid: null
    gridid: null
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    note: null
    orcidid: https://orcid.org/0000-0002-9564-8146
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Andrew Nelson
    note: null
    orcidid: https://orcid.org/0000-0001-9896-1739
    rorid: null
    typeEntity: null
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Keriayn Smith
    note: null
    orcidid: https://orcid.org/0000-0002-4351-2765
    rorid: null
    typeEntity: null
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Tian Hong
    note: null
    orcidid: https://orcid.org/0000-0002-8212-7050
    rorid: null
    typeEntity: null
    typeRole:
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    url: null
  description: EssSubgraph is a model algorithm that integrates omics data and network
    data to predict essential genes.
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://github.com/wenmm/EssSubgraph/tree/main
  download:
  - note: null
    type: Source code
    url: https://github.com/wenmm/EssSubgraph/tree/main
    version: null
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: python EssSubgraph.py --epochs 200 --device 0 --dataset ./data/esssubgraph_human_pc50_string.pkl
    input: []
    note: null
    operation:
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene prediction
      uri: http://edamontology.org/operation_2454
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein-protein interaction analysis
      uri: http://edamontology.org/operation_2949
    output: []
  homepage: https://github.com/wenmm/EssSubgraph/tree/main
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-08-20T18:13:15.065957Z'
  license: GPL-3.0
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  - note: null
    type:
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    url: https://github.com/wenmm/EssSubgraph/tree/main
  maturity: Mature
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  operatingSystem:
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  - Mac
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  otherID: []
  owner: haimei
  publication:
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    metadata: null
    note: https://www.biorxiv.org/content/10.1101/2025.07.21.665218v1.abstract
    pmcid: null
    pmid: null
    type:
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    version: null
  relation:
  - biotoolsID: MTGCN
    type: includes
  toolType:
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  topic:
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  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Biomarkers
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  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-08-19T13:03:53.135066Z'
  biotoolsCURIE: biotools:rna-disease-predictor
  biotoolsID: rna-disease-predictor
  collectionID: []
  confidence_flag: null
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  description: PanGIA is a deep learning model for predicting ncRNA-disease associations.
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  download: []
  editPermission:
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- accessibility: null
  additionDate: '2025-08-18T11:50:36.465851Z'
  biotoolsCURIE: biotools:ve3-genomix
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  description: VE3's Genomics Accelerator Solution optimizes genomic data processing
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    name: Seyyed Amin Seyyed Rezaei
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    typeEntity: Person
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    url: https://matheat.streamlit.app/
  description: MatHeat transforms your gene expression data into powerful insights,
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  description: Workflow optimized for the analysis of rare diseases, designed to detect
    SNVs and INDELs in targeted sequencing data (CES/WES) and whole genome sequencing
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  - note: null
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  - term: Bioinformatics
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  additionDate: '2025-08-12T08:52:25.808752Z'
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  description: Pipeline based on Nextflow and nf-core for long-read DNA sequencing
    analysis.
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  - note: null
    type:
    - User manual
    url: https://github.com/TBLabFJD/WHALE
  download: []
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  homepage: https://github.com/TBLabFJD/WHALE
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  name: WHALE-FJD
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    uri: http://edamontology.org/topic_0622
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
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  description: Germline variant calling pipeline implemented in Nextflow which performs
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    targeted sequencing (gene panels and WES) and whole genome sequencing.
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    type:
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  biotoolsCURIE: biotools:fjd-pipeline
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    fundrefid: null
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    name: Bioinformatics Unit IIS-FJD
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    url: https://www.translationalbioinformaticslab.es/
  description: 'Pipeline for Single Nucleotide Variants (SNVs) and Copy Number Variation
    (CNVs) variant calling. This tool is no longer maintained. See the new version:
    PARROT-FJD.'
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  - note: null
    type:
    - User manual
    url: https://github.com/TBLabFJD/VariantCallingFJD
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    input:
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      format:
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        uri: http://edamontology.org/format_2545
    note: null
    operation:
    - term: Variant calling
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    output:
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      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
  homepage: https://github.com/TBLabFJD/VariantCallingFJD
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  language:
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  - R
  - Bash
  lastUpdate: '2025-08-12T08:31:29.574795Z'
  license: CC-BY-NC-SA-4.0
  link:
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  otherID: []
  owner: iiancu
  publication:
  - doi: 10.1038/s41525-021-00278-6
    metadata:
      abstract: Clinical exome (CE) sequencing has become a first-tier diagnostic
        test for hereditary diseases; however, its diagnostic rate is around 30–50%.
        In this study, we aimed to increase the diagnostic yield of CE using a custom
        reanalysis algorithm. Sequencing data were available for three cohorts using
        two commercial protocols applied as part of the diagnostic process. Using
        these cohorts, we compared the performance of general and clinically relevant
        variant calling and the efficacy of an in-house bioinformatic protocol (FJD-pipeline)
        in detecting causal variants as compared to commercial protocols. On the whole,
        the FJD-pipeline detected 99.74% of the causal variants identified by the
        commercial protocol in previously solved cases. In the unsolved cases, FJD-pipeline
        detects more INDELs and non-exonic variants, and is able to increase the diagnostic
        yield in 2.5% and 3.2% in the re-analysis of 78 cancer and 62 cardiovascular
        cases. These results were considered to design a reanalysis, filtering and
        prioritization algorithm that was tested by reassessing 68 inconclusive cases
        of monoallelic autosomal recessive retinal dystrophies increasing the diagnosis
        by 4.4%. In conclusion, a guided NGS reanalysis of unsolved cases increases
        the diagnostic yield in genetic disorders, making it a useful diagnostic tool
        in medical genetics.
      authors:
      - name: Romero R.
      - name: de la Fuente L.
      - name: Del Pozo-Valero M.
      - name: Riveiro-Alvarez R.
      - name: Trujillo-Tiebas M.J.
      - name: Martin-Merida I.
      - name: Avila-Fernandez A.
      - name: Iancu I.-F.
      - name: Perea-Romero I.
      - name: Nunez-Moreno G.
      - name: Damian A.
      - name: Rodilla C.
      - name: Almoguera B.
      - name: Corton M.
      - name: Ayuso C.
      - name: Minguez P.
      citationCount: 12
      date: '2022-12-01T00:00:00Z'
      journal: npj Genomic Medicine
      title: An evaluation of pipelines for DNA variant detection can guide a reanalysis
        protocol to increase the diagnostic ratio of genetic diseases
    note: null
    pmcid: PMC8795168
    pmid: '35087072'
    type:
    - Usage
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  topic:
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    uri: http://edamontology.org/topic_0091
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-08-07T10:25:22.118390Z'
  biotoolsCURIE: biotools:macsylib
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    name: Eduardo P.C. Rocha
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    name: Sophie Abby
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    url: null
  description: a Python package library that allow to model and detect macromolecular
    systems, genetic pathways… by similarity search in prokaryotes datasets.
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    type:
    - General
    url: https://macsylib.readthedocs.io/en/latest/
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    input:
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    note: null
    operation:
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    output:
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      format: []
  homepage: https://github.com/gem-pasteur/macsylib
  homepage_status: 0
  language:
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  lastUpdate: '2025-08-11T12:14:42.322020Z'
  license: GPL-3.0
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    metadata:
      abstract: 'Complex cellular functions are usually encoded by a set of genes
        in one or a few orga-nized genetic loci in microbial genomes. Macromolecular
        System Finder (MacSyFinder) is a program that uses these properties to model
        and then annotate cellular functions in microbial genomes. This is done by
        integrating the identification of each individual gene at the level of the
        molecular system. We hereby present a major release of MacSyFinder (version
        2) coded in Python 3. The code was improved and rationalized to facilitate
        future maintainability. Several new features were added to allow more flexible
        modelling of the systems. We introduce a more intuitive and comprehensive
        search engine to identify all the best candidate systems and sub-optimal ones
        that respect the models’ constraints. We also introduce the novel macsydata
        companion tool that enables the easy installation and broad distribution of
        the models developed for MacSyFinder (macsy-models) from GitHub repositories.
        Finally, we have updated and improved MacSyFinder popular mod-els: TXSScan
        to identify protein secretion systems, TFFscan to identify type IV filaments,
        CONJscan to identify conjugative systems, and CasFinder to identify CRISPR
        associated proteins. MacSyFinder and the updated models are available at:
        https://github.com/gem-pasteur/macsyfinder.'
      authors:
      - name: Neron B.
      - name: Denise R.
      - name: Coluzzi C.
      - name: Touchon M.
      - name: Rocha E.P.C.
      - name: Abby S.S.
      citationCount: 43
      date: '2023-01-01T00:00:00Z'
      journal: Peer Community Journal
      title: 'MacSyFinder v2: Improved modelling and search engine to identify molecular
        systems in genomes'
    note: null
    pmcid: null
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    type: []
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  validated: 0
  version:
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next: ?page=2
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