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  description: A tool for find repetitive sequences or sequence enrichment regions
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  homepage: https://github.com/rennmeng/SERtool
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  language:
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  - Rust
  lastUpdate: '2025-09-25T00:29:07.274216Z'
  license: MIT
  link:
  - note: null
    type:
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  maturity: Legacy
  name: SERtool
  operatingSystem:
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  otherID: []
  owner: rennmeng
  publication: []
  relation: []
  toolType:
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  - Command-line tool
  topic:
  - term: Sequence composition, complexity and repeats
    uri: http://edamontology.org/topic_0157
  validated: 0
  version:
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- accessibility: null
  additionDate: '2025-09-23T08:54:29.614497Z'
  biotoolsCURIE: biotools:constava
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    note: W.V., D.R. and G.O. conceptualised the study. W.V., D.R., G.O. and D.B.
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    rorid: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://bio2byte.be/people/38
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    name: David Bickel
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    orcidid: https://orcid.org/0000-0003-0332-8338
    rorid: null
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    typeRole:
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    url: https://bio2byte.be/people/48
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    name: Daniele Raimondi
    note: W.V., D.R. and G.O. conceptualised the study. W.V., D.R., G.O. and D.B.
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    name: Gabriele Orlando
    note: W.V., D.R. and G.O. conceptualised the study. G.O. developed the initial
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    name: Joel Roca-Martinez
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  description: Constava calculates conformational states probability and conformational
    state variability from protein structure ensembles.
  documentation:
  - note: "Table of content: \nDescription\nInstallation\nPrerequisites\nInstallation\
      \ through PyPI\nInstallation from the source\nUsage\nExecution from the command\
      \ line\nExtracting backbone dihedrals from a trajectory\nAnalyzing a conformational\
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    url: https://pypi.org/project/constava/
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  function:
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        term: Protein geometry data
        uri: http://edamontology.org/data_2991
      format: []
    note: This command analyzes the provided backbone dihedral angles and infers the
      propensities for each residue to reside in a given conformational state.
    operation:
    - term: Protein geometry calculation
      uri: http://edamontology.org/operation_0249
    output:
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        term: Amino acid property
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  language:
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    metadata:
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      journal: NAR Genomics and Bioinformatics
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    note: null
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      journal: Journal of Molecular Biology
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    note: null
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    type:
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  toolType:
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  topic:
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  validated: 0
  version:
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- accessibility: Open access
  additionDate: '2025-09-23T08:51:10.252083Z'
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  description: PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based
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      date: '2024-12-01T00:00:00Z'
      journal: Nature Communications
      title: PICNIC accurately predicts condensate-forming proteins regardless of
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    note: null
    pmcid: PMC11634905
    pmid: '39663388'
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    version: 1.0.0
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  toolType:
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  - Web service
  topic:
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    uri: http://edamontology.org/topic_3474
  validated: 0
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- accessibility: Open access
  additionDate: '2025-09-22T12:18:43.568886Z'
  biotoolsCURIE: biotools:twistmethylflow
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  description: This Nextflow pipeline is designed for the analysis of Twist NGS Methylation
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    methylation analysis, and post-processing. It integrates various tools and custom
    scripts to provide a comprehensive analysis workflow.
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  - note: null
    type:
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    input: []
    note: null
    operation:
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  - Bash
  - Python
  - JavaScript
  - Other
  lastUpdate: '2025-09-22T12:35:30.590353Z'
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  link:
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  name: TwistMethylFlow
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    type: uses
  - biotoolsID: qualimap
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    type: uses
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
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  additionDate: '2024-12-21T15:24:55.662209Z'
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  description: StripePy recognizes architectural stripes in 3C and Hi-C contact maps
    using geometric reasoning
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    type: Container file
    url: https://hub.docker.com/r/paulsengroup/stripepy
    version: null
  - note: null
    type: Test data
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    type: Binaries
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  additionDate: '2025-09-17T13:10:30.804502Z'
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  description: GFFx is a Rust-based toolkit for ultra-fast and scalable genome annotation
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  function:
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        term: Sequence features
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    note: null
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    note: null
    operation:
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    output:
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        term: Alignment
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      - term: CRAM
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    note: null
    operation:
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  toolType:
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  description: Miniasm is a very fast OLC-based de novo assembler for noisy long reads.
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  description: 'Fed-BioMed is an open-source research and development initiative for
    translating federated learning into real-world medical applications.

    The community of Fed-BioMed gathers experts in medical engineering, machine learning,
    communication, and security.

    We all contribute to provide an open, user-friendly, and trusted framework for
    deploying the state-of-the-art of federated learning in sensitive environments,
    such as in hospitals and health data lakes.'
  documentation:
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      format:
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      - term: PNG
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    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format:
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        uri: http://edamontology.org/format_3752
    note: Federated training of ML models for generic data and several task types,
      including image analysis, tabular data, classification, regression, etc..
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      uri: http://edamontology.org/operation_3443
    - term: Statistical calculation
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  name: Fed-BioMed
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    metadata: null
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      journal: npj Digital Medicine
      title: Real world federated learning with a knowledge distilled transformer
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  version:
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  additionDate: '2021-01-18T08:45:43Z'
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  description: 'Simulation of small-angle X-ray scattering data of biological macromolecules
    in solution.


    Biological small angle scattering.


    Simulation of 2-D SAXS patterns and 1-D profiles from biological macromolecules
    in solution.


    Post all your questions about IMSIM to the ATSAS Forum.


    ATSAS online : Forum : User information : EMBL Hamburg.


    Simulation of two-dimensional small angle X-ray scattering patterns and, further,
    one dimensional profiles from biological macromolecules in solution.'
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  publication:
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    metadata:
      abstract: © 2020.This article presents IMSIM, an application to simulate two-dimensional
        small-angle X-ray scattering patterns and, further, one-dimensional profiles
        from biological macromolecules in solution. IMSIM implements a statistical
        approach yielding two-dimensional images in TIFF, CBF or EDF format, which
        may be readily processed by existing data-analysis pipelines. Intensities
        and error estimates of one-dimensional patterns obtained from the radial average
        of the two-dimensional images exhibit the same statistical properties as observed
        with actual experimental data. With initial input on an absolute scale, [cm-1]/c[mgml-1],
        the simulated data frames may also be scaled to absolute scale such that the
        forward scattering after subtraction of the background is proportional to
        the molecular weight of the solute. The effects of changes of concentration,
        exposure time, flux, wavelength, sample-detector distance, detector dimensions,
        pixel size, and the mask as well as incident beam position can be considered
        for the simulation. The simulated data may be used in method development,
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        setup for a project prior to the application and use of the actual beamtime.
        IMSIM is available as part of the ATSAS software package (3.0.0) and is freely
        available for academic use (http://www.embl-hamburg.de/biosaxs/download.html).
      authors:
      - name: Franke D.
      - name: Hajizadeh N.R.
      - name: Svergun D.I.
      citationCount: 3
      date: '2020-04-01T00:00:00Z'
      journal: Journal of Applied Crystallography
      title: Simulation of small-angle X-ray scattering data of biological macromolecules
        in solution
    note: null
    pmcid: PMC7133063
    pmid: '32280325'
    type: []
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  toolType:
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  topic:
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  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 0
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  cost: Free of charge
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  description: Navpa is non-software, deriving its name from nav pa', Klingon for
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    violation of published works such as maps. Navpa itself does nothing, including
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    can handle the special case of a tool with no inputs, outputs or operations.
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  toolType:
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  topic:
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  - term: Genetics
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  description: Evolutionary simulation framework that combines a powerful engine for
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  license: GPL-3.0
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  - note: GitHub repository for SLiM
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  publication:
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    metadata:
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      citationCount: 541
      date: '2019-03-01T00:00:00Z'
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    type:
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  - doi: 10.1093/molbev/msy237
    metadata:
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      authors: []
      citationCount: 13
      date: '2019-05-01T00:00:00Z'
      journal: Molecular Biology and Evolution
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    type:
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  - doi: 10.1111/1755-0998.12968
    metadata:
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      citationCount: 123
      date: '2019-03-01T00:00:00Z'
      journal: Molecular Ecology Resources
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    note: B.C. Haller, J. Galloway, J. Kelleher, P.W. Messer, P.L. Ralph. (2019).
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    type:
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  - doi: 10.1101/2025.08.07.669155
    metadata: null
    note: 'BC Haller, PL Ralph, PW Messer. (2025). SLiM 5: Eco-evolutionary simulations
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    type:
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  - doi: 10.1086/723601
    metadata:
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      authors: []
      citationCount: 124
      date: '2023-05-01T00:00:00Z'
      journal: American Naturalist
      title: 'SLiM 4: Multispecies Eco-Evolutionary Modeling'
    note: 'B.C. Haller, P.W. Messer. (2023). SLiM 4: Multispecies eco-evolutionary
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    pmcid: PMC10793872
    pmid: '37130229'
    type:
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    version: '4.0'
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  toolType:
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  - Desktop application
  topic:
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  - term: Molecular interactions, pathways and networks
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  - term: Genetic variation
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  - term: Evolutionary biology
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  validated: 0
  version:
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- accessibility: Open access
  additionDate: '2025-09-03T07:50:00.879412Z'
  biotoolsCURIE: biotools:cats-rf
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  confidence_flag: null
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  description: CATS-rf evaluates transcriptome assemblies using RNA-seq reads without
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  documentation:
  - note: null
    type:
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  - term: Omics
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  - term: Biology
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  description: Atomic Charge Calculator II (ACC II) is a web application for fast
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  description: The PASTEClassifier (Pseudo Agent System for Transposable Elements
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  - term: Probes and primers
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  - note: null
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  - note: YouTube training videos
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  owner: ben_s
  publication:
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  topic:
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  validated: 1
  version:
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        term: RNA sequence
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    note: null
    operation:
    - term: Sequence assembly validation
      uri: http://edamontology.org/operation_3180
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      format: []
  homepage: https://github.com/bodulic/CATS-rb
  homepage_status: 0
  language:
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  - Bash
  lastUpdate: '2025-09-03T08:01:20.176094Z'
  license: MIT
  link:
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  owner: bodulic
  publication:
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    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
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  toolType:
  - Command-line tool
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  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Biology
    uri: http://edamontology.org/topic_3070
  - term: Biosciences
    uri: http://edamontology.org/topic_4019
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  biotoolsCURIE: biotools:bakta
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    rorid: null
    typeEntity: Person
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    - Maintainer
    url: https://github.com/oschwengers
  - email: null
    fundrefid: null
    gridid: null
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    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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  description: Rapid & standardized annotation of bacterial genomes, MAGs & plasmids
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    type:
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    - Contributions policy
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  - note: null
    type:
    - User manual
    url: https://bakta.readthedocs.io/
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        uri: http://edamontology.org/format_1929
    - data:
        term: Sequence features metadata
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    - data:
        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
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    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
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        uri: http://edamontology.org/format_1929
    - data:
        term: Hidden Markov model
        uri: http://edamontology.org/data_1364
      format:
      - term: HMMER3
        uri: http://edamontology.org/format_3329
    note: null
    operation:
    - term: Genome annotation
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    - data:
        term: Feature table
        uri: http://edamontology.org/data_1270
      format:
      - term: GenBank format
        uri: http://edamontology.org/format_1936
      - term: EMBL format
        uri: http://edamontology.org/format_1927
      - term: GFF3
        uri: http://edamontology.org/format_1975
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    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
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        uri: http://edamontology.org/format_1929
    - data:
        term: Nucleic acid sequence record
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      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Score
        uri: http://edamontology.org/data_1772
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://github.com/oschwengers/bakta
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-09-01T07:59:35.735958Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/oschwengers/bakta
  - note: null
    type:
    - Issue tracker
    url: https://github.com/oschwengers/bakta/issues
  - note: null
    type:
    - Other
    url: https://bioconda.github.io/recipes/bakta/README.html
  - note: null
    type:
    - Service
    url: https://bakta.computational.bio
  maturity: Mature
  name: Bakta
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: oschwengers
  publication:
  - doi: 10.1099/mgen.0.000685
    metadata:
      abstract: ''
      authors: []
      citationCount: 582
      date: '2021-01-01T00:00:00Z'
      journal: Microbial Genomics
      title: 'Bakta: Rapid and standardized annotation of bacterial genomes via alignment-free
        sequence identification'
    note: null
    pmcid: PMC8743544
    pmid: '34739369'
    type:
    - Primary
    version: '1.1'
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    type: uses
  - biotoolsID: hmmer3
    type: uses
  - biotoolsID: infernal
    type: uses
  - biotoolsID: trnascan-se
    type: uses
  - biotoolsID: blast
    type: uses
  - biotoolsID: aragorn
    type: uses
  - biotoolsID: pilercr
    type: uses
  - biotoolsID: deepsig
    type: uses
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - v1.11.4
- accessibility: Open access
  additionDate: '2025-06-17T20:53:54.551980Z'
  biotoolsCURIE: biotools:metasbt
  biotoolsID: metasbt
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: fabio.cumbo@gmail.com
    fundrefid: null
    gridid: null
    name: Fabio Cumbo
    note: null
    orcidid: https://orcid.org/0000-0003-2920-5838
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    - Support
    url: https://cumbof.github.io
  description: MetaSBT is a scalable framework designed to automatically index microbial
    genomes and accurately characterize metagenome-assembled genomes using Sequence
    Bloom Trees.
  documentation:
  - note: Official MetaSBT Wiki
    type:
    - General
    url: https://github.com/cumbof/MetaSBT/wiki
  download:
  - note: null
    type: Software package
    url: https://pypi.io/packages/source/m/metasbt/metasbt-0.1.4.post1.tar.gz
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  homepage: https://github.com/cumbof/MetaSBT
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  license: MIT
  link:
  - note: Framework Repository
    type:
    - Repository
    url: https://github.com/cumbof/MetaSBT
  - note: Repository with Public MetaSBT Databases
    type:
    - Repository
    url: https://github.com/cumbof/MetaSBT-DBs
  maturity: Mature
  name: MetaSBT
  operatingSystem:
  - Linux
  otherID: []
  owner: fabiocumbo
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  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version:
  - 0.1.4.post1
  - 0.1.5
- accessibility: null
  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
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - 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
  name: VAPOR
  operatingSystem: []
  otherID: []
  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
        the proportion of mapped reads by up to 13.3% compared to standard references.
        VAPOR has the potential to improve the robustness of bioinformatics pipelines
        for surveillance and could be adapted to other RNA viruses.'
      authors:
      - name: Southgate J.A.
      - 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: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version:
  - 1.0.3
- 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:
    - Developer
    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
    url: https://pypi.org/project/img2catalog/
    version: null
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Query and retrieval
      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
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  - Linux
  - Mac
  - Windows
  otherID: []
  owner: agjharms
  publication: []
  relation:
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    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: FAIR data
    uri: http://edamontology.org/topic_4012
  validated: 0
  version:
  - 1.0.1
- accessibility: Open access
  additionDate: '2022-03-21T17:29:40.559556Z'
  biotoolsCURIE: biotools:fjd-pipeline
  biotoolsID: fjd-pipeline
  collectionID:
  - IMPaCT-Data
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: pablo.minguez@quironsalud.es
    fundrefid: null
    gridid: null
    name: Bioinformatics Unit IIS-FJD
    note: null
    orcidid: https://orcid.org/0000-0003-4099-9421
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    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.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/TBLabFJD/VariantCallingFJD
  download: []
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Tool name (FASTA)
        uri: http://edamontology.org/data_1193
      format:
      - term: FASTQ-like format
        uri: http://edamontology.org/format_2545
    note: null
    operation:
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Copy number variation detection
      uri: http://edamontology.org/operation_3961
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
  homepage: https://github.com/TBLabFJD/VariantCallingFJD
  homepage_status: 0
  language:
  - Python
  - R
  - Bash
  lastUpdate: '2025-08-12T08:31:29.574795Z'
  license: CC-BY-NC-SA-4.0
  link:
  - note: GitHub repository
    type:
    - Repository
    url: https://github.com/TBLabFJD/VariantCallingFJD
  maturity: Emerging
  name: FJD-pipeline
  operatingSystem:
  - Linux
  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
    version: null
  relation:
  - biotoolsID: priorr
    type: usedBy
  - biotoolsID: parrot-fjd
    type: hasNewVersion
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-08-10T14:15:14.443314Z'
  biotoolsCURIE: biotools:genex_workflow
  biotoolsID: genex_workflow
  collectionID: []
  confidence_flag: tool
  cost: Free of charge (with restrictions)
  credit: []
  description: This is a computational workflow for detecting coordinates of microbial-like
    or human-like sequences in eukaryotic and prokaryotic reference genomes. The workflow
    accepts a reference genome in FASTA-format and outputs coordinates of microbial-like
    (human-like) regions in BED-format. The workflow builds a Bowtie2 index of the
    reference genome and aligns pre-computed microbial (GTDB v.214 or NCBI RefSeq
    release 213) or human (hg38) pseudo-reads to the reference, then custom scripts
    are used for detection of the positions of covered regions and quantification
    of most abundant microbial species, the latter is only when screening for microbial-like
    sequences in eukaryotic references.
  documentation: []
  download: []
  editPermission:
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  elixirNode: []
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  elixir_badge: 0
  function: []
  homepage: https://github.com/NikolayOskolkov/MCWorkflow
  homepage_status: 0
  language: []
  lastUpdate: '2025-08-10T14:20:33.908626Z'
  license: CC0-1.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/NikolayOskolkov/MCWorkflow
  maturity: Mature
  name: GENEX workflow
  operatingSystem: []
  otherID: []
  owner: Nikolay_Oskolkov
  publication:
  - doi: 10.1101/2025.03.19.644176
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2024-02-02T14:42:46.030561Z'
  biotoolsCURIE: biotools:hictk
  biotoolsID: hictk
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Blazing fast toolkit to work with .hic and .cool files
  documentation:
  - note: null
    type:
    - User manual
    url: https://hictk.readthedocs.io
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictk/releases
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictk
    version: null
  - note: null
    type: Container file
    url: https://github.com/paulsengroup/hictk/pkgs/container/hictk
    version: null
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  homepage: https://github.com/paulsengroup/hictk
  homepage_status: 0
  language: []
  lastUpdate: '2025-08-04T21:43:36.678557Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictk
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictk/issues
  - note: null
    type:
    - Helpdesk
    url: https://github.com/paulsengroup/hictk/discussions
  maturity: Mature
  name: hictk
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 1
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: hictkpy
    type: usedBy
  - biotoolsID: hictkr
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  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
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  - 2.1.3
  - 2.1.4
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  additionDate: '2025-07-30T10:37:08.226825Z'
  biotoolsCURIE: biotools:eukrep
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  description: Classification of Eukaryotic and Prokaryotic sequences from metagenomic
    datasets
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  function:
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    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_2478
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  homepage: https://github.com/patrickwest/EukRep
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-30T10:37:08.229304Z'
  license: MIT
  link: []
  maturity: null
  name: EukRep
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  owner: m.bernt
  publication:
  - doi: 10.1101/gr.228429.117
    metadata:
      abstract: Microbial eukaryotes are integral components of natural microbial
        communities, and their inclusion is critical for many ecosystem studies, yet
        the majority of published metagenome analyses ignore eukaryotes. In order
        to include eukaryotes in environmental studies, we propose a method to recover
        eukaryotic genomes from complex metagenomic samples. A key step for genome
        recovery is separation of eukaryotic and prokaryotic fragments. We developed
        a k-mer-based strategy, EukRep, for eukaryotic sequence identification and
        applied it to environmental samples to show that it enables genome recovery,
        genome completeness evaluation, and prediction of metabolic potential. We
        used this approach to test the effect of addition of organic carbon on a geyser-associated
        microbial community and detected a substantial change of the community metabolism,
        with selection against almost all candidate phyla bacteria and archaea and
        for eukaryotes. Near complete genomes were reconstructed for three fungi placed
        within the Eurotiomycetes and an arthropod. While carbon fixation and sulfur
        oxidation were important functions in the geyser community prior to carbon
        addition, the organic carbon-impacted community showed enrichment for secreted
        proteases, secreted lipases, cellulose targeting CAZymes, and methanol oxidation.
        We demonstrate the broader utility of EukRep by reconstructing and evaluating
        relatively high-quality fungal, protist, and rotifer genomes from complex
        environmental samples. This approach opens the way for cultivation-independent
        analyses of whole microbial communities.
      authors:
      - name: West P.T.
      - name: Probst A.J.
      - name: Grigoriev I.V.
      - name: Thomas B.C.
      - name: Banfield J.F.
      citationCount: 143
      date: '2018-04-01T00:00:00Z'
      journal: Genome Research
      title: Genome-reconstruction for eukaryotes from complex natural microbial communities
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-07-24T07:14:51.692628Z'
  biotoolsCURIE: biotools:eukcc
  biotoolsID: eukcc
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: EukCC is a completeness and contamination estimator for metagenomic
    assembled microbial eukaryotic genomes.
  documentation: []
  download: []
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  function:
  - cmd: null
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format: []
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format: []
    note: null
    operation:
    - term: Sequence assembly validation
      uri: http://edamontology.org/operation_3180
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format: []
  homepage: https://github.com/EBI-Metagenomics/EukCC
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-24T07:14:51.694965Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: EukCC
  operatingSystem: []
  otherID: []
  owner: m.bernt
  publication:
  - doi: 10.1186/s13059-020-02155-4
    metadata:
      abstract: Microbial eukaryotes constitute a significant fraction of biodiversity
        and have recently gained more attention, but the recovery of high-quality
        metagenomic assembled eukaryotic genomes is limited by the current availability
        of tools. To help address this, we have developed EukCC, a tool for estimating
        the quality of eukaryotic genomes based on the automated dynamic selection
        of single copy marker gene sets. We demonstrate that our method outperforms
        current genome quality estimators, particularly for estimating contamination,
        and have applied EukCC to datasets derived from two different environments
        to enable the identification of novel eukaryote genomes, including one from
        the human skin.
      authors:
      - name: Saary P.
      - name: Mitchell A.L.
      - name: Finn R.D.
      citationCount: 70
      date: '2020-09-10T00:00:00Z'
      journal: Genome Biology
      title: Estimating the quality of eukaryotic genomes recovered from metagenomic
        analysis with EukCC
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-07-23T10:27:57.572501Z'
  biotoolsCURIE: biotools:etenlnc
  biotoolsID: etenlnc
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  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: barah@tezu.ernet.in
    fundrefid: null
    gridid: null
    name: Pankaj Barah
    note: Assistant Professor at Department of Molecular Biology and Biotechnology,
      Tezpur University.
    orcidid: https://orcid.org/0000-0001-7039-7996
    rorid: null
    typeEntity: Person
    typeRole:
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    - Maintainer
    url: https://www.tezu.ernet.in/dmbbt/profile/34
  - email: prangannathofficial@gmai.com
    fundrefid: null
    gridid: null
    name: Prangan Nath
    note: null
    orcidid: https://orcid.org/0000-0002-9451-7822
    rorid: null
    typeEntity: Person
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    url: null
  description: ETENLNC (End-To-End-Novel-NonCoding) is a lncRNA identification and
    analysis framework
  documentation:
  - note: A detailed guide on running ETENLNC using the demo data can be found in
      the ETENLNC manual (supplementary to our publication)
    type:
    - Command-line options
    url: https://github.com/EvolOMICS-TU/ETENLNC
  download:
  - note: Sample/demo data for ETENLNC. A detailed guide on running ETENLNC using
      the demo data can be found in the ETENLNC manual (supplementary to our publication).
    type: Biological data
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  function:
  - cmd: bash ETENLNC_docker.sh
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Genome map
        uri: http://edamontology.org/data_1288
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: GTF
        uri: http://edamontology.org/format_2306
      - term: GFF
        uri: http://edamontology.org/format_2305
    note: Run ETENLNC on paired-end raw RNA-Seq (.fastq) data
    operation:
    - term: Read pre-processing
      uri: http://edamontology.org/operation_3219
    - term: Sequencing quality control
      uri: http://edamontology.org/operation_3218
    - term: Transcriptome assembly
      uri: http://edamontology.org/operation_3258
    - term: Nucleic acid feature detection
      uri: http://edamontology.org/operation_0415
    - term: RNA-Seq quantification
      uri: http://edamontology.org/operation_3800
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Heat map generation
      uri: http://edamontology.org/operation_0531
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: k-mer counting
      uri: http://edamontology.org/operation_3472
    - term: Protein-nucleic acid interaction analysis
      uri: http://edamontology.org/operation_0389
    - term: RNA-binding protein prediction
      uri: http://edamontology.org/operation_3901
    - term: Gene regulatory network prediction
      uri: http://edamontology.org/operation_2437
    output:
    - data:
        term: Nucleic acid sequence record
        uri: http://edamontology.org/data_2887
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Protein interaction data
        uri: http://edamontology.org/data_0906
      format: []
  homepage: https://github.com/EvolOMICS-TU/ETENLNC
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-23T10:27:57.574879Z'
  license: GPL-3.0
  link:
  - note: GitHub Repo
    type:
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    url: https://github.com/EvolOMICS-TU/ETENLNC
  maturity: Emerging
  name: ETENLNC
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: prangannath
  publication:
  - doi: 10.1016/j.compbiolchem.2024.108140
    metadata:
      abstract: 'Long non-coding RNAs (lncRNAs) play crucial roles in the regulation
        of gene expression and maintenance of genomic integrity through various interactions
        with DNA, RNA, and proteins. The availability of large-scale sequence data
        from various high-throughput platforms has opened possibilities to identify,
        predict, and functionally annotate lncRNAs. As a result, there is a growing
        demand for an integrative computational framework capable of identifying known
        lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory
        interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding),
        a user-friendly, integrative, open-source, scalable, and modular computational
        framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC
        employs six stringent filtration steps to identify novel lncRNAs, performs
        differential expression analysis of mRNA and lncRNA transcripts, and predicts
        regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked
        ETENLNC against six existing tools and optimized it for desktop workstations
        and high-performance computing environments using data from three different
        species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.'
      authors:
      - name: Nath P.
      - name: Bhuyan K.
      - name: Bhattacharyya D.K.
      - name: Barah P.
      citationCount: 2
      date: '2024-10-01T00:00:00Z'
      journal: Computational Biology and Chemistry
      title: 'ETENLNC: An end to end lncRNA identification and analysis framework
        to facilitate construction of known and novel lncRNA regulatory networks'
    note: null
    pmcid: null
    pmid: '38996755'
    type:
    - Primary
    version: '.0'
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    type: uses
  - biotoolsID: salmon
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  - biotoolsID: lnctar
    type: uses
  - biotoolsID: capsule-lpi
    type: uses
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    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  validated: 0
  version:
  - '1.0'
- accessibility: null
  additionDate: '2025-07-21T09:36:40.711284Z'
  biotoolsCURIE: biotools:nnunet
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  description: nnU-Net is a self-configuring method for deep learning-based biomedical
    image segmentation, developed by the Applied Computer Vision Lab (ACVL) of Helmholtz
    Imaging and the Division of Medical Image Computing at the German Cancer Research
    Center (DKFZ). It is designed to automatically adapt to a given dataset, analyzing
    the provided training cases to configure a matching U-Net-based segmentation pipeline
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  documentation: []
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  homepage: https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1
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  language:
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  lastUpdate: '2025-07-21T15:06:33.111687Z'
  license: Apache-2.0
  link: []
  maturity: null
  name: nnUNet
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  owner: ASHISR
  publication:
  - doi: 10.1038/s41592-020-01008-z
    metadata:
      abstract: Biomedical imaging is a driver of scientific discovery and a core
        component of medical care and is being stimulated by the field of deep learning.
        While semantic segmentation algorithms enable image analysis and quantification
        in many applications, the design of respective specialized solutions is non-trivial
        and highly dependent on dataset properties and hardware conditions. We developed
        nnU-Net, a deep learning-based segmentation method that automatically configures
        itself, including preprocessing, network architecture, training and post-processing
        for any new task. The key design choices in this process are modeled as a
        set of fixed parameters, interdependent rules and empirical decisions. Without
        manual intervention, nnU-Net surpasses most existing approaches, including
        highly specialized solutions on 23 public datasets used in international biomedical
        segmentation competitions. We make nnU-Net publicly available as an out-of-the-box
        tool, rendering state-of-the-art segmentation accessible to a broad audience
        by requiring neither expert knowledge nor computing resources beyond standard
        network training.
      authors:
      - name: Isensee F.
      - name: Jaeger P.F.
      - name: Kohl S.A.A.
      - name: Petersen J.
      - name: Maier-Hein K.H.
      citationCount: 5362
      date: '2021-02-01T00:00:00Z'
      journal: Nature Methods
      title: 'nnU-Net: a self-configuring method for deep learning-based biomedical
        image segmentation'
    note: null
    pmcid: null
    pmid: null
    type:
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  toolType:
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  validated: 0
  version:
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- accessibility: Open access
  additionDate: '2022-09-17T13:13:51.109638Z'
  biotoolsCURIE: biotools:physiboss
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  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: alfonso.valencia@bsc.es
    fundrefid: null
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    name: Alfonso Valencia
    note: null
    orcidid: https://orcid.org/0000-0002-8937-6789
    rorid: null
    typeEntity: Person
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  - email: null
    fundrefid: null
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    name: Laurence Calzone
    note: null
    orcidid: https://orcid.org/0000-0002-7835-1148
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Emmanuel Barillot
    note: null
    orcidid: https://orcid.org/0000-0003-2724-2002
    rorid: null
    typeEntity: null
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Miguel Ponce-de-Leon
    note: null
    orcidid: https://orcid.org/0000-0002-7496-844X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Arnau Montagud
    note: null
    orcidid: https://orcid.org/0000-0002-7696-1241
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A sustainable integration of stochastic Boolean and agent-based modelling
    frameworks.
  documentation:
  - note: null
    type:
    - Code of conduct
    url: https://github.com/PhysiBoSS/PhysiBoSS/blob/master/CODE_OF_CONDUCT.md
  download: []
  editPermission:
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    - vincent-noel
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    type: group
  elixirCommunity: []
  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    output: []
  homepage: https://github.com/PhysiBoSS/PhysiBoSS
  homepage_status: 0
  language:
  - C++
  - Python
  - MATLAB
  lastUpdate: '2025-07-21T09:32:03.727618Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
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    url: https://github.com/PhysiBoSS/
  maturity: null
  name: PhysiBoSS
  operatingSystem:
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  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1101/2022.01.06.468363
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2025-07-15T16:04:03.513732Z'
  biotoolsCURIE: biotools:GevaertLab_DUNE
  biotoolsID: GevaertLab_DUNE
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Thomas Barba
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
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    url: null
  description: A versatile neuroimaging encoder for extracting deep features from
    multisequence brain MRIs
  documentation:
  - note: null
    type:
    - Quick start guide
    url: https://github.com/gevaertlab/DUNE/blob/main/README.md
  download:
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    type: Source code
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  function:
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    input: []
    note: Extracts features from
    operation:
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      uri: http://edamontology.org/operation_3937
    output: []
  homepage: https://github.com/gevaertlab/DUNE
  homepage_status: 0
  language:
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  lastUpdate: '2025-07-15T16:04:03.516083Z'
  license: Apache-2.0
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  - note: null
    type:
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  maturity: Mature
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  operatingSystem:
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  otherID: []
  owner: tbarba
  publication:
  - doi: 10.1101/2025.02.24.25322787
    metadata: null
    note: null
    pmcid: null
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    type: []
    version: null
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  validated: 0
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    (kmax). Analyzing the k-mer spectrum across multiple values of k can be done in
    seconds. This program is designed for bioinformatics researchers and scientists.
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    - data:
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      format:
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    - data:
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      format:
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    note: null
    operation:
    - term: k-mer counting
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      format: []
    - data:
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        uri: http://edamontology.org/data_1265
      format: []
    - data:
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  name: UniversalMer
  operatingSystem:
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  - Mac
  otherID: []
  owner: Ugrammer
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
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    uri: http://edamontology.org/topic_0091
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 2.0.0
- accessibility: null
  additionDate: '2017-08-03T18:49:51Z'
  biotoolsCURIE: biotools:moccasin
  biotoolsID: moccasin
  collectionID: []
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  credit:
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    fundrefid: null
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    name: Michael Hucka,
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://github.com/sbmlteam/moccasin
  description: Designed to convert certain basic forms of ODE simulation models written
    in MATLAB or Octave and translate them into SBML format.
  documentation:
  - note: null
    type:
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    url: https://github.com/sbmlteam/moccasin/blob/master/README.md
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  homepage: https://github.com/sbmlteam/moccasin
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-08T14:13:52.296025Z'
  license: null
  link:
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  maturity: null
  name: MOCCASIN
  operatingSystem:
  - Linux
  otherID: []
  owner: frankbergmann
  publication:
  - doi: null
    metadata:
      abstract: © 2016 The Author 2016. Published by Oxford University Press.MATLAB
        is popular in biological research for creating and simulating models that
        use ordinary differential equations (ODEs). However, sharing or using these
        models outside of MATLAB is often problematic. A community standard such as
        Systems Biology Markup Language (SBML) can serve as a neutral exchange format,
        but translating models from MATLAB to SBML can be challenging - especially
        for legacy models not written with translation in mind. We developed MOCCASIN
        (Model ODE Converter for Creating Automated SBML INteroperability) to help.
        MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks
        into the SBML format.
      authors:
      - name: Gomez H.F.
      - name: Hucka M.
      - name: Keating S.M.
      - name: Nudelman G.
      - name: Iber D.
      - name: Sealfon S.C.
      citationCount: 7
      date: '2016-06-15T00:00:00Z'
      journal: Bioinformatics
      title: 'MOCCASIN: Converting MATLAB ODE models to SBML'
    note: null
    pmcid: null
    pmid: '26861819'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Simulation experiment
    uri: http://edamontology.org/topic_3524
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-01-17T15:07:47Z'
  biotoolsCURIE: biotools:copasi
  biotoolsID: copasi
  collectionID:
  - de.NBI
  - EBI Training Tools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://copasi.org/About/Team/
  - email: frank.bergmann@bioquant.uni-heidelberg.de
    fundrefid: null
    gridid: null
    name: Frank T. Bergmann
    note: null
    orcidid: https://orcid.org/0000-0001-5553-4702
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Open-source software application for creating and solving mathematical
    models of biological processes such as metabolic networks, cell-signaling pathways,
    regulatory networks, infectious diseases, and many others. It includes features
    to define models of biological processes, simulate and analyze these models, generate
    analysis reports, and import/export models in SBML format.
  documentation:
  - note: null
    type:
    - User manual
    url: http://copasi.org/Support/User_Manual/
  download:
  - note: Source and binary packages are available for download.
    type: Binaries
    url: http://copasi.org/Download/
    version: null
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  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
    note: null
    operation:
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Metabolic network modelling
      uri: http://edamontology.org/operation_3660
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
  homepage: http://copasi.org/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-07-07T11:17:37.638447Z'
  license: Artistic-2.0
  link:
  - note: User Forum
    type:
    - Discussion forum
    url: https://groups.google.com/g/copasi-user-forum
  - note: Issue tracker
    type:
    - Issue tracker
    url: http://tracker.copasi.org/
  - note: Github Repo
    type:
    - Repository
    url: https://github.com/copasi/COPASI
  - note: null
    type:
    - Social media
    url: https://fosstodon.org/@copasi
  maturity: Mature
  name: COPASI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: frankbergmann
  publication:
  - doi: 10.1093/bioinformatics/btl485
    metadata:
      abstract: 'Motivation: Simulation and modeling is becoming a standard approach
        to understand complex biochemical processes. Therefore, there is a big need
        for software tools that allow access to diverse simulation and modeling methods
        as well as support for the usage of these methods. Results: Here, we present
        COPASI, a platform-independent and user-friendly biochemical simulator that
        offers several unique features. We discuss numerical issues with these features;
        in particular, the criteria to switch between stochastic and deterministic
        simulation methods, hybrid deterministic-stochastic methods, and the importance
        of random number generator numerical resolution in stochastic simulation.
        © 2006 Oxford University Press.'
      authors:
      - name: Hoops S.
      - name: Gauges R.
      - name: Lee C.
      - name: Pahle J.
      - name: Simus N.
      - name: Singhal M.
      - name: Xu L.
      - name: Mendes P.
      - name: Kummer U.
      citationCount: 2004
      date: '2006-12-15T00:00:00Z'
      journal: Bioinformatics
      title: COPASI - A COmplex PAthway SImulator
    note: null
    pmcid: null
    pmid: '17032683'
    type: []
    version: null
  - doi: 10.1007/978-1-59745-525-1_2
    metadata:
      abstract: Computational modeling and simulation of biochemical networks is at
        the core of systems biology and this includes many types of analyses that
        can aid understanding of how these systems work. COPASI is a generic software
        package for modeling and simulation of biochemical networks which provides
        many of these analyses in convenient ways that do not require the user to
        program or to have deep knowledge of the numerical algorithms. Here we provide
        a description of how these modeling techniques can be applied to biochemical
        models using COPASI. The focus is both on practical aspects of software usage
        as well as on the utility of these analyses in aiding biological understanding.
        Practical examples are described for steady-state and time-course simulations,
        stoichiometric analyses, parameter scanning, sensitivity analysis (including
        metabolic control analysis), global optimization, parameter estimation, and
        stochastic simulation. The examples used are all published models that are
        available in the BioModels database in SBML format. © 2009 Humana Press.
      authors:
      - name: Mendes P.
      - name: Hoops S.
      - name: Sahle S.
      - name: Gauges R.
      - name: Dada J.
      - name: Kummer U.
      citationCount: 171
      date: '2009-12-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Computational modeling of biochemical networks using COPASI
    note: null
    pmcid: null
    pmid: '19399433'
    type: []
    version: null
  - doi: 10.1016/j.jbiotec.2017.06.1200
    metadata:
      abstract: COPASI is software used for the creation, modification, simulation
        and computational analysis of kinetic models in various fields. It is open-source,
        available for all major platforms and provides a user-friendly graphical user
        interface, but is also controllable via the command line and scripting languages.
        These are likely reasons for its wide acceptance. We begin this review with
        a short introduction describing the general approaches and techniques used
        in computational modeling in the biosciences. Next we introduce the COPASI
        package, and its capabilities, before looking at typical applications of COPASI
        in biotechnology.
      authors:
      - name: Bergmann F.T.
      - name: Hoops S.
      - name: Klahn B.
      - name: Kummer U.
      - name: Mendes P.
      - name: Pahle J.
      - name: Sahle S.
      citationCount: 81
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: COPASI and its applications in biotechnology
    note: null
    pmcid: PMC5623632
    pmid: '28655634'
    type: []
    version: null
  relation:
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    type: usedBy
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    type: usedBy
  - biotoolsID: biosimulations
    type: includedIn
  - biotoolsID: sbmlwebapp
    type: usedBy
  - biotoolsID: libsbml
    type: uses
  toolType:
  - Command-line tool
  - Library
  - Desktop application
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: null
  additionDate: '2025-07-07T09:04:03.787249Z'
  biotoolsCURIE: biotools:sqanti3
  biotoolsID: sqanti3
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: ana.conesa@csic.es
    fundrefid: null
    gridid: null
    name: Ana Conesa
    note: null
    orcidid: https://orcid.org/0000-0001-9597-311X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: pablo.atienza@csic.es
    fundrefid: null
    gridid: null
    name: Pablo Atienza
    note: Maintainer of SQANTI3
    orcidid: https://orcid.org/0009-0002-1093-693X
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  - email: fabian.robledo@csic.es
    fundrefid: null
    gridid: null
    name: Fabián Robledo
    note: Maintainer of SQANTI3 and ELIXIR-related contact
    orcidid: https://orcid.org/0009-0005-9047-3315
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  description: "SQANTI3 constitutes the first module of the Functional IsoTranscriptomics\
    \ (FIT) pipeline, which is an end-to-end strategy to perform isoform-level bioinformatics\
    \ analyses. \n\nThe SQANTI3 tool is designed to enable quality control and filtering\
    \ of long read-defined transcriptomes, which are often rich in artifacts and false-positive\
    \ isoforms. \n\nTherefore, a good curation of the transcriptome is indispensable\
    \ to proceed with FIT analysis and produce valid, biologically sound conclusions/hypothesis."
  documentation:
  - note: SQANTI3 main documentation resource
    type:
    - General
    url: https://github.com/ConesaLab/SQANTI3/wiki
  - note: null
    type:
    - Code of conduct
    url: https://github.com/ConesaLab/SQANTI3/blob/master/CODE_OF_CONDUCT.md
  - note: Command-line options and instructions for the QC submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-Quality-Control
  - note: Command-line options and instructions for the filter submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-filter
  - note: Command-line options and instructions for the rescue submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-rescue
  - note: Instructions to install and use sqanti, either in docker or apptainer containers
      or in a linux system
    type:
    - Installation instructions
    url: https://github.com/ConesaLab/SQANTI3/wiki/Dependencies-and-installation
  - note: Basic tutorial with examples to start using SQANTI3
    type:
    - Quick start guide
    url: https://github.com/ConesaLab/SQANTI3/wiki/Tutorial:-running-SQANTI3-on-an-example-dataset
  - note: Changelog and release notes for every version
    type:
    - Release notes
    url: https://github.com/ConesaLab/SQANTI3/releases
  - note: Citation instructions are on the end of the Github repository's main page
    type:
    - Citation instructions
    url: https://github.com/ConesaLab/SQANTI3?tab=readme-ov-file#how-to-cite-sqanti3
  - note: Benchmarking about the resources needed to run sqanti3 with multiple cores
    type:
    - Other
    url: https://github.com/ConesaLab/SQANTI3/wiki/SQANTI3-memory-requeriments-and-paralellization
  download:
  - note: null
    type: Downloads page
    url: https://github.com/ConesaLab/SQANTI3/releases
    version: null
  - note: Version 5.5 download link from github
    type: Binaries
    url: https://github.com/ConesaLab/SQANTI3/releases/download/v5.5/SQANTI3_v5.5.zip
    version: '5.5'
  editPermission:
    authors:
    - Fabian-RY
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  homepage: https://github.com/ConesaLab/SQANTI3
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  language:
  - Python
  lastUpdate: '2025-07-07T09:35:33.280160Z'
  license: GPL-3.0
  link:
  - note: Github repository for source code access and download
    type:
    - Repository
    url: https://github.com/ConesaLab/SQANTI3
  maturity: Mature
  name: SQANTI3
  operatingSystem:
  - Linux
  otherID: []
  owner: Fabian-RY
  publication:
  - doi: 10.1038/s41592-024-02229-2
    metadata:
      abstract: SQANTI3 offers a flexible tool for quality control, curation and annotation
        of long-read RNA sequencing data. SQANTI3 is a tool designed for the quality
        control, curation and annotation of long-read transcript models obtained with
        third-generation sequencing technologies. Leveraging its annotation framework,
        SQANTI3 calculates quality descriptors of transcript models, junctions and
        transcript ends. With this information, potential artifacts can be identified
        and replaced with reliable sequences. Furthermore, the integrated functional
        annotation feature enables subsequent functional iso-transcriptomics analyses.
      authors:
      - name: Pardo-Palacios F.J.
      - name: Arzalluz-Luque A.
      - name: Kondratova L.
      - name: Salguero P.
      - name: Mestre-Tomas J.
      - name: Amorin R.
      - name: Estevan-Morio E.
      - name: Liu T.
      - name: Nanni A.
      - name: McIntyre L.
      - name: Tseng E.
      - name: Conesa A.
      citationCount: 41
      date: '2024-05-01T00:00:00Z'
      journal: Nature Methods
      title: 'SQANTI3: curation of long-read transcriptomes for accurate identification
        of known and novel isoforms'
    note: Publication of SQANTI3
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '5.1'
  - doi: 10.1101/gr.222976.117
    metadata:
      abstract: High-throughput sequencing of full-length transcripts using long reads
        has paved the way for the discovery of thousands of novel transcripts, even
        in well-annotated mammalian species. The advances in sequencing technology
        have created a need for studies and tools that can characterize these novel
        variants. Here, we present SQANTI, an automated pipeline for the classification
        of long-read transcripts that can assess the quality of data and the preprocessing
        pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse
        transcriptome using Pacific Biosciences (PacBio) long reads and illustrate
        how the tool is effective in characterizing and describing the composition
        of the full-length transcriptome. We perform extensive evaluation of ToFU
        PacBio transcripts by PCR to reveal that an important number of the novel
        transcripts are technical artifacts of the sequencing approach and that SQANTI
        quality descriptors can be used to engineer a filtering strategy to remove
        them. Most novel transcripts in this curated transcriptome are novel combinations
        of existing splice sites, resulting more frequently in novel ORFs than novel
        UTRs, and are enriched in both general metabolic and neural-specific functions.
        We show that these new transcripts have a major impact in the correct quantification
        of transcript levels by state-of-the-art short-read-based quantification algorithms.
        By comparing our iso-transcriptome with public proteomics databases, we find
        that alternative isoforms are elusive to proteogenomics detection. SQANTI
        allows the user to maximize the analytical outcome of long-read technologies
        by providing the tools to deliver quality-evaluated and curated full-length
        transcriptomes.
      authors:
      - name: Tardaguila M.
      - name: De La Fuente L.
      - name: Marti C.
      - name: Pereira C.
      - name: Pardo-Palacios F.J.
      - name: Del Risco H.
      - name: Ferrell M.
      - name: Mellado M.
      - name: Macchietto M.
      - name: Verheggen K.
      - name: Edelmann M.
      - name: Ezkurdia I.
      - name: Vazquez J.
      - name: Tress M.
      - name: Mortazavi A.
      - name: Martens L.
      - name: Rodriguez-Navarro S.
      - name: Moreno-Manzano V.
      - name: Conesa A.
      citationCount: 256
      date: '2018-03-01T00:00:00Z'
      journal: Genome Research
      title: 'SQANTI: Extensive characterization of long-read transcript sequences
        for quality control in full-length transcriptome identification and quantification'
    note: Original SQANTI publication
    pmcid: null
    pmid: null
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  validated: 0
  version:
  - '5.5'
- accessibility: null
  additionDate: '2020-01-14T08:51:14Z'
  biotoolsCURIE: biotools:RepeatModeler2
  biotoolsID: RepeatModeler2
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: RepeatModeler is a de novo transposable element (TE) family identification
    and modeling package. At the heart of RepeatModeler are three de-novo repeat finding
    programs ( RECON, RepeatScout and LtrHarvest/Ltr_retriever ) which employ complementary
    computational methods for identifying repeat element boundaries and family relationships
    from sequence data.
  documentation: []
  download: []
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    type: public
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: de Novo sequencing
      uri: http://edamontology.org/operation_3644
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    output: []
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  homepage_status: 0
  language:
  - Perl
  lastUpdate: '2025-07-01T04:28:03.302129Z'
  license: OSL-2.0
  link:
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    type:
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    url: https://github.com/Dfam-consortium/RepeatModeler/blob/master/LICENSE
  maturity: Mature
  name: RepeatModeler2
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1101/856591
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
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    type: isNewVersionOf
  toolType:
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  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Sequence composition, complexity and repeats
    uri: http://edamontology.org/topic_0157
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Mobile genetic elements
    uri: http://edamontology.org/topic_0798
  validated: 1
  version:
  - 2.0.7
- accessibility: Open access
  additionDate: '2025-06-18T09:20:19.874306Z'
  biotoolsCURIE: biotools:chopper
  biotoolsID: chopper
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: decosterwouter@gmail.com
    fundrefid: null
    gridid: null
    name: Wouter de Coster
    note: null
    orcidid: https://orcid.org/0000-0002-5248-8197
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: https://gigabaseorgigabyte.wordpress.com
  description: Rust implementation of NanoFilt+NanoLyse, both originally written in
    Python. This tool, intended for long read sequencing such as PacBio or ONT, filters
    and trims a fastq file.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: 'gunzip -c reads.fastq.gz | chopper -q 10 -l 500 | gzip > filtered_reads.fastq.gz

      chopper -q 10 -l 500 -i reads.fastq > filtered_reads.fastq

      chopper -q 10 -l 500 -i reads.fastq.gz | gzip > filtered_reads.fastq.gz'
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Scatter plot plotting
      uri: http://edamontology.org/operation_2940
    - term: Box-Whisker plot plotting
      uri: http://edamontology.org/operation_2943
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: JPG
        uri: http://edamontology.org/format_3579
  homepage: https://github.com/wdecoster/chopper
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-30T15:46:01.095401Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/wdecoster/chopper
  maturity: Mature
  name: Chopper
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Keiler_Collier
  publication:
  - doi: 10.1093/bioinformatics/btad311
    metadata:
      abstract: 'Increases in the cohort size in long-read sequencing projects necessitate
        more efficient software for quality assessment and processing of sequencing
        data from Oxford Nanopore Technologies and Pacific Biosciences. Here, we describe
        novel tools for summarizing experiments, filtering datasets, visualizing phased
        alignments results, and updates to the NanoPack software suite. Availability
        and implementation: The cramino, chopper, kyber, and phasius tools are written
        in Rust and available as executable binaries without requiring installation
        or managing dependencies. Binaries build on musl are available for broad compatibility.
        NanoPlot and NanoComp are written in Python3. Links to the separate tools
        and their documentation can be found at https://github.com/wdecoster/nanopack.
        All tools are compatible with Linux, Mac OS, and the MS Windows Subsystem
        for Linux and are released under the MIT license. The repositories include
        test data, and the tools are continuously tested using GitHub Actions and
        can be installed with the conda dependency manager.'
      authors:
      - name: De Coster W.
      - name: Rademakers R.
      citationCount: 211
      date: '2023-05-01T00:00:00Z'
      journal: Bioinformatics
      title: 'NanoPack2: population-scale evaluation of long-read sequencing data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: nanopack
    type: isNewVersionOf
  toolType:
  - Command-line tool
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - v0.10.0b
- accessibility: Open access
  additionDate: '2024-03-18T14:51:49.667412Z'
  biotoolsCURIE: biotools:compleasm
  biotoolsID: compleasm
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: neng@ds.dfci.harvard.edu
    fundrefid: null
    gridid: null
    name: Neng Huang
    note: null
    orcidid: https://orcid.org/0000-0001-7187-0749
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'Compleasm: a faster and more accurate reimplementation of BUSCO.

    It provides measures for quantitative assessment of genome assembly, gene set,
    and transcriptome completeness based on evolutionarily informed expectations of
    gene content from near-universal single-copy orthologs.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/huangnengCSU/compleasm/blob/0.2.6/README.md
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: compleasm run -l "$lineage" -a assembly.fa -o output_prefix
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
    note: Runs compleasm using the BUSCO set corresponding to the lineage given.
    operation:
    - term: Sequence assembly validation
      uri: http://edamontology.org/operation_3180
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format: []
  homepage: https://github.com/huangnengCSU/compleasm
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-30T15:30:41.266812Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/huangnengCSU/compleasm
  - note: null
    type:
    - Issue tracker
    url: https://github.com/huangnengCSU/compleasm/issues
  - note: List of accepted lineages (taxonomic groups with curated BUSCO sets)
    type:
    - Other
    url: https://busco.ezlab.org/list_of_lineages.html
  maturity: Mature
  name: Compleasm
  operatingSystem: []
  otherID: []
  owner: rlibouban
  publication:
  - doi: 10.1093/bioinformatics/btad595
    metadata:
      abstract: 'Motivation: Evaluating the gene completeness is critical to measuring
        the quality of a genome assembly. An incomplete assembly can lead to errors
        in gene predictions, annotation, and other downstream analyses. Benchmarking
        Universal Single-Copy Orthologs (BUSCO) is a widely used tool for assessing
        the completeness of genome assembly by testing the presence of a set of single-copy
        orthologs conserved across a wide range of taxa. However, BUSCO is slow particularly
        for large genome assemblies. It is cumbersome to apply BUSCO to a large number
        of assemblies. Results: Here, we present compleasm, an efficient tool for
        assessing the completeness of genome assemblies. Compleasm utilizes the miniprot
        protein-to-genome aligner and the conserved orthologous genes from BUSCO.
        It is 14 times faster than BUSCO for human assemblies and reports a more accurate
        completeness of 99.6% than BUSCO''s 95.7%, which is in close agreement with
        the annotation completeness of 99.5% for T2T-CHM13.'
      authors:
      - name: Huang N.
      - name: Li H.
      citationCount: 95
      date: '2023-10-01T00:00:00Z'
      journal: Bioinformatics
      title: 'compleasm: a faster and more accurate reimplementation of BUSCO'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: busco
    type: isNewVersionOf
  toolType:
  - Command-line tool
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - v.0.2.5
- accessibility: null
  additionDate: '2025-06-30T15:03:36.801398Z'
  biotoolsCURIE: biotools:lexicmap
  biotoolsID: lexicmap
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: shenwei356@gmail.com
    fundrefid: null
    gridid: null
    name: Wei Shen
    note: null
    orcidid: https://orcid.org/0000-0002-8099-8258
    rorid: null
    typeEntity: null
    typeRole: []
    url: http://shenwei.me
  description: LexicMap is a nucleotide sequence alignment tool for efficiently querying
    gene, plasmid, viral, or long-read sequences (>100 bp) against up to millions
    of prokaryotic genomes.
  documentation:
  - note: null
    type:
    - User manual
    url: https://bioinf.shenwei.me/LexicMap/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://bioinf.shenwei.me/LexicMap/
  homepage_status: 0
  language:
  - Other
  lastUpdate: '2025-06-30T15:05:12.697639Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/shenwei356/LexicMap
  maturity: null
  name: LexicMap
  operatingSystem: []
  otherID: []
  owner: smeds
  publication:
  - doi: 10.1101/2024.08.30.610459
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic: []
  validated: 0
  version:
  - v0.7.0
  - v0.6.1
  - v0.6.0
  - v0.5.0
  - v0.4.0
  - v0.3.0
  - v0.2.0
  - v0.1.0
- accessibility: Open access
  additionDate: '2024-07-12T13:51:31.192159Z'
  biotoolsCURIE: biotools:fantasia
  biotoolsID: fantasia
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: FANTASIA (Functional ANnoTAtion based on embedding space SImilArity)
    is a pipeline for annotating GO terms in protein sequence files using GOPredSim
    with the protein language model ProtT5
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure alignment
        uri: http://edamontology.org/data_1481
      format: []
    note: null
    operation:
    - term: Phylogenetic analysis
      uri: http://edamontology.org/operation_0324
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    output: []
  homepage: https://github.com/MetazoaPhylogenomicsLab/FANTASIA
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-28T15:45:24.390300Z'
  license: null
  link: []
  maturity: null
  name: FANTASIA
  operatingSystem: []
  otherID: []
  owner: arojas
  publication:
  - doi: 10.1101/2024.02.28.582465
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1101/2024.02.14.580341
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 2.1.0
- accessibility: Open access
  additionDate: '2025-06-25T13:31:29.384393Z'
  biotoolsCURIE: biotools:fantasiav2
  biotoolsID: fantasiav2
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fmpercan@upo.es
    fundrefid: null
    gridid: null
    name: Francisco Miguel Pérez Canales
    note: Programmer
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Documentor
    - Maintainer
    - Support
    - Provider
    url: http://www.bioinfocb.es/
  - email: a.rojas.m@csic.es
    fundrefid: null
    gridid: null
    name: Ana M Rojas Mendoza
    note: Scientific concept and functionalities
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Contributor
    - Documentor
    - Support
    url: http://www.bioinfocb.es/
  - email: rosa.fernandez@ibe.upf-csic.es
    fundrefid: null
    gridid: null
    name: Rosa Fernandez
    note: Scientific concept and functionalities
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Contributor
    - Documentor
    url: https://www.metazomics.com/
  - email: fraruimot@alum.us.es
    fundrefid: null
    gridid: null
    name: Francisco J. Ruiz Mota
    note: Junior developer
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: http://www.bioinfocb.es/
  - email: gemma.martinez@ibe.upf-csic.es
    fundrefid: null
    gridid: null
    name: Gemma Martinez Redondo
    note: Contributed  as developer of the first version of FANTASIA V1
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    url: https://www.metazomics.com/
  description: FANTASIA (Functional ANnoTAtion based on embedding space SImilArity)
    is a pipeline for protein annotating via GO term transference using the embedding
    space. FANTASIA’s latest developments include additional protein language models
    and provide enhanced functionalities.
  documentation:
  - note: Full documetnation with user cases, benchmarking, and cluster implementations
    type:
    - General
    url: https://fantasia.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors:
    - frapercan
    type: group
  elixirCommunity:
  - 3D-BioInfo
  - Proteomics
  elixirNode:
  - Spain
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    output: []
  homepage: https://github.com/CBBIO/FANTASIA
  homepage_status: 0
  language:
  - Python
  - SQL
  lastUpdate: '2025-06-25T14:34:50.629045Z'
  license: MIT
  link:
  - note: Main repository contains documentation  from latest version
    type:
    - Repository
    url: https://github.com/CBBIO/FANTASIA
  - note: Main repository contains documentation  from linitial version based on Bioembeddings
      implementation.
    type:
    - Repository
    url: https://github.com/MetazoaPhylogenomicsLab/FANTASIA
  - note: Recommended tool for the Earth Biogenome Project
    type:
    - Software catalogue
    url: https://www.earthbiogenome.org/report-on-annotation-recommended-tools
  maturity: Mature
  name: FANTASIAV2
  operatingSystem:
  - Linux
  otherID: []
  owner: arojas
  publication:
  - doi: 10.1093/nargab/lqae078
    metadata:
      abstract: Protein language models have been tested and proved to be reliable
        when used on curated datasets but have not yet been applied to full proteomes.
        Accordingly, we tested how two different machine learning-based methods performed
        when decoding functional information from the proteomes of selected model
        organisms. We found that protein language models are more precise and informative
        than deep learning methods for all the species tested and across the three
        gene ontologies studied, and that they better recover functional information
        from transcriptomic experiments. The results obtained indicate that these
        language models are likely to be suitable for large-scale annotation and downstream
        analyses, and we recommend a guide for their use.
      authors:
      - name: Barrios-Nunez I.
      - name: Martinez-Redondo G.I.
      - name: Medina-Burgos P.
      - name: Cases I.
      - name: Fernandez R.
      - name: Rojas A.M.
      citationCount: 1
      date: '2024-09-01T00:00:00Z'
      journal: NAR Genomics and Bioinformatics
      title: Decoding functional proteome information in model organisms using protein
        language models
    note: null
    pmcid: null
    pmid: null
    type:
    - Usage
    version: null
  - doi: 10.1101/2024.02.28.582465
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Molecular evolution
    uri: http://edamontology.org/topic_3945
  - term: Natural language processing
    uri: http://edamontology.org/topic_0218
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  - term: Open science
    uri: http://edamontology.org/topic_4010
  validated: 0
  version:
  - 2.8.0
- accessibility: null
  additionDate: '2017-08-03T18:53:02Z'
  biotoolsCURIE: biotools:oncocnv
  biotoolsID: oncocnv
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: infos@oncodna.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.oncodna.com/
  - email: valentina.boeva@curie.fr
    fundrefid: null
    gridid: null
    name: Valentina Boeva
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Package to detect copy number changes in Deep Sequencing data developed
    by OncoDNA.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BoevaLab/ONCOCNV/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  homepage: https://oncocnv.curie.fr/
  homepage_status: 0
  language:
  - R
  - Perl
  lastUpdate: '2025-06-25T13:52:15.437225Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/oncocnv-5-4-detection-of-copy-number-changes-in-deep-sequencing-data.html
  maturity: null
  name: ONCOCNV
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: aminoacid
  publication:
  - doi: 10.1093/bioinformatics/btu436
    metadata: null
    note: null
    pmcid: PMC4253825
    pmid: '25016581'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Data quality management
    uri: http://edamontology.org/topic_3572
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2018-05-28T14:50:09Z'
  biotoolsCURIE: biotools:deepsig
  biotoolsID: deepsig
  collectionID:
  - Bologna Biocomputing Group
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it
  description: Prediction of secretory signal peptides in protein sequences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/deepsig
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/deepsig
    version: 1.2.5
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/deepsig
    version: 1.2.5
  editPermission:
    authors:
    - savo
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://busca.biocomp.unibo.it/deepsig/
  homepage_status: 0
  language:
  - Python
  - C++
  lastUpdate: '2025-06-19T11:55:09.017105Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: DeepSig
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btx818
    metadata:
      abstract: Motivation The identification of signal peptides in protein sequences
        is an important step toward protein localization and function characterization.
        Results Here, we present DeepSig, an improved approach for signal peptide
        detection and cleavage-site prediction based on deep learning methods. Comparative
        benchmarks performed on an updated independent dataset of proteins show that
        DeepSig is the current best performing method, scoring better than other available
        state-of-the-art approaches on both signal peptide detection and precise cleavage-site
        identification. Availability and implementation DeepSig is available as both
        standalone program and web server at https://deepsig.biocomp.unibo.it. All
        datasets used in this study can be obtained from the same website.
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 96
      date: '2018-05-15T00:00:00Z'
      journal: Bioinformatics
      title: 'DeepSig: Deep learning improves signal peptide detection in proteins'
    note: null
    pmcid: PMC5946842
    pmid: '29280997'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - 1.2.5
- accessibility: null
  additionDate: '2025-05-22T12:06:46.866961Z'
  biotoolsCURIE: biotools:breast_dense_tissue_segmentation
  biotoolsID: breast_dense_tissue_segmentation
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit: []
  description: The tool takes a digital mammogram and performs an automatic segmentation
    of the breast area and the dense tissue. After the mammogram segmentation, the
    tool returns a DICOM-SEG image with both the dense tissue and the breast tissue
    mask combined.
  documentation: []
  download: []
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  function:
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    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_3553
    output: []
  homepage: https://pubmed.ncbi.nlm.nih.gov/36010173/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-19T06:23:14.022250Z'
  license: Proprietary
  link: []
  maturity: null
  name: Breast Dense Tissue Segmentation
  operatingSystem: []
  otherID: []
  owner: dsilveira
  publication:
  - doi: 10.3390/diagnostics12081822
    metadata:
      abstract: Breast density assessed from digital mammograms is a known biomarker
        related to a higher risk of developing breast cancer. Supervised learning
        algorithms have been implemented to determine this. However, the performance
        of these algorithms depends on the quality of the ground-truth information,
        which expert readers usually provide. These expert labels are noisy approximations
        to the ground truth, as there is both intra- and inter-observer variability
        among them. Thus, it is crucial to provide a reliable method to measure breast
        density from mammograms. This paper presents a fully automated method based
        on deep learning to estimate breast density, including breast detection, pectoral
        muscle exclusion, and dense tissue segmentation. We propose a novel confusion
        matrix (CM)—YNet model for the segmentation step. This architecture includes
        networks to model each radiologist’s noisy label and gives the estimated ground-truth
        segmentation as well as two parameters that allow interaction with a threshold-based
        labeling tool. A multi-center study involving 1785 women whose “for presentation”
        mammograms were obtained from 11 different medical facilities was performed.
        A total of 2496 mammograms were used as the training corpus, and 844 formed
        the testing corpus. Additionally, we included a totally independent dataset
        from a different center, composed of 381 women with one image per patient.
        Each mammogram was labeled independently by two expert radiologists using
        a threshold-based tool. The implemented CM-Ynet model achieved the highest
        DICE score averaged over both test datasets (0.82 ± 0.14) when compared to
        the closest dense-tissue segmentation assessment from both radiologists. The
        level of concordance between the two radiologists showed a DICE score of (0.76
        ± 0.17). An automatic breast density estimator based on deep learning exhibited
        higher performance when compared with two experienced radiologists. This suggests
        that modeling each radiologist’s label allows for better estimation of the
        unknown ground-truth segmentation. The advantage of the proposed model is
        that it also provides the threshold parameters that enable user interaction
        with a threshold-based tool.
      authors:
      - name: Larroza A.
      - name: Perez-Benito F.J.
      - name: Perez-Cortes J.-C.
      - name: Roman M.
      - name: Pollan M.
      - name: Perez-Gomez B.
      - name: Salas-Trejo D.
      - name: Casals M.
      - name: Llobet R.
      citationCount: 5
      date: '2022-08-01T00:00:00Z'
      journal: Diagnostics
      title: 'Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based
        and Mask-Based Approach'
    note: null
    pmcid: PMC9406546
    pmid: '36010173'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  validated: 0
  version:
  - v1.0.6
- accessibility: null
  additionDate: '2025-05-22T14:58:20.772120Z'
  biotoolsCURIE: biotools:extended_a_priori_probability_tool
  biotoolsID: extended_a_priori_probability_tool
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit: []
  description: The tool provides a semi-supervised metric (EAPP) for binary classification
    tasks that considers not only the a priori probability but also some possible
    bias present in the dataset, as well as other features that could provide a relatively
    trivial separability of the target classes. Therefore, it allows for evaluating
    the ease or complexity of the task or bias of the data beyond the well-established
    baseline for any binary classification.
  documentation: []
  download: []
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data analysis
      uri: http://edamontology.org/operation_2945
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Image analysis
      uri: http://edamontology.org/operation_3443
    output: []
  homepage: https://ieeexplore.ieee.org/document/9950248
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-19T06:22:40.072300Z'
  license: Proprietary
  link: []
  maturity: null
  name: EAPP - Extended a Priori Probability tool
  operatingSystem: []
  otherID: []
  owner: dsilveira
  publication:
  - doi: 10.1109/ACCESS.2022.3221936
    metadata:
      abstract: The a priori probability of a dataset is usually used as a baseline
        for comparing a particular algorithm's accuracy in a given binary classification
        task. ZeroR is the simplest algorithm for this, predicting the majority class
        for all examples. However, this is an extremely simple approach that has no
        predictive power and does not describe other dataset features that could lead
        to a more demanding baseline. In this paper, we present the Extended A Priori
        Probability (EAPP), a novel semi-supervised baseline metric for binary classification
        tasks that considers not only the a priori probability but also some possible
        bias present in the dataset as well as other features that could provide a
        relatively trivial separability of the target classes. The approach is based
        on the area under the ROC curve (AUC ROC), known to be quite insensitive to
        class imbalance. The procedure involves multiobjective feature extraction
        and a clustering stage in the input space with autoencoders and a subsequent
        combinatory weighted assignation from clusters to classes depending on the
        distance to nearest clusters for each class. Class labels are then assigned
        to establish the combination that maximizes AUC ROC for each number of clusters
        considered. To avoid overfit in the combined feature extraction and clustering
        method, a cross-validation scheme is performed in each case. EAPP is defined
        for different numbers of clusters, starting from the inverse of the minority
        class proportion, which is useful for a fair comparison among diversely imbalanced
        datasets. A high EAPP usually relates to an easy binary classification task,
        but it also may be due to a significant coarse-grained bias in the dataset,
        when the task is previously known to be difficult. This metric represents
        a baseline beyond the a priori probability to assess the actual capabilities
        of binary classification models.
      authors:
      - name: Castello V.O.
      - name: Perez-Benito F.J.
      - name: Catala O.D.T.
      - name: Igual I.S.
      - name: Llobet R.
      - name: Perez-Cortes J.-C.
      citationCount: 0
      date: '2022-01-01T00:00:00Z'
      journal: IEEE Access
      title: 'Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine
        Learning Binary Classification Tasks'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 0
  version:
  - v1.0.1
- accessibility: Open access
  additionDate: '2021-05-27T09:02:10Z'
  biotoolsCURIE: biotools:qiime2
  biotoolsID: qiime2
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: greg.caporaso@nau.edu
    fundrefid: null
    gridid: null
    name: Greg Caporaso
    note: null
    orcidid: https://orcid.org/0000-0002-8865-1670
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: https://cap-lab.bio
  description: QIIME 2 is an AI-ready microbiome multi-omics data science platform
    that is trusted, free, open source, extensible, and community developed and supported
    bioinformatics.
  documentation:
  - note: User documentation for amplicon analysis, oriented to new users.
    type:
    - General
    url: https://amplicon-docs.qiime2.org
  - note: User documentation for metagenome analysis, oriented to new users.
    type:
    - Installation instructions
    url: https://moshpit.qiime2.org
  - note: User documentation, oriented toward power users.
    type:
    - User manual
    url: https://use.qiime2.org
  - note: Developer documentation, including a plugin development tutorial.
    type:
    - API documentation
    url: https://develop.qiime2.org
  - note: 'The QIIME 2 Library Stacks: a resource for discovering relevant documentation.'
    type:
    - Other
    url: https://library.qiime2.org/books
  download: []
  editPermission:
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    type: private
  elixirCommunity:
  - Galaxy
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Demultiplexing
      uri: http://edamontology.org/operation_3933
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output: []
  homepage: https://qiime2.org
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-18T21:00:46.185472Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/qiime2
  - note: null
    type:
    - Software catalogue
    url: https://library.qiime2.org
  - note: null
    type:
    - Discussion forum
    url: https://forum.qiime2.org
  - note: Site for viewing and interacting with QIIME 2 results.
    type:
    - Other
    url: https://view.qiime2.org
  maturity: Mature
  name: QIIME 2
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: gregcaporaso
  publication:
  - doi: 10.1038/s41587-019-0209-9
    metadata:
      abstract: ''
      authors:
      - name: Bolyen E.
      - name: Rideout J.R.
      - name: Dillon M.R.
      - name: Bokulich N.A.
      - name: Abnet C.C.
      - name: Al-Ghalith G.A.
      - name: Alexander H.
      - name: Alm E.J.
      - name: Arumugam M.
      - name: Asnicar F.
      - name: Bai Y.
      - name: Bisanz J.E.
      - name: Bittinger K.
      - name: Brejnrod A.
      - name: Brislawn C.J.
      - name: Brown C.T.
      - name: Callahan B.J.
      - name: Caraballo-Rodriguez A.M.
      - name: Chase J.
      - name: Cope E.K.
      - name: Da Silva R.
      - name: Diener C.
      - name: Dorrestein P.C.
      - name: Douglas G.M.
      - name: Durall D.M.
      - name: Duvallet C.
      - name: Edwardson C.F.
      - name: Ernst M.
      - name: Estaki M.
      - name: Fouquier J.
      - name: Gauglitz J.M.
      - name: Gibbons S.M.
      - name: Gibson D.L.
      - name: Gonzalez A.
      - name: Gorlick K.
      - name: Guo J.
      - name: Hillmann B.
      - name: Holmes S.
      - name: Holste H.
      - name: Huttenhower C.
      - name: Huttley G.A.
      - name: Janssen S.
      - name: Jarmusch A.K.
      - name: Jiang L.
      - name: Kaehler B.D.
      - name: Kang K.B.
      - name: Keefe C.R.
      - name: Keim P.
      - name: Kelley S.T.
      - name: Knights D.
      - name: Koester I.
      - name: Kosciolek T.
      - name: Kreps J.
      - name: Langille M.G.I.
      - name: Lee J.
      - name: Ley R.
      - name: Liu Y.-X.
      - name: Loftfield E.
      - name: Lozupone C.
      - name: Maher M.
      - name: Marotz C.
      - name: Martin B.D.
      - name: McDonald D.
      - name: McIver L.J.
      - name: Melnik A.V.
      - name: Metcalf J.L.
      - name: Morgan S.C.
      - name: Morton J.T.
      - name: Naimey A.T.
      - name: Navas-Molina J.A.
      - name: Nothias L.F.
      - name: Orchanian S.B.
      - name: Pearson T.
      - name: Peoples S.L.
      - name: Petras D.
      - name: Preuss M.L.
      - name: Pruesse E.
      - name: Rasmussen L.B.
      - name: Rivers A.
      - name: Robeson M.S.
      - name: Rosenthal P.
      - name: Segata N.
      - name: Shaffer M.
      - name: Shiffer A.
      - name: Sinha R.
      - name: Song S.J.
      - name: Spear J.R.
      - name: Swafford A.D.
      - name: Thompson L.R.
      - name: Torres P.J.
      - name: Trinh P.
      - name: Tripathi A.
      - name: Turnbaugh P.J.
      - name: Ul-Hasan S.
      - name: van der Hooft J.J.J.
      - name: Vargas F.
      - name: Vazquez-Baeza Y.
      - name: Vogtmann E.
      - name: von Hippel M.
      - name: Walters W.
      - name: Wan Y.
      - name: Wang M.
      - name: Warren J.
      - name: Weber K.C.
      - name: Williamson C.H.D.
      - name: Willis A.D.
      - name: Xu Z.Z.
      - name: Zaneveld J.R.
      - name: Zhang Y.
      - name: Zhu Q.
      - name: Knight R.
      - name: Caporaso J.G.
      citationCount: 14289
      date: '2019-08-01T00:00:00Z'
      journal: Nature Biotechnology
      title: Reproducible, interactive, scalable and extensible microbiome data science
        using QIIME 2
    note: null
    pmcid: PMC7015180
    pmid: '31341288'
    type:
    - Primary
    version: '2.0'
  relation:
  - biotoolsID: qiime
    type: isNewVersionOf
  toolType:
  - Suite
  - Command-line tool
  - Library
  topic:
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version:
  - '2.0'
- accessibility: Open access (with restrictions)
  additionDate: '2021-07-06T20:27:27Z'
  biotoolsCURIE: biotools:nanoplot
  biotoolsID: nanoplot
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Wouter De Coster
    note: null
    orcidid: https://orcid.org/0000-0002-5248-8197
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://gigabaseorgigabyte.wordpress.com/
  description: NanoPlot is a tool with various visualizations of sequencing data in
    bam, cram, fastq, fasta or platform-specific TSV summaries, mainly intended for
    long-read sequencing from Oxford Nanopore Technologies and Pacific Biosciences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/wdecoster/NanoPlot
  download:
  - note: null
    type: Command-line specification
    url: https://anaconda.org/bioconda/nanoplot
    version: null
  - note: null
    type: Command-line specification
    url: https://pypi.org/project/NanoPlot/
    version: null
  editPermission:
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    type: public
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
      - term: nucleotide
        uri: http://edamontology.org/format_1207
    note: null
    operation:
    - term: Scatter plot plotting
      uri: http://edamontology.org/operation_2940
    - term: Box-Whisker plot plotting
      uri: http://edamontology.org/operation_2943
    output: []
  homepage: https://github.com/wdecoster/NanoPlot
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-18T12:31:48.762608Z'
  license: GPL-3.0
  link:
  - note: Issue tracker and most up to date software version
    type:
    - Repository
    url: https://github.com/wdecoster/NanoPlot
  - note: Web service with more limited options compared to the command line tool
    type:
    - Service
    url: http://nanoplot.bioinf.be/
  maturity: Mature
  name: NanoPlot
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: wdecoster
  publication:
  - doi: 10.1093/bioinformatics/bty149
    metadata:
      abstract: 'Summary: Here we describe NanoPack, a set of tools developed for
        visualization and processing of long-read sequencing data from Oxford Nanopore
        Technologies and Pacific Biosciences. Availability and implementation: The
        NanoPack tools are written in Python3 and released under the GNU GPL3.0 License.
        The source code can be found at https://github.com/wdecoster/nanopack, together
        with links to separate scripts and their documentation. The scripts are compatible
        with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available
        as a graphical user interface, a web service at http://nanoplot.bioinf.be
        and command line tools.'
      authors:
      - name: De Coster W.
      - name: D'Hert S.
      - name: Schultz D.T.
      - name: Cruts M.
      - name: Van Broeckhoven C.
      citationCount: 1840
      date: '2018-08-01T00:00:00Z'
      journal: Bioinformatics
      title: 'NanoPack: Visualizing and processing long-read sequencing data'
    note: null
    pmcid: PMC6061794
    pmid: '29547981'
    type:
    - Method
    version: null
  relation:
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    type: includedIn
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - v.1.42.0
- accessibility: Open access
  additionDate: '2019-09-13T12:51:16Z'
  biotoolsCURIE: biotools:kraken2
  biotoolsID: kraken2
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Derrick E. Wood
    note: null
    orcidid: http://orcid.org/0000-0002-7429-1854
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jennifer Lu
    note: null
    orcidid: http://orcid.org/0000-0001-9167-2002
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: langmea@cs.jhu.edu
    fundrefid: null
    gridid: null
    name: Ben Langmead
    note: null
    orcidid: http://orcid.org/0000-0003-2437-1976
    rorid: null
    typeEntity: Person
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    url: null
  description: 'Kraken 2 is the newest version of Kraken, a taxonomic classification
    system using exact k-mer matches to achieve high accuracy and fast classification
    speeds. This classifier matches each k-mer within a query sequence to the lowest
    common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments
    inform the classification algorithm.

    Any assumption that Kraken’s raw read assignments can be directly translated into
    species or strain-level abundance estimates is flawed. Bracken (Bayesian Reestimation
    of Abundance after Classification with KrakEN), estimates species abundances in
    metagenomics samples by probabilistically re-distributing reads in the taxonomic
    tree. (Lu, Jennifer et al. “Bracken: estimating species abundance in metagenomics
    data.”)'
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/DerrickWood/kraken2/wiki/Manual
  - note: Links to multiple Kraken 2 and bracken databases and indexes
    type:
    - User manual
    url: https://benlangmead.github.io/aws-indexes/k2
  download:
  - note: null
    type: Source code
    url: https://github.com/DerrickWood/kraken2/archive/v2.0.8-beta.tar.gz
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  editPermission:
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    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: '`kraken2 --db <kraken2_database> <input.fastq>`'
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://ccb.jhu.edu/software/kraken2/
  homepage_status: 0
  language:
  - C++
  - Perl
  lastUpdate: '2025-06-18T12:24:26.657325Z'
  license: MIT
  link:
  - note: null
    type:
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    url: https://github.com/DerrickWood/kraken2
  - note: null
    type:
    - Issue tracker
    url: https://github.com/DerrickWood/kraken2/issues
  maturity: null
  name: kraken2
  operatingSystem: []
  otherID: []
  owner: anand-anshu
  publication:
  - doi: 10.1101/762302
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: kraken
    type: isNewVersionOf
  - biotoolsID: bracken
    type: usedBy
  toolType:
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  topic:
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  validated: 1
  version:
  - 2.0.8-beta
next: ?page=2
previous: null
