count: 8851
list:
- accessibility: Open access
  additionDate: '2019-09-28T12:06:10Z'
  biotoolsCURIE: biotools:AMICI
  biotoolsID: AMICI
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: fabian_froehlich@hms.harvard.edu
    fundrefid: null
    gridid: null
    name: Fabian Fröhlich
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    - Developer
    url: null
  - email: jan.hasenauer@uni-bonn.de
    fundrefid: null
    gridid: null
    name: Jan Hasenauer
    note: null
    orcidid: https://orcid.org/0000-0002-4935-3312
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: daniel.weindl@uni-bonn.de
    fundrefid: null
    gridid: null
    name: Daniel Weindl
    note: null
    orcidid: https://orcid.org/0000-0001-9963-6057
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    - Developer
    url: null
  description: 'AMICI provides a multi-language (Python, C++, Matlab) interface for
    the SUNDIALS solvers CVODES (for ordinary differential equations) and IDAS (for
    algebraic differential equations). AMICI allows the user to read differential
    equation models specified as SBML or PySB and automatically compiles such models
    into .mex simulation files (Matlab), C++ executables or Python modules.


    Beyond forward integration, the compiled simulation file also allows for forward
    sensitivity analysis, steady state sensitivity analysis and adjoint sensitivity
    analysis for likelihood-based output functions.


    The interface was designed to provide routines for efficient gradient computation
    in parameter estimation of biochemical reaction models but it is also applicable
    to a wider range of differential equation constrained optimization problems.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://amici.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  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: PEtab
        uri: http://edamontology.org/format_4015
    note: null
    operation:
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output:
    - data:
        term: Trajectory data
        uri: http://edamontology.org/data_3870
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/AMICI-dev/AMICI
  homepage_status: 0
  language:
  - MATLAB
  - C++
  - Python
  lastUpdate: '2025-05-08T07:28:52.817175Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/AMICI-dev/AMICI/issues
  maturity: Mature
  name: AMICI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: dweindl
  publication:
  - doi: 10.1093/bioinformatics/btab227
    metadata:
      abstract: Ordinary differential equation models facilitate the understanding
        of cellular signal transduction and other biological processes. However, for
        large and comprehensive models, the computational cost of simulating or calibrating
        can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB
        that provides efficient simulation and sensitivity analysis routines tailored
        for scalable, gradient-based parameter estimation and uncertainty quantification.
      authors:
      - name: Frohlich F.
      - name: Weindl D.
      - name: Schalte Y.
      - name: Pathirana D.
      - name: Paszkowski L.
      - name: Lines G.T.
      - name: Stapor P.
      - name: Hasenauer J.
      citationCount: 37
      date: '2021-10-15T00:00:00Z'
      journal: Bioinformatics
      title: 'AMICI: high-performance sensitivity analysis for large ordinary differential
        equation models'
    note: null
    pmcid: PMC8545331
    pmid: '33821950'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-05-07T12:50:07.458170Z'
  biotoolsCURIE: biotools:galaxy_ecology
  biotoolsID: galaxy_ecology
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: yvan.le-bras@mnhn.fr
    fundrefid: null
    gridid: null
    name: Yvan Le Bras
    note: null
    orcidid: https://orcid.org/0000-0002-8504-068X
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: grid.410350.3
    name: Muséum national d'Histoire naturelle
    note: null
    orcidid: null
    rorid: 03wkt5x30
    typeEntity: Institute
    typeRole: []
    url: null
  description: Galaxy Ecology is mainly developed by the French Data Terra research
    infrastructure Biodiversity data hub (PNDB). Its dedication is to provide tools
    for biodiversity data management and analysis for the Galaxy platform. Tools are
    useable through dedicated Galaxy instances hosted by Galaxy Europe (ecology.usegalaxy.eu)
    and Galaxy France (ecology.usegalaxy.fr).
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/galaxyecology/tools-ecology
  homepage_status: 0
  language: []
  lastUpdate: '2025-05-07T12:50:07.461506Z'
  license: MIT
  link:
  - note: European Galaxy Ecology instance
    type:
    - Galaxy service
    url: https://ecology.usegalaxy.eu
  - note: French Galaxy Ecology instance
    type:
    - Galaxy service
    url: https://ecology.usegalaxy.fr
  maturity: Mature
  name: Galaxy Ecology
  operatingSystem: []
  otherID: []
  owner: ylebras
  publication:
  - doi: 10.1093/gigascience/giae122
    metadata:
      abstract: Numerous conceptual frameworks exist for best practices in research
        data and analysis (e.g., Open Science and FAIR principles). In practice, there
        is a need for further progress to improve transparency, reproducibility, and
        confidence in ecology. Here, we propose a practical and operational framework
        for researchers and experts in ecology to achieve best practices for building
        analytical procedures from individual research projects to production-level
        analytical pipelines. We introduce the concept of atomization to identify
        analytical steps that support generalization by allowing us to go beyond single
        analyses. The term atomization is employed to convey the idea of single analytical
        steps as "atoms"composing an analytical procedure. When generalized, "atoms"can
        be used in more than a single case analysis. These guidelines were established
        during the development of the Galaxy-Ecology initiative, a web platform dedicated
        to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way
        to reach higher levels of reproducibility in ecological sciences by increasing
        the accessibility and reusability of analytical workflows once atomized and
        generalized.
      authors:
      - name: Royaux C.
      - name: Mihoub J.-B.
      - name: Josse M.
      - name: Pelletier D.
      - name: Norvez O.
      - name: Reecht Y.
      - name: Fouilloux A.
      - name: Rasche H.
      - name: Hiltemann S.
      - name: Batut B.
      - name: Marc E.
      - name: Seguineau P.
      - name: Masse G.
      - name: Amosse A.
      - name: Bissery C.
      - name: Lorrilliere R.
      - name: Martin A.
      - name: Bas Y.
      - name: Virgoulay T.
      - name: Chambon V.
      - name: Arnaud E.
      - name: Michon E.
      - name: Urfer C.
      - name: Trigodet E.
      - name: Delannoy M.
      - name: Lois G.
      - name: Julliard R.
      - name: Gruning B.
      - name: Le Bras Y.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
      journal: GigaScience
      title: 'Guidance framework to apply best practices in ecological data analysis:
        lessons learned from building Galaxy-Ecology'
    note: null
    pmcid: null
    pmid: null
    type:
    - Review
    version: null
  relation:
  - biotoolsID: galaxy
    type: uses
  - biotoolsID: tiaas
    type: uses
  - biotoolsID: Galaxy_Tools
    type: uses
  - biotoolsID: galaxy_france
    type: includedIn
  toolType: []
  topic:
  - term: Environmental sciences
    uri: http://edamontology.org/topic_3855
  - term: Ecology
    uri: http://edamontology.org/topic_0610
  validated: 0
  version:
  - 1.0.0
- accessibility: Open access
  additionDate: '2016-07-12T10:54:05Z'
  biotoolsCURIE: biotools:pia
  biotoolsID: pia
  collectionID:
  - KNIME
  - de.NBI
  - Proteomics
  - BioInfra.Prot
  - CUBiMed.RUB
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: julian.uszkoreit@rub.de
    fundrefid: null
    gridid: null
    name: Julian Uszkoreit
    note: null
    orcidid: http://orcid.org/0000-0001-7522-4007
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: cubimed@rub.de
    fundrefid: null
    gridid: null
    name: CUBiMed.RUB
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: The main focus lays on the integrated inference algorithms, concluding
    the proteins from a set of identified spectra. But it also allows you to integrate
    results of various search engines, inspect your peptide spectrum matches, calculate
    FDR values across different results and visualize the correspondence between PSMs,
    peptides and proteins.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/medbioinf/pia/wiki
  download:
  - note: null
    type: Source code
    url: https://github.com/mpc-bioinformatics/pia
    version: null
  - note: null
    type: Software package
    url: http://bioconda.github.io/recipes/pia/README.html
    version: null
  - note: null
    type: Binaries
    url: https://github.com/mpc-bioinformatics/pia/releases
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/julianusz/pia
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Proteomics
  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry spectra
        uri: http://edamontology.org/data_0943
      format:
      - term: Mascot .dat file
        uri: http://edamontology.org/format_3713
      - term: mzIdentML
        uri: http://edamontology.org/format_3247
      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: PRIDE XML
        uri: http://edamontology.org/format_3684
      - term: X!Tandem XML
        uri: http://edamontology.org/format_3711
      - term: MSF
        uri: http://edamontology.org/format_3702
      - term: mzTab
        uri: http://edamontology.org/format_3681
    note: PIA allows you to inspect the results of common proteomics spectrum identification
      search engines, combine them seamlessly and conduct statistical analyses. The
      main focus of PIA lays on the integrated inference algorithms, i.e. concluding
      the proteins from a set of identified spectra. But it also allows you to inspect
      your peptide spectrum matches, calculate FDR values across different search
      engine results and visualize the correspondence between PSMs, peptides and proteins.
      Search engine results in several formats peptide spectrum matches (PSMs) and
      peptides Inferred Proteins
    operation:
    - term: Protein identification
      uri: http://edamontology.org/operation_3767
    - term: Target-Decoy
      uri: http://edamontology.org/operation_3649
    output:
    - data:
        term: Peptide identification
        uri: http://edamontology.org/data_0945
      format:
      - term: Sequence feature table format (text)
        uri: http://edamontology.org/format_2206
      - term: KNIME datatable format
        uri: http://edamontology.org/format_3765
      - term: idXML
        uri: http://edamontology.org/format_3764
      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: mzTab
        uri: http://edamontology.org/format_3681
      - term: mzIdentML
        uri: http://edamontology.org/format_3247
    - data:
        term: Protein identifier
        uri: http://edamontology.org/data_0989
      format:
      - term: Sequence feature table format (text)
        uri: http://edamontology.org/format_2206
      - term: KNIME datatable format
        uri: http://edamontology.org/format_3765
      - term: idXML
        uri: http://edamontology.org/format_3764
      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: mzTab
        uri: http://edamontology.org/format_3681
      - term: mzIdentML
        uri: http://edamontology.org/format_3247
  homepage: https://github.com/medbioinf/pia
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2025-05-02T13:15:26.717477Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/medbioinf/pia
  maturity: Mature
  name: PIA - Protein Inference Algorithms
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: julianu
  publication:
  - doi: 10.1021/acs.jproteome.5b00121
    metadata:
      abstract: Protein inference connects the peptide spectrum matches (PSMs) obtained
        from database search engines back to proteins, which are typically at the
        heart of most proteomics studies. Different search engines yield different
        PSMs and thus different protein lists. Analysis of results from one or multiple
        search engines is often hampered by different data exchange formats and lack
        of convenient and intuitive user interfaces. We present PIA, a flexible software
        suite for combining PSMs from different search engine runs and turning these
        into consistent results. PIA can be integrated into proteomics data analysis
        workflows in several ways. A user-friendly graphical user interface can be
        run either locally or (e.g., for larger core facilities) from a central server.
        For automated data processing, stand-alone tools are available. PIA implements
        several established protein inference algorithms and can combine results from
        different search engines seamlessly. On several benchmark data sets, we show
        that PIA can identify a larger number of proteins at the same protein FDR
        when compared to that using inference based on a single search engine. PIA
        supports the majority of established search engines and data in the mzIdentML
        standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.
      authors:
      - name: Uszkoreit J.
      - name: Maerkens A.
      - name: Perez-Riverol Y.
      - name: Meyer H.E.
      - name: Marcus K.
      - name: Stephan C.
      - name: Kohlbacher O.
      - name: Eisenacher M.
      citationCount: 57
      date: '2015-07-02T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface'
    note: null
    pmcid: null
    pmid: '25938255'
    type:
    - Primary
    version: null
  - doi: 10.1021/acs.jproteome.8b00723
    metadata:
      abstract: Proteomics using LC-MS/MS has become one of the main methods to analyze
        the proteins in biological samples in high-throughput. But the existing mass-spectrometry
        instruments are still limited with respect to resolution and measurable mass
        ranges, which is one of the main reasons why shotgun proteomics is the major
        approach. Here proteins are digested, which leads to the identification and
        quantification of peptides instead. While often neglected, the important step
        of protein inference needs to be conducted to infer from the identified peptides
        to the actual proteins in the original sample. In this work, we highlight
        some of the previously published and newly added features of the tool PIA
        - Protein Inference Algorithms, which helps the user with the protein inference
        of measured samples. We also highlight the importance of the usage of PSI
        standard file formats, as PIA is the only current software supporting all
        available standards used for spectrum identification and protein inference.
        Additionally, we briefly describe the benefits of working with workflow environments
        for proteomics analyses and show the new features of the PIA nodes for the
        KNIME Analytics Platform. Finally, we benchmark PIA against a recently published
        data set for isoform detection. PIA is open source and available for download
        on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the
        community extensions inside the KNIME analytics platform.
      authors:
      - name: Uszkoreit J.
      - name: Perez-Riverol Y.
      - name: Eggers B.
      - name: Marcus K.
      - name: Eisenacher M.
      citationCount: 30
      date: '2019-02-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: Protein Inference Using PIA Workflows and PSI Standard File Formats
    note: null
    pmcid: null
    pmid: '30474983'
    type: []
    version: null
  relation:
  - biotoolsID: knime
    type: uses
  toolType:
  - Command-line tool
  - Library
  - Desktop application
  - Workflow
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Protein variants
    uri: http://edamontology.org/topic_3120
  validated: 1
  version:
  - '1.5'
- accessibility: Open access (with restrictions)
  additionDate: '2025-04-30T10:28:37.002350Z'
  biotoolsCURIE: biotools:eucaim_wizard_tool
  biotoolsID: eucaim_wizard_tool
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit: []
  description: The EUCAIM Wizard Tool performs an analysis of data re-identification
    risks of imaging and clinical data that follow the EUCAIM CDM. It includes and
    uses an EUCAIM specific configuration of the ARX Data Anonymization Tool (biotools:arx)  for
    further anonymization, by supporting a wide variety of privacy and risk models
    as well methods for analyzing the usefulness of output data.
  documentation: []
  download: []
  editPermission:
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    type: private
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Anonymisation
      uri: http://edamontology.org/operation_3283
    output: []
  homepage: https://harbor.eucaim.cancerimage.eu/harbor/
  homepage_status: 0
  language: []
  lastUpdate: '2025-05-01T12:43:46.544425Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/goharbor/harbor
  maturity: null
  name: EUCAIM Wizard Tool
  operatingSystem: []
  otherID: []
  owner: vkalokyri
  publication: []
  relation:
  - biotoolsID: arx
    type: includes
  - biotoolsID: arx
    type: uses
  toolType: []
  topic: []
  validated: 0
  version:
  - beta1.0
- accessibility: Open access
  additionDate: '2019-08-09T13:16:33Z'
  biotoolsCURIE: biotools:pyOpenMS
  biotoolsID: pyOpenMS
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hannes.rost@utoronto.ca
    fundrefid: null
    gridid: null
    name: Hannes L. Rost
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Python library for mass spectrometry, specifically for the analysis
    of proteomics and metabolomics data.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pyopenms.readthedocs.io
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/OpenMS/pyopenms-extra/blob/master/docs/source/index.rst
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-04-30T11:39:29.549782Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/OpenMS/pyopenms-extra
  - note: null
    type:
    - Issue tracker
    url: https://github.com/OpenMS/pyopenms-extra/issues
  maturity: Mature
  name: pyOpenMS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: samwein
  publication:
  - doi: 10.7287/PEERJ.PREPRINTS.27736
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-06-18T11:06:01Z'
  biotoolsCURIE: biotools:OpenSWATH
  biotoolsID: OpenSWATH
  collectionID:
  - ms-utils
  - Proteomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hroest@stanford.edu
    fundrefid: null
    gridid: null
    name: Hannes Röst
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: gr2578@cumc.columbia.edu
    fundrefid: null
    gridid: null
    name: George Rosenberger
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: OpenSWATH is a proteomics software that allows analysis of LC-MS/MS
    DIA (data independent acquisition) and implemented as part of OpenMS.
  documentation:
  - note: null
    type:
    - User manual
    url: https://buildmedia.readthedocs.org/media/pdf/openswath/latest/openswath.pdf
  download:
  - note: null
    type: Software package
    url: http://www.openswath.org/en/latest/docs/binaries.html
    version: null
  - note: null
    type: Source code
    url: http://www.openswath.org/en/latest/docs/sources.html
    version: null
  editPermission:
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    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Experimental measurement
        uri: http://edamontology.org/data_3108
      format:
      - term: WIFF format
        uri: http://edamontology.org/format_3710
      - term: Mass spectrometry data format
        uri: http://edamontology.org/format_3245
    note: null
    operation:
    - term: Formatting
      uri: http://edamontology.org/operation_0335
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: SQLite format
        uri: http://edamontology.org/format_3621
      - term: mzXML
        uri: http://edamontology.org/format_3654
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: SQLite format
        uri: http://edamontology.org/format_3621
      - term: mzXML
        uri: http://edamontology.org/format_3654
    - data:
        term: Mass spectrometry spectra
        uri: http://edamontology.org/data_0943
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: TraML
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    note: null
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    - term: Chromatogram visualisation
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    - term: Target-Decoy
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    output:
    - data:
        term: Peptide mass fingerprint
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      format:
      - term: featureXML
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      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: SQLite format
        uri: http://edamontology.org/format_3621
  homepage: http://www.openswath.org
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-04-30T11:39:29.102190Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/OpenSWATH/docker
  - note: null
    type:
    - Repository
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    - Mailing list
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  name: OpenSWATH
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  otherID: []
  owner: samwein
  publication:
  - doi: null
    metadata:
      abstract: ''
      authors:
      - name: Rost H.L.
      - name: Rosenberger G.
      - name: Navarro P.
      - name: Gillet L.
      - name: Miladinovia S.M.
      - name: Schubert O.T.
      - name: Wolski W.
      - name: Collins B.C.
      - name: Malmstrom J.
      - name: Malmstrom L.
      - name: Aebersold R.
      citationCount: 409
      date: '2014-01-01T00:00:00Z'
      journal: Nature Biotechnology
      title: OpenSWATH enables automated, targeted analysis of data-independent acquisition
        MS data
    note: null
    pmcid: null
    pmid: '24727770'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - OpenMS 2.4.0
- accessibility: Open access
  additionDate: '2025-04-29T17:07:56.642694Z'
  biotoolsCURIE: biotools:biosurfdb
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  cost: Free of charge
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    fundrefid: null
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    name: Jorge S. Oliveira
    note: null
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    rorid: null
    typeEntity: Person
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    - Primary contact
    url: https://web.tecnico.ulisboa.pt/jorge.oliveira/
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    name: Ana Teresa Freitas
    note: null
    orcidid: http://orcid.org/0000-0003-4638-2879
    rorid: null
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    fundrefid: null
    gridid: null
    name: Ana Tereza Ribeiro de Vasconcelos
    note: null
    orcidid: http://orcid.org/0000-0002-4632-2086
    rorid: null
    typeEntity: Person
    typeRole:
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    note: null
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  - email: gpo@biodata.pt
    fundrefid: null
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    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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  description: BioSurfDB is a online database for biodegradation and biosurfactants.
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  download: []
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  function:
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    input:
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        term: DNA sequence
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      format: []
    - data:
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      format: []
    note: null
    operation:
    - term: Local alignment
      uri: http://edamontology.org/operation_0495
    output:
    - data:
        term: Nucleic acid sequence alignment
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      format: []
    - data:
        term: Hybrid sequence alignment
        uri: http://edamontology.org/data_1385
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    input: []
    note: null
    operation:
    - term: Sequence database search (by property)
      uri: http://edamontology.org/operation_0349
    output:
    - data:
        term: Gene symbol
        uri: http://edamontology.org/data_1026
      format: []
    - data:
        term: Gene ID (NCBI)
        uri: http://edamontology.org/data_1027
      format: []
    - data:
        term: Gene name
        uri: http://edamontology.org/data_2299
      format: []
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format: []
  homepage: https://www.biosurfdb.org
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-29T17:35:41.655075Z'
  license: null
  link: []
  maturity: Mature
  name: BioSurfDB
  operatingSystem: []
  otherID: []
  owner: ELIXIR-PT
  publication:
  - doi: 10.1093/database/bav033
    metadata:
      abstract: 'Crude oil extraction, transportation and use provoke the contamination
        of countless ecosystems. Therefore, bioremediation through surfactants mobilization
        or biodegradation is an important subject, both economically and environmentally.
        Bioremediation research had a great boost with the recent advances in Metagenomics,
        as it enabled the sequencing of uncultured microorganisms providing new insights
        on surfactant-producing and/or oil-degrading bacteria. Many research studies
        are making available genomic data from unknown organisms obtained from metagenomics
        analysis of oil-contaminated environmental samples. These new datasets are
        presently demanding the development of new tools and data repositories tailored
        for the biological analysis in a context of bioremediation data analysis.
        This work presents BioSurfDB, www.biosurfdb.org, a curated relational information
        system integrating data from: (i) metagenomes; (ii) organisms; (iii) biodegradation
        relevant genes; proteins and their metabolic pathways; (iv) bioremediation
        experiments results, with specific pollutants treatment efficiencies by surfactant
        producing organisms; and (v) a biosurfactant-curated list, grouped by producing
        organism, surfactant name, class and reference. The main goal of this repository
        is to gather information on the characterization of biological compounds and
        mechanisms involved in biosurfactant production and/or biodegradation and
        make it available in a curated way and associated with a number of computational
        tools to support studies of genomic and metagenomic data.'
      authors:
      - name: Oliveira J.S.
      - name: Araujo W.
      - name: Sales A.I.L.
      - name: De Brito Guerra A.
      - name: Da Silva Araujo S.C.
      - name: De Vasconcelos A.T.R.
      - name: Agnez-Lima L.F.
      - name: Freitas A.T.
      citationCount: 30
      date: '2015-01-01T00:00:00Z'
      journal: Database
      title: 'BioSurfDB: Knowledge and algorithms to support biosurfactants and biodegradation
        studies'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Enzymes
    uri: http://edamontology.org/topic_0821
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2018-07-09T23:06:59Z'
  biotoolsCURIE: biotools:metexplore
  biotoolsID: metexplore
  collectionID:
  - elixir-fr-sdp-2019
  - EBI Training Tools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: fabien.jourdan@inra.fr
    fundrefid: null
    gridid: null
    name: Fabien JOURDAN
    note: null
    orcidid: https://orcid.org/0000-0001-9401-2894
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://sites.google.com/site/fabienjourdan/
  - email: ludovic.cottret@inra.fr
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: metexplore@oulouse.inra.fr
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: MetaboHub
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole: []
    url: null
  description: Metabolic network curation, visualisation and omics data analysis.
    It is possible to curate and annotate metabolic networks in a collaborative environment.
    Several tools are available for metabolomics data mapping in networks and visualisation.
  documentation:
  - note: User documentation on MetExplore main features.
    type:
    - User manual
    url: https://metexplore.toulouse.inra.fr/metexplore-doc/index.php
  - note: MetExploreViz (metabolic network visualsiation) documentation.
    type:
    - User manual
    url: https://metexplore.toulouse.inra.fr/metexploreViz/doc/documentation.php
  - note: MetExplore webservice documentation
    type:
    - API documentation
    url: https://metexplore.toulouse.inra.fr/metexplore-webservice-documentation/
  - note: How to use MetExplore to map metabolomics data onto metabolic networks.
    type:
    - Training material
    url: https://metexplore.pages.mia.inra.fr/metexplore-training/
  download: []
  editPermission:
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    - Jennifer
    type: group
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  function:
  - cmd: null
    input:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
    note: null
    operation:
    - term: Pathway or network analysis
      uri: http://edamontology.org/operation_2497
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
      - term: GML
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      - term: SBML
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      - term: PNG
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  - cmd: null
    input: []
    note: null
    operation:
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    output: []
  homepage: http://www.metexplore.fr/
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-28T07:17:11.601858Z'
  license: null
  link: []
  maturity: Mature
  name: MetExplore
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: fabienjourdan
  publication:
  - doi: 10.1093/nar/gky301
    metadata:
      abstract: Metabolism of an organism is composed of hundreds to thousands of
        interconnected biochemical reactions responding to environmental or genetic
        constraints. This metabolic network provides a rich knowledge to contextualize
        omics data and to elaborate hypotheses on metabolic modulations. Nevertheless,
        performing this kind of integrative analysis is challenging for end users
        with not sufficiently advanced computer skills since it requires the use of
        various tools and web servers. MetExplore offers an all-in-one online solution
        composed of interactive tools for metabolic network curation, network exploration
        and omics data analysis. In particular, it is possible to curate and annotate
        metabolic networks in a collaborative environment. The network exploration
        is also facilitated in MetExplore by a system of interactive tables connected
        to a powerful network visualization module. Finally, the contextualization
        of metabolic elements in the network and the calculation of over-representation
        statistics make it possible to interpret any kind of omics data.
      authors:
      - name: Cottret L.
      - name: Frainay C.
      - name: Chazalviel M.
      - name: Cabanettes F.
      - name: Gloaguen Y.
      - name: Camenen E.
      - name: Merlet B.
      - name: Heux S.
      - name: Portais J.-C.
      - name: Poupin N.
      - name: Vinson F.
      - name: Jourdan F.
      citationCount: 92
      date: '2018-07-02T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MetExplore: Collaborative edition and exploration of metabolic networks'
    note: null
    pmcid: PMC6030842
    pmid: '29718355'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkq312
    metadata:
      abstract: High-throughput metabolomic experiments aim at identifying and ultimately
        quantifying all metabolites present in biological systems. The metabolites
        are interconnected through metabolic reactions, generally grouped into metabolic
        pathways. Classical metabolic maps provide a relational context to help interpret
        metabolomics experiments and a wide range of tools have been developed to
        help place metabolites within metabolic pathways. However, the representation
        of metabolites within separate disconnected pathways overlooks most of the
        connectivity of the metabolome. By definition, reference pathways cannot integrate
        novel pathways nor show relationships between metabolites that may be linked
        by common neighbours without being considered as joint members of a classical
        biochemical pathway. MetExplore is a web server that offers the possibility
        to link metabolites identified in untargeted metabolomics experiments within
        the context of genome-scale reconstructed metabolic networks. The analysis
        pipeline comprises mapping metabolomics data onto the specific metabolic network
        of an organism, then applying graph-based methods and advanced visualization
        tools to enhance data analysis. The MetExplore web server is freely accessible
        at http://metexplore.toulouse.inra.fr. © The Author(s) 2010. Published by
        Oxford University Press.
      authors:
      - name: Cottret L.
      - name: Wildridge D.
      - name: Vinson F.
      - name: Barrett M.P.
      - name: Charles H.
      - name: Sagot M.-F.
      - name: Jourdan F.
      citationCount: 144
      date: '2010-05-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MetExplore: A web server to link metabolomic experiments and genome-scale
        metabolic networks'
    note: null
    pmcid: PMC2896158
    pmid: '20444866'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btx588
    metadata:
      abstract: Summary MetExploreViz is an open source web component that can be
        easily embedded in any web site. It provides features dedicated to the visualization
        of metabolic networks and pathways and thus offers a flexible solution to
        analyse omics data in a biochemical context.
      authors:
      - name: Chazalviel M.
      - name: Frainay C.
      - name: Poupin N.
      - name: Vinson F.
      - name: Merlet B.
      - name: Gloaguen Y.
      - name: Cottret L.
      - name: Jourdan F.
      citationCount: 36
      date: '2018-01-15T00:00:00Z'
      journal: Bioinformatics
      title: 'MetExploreViz: Web component for interactive metabolic network visualization'
    note: null
    pmcid: PMC5860210
    pmid: '28968733'
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Web application
  - Web service
  - Workbench
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 1
  version:
  - '2.40'
- accessibility: Open access (with restrictions)
  additionDate: '2017-04-22T17:35:18Z'
  biotoolsCURIE: biotools:gemma
  biotoolsID: gemma
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: pavlab-support@msl.ubc.ca
    fundrefid: null
    gridid: null
    name: Pavlidis Lab Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: https://pavlab.msl.ubc.ca/
  description: Gemma provides data, experimental design annotations, and differential
    expression analysis results for thousands of microarray and RNA-seq experiments.
    We re-analyze raw data from public sources (primarily NCBI GEO), annotate experimental
    conditions, conduct quality control and compute differential expression using
    standardized procedures. We have especially good coverage of experiments relevant
    to the nervous system.
  documentation:
  - note: null
    type:
    - General
    url: https://pavlidislab.github.io/Gemma/
  - note: Documentation for the Gemma REST API.
    type:
    - API documentation
    url: https://gemma.msl.ubc.ca/resources/restapidocs/
  download:
  - note: null
    type: Downloads page
    url: https://github.com/PavlidisLab/Gemma/releases
    version: null
  editPermission:
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    type: group
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  function:
  - cmd: null
    input:
    - data:
        term: Gene expression matrix
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      format: []
    - data:
        term: Raw microarray data
        uri: http://edamontology.org/data_3110
      format: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    output:
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format: []
    - data:
        term: Quality control report
        uri: http://edamontology.org/data_3914
      format: []
  homepage: https://gemma.msl.ubc.ca/
  homepage_status: 0
  language:
  - Java
  - JavaScript
  lastUpdate: '2025-04-22T22:09:03.811075Z'
  license: CC-BY-NC-4.0
  link:
  - note: null
    type:
    - Repository
    - Issue tracker
    url: https://github.com/PavlidisLab/Gemma
  maturity: Mature
  name: Gemma
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: artrymix
  publication:
  - doi: 10.1093/bioinformatics/btp259
    metadata:
      abstract: 'Motivation: Many microarray datasets are available online with formalized
        standards describing the probe sequences and expression values. Unfortunately,
        the description, conditions and parameters of the experiments are less commonly
        formalized and often occur as natural language text. This hinders searching,
        high-throughput analysis, organization and integration of the datasets. Results:
        We use the lexical resources and software tools from the Unified Medical Language
        System (UMLS) to extract concepts from text. We then link the UMLS concepts
        to classes in open biomedical ontologies. The result is accessible and clear
        semantic annotations of gene expression experiments. We applied the method
        to 595 expression experiments from Gemma, a resource for re-use and meta-analysis
        of gene expression profiling data. We evaluated and corrected all stages of
        the annotation process. The majority of missed annotations were due to a lack
        of cross-references. The most error-prone stage was the extraction of concepts
        from phrases. Final review of the annotations in context of the experiments
        revealed 89% precision. A naive system, lacking the phrase to concept corrections
        is 68% precise. We have integrated this annotation pipeline into Gemma. ©
        2009 The Author(s).'
      authors:
      - name: French L.
      - name: Lane S.
      - name: Law T.
      - name: Xu L.
      - name: Pavlidis P.
      citationCount: 9
      date: '2009-06-09T00:00:00Z'
      journal: Bioinformatics
      title: Application and evaluation of automated semantic annotation of gene expression
        experiments
    note: null
    pmcid: PMC2687992
    pmid: '19376825'
    type:
    - Primary
    version: null
  - doi: 10.1093/database/baab006
    metadata:
      abstract: 'Vast amounts of transcriptomic data reside in public repositories,
        but effective reuse remains challenging. Issues include unstructured dataset
        metadata, inconsistent data processing and quality control, and inconsistent
        probe-gene mappings across microarray technologies. Thus, extensive curation
        and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics
        system was created to help address these issues. Gemma consists of a database
        of curated transcriptomic datasets, analytical software, a web interface and
        web services. Here we present an update on Gemma''s holdings, data processing
        and analysis pipelines, our curation guidelines, and software features. As
        of June 2020, Gemma contains 10 811 manually curated datasets (primarily human,
        mouse and rat), over 395 000 samples and hundreds of curated transcriptomic
        platforms (both microarray and RNA sequencing). Dataset topics were represented
        with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic
        annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of
        conditions and tissues, it captures a large majority of available brain-related
        datasets, accounting for 34% of its holdings. Users can access the curated
        data and differential expression analyses through the Gemma website, RESTful
        service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html'
      authors:
      - name: Lim N.
      - name: Tesar S.
      - name: Belmadani M.
      - name: Poirier-Morency G.
      - name: Mancarci B.O.
      - name: Sicherman J.
      - name: Jacobson M.
      - name: Leong J.
      - name: Tan P.
      - name: Pavlidis P.
      citationCount: 21
      date: '2021-01-01T00:00:00Z'
      journal: Database
      title: Curation of over 10 000 transcriptomic studies to enable data reuse
    note: null
    pmcid: PMC7904053
    pmid: '33599246'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: rsem
    type: uses
  - biotoolsID: multiqc
    type: uses
  - biotoolsID: cutadapt
    type: uses
  - biotoolsID: star
    type: uses
  - biotoolsID: ncbi_geo
    type: uses
  - biotoolsID: sradb
    type: uses
  - biotoolsID: ncbi_gene
    type: uses
  toolType:
  - Web application
  - Web API
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
  validated: 1
  version:
  - 1.31.13
- accessibility: Open access
  additionDate: '2019-05-27T16:59:15Z'
  biotoolsCURIE: biotools:SLiM_software
  biotoolsID: SLiM_software
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: messer@cornell.edu
    fundrefid: null
    gridid: null
    name: Philipp Messer
    note: null
    orcidid: https://orcid.org/0000-0001-8453-9377
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://messerlab.org
  - email: bhaller@benhaller.com
    fundrefid: null
    gridid: null
    name: Benjamin C. Haller
    note: null
    orcidid: https://orcid.org/0000-0003-1874-8327
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://benhaller.com
  description: Evolutionary simulation framework that combines a powerful engine for
    population genetic simulations with the capability of modeling arbitrarily complex
    evolutionary scenarios.  Includes a graphical modeling environment.
  documentation:
  - note: The manual for SLiM itself
    type:
    - User manual
    url: http://benhaller.com/slim/SLiM_Manual.pdf
  - note: The manual for Eidos, the scripting language used by SLiM
    type:
    - User manual
    url: http://benhaller.com/slim/Eidos_Manual.pdf
  - note: Quick reference sheets for SLiM and Eidos
    type:
    - Quick start guide
    url: http://benhaller.com/slim/SLiMEidosRefSheets.zip
  download:
  - note: A source archive for the command-line `slim` tool only.  Complete source
      code is on GitHub, but most platforms have an installer anyway; see the manual,
      chapter 2, for installation instructions.
    type: Source code
    url: http://benhaller.com/slim/SLiM.zip
    version: null
  - note: The GitHub page for the current release version, to obtain full source code.
    type: Downloads page
    url: https://github.com/MesserLab/SLiM/releases/latest
    version: null
  editPermission:
    authors: []
    type: private
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  function:
  - cmd: null
    input: []
    note: Run individual-based eco-evolutionary simulations with explicit genetics
    operation:
    - term: DNA substitution modelling
      uri: http://edamontology.org/operation_0550
    - term: Sequence generation
      uri: http://edamontology.org/operation_0230
    - term: Ecological modelling
      uri: http://edamontology.org/operation_3946
    output: []
  homepage: https://messerlab.org/slim/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-04-18T17:40:38.228809Z'
  license: GPL-3.0
  link:
  - note: SLiM home page in the Messer Lab website
    type:
    - Software catalogue
    url: https://messerlab.org/slim/
  - note: GitHub repository for SLiM
    type:
    - Repository
    url: https://github.com/MesserLab/SLiM
  - note: Discussion forum for SLiM questions
    type:
    - Discussion forum
    url: https://groups.google.com/g/slim-discuss
  - note: Announcements mailing list
    type:
    - Mailing list
    url: https://groups.google.com/g/slim-announce
  maturity: Mature
  name: SLiM
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: bchaller
  publication:
  - doi: 10.1093/molbev/msy228
    metadata:
      abstract: With the desire to model population genetic processes under increasingly
        realistic scenarios, forward genetic simulations have become a critical part
        of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation
        framework is one of the most powerful and widely used tools in this area.
        However, its foundation in the Wright-Fisher model has been found to pose
        an obstacle to implementing many types of models; it is difficult to adapt
        the Wright-Fisher model, with its many assumptions, to modeling ecologically
        realistic scenarios such as explicit space, overlapping generations, individual
        variation in reproduction, density-dependent population regulation, individual
        variation in dispersal or migration, local extinction and recolonization,
        mating between subpopulations, age structure, fitness-based survival and hard
        selection, emergent sex ratios, and so forth. In response to this need, we
        here introduce SLiM 3, which contains two key advancements aimed at abolishing
        these limitations. First, the new non-Wright-Fisher or "nonWF" model type
        provides a much more flexible foundation that allows the easy implementation
        of all of the above scenarios and many more. Second, SLiM 3 adds support for
        continuous space, including spatial interactions and spatial maps of environmental
        variables. We provide a conceptual overview of these new features, and present
        several example models to illustrate their use.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 504
      date: '2019-03-01T00:00:00Z'
      journal: Molecular Biology and Evolution
      title: 'SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model'
    note: 'B.C. Haller, P.W. Messer. (2019). SLiM 3: Forward genetic simulations beyond
      the Wright–Fisher Model. Molecular Biology and Evolution 36(3), 632–637.'
    pmcid: PMC6389312
    pmid: '30517680'
    type:
    - Other
    version: null
  - doi: 10.1093/molbev/msy237
    metadata:
      abstract: The SLiM forward genetic simulation framework has proved to be a powerful
        and flexible tool for population genetic modeling. However, as a complex piece
        of software with many features that allow simulating a diverse assortment
        of evolutionary models, its initial learning curve can be difficult. Here
        we provide a step-by-step demonstration of how to build a simple evolutionary
        model in SLiM 3, to help new users get started. We will begin with a panmictic
        neutral model, and build up to a model of the evolution of a polygenic quantitative
        trait under selection for an environmental phenotypic optimum.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 12
      date: '2019-05-01T00:00:00Z'
      journal: Molecular Biology and Evolution
      title: Evolutionary Modeling in SLiM 3 for Beginners
    note: B.C. Haller, P.W. Messer. (2019). Evolutionary modeling in SLiM 3 for beginners.
      Molecular Biology and Evolution 36(5), 1101–1109.
    pmcid: PMC6501880
    pmid: '30590560'
    type:
    - Usage
    version: null
  - doi: 10.1111/1755-0998.12968
    metadata:
      abstract: 'There is an increasing demand for evolutionary models to incorporate
        relatively realistic dynamics, ranging from selection at many genomic sites
        to complex demography, population structure, and ecological interactions.
        Such models can generally be implemented as individual-based forward simulations,
        but the large computational overhead of these models often makes simulation
        of whole chromosome sequences in large populations infeasible. This situation
        presents an important obstacle to the field that requires conceptual advances
        to overcome. The recently developed tree-sequence recording method (Kelleher,
        Thornton, Ashander, & Ralph, 2018), which stores the genealogical history
        of all genomes in the simulated population, could provide such an advance.
        This method has several benefits: (1) it allows neutral mutations to be omitted
        entirely from forward-time simulations and added later, thereby dramatically
        improving computational efficiency; (2) it allows neutral burn-in to be constructed
        extremely efficiently after the fact, using “recapitation”; (3) it allows
        direct examination and analysis of the genealogical trees along the genome;
        and (4) it provides a compact representation of a population''s genealogy
        that can be analysed in Python using the msprime package. We have implemented
        the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary
        simulation software package) and extended it to allow the recording of non-neutral
        mutations, greatly broadening the utility of this method. To demonstrate the
        versatility and performance of this approach, we showcase several practical
        applications that would have been beyond the reach of previously existing
        methods, opening up new horizons for the modelling and exploration of evolutionary
        processes.'
      authors:
      - name: Haller B.C.
      - name: Galloway J.
      - name: Kelleher J.
      - name: Messer P.W.
      - name: Ralph P.L.
      citationCount: 109
      date: '2019-03-01T00:00:00Z'
      journal: Molecular Ecology Resources
      title: Tree-sequence recording in SLiM opens new horizons for forward-time simulation
        of whole genomes
    note: B.C. Haller, J. Galloway, J. Kelleher, P.W. Messer, P.L. Ralph. (2019).
      Tree-sequence recording in SLiM opens new horizons for forward-time simulation
      of whole genomes. Molecular Ecology Resources 19(2), 552–566.
    pmcid: PMC6393187
    pmid: '30565882'
    type:
    - Method
    version: null
  - doi: 10.1086/723601
    metadata:
      abstract: The SLiM software framework for genetically explicit forward simulation
        has been widely used in population genetics. However, it has been largely
        restricted to modeling only a single species, which has limited its broader
        utility in evolutionary biology. Indeed, to our knowledge no general-purpose,
        flexible modeling framework exists that provides support for simulating multiple
        species while also providing other key features, such as explicit genetics
        and continuous space. The lack of such software has limited our ability to
        model higher biological levels such as communities, eco-systems, coevolutionary
        and eco-evolutionary processes, and bio-diversity, which is crucial for many
        purposes, from extending our basic understanding of evolutionary ecology to
        informing conservation and management decisions. We here announce the release
        of SLiM 4, which fills this important gap by adding support for multiple species,
        including ecological interactions between species such as predation, parasitism,
        and mutualism, and illustrate its new features with examples.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 79
      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
      modeling. The American Naturalist 201(5), E127–E139.'
    pmcid: PMC10793872
    pmid: '37130229'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Desktop application
  topic:
  - term: Ecology
    uri: http://edamontology.org/topic_0610
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  validated: 0
  version:
  - '5.0'
- accessibility: Open access
  additionDate: '2025-04-16T08:53:57.270825Z'
  biotoolsCURIE: biotools:openvre
  biotoolsID: openvre
  collectionID:
  - EUCAIM
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: laia.codo@bsc.es
    fundrefid: 10.13039/501100006433
    gridid: grid.10097.3f
    name: Laia Codó
    note: Senior Researcher at Barcelona Supercomputing Center. Lead of Trusted Research
      Environments and Cloud Computing at INB Lab.
    orcidid: https://orcid.org/0000-0002-6797-8746
    rorid: 05sd8tv96
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.bsc.es/codo-laia
  - email: gelpi@ub.edu
    fundrefid: 10.13039/501100005774
    gridid: grid.5841.8
    name: Josep Lluís Gelpí
    note: Professor at University of Barcelona and Team Leader at BSC's INB Laboratory
    orcidid: http://orcid.org/0000-0002-0566-7723
    rorid: 021018s57
    typeEntity: Person
    typeRole:
    - Contributor
    url: https://webgrec.ub.edu/webpages/000011/ang/gelpi.ub.edu.html
  description: The Open Virtual Research Environment is a general purpose analysis
    platform that can be tailored for domain specific analyses.
  documentation:
  - note: Getting Started Guide
    type:
    - Quick start guide
    url: http://vre.multiscalegenomics.eu/help/starting.php
  download:
  - note: null
    type: Source code
    url: https://github.com/inab/openVRE
    version: '1.0'
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    note: null
    operation:
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      uri: http://edamontology.org/operation_3760
    output: []
  homepage: https://github.com/inab/openVRE/wiki
  homepage_status: 0
  language:
  - PHP
  - Python
  - JavaScript
  lastUpdate: '2025-04-16T08:53:57.280528Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: openVRE
  operatingSystem:
  - Linux
  otherID: []
  owner: gelpi@ub.edu
  publication: []
  relation:
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    type: isNewVersionOf
  toolType:
  - Bioinformatics portal
  - Workbench
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2019-08-26T14:34:55Z'
  biotoolsCURIE: biotools:MINERVA_Platform
  biotoolsID: MINERVA_Platform
  collectionID:
  - ELIXIR-LU
  - LCSB
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'The MINERVA (Molecular Interaction NEtwoRk VisuAlization) platform
    is a standalone webserver for visualization, exploration and management of molecular
    networks encoded in SBGN-compliant format, including files produced using CellDesigner
    or SBGN editors. Visualization of uploaded networks generated by the platform
    is accessible via a web browser to all viewers with the weblink to the resource.


    The MINERVA Platform is a webservice using the Java Server Faces 2 technology.
    The server side, including data parsing, integration, annotation and verification,
    is implemented in Java. The platform uses the Postgres SQL database for data storage
    and the Hibernate framework as a middle layer between web server and database.
    The user web-interface is generated using React.js. The displayed content is visualized
    by OpenLayers API, dedicated JavaScript and CSS.'
  documentation:
  - note: null
    type:
    - Quick start guide
    - Release notes
    - User manual
    - API documentation
    - Citation instructions
    - Terms of use
    url: https://minerva.uni.lu
  download:
  - note: Installation instructions, including debian package, virtual machine images
      and docker containers.
    type: Other
    url: https://minerva.pages.uni.lu/doc/install/
    version: 13.1.3 - 18.1.1
  editPermission:
    authors:
    - sascha.herzinger
    type: group
  elixirCommunity: []
  elixirNode:
  - Luxembourg
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output: []
  homepage: https://minerva.uni.lu
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-14T09:01:54.711218Z'
  license: AGPL-3.0
  link:
  - note: GiLab repository for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/core/
  - note: Issue tracker for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/core/-/issues
  - note: GiLab repository for frontend functionalities
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/frontend
  - note: Issue tracker for frontend functionalities
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/frontend/-/issues
  maturity: Mature
  name: The MINERVA Platform
  operatingSystem: []
  otherID: []
  owner: mjostaszewski
  publication:
  - doi: 10.1038/npjsba.2016.20
    metadata:
      abstract: Our growing knowledge about various molecular mechanisms is becoming
        increasingly more structured and accessible. Different repositories of molecular
        interactions and available literature enable construction of focused and high-quality
        molecular interaction networks. Novel tools for curation and exploration of
        such networks are needed, in order to foster the development of a systems
        biology environment. In particular, solutions for visualization, annotation
        and data cross-linking will facilitate usage of network-encoded knowledge
        in biomedical research. To this end we developed the MINERVA (Molecular Interaction
        NEtwoRks VisuAlization) platform, a standalone webservice supporting curation,
        annotation and visualization of molecular interaction networks in Systems
        Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated
        content annotation and verification for improved quality control. The end
        users can explore and interact with hosted networks, and provide direct feedback
        to content curators. MINERVA enables mapping drug targets or overlaying experimental
        data on the visualized networks. Extensive export functions enable downloading
        areas of the visualized networks as SBGN-compliant models for efficient reuse
        of hosted networks. The software is available under Affero GPL 3.0 as a Virtual
        Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/.
        We believe that MINERVA is an important contribution to systems biology community,
        as its architecture enables set-up of locally or globally accessible SBGN-oriented
        repositories of molecular interaction networks. Its functionalities allow
        overlay of multiple information layers, facilitating exploration of content
        and interpretation of data. Moreover, annotation and verification workflows
        of MINERVA improve the efficiency of curation of networks, allowing life-science
        researchers to better engage in development and use of biomedical knowledge
        repositories.
      authors:
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Satagopam V.
      - name: Gebel S.
      - name: Mazein A.
      - name: Kuzma M.
      - name: Zorzan S.
      - name: McGee F.
      - name: Otjacques B.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 60
      date: '2016-01-01T00:00:00Z'
      journal: npj Systems Biology and Applications
      title: MINERVA—A platform for visualization and curation of molecular interaction
        networks
    note: null
    pmcid: PMC5516855
    pmid: '28725475'
    type:
    - Primary
    version: '10.0'
  - doi: 10.1093/bioinformatics/btz286
    metadata:
      abstract: 'Summary: The complexity of molecular networks makes them difficult
        to navigate and interpret, creating a need for specialized software. MINERVA
        is a web platform for visualization, exploration and management of molecular
        networks. Here, we introduce an extension to MINERVA architecture that greatly
        facilitates the access and use of the stored molecular network data. It allows
        to incorporate such data in analytical pipelines via a programmatic access
        interface, and to extend the platform''s visual exploration and analytics
        functionality via plugin architecture. This is possible for any molecular
        network hosted by the MINERVA platform encoded in well-recognized systems
        biology formats. To showcase the possibilities of the plugin architecture,
        we have developed several plugins extending the MINERVA core functionalities.
        In the article, we demonstrate the plugins for interactive tree traversal
        of molecular networks, for enrichment analysis and for mapping and visualization
        of known disease variants or known adverse drug reactions to molecules in
        the network. Availability and implementation: Plugins developed and maintained
        by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/.
        The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.'
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Smula E.
      - name: Schneider R.
      citationCount: 24
      date: '2019-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'MINERVA API and plugins: Opening molecular network analysis and visualization
        to the community'
    note: null
    pmcid: PMC6821317
    pmid: '31074494'
    type:
    - Primary
    version: 12.2.3
  - doi: 10.1093/bib/bbz067
    metadata:
      abstract: The understanding of complex biological networks often relies on both
        a dedicated layout and a topology. Currently, there are three major competing
        layout-aware systems biology formats, but there are no software tools or software
        libraries supporting all of them. This complicates the management of molecular
        network layouts and hinders their reuse and extension. In this paper, we present
        a high-level overview of the layout formats in systems biology, focusing on
        their commonalities and differences, review their support in existing software
        tools, libraries and repositories and finally introduce a new conversion module
        within the MINERVA platform. The module is available via a REST API and offers,
        besides the ability to convert between layout-aware systems biology formats,
        the possibility to export layouts into several graphical formats. The module
        enables conversion of very large networks with thousands of elements, such
        as disease maps or metabolic reconstructions, rendering it widely applicable
        in systems biology.
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Hasenauer J.
      - name: Schneider R.
      citationCount: 15
      date: '2019-07-10T00:00:00Z'
      journal: Briefings in Bioinformatics
      title: Closing the gap between formats for storing layout information in systems
        biology
    note: null
    pmcid: PMC7373180
    pmid: '31273380'
    type:
    - Primary
    version: 13.1.1
  - doi: 10.1089/big.2015.0057
    metadata:
      abstract: Translational medicine is a domain turning results of basic life science
        research into new tools and methods in a clinical environment, for example,
        as new diagnostics or therapies. Nowadays, the process of translation is supported
        by large amounts of heterogeneous data ranging from medical data to a whole
        range of -omics data. It is not only a great opportunity but also a great
        challenge, as translational medicine big data is difficult to integrate and
        analyze, and requires the involvement of biomedical experts for the data processing.
        We show here that visualization and interoperable workflows, combining multiple
        complex steps, can address at least parts of the challenge. In this article,
        we present an integrated workflow for exploring, analysis, and interpretation
        of translational medicine data in the context of human health. Three Web services
        - tranSMART, a Galaxy Server, and a MINERVA platform - are combined into one
        big data pipeline. Native visualization capabilities enable the biomedical
        experts to get a comprehensive overview and control over separate steps of
        the workflow. The capabilities of tranSMART enable a flexible filtering of
        multidimensional integrated data sets to create subsets suitable for downstream
        processing. A Galaxy Server offers visually aided construction of analytical
        pipelines, with the use of existing or custom components. A MINERVA platform
        supports the exploration of health and disease-related mechanisms in a contextualized
        analytical visualization system. We demonstrate the utility of our workflow
        by illustrating its subsequent steps using an existing data set, for which
        we propose a filtering scheme, an analytical pipeline, and a corresponding
        visualization of analytical results. The workflow is available as a sandbox
        environment, where readers can work with the described setup themselves. Overall,
        our work shows how visualization and interfacing of big data processing services
        facilitate exploration, analysis, and interpretation of translational medicine
        data.
      authors:
      - name: Satagopam V.
      - name: Gu W.
      - name: Eifes S.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Gebel S.
      - name: Barbosa-Silva A.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 38
      date: '2016-06-01T00:00:00Z'
      journal: Big Data
      title: Integration and Visualization of Translational Medicine Data for Better
        Understanding of Human Diseases
    note: null
    pmcid: PMC4932659
    pmid: '27441714'
    type:
    - Usage
    version: '10.0'
  - doi: 10.1016/j.envpol.2019.04.005
    metadata:
      abstract: 'Polychlorinated biphenyls (PCBs) are a class of widespread environmental
        pollutants, commonly found in human blood, that have been suggested to be
        linked to the occurrence of sporadic Parkinson''s disease (PD). It has been
        reported that some non-coplanar PCBs accumulate in the brains of female PD
        patients. To improve our understanding of the association between PCB exposure
        and PD risk we have applied whole transcriptome gene expression analysis in
        blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly,
        we observe that in females, blood levels of non-coplanar PCBs appear to be
        associated with expression levels of PD-specific genes. However, no such association
        was detected in males. Among the 131 PD-specific genes affected, 39 have been
        shown to display similar changes in expression levels in the substantia nigra
        of deceased PD patients. Especially among the down-regulated genes, transcripts
        of genes involved in neurotransmitter vesicle-related functions were predominant.
        Capsule: Plasma PCB levels are associated with gene expression changes in
        females only, resulting in brain-related genes changing in blood cells of
        healthy individuals exposed to PCBs.'
      authors:
      - name: Bohler S.
      - name: Krauskopf J.
      - name: Espin-Perez A.
      - name: Gebel S.
      - name: Palli D.
      - name: Rantakokko P.
      - name: Kiviranta H.
      - name: Kyrtopoulos S.A.
      - name: Balling R.
      - name: Kleinjans J.
      citationCount: 5
      date: '2019-07-01T00:00:00Z'
      journal: Environmental Pollution
      title: Genes associated with Parkinson's disease respond to increasing polychlorinated
        biphenyl levels in the blood of healthy females
    note: null
    pmcid: null
    pmid: '30991279'
    type: []
    version: 13.1.1
  relation:
  - biotoolsID: pathvisio
    type: uses
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    type: uses
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    type: uses
  toolType: []
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Translational medicine
    uri: http://edamontology.org/topic_3342
  validated: 0
  version:
  - 13.1.3
  - 13.2.0
  - 14.0.13
  - 15.0.3
  - 16.4.0
  - 17.1.3
  - 18.1.1
- accessibility: Open access
  additionDate: '2025-04-11T20:52:47.895200Z'
  biotoolsCURIE: biotools:opencloning
  biotoolsID: opencloning
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: manulera14@gmail.com
    fundrefid: null
    gridid: null
    name: Manuel Lera-Ramirez
    note: null
    orcidid: https://orcid.org/0000-0002-8666-9746
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://github.com/manulera
  description: An Open-Source web application to plan and document cloning and genome
    engineering
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/manulera/OpenCloning
  download: []
  editPermission:
    authors: []
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  function:
  - cmd: null
    input: []
    note: null
    operation:
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pairwise sequence alignment
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Nucleic acid design
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Restriction digest
      uri: http://edamontology.org/operation_0365
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence generation
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence assembly validation
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Chromatogram visualisation
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    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Map drawing
      uri: http://edamontology.org/operation_0573
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Network visualisation
      uri: http://edamontology.org/operation_3925
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    output: []
  homepage: https://opencloning.org/
  homepage_status: 0
  language:
  - Python
  - JavaScript
  lastUpdate: '2025-04-11T20:53:03.390161Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/manulera/OpenCloning
  - note: Hosted web app
    type:
    - Service
    url: https://opencloning.org/
  - note: Backend code
    type:
    - Repository
    url: https://github.com/manulera/OpenCloning_backend
  - note: null
    type:
    - Repository
    url: https://github.com/manulera/OpenCloning_frontend
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  name: OpenCloning
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  owner: manulera
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  toolType:
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  - Web API
  - Library
  topic:
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  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  - term: Synthetic biology
    uri: http://edamontology.org/topic_3895
  validated: 0
  version: []
- accessibility: Open access
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  confidence_flag: null
  cost: Free of charge
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  description: The PR2 reference sequence database was initiated in 2010 in the frame
    of the BioMarks project from work that had developed in the previous ten years
    in the Plankton Group of the Station Biologique of Roscoff. It aims to provide
    a reference database of carefully annotated 18S rRNA sequences using nine unique
    taxonomic fields (from domain to species). At present, it contains over 240,000
    sequences. Although it focuses on protists, it also contains sequences from metazoa,
    fungi and plants as well a limited set of 16S sequences from plastids and bacteria.
    Several metadata fields are available for many sequences, including geo-localisation,
    whether it originates from a culture or a natural sample, host type etc… The annotation
    of PR2 is performed by experts from each taxonomic groups.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pr2database.github.io/pr2database/articles/pr2database.html
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  - note: null
    type: Downloads page
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  homepage: https://app.pr2-database.org/
  homepage_status: 0
  language:
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  lastUpdate: '2025-04-11T14:01:09.249274Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/pr2database/pr2database/releases
  - note: null
    type:
    - Issue tracker
    url: https://github.com/pr2database/pr2database/issues
  - note: null
    type:
    - Other
    url: https://pr2-database.org/
  maturity: Mature
  name: Protists Ribosomal Reference Database
  operatingSystem: []
  otherID: []
  owner: vaulot
  publication:
  - doi: 10.1093/nar/gks1160
    metadata:
      abstract: The interrogation of genetic markers in environmental meta-barcoding
        studies is currently seriously hindered by the lack of taxonomically curated
        reference data sets for the targeted genes. The Protist Ribosomal Reference
        database (PR2, http://ssu-rrna.org/) provides a unique access to eukaryotic
        small sub-unit (SSU) ribosomal RNA and DNA sequences, with curated taxonomy.
        The database mainly consists of nuclear-encoded protistan sequences. However,
        metazoans, land plants, macrosporic fungi and eukaryotic organelles (mitochondrion,
        plastid and others) are also included because they are useful for the analysis
        of high-troughput sequencing data sets. Introns and putative chimeric sequences
        have been also carefully checked. Taxonomic assignation of sequences consists
        of eight unique taxonomic fields. In total, 136866 sequences are nuclear encoded,
        45708 (36 501 mitochondrial and 9657 chloroplastic) are from organelles, the
        remaining being putative chimeric sequences. The website allows the users
        to download sequences from the entire and partial databases (including representative
        sequences after clustering at a given level of similarity). Different web
        tools also allow searches by sequence similarity. The presence of both rRNA
        and rDNA sequences, taking into account introns (crucial for eukaryotic sequences),
        a normalized eight terms ranked-taxonomy and updates of new GenBank releases
        were made possible by a long-term collaboration between experts in taxonomy
        and computer scientists. © The Author(s) 2012.
      authors:
      - name: Guillou L.
      - name: Bachar D.
      - name: Audic S.
      - name: Bass D.
      - name: Berney C.
      - name: Bittner L.
      - name: Boutte C.
      - name: Burgaud G.
      - name: De Vargas C.
      - name: Decelle J.
      - name: Del Campo J.
      - name: Dolan J.R.
      - name: Dunthorn M.
      - name: Edvardsen B.
      - name: Holzmann M.
      - name: Kooistra W.H.C.F.
      - name: Lara E.
      - name: Le Bescot N.
      - name: Logares R.
      - name: Mahe F.
      - name: Massana R.
      - name: Montresor M.
      - name: Morard R.
      - name: Not F.
      - name: Pawlowski J.
      - name: Probert I.
      - name: Sauvadet A.-L.
      - name: Siano R.
      - name: Stoeck T.
      - name: Vaulot D.
      - name: Zimmermann P.
      - name: Christen R.
      citationCount: 1456
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The Protist Ribosomal Reference database (PR2): A catalog of unicellular
        eukaryote Small Sub-Unit rRNA sequences with curated taxonomy'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  - Web application
  topic:
  - term: Metabarcoding
    uri: http://edamontology.org/topic_4038
  validated: 0
  version:
  - 5.1.0
- accessibility: Open access
  additionDate: '2022-11-03T14:53:22.145643Z'
  biotoolsCURIE: biotools:rdmkit
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  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: RDMkit is the Research Data Management toolkit for Life Sciences, providing
    best practices and guidelines to for FAIR (Findable, Accessible, Interoperable
    and Reusable) data management.
  documentation: []
  download:
  - note: null
    type: Downloads page
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    version: null
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  elixirNode:
  - UK
  - Norway
  - Belgium
  - Germany
  - Finland
  - Spain
  elixirPlatform:
  - Data
  - Interoperability
  - Training
  elixir_badge: 0
  function: []
  homepage: https://rdmkit.elixir-europe.org
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-04-11T13:03:16.201584Z'
  license: MIT
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  - note: null
    type:
    - Repository
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  maturity: null
  name: RDMkit
  operatingSystem:
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  - Linux
  - Windows
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  owner: munazah
  publication: []
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  toolType:
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  topic: []
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2020-09-03T19:08:44Z'
  biotoolsCURIE: biotools:plaza_4.0
  biotoolsID: plaza_4.0
  collectionID:
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  - VIB
  - UGent
  confidence_flag: tool
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Klaas Vandepoele
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
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  description: 'Integrative resource for functional, evolutionary and comparative
    plant genomics.


    PLAZA is an access point for plant comparative genomics, centralizing genomic
    data produced by different genome sequencing initiatives.


    This intermediate update of the PLAZA platform, with a focus on the Monocots,
    became necessary due to substantial annotation updates to species present in the
    4.0 PLAZA instances.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://bioinformatics.psb.ugent.be/plaza/documentation
  download: []
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  function:
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    input: []
    note: null
    operation:
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    - term: Phylogenetic tree annotation
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    - term: Phylogenetic tree visualisation
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    output: []
  homepage: https://bioinformatics.psb.ugent.be/plaza
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-11T09:29:36.505334Z'
  license: null
  link: []
  maturity: null
  name: PLAZA 4.0
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  - Linux
  - Windows
  otherID: []
  owner: bits@vib.be
  publication:
  - doi: 10.1093/NAR/GKX1002
    metadata:
      abstract: PLAZA (https://bioinformatics.psb.ugent.be/plaza) is a plant-oriented
        online resource for comparative, evolutionary and functional genomics. The
        PLAZA platform consists of multiple independent instances focusing on different
        plant clades, while also providing access to a consistent set of reference
        species. Each PLAZA instance contains structural and functional gene annotations,
        gene family data and phylogenetic trees and detailed gene colinearity information.
        A user-friendly web interface makes the necessary tools and visualizations
        accessible, specific for each data type. Here we present PLAZA 4.0, the latest
        iteration of the PLAZA framework. This version consists of two new instances
        (Dicots 4.0 and Monocots 4.0) providing a large increase in newly available
        species, and offers access to updated and newly implemented tools and visualizations,
        helping users with the ever-increasing demands for complex and in-depth analyzes.
        The total number of species across both instances nearly doubles from 37 species
        in PLAZA 3.0 to 71 species in PLAZA 4.0, with a much broader coverage of crop
        species (e.g. wheat, palm oil) and species of evolutionary interest (e.g.
        spruce, Marchantia). The new PLAZA instances can also be accessed by a programming
        interface through a RESTful web service, thus allowing bioinformaticians to
        optimally leverage the power of the PLAZA platform.
      authors:
      - name: Van Bel M.
      - name: Diels T.
      - name: Vancaester E.
      - name: Kreft L.
      - name: Botzki A.
      - name: Van De Peer Y.
      - name: Coppens F.
      - name: Vandepoele K.
      citationCount: 338
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'PLAZA 4.0: An integrative resource for functional, evolutionary and
        comparative plant genomics'
    note: null
    pmcid: PMC5753339
    pmid: '29069403'
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Plant biology
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  - term: Phylogenetics
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  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  validated: 0
  version: []
- accessibility: Open access (with restrictions)
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  biotoolsCURIE: biotools:clowm
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  confidence_flag: tool
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Daniel Göbel
    note: null
    orcidid: https://orcid.org/0009-0009-9985-3823
    rorid: null
    typeEntity: Person
    typeRole:
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    - Developer
    - Primary contact
    url: https://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=223066601
  - email: mbeckste@cebitec.uni-bielefeld.de
    fundrefid: null
    gridid: null
    name: Michael Beckstette
    note: null
    orcidid: https://orcid.org/0000-0002-3707-1692
    rorid: null
    typeEntity: Person
    typeRole:
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    - Documentor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Bielefeld University
    note: null
    orcidid: null
    rorid: 02hpadn98
    typeEntity: Institute
    typeRole:
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    url: https://www.uni-bielefeld.de
  - email: null
    fundrefid: null
    gridid: null
    name: Genome Informatics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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    url: https://gi.cebitec.uni-bielefeld.de
  - email: null
    fundrefid: null
    gridid: null
    name: NFDI4Microbiota
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
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    url: https://nfdi4microbiota.de
  description: Data analysis in the life sciences often suffers from reproducibility,
    standardization and accessibility issues and these problems become even more severe
    with the escalating volume and complexity of data. While best-practice data analysis
    workflows try to tackle these challenges, many existing workflows still grapple
    with scalability issues, limited portability due to tight coupling with the execution
    environment and accessibility constraints arising from the absence of user-friendly
    interfaces. To overcome these issues, we created CloWM, a comprehensive cloud-based
    workflow management platform that allows the generic transformation of command-line
    driven workflows into user-friendly web-based services. CloWM offers the seamless
    integration of (1) scientific workflows written in the Nextflow DSL, (2) robust
    data storage, (3) a highly scalable compute layer for data-intensive analysis
    tasks and (4) a user-friendly interface.
  documentation:
  - note: null
    type:
    - API documentation
    url: https://clowm.bi.denbi.de/api/v1/docs
  - note: null
    type:
    - Terms of use
    url: https://clowm.bi.denbi.de/terms
  download:
  - note: null
    type: API specification
    url: https://clowm.bi.denbi.de/api/v1/openapi.json
    version: null
  - note: null
    type: Source code
    url: https://gitlab.ub.uni-bielefeld.de/cmg/clowm
    version: null
  editPermission:
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  lastUpdate: '2025-04-09T14:21:50.981521Z'
  license: Apache-2.0
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  operatingSystem: []
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  owner: dgoebel
  publication:
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    metadata: null
    note: null
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    type:
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    version: null
  relation:
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    type: includes
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  - Bioinformatics portal
  topic:
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    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:14:05.166974Z'
  biotoolsCURIE: biotools:mudoger
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  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: ulisses.rocha@ufz.de
    fundrefid: null
    gridid: null
    name: Ulisses Rocha
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: The Multi-Domain Genome Recovery v1.0 (MuDoGeR v1.0) framework is a
    tool developed to help users to recover Metagenome-Assembled Genomes and Uncultivated
    Viral Genomes from whole-genome sequence (WGS) samples simultaneously. The MuDoGeR
    v1.0 framework acts as a wrapper for several tools. It was designed to be an easy-to-use
    tool that outputs ready-to-use comprehensive files.
  documentation: []
  download: []
  editPermission:
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: DNA sequence
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      format:
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        uri: http://edamontology.org/format_1930
    note: null
    operation:
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    output: []
  homepage: https://github.com/mdsufz/MuDoGeR
  homepage_status: 0
  language:
  - Shell
  - R
  - Python
  lastUpdate: '2025-04-09T07:23:51.614291Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/mdsufz/MuDoGeR
  maturity: null
  name: MuDoGeR
  operatingSystem:
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  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1111/1755-0998.13904
    metadata:
      abstract: Several computational frameworks and workflows that recover genomes
        from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is
        difficult for scientists with little bioinformatics experience to evaluate
        quality, annotate genes, dereplicate, assign taxonomy and calculate relative
        abundance and coverage of genomes belonging to different domains. MuDoGeR
        is a user-friendly tool tailored for those familiar with Unix command-line
        environment that makes it easy to recover genomes of prokaryotes, eukaryotes
        and viruses from metagenomes, either alone or in combination. We tested MuDoGeR
        using 24 individual-isolated genomes and 574 metagenomes, demonstrating the
        applicability for a few samples and high throughput. While MuDoGeR can recover
        eukaryotic viral sequences, its characterization is predominantly skewed towards
        bacterial and archaeal viruses, reflecting the field's current state. However,
        acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate
        updates and integrate new tools, ensuring its ongoing relevance in the rapidly
        evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR.
        Additionally, MuDoGeR is also available as a Singularity container.
      authors:
      - name: Rocha U.
      - name: CoelhoKasmanas J.
      - name: Kallies R.
      - name: Saraiva J.P.
      - name: Toscan R.B.
      - name: Stefanic P.
      - name: Bicalho M.F.
      - name: BorimCorrea F.
      - name: Basturk M.N.
      - name: Fousekis E.
      - name: VianaBarbosa L.M.
      - name: Plewka J.
      - name: Probst A.J.
      - name: Baldrian P.
      - name: Stadler P.F.
      citationCount: 5
      date: '2024-02-01T00:00:00Z'
      journal: Molecular Ecology Resources
      title: 'MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy'
    note: null
    pmcid: null
    pmid: '37994269'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-04-08T12:40:26.806570Z'
  biotoolsCURIE: biotools:marinemetagenomedb
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  description: MarineMetagenomeDB provides standardized and manually curated metadata
    for 11,449 marine metagenomes from SRA and MG-RAST. It enables users to search,
    filter, visualize, and download metadata through a web application equipped with
    quick and advanced search options, interactive map selection, and export tools.
    The database enhances data findability and reuse for comparative and large-scale
    meta-analyses in marine microbiome research.
  documentation: []
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  homepage_status: 0
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  name: MarineMetagenomeDB
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  owner: andersonavilasantos
  publication:
  - doi: 10.1186/s40793-022-00449-7
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
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    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Metagenomic sequencing
    uri: http://edamontology.org/topic_3837
  - term: Sample collections
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  validated: 0
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- accessibility: Open access
  additionDate: '2025-04-08T12:16:36.396210Z'
  biotoolsCURIE: biotools:animalassociatedmetagenomedb
  biotoolsID: animalassociatedmetagenomedb
  collectionID:
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  confidence_flag: null
  cost: null
  credit: []
  description: The AnimalAssociatedMetagenomeDB (AAMDB) provides standardized and
    manually curated metadata for 10,885 non-human, animal-associated metagenomes
    collected from public repositories such as SRA and MG-RAST. It allows users to
    search, filter, visualize, and export metadata through an interactive web application
    featuring quick and advanced search options, geographic map-based selection, and
    support for raw data download. The tool facilitates the discovery and reuse of
    metagenomic data in biodiversity and microbiome studies.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  homepage: https://webapp.ufz.de/aamdb/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-04-08T12:38:13.124276Z'
  license: null
  link: []
  maturity: null
  name: AnimalAssociatedMetagenomeDB
  operatingSystem: []
  otherID: []
  owner: andersonavilasantos
  publication:
  - doi: 10.1186/s42523-023-00267-3
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Metagenomic sequencing
    uri: http://edamontology.org/topic_3837
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Sample collections
    uri: http://edamontology.org/topic_3277
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2021-09-22T08:33:22.081093Z'
  biotoolsCURIE: biotools:ramclustr
  biotoolsID: ramclustr
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: Corey.Broeckling@ColoState.EDU
    fundrefid: null
    gridid: null
    name: Corey Broeckling
    note: null
    orcidid: https://orcid.org/0000-0002-6158-827X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A feature clustering algorithm for non-targeted mass spectrometric
    metabolomics data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Feature table
        uri: http://edamontology.org/data_1270
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Sample annotation
        uri: http://edamontology.org/data_3113
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Imputation
      uri: http://edamontology.org/operation_3557
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    - term: Correlation
      uri: http://edamontology.org/operation_3465
    output:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format: []
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: https://github.com/cbroeckl/RAMClustR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-04-02T11:37:27.390711Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Galaxy service
    url: https://umsa.cerit-sc.cz/root?tool_id=toolshed.g2.bx.psu.edu/repos/recetox/ramclustr/ramclustr/1.3.0+galaxy0
  maturity: Emerging
  name: RAMClustR
  operatingSystem: []
  otherID: []
  owner: recetox-specdatri
  publication:
  - doi: 10.1021/ac501530d
    metadata:
      abstract: 'Metabolomic data are frequently acquired using chromatographically
        coupled mass spectrometry (MS) platforms. For such datasets, the first step
        in data analysis relies on feature detection, where a feature is defined by
        a mass and retention time. While a feature typically is derived from a single
        compound, a spectrum of mass signals is more a more-accurate representation
        of the mass spectrometric signal for a given metabolite. Here, we report a
        novel feature grouping method that operates in an unsupervised manner to group
        signals from MS data into spectra without relying on predictability of the
        in-source phenomenon. We additionally address a fundamental bottleneck in
        metabolomics, annotation of MS level signals, by incorporating indiscriminant
        MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS
        and idMS/MS data, and feature-feature relationships are determined simultaneously
        from the MS and idMS/MS data. This approach facilitates identification of
        metabolites using in-source MS and/or idMS/MS spectra from a single experiment,
        reduces quantitative analytical variation compared to single-feature measures,
        and decreases false positive annotations of unpredictable phenomenon as novel
        compounds. This tool is released as a freely available R package, called RAMClustR,
        and is sufficiently versatile to group features from any chromatographic-spectrometric
        platform or feature-finding software. © 2014 American Chemical Society.'
      authors:
      - name: Broeckling C.D.
      - name: Afsar F.A.
      - name: Neumann S.
      - name: Ben-Hur A.
      - name: Prenni J.E.
      citationCount: 201
      date: '2014-07-15T00:00:00Z'
      journal: Analytical Chemistry
      title: 'RAMClust: A novel feature clustering method enables spectral-matching-based
        annotation for metabolomics data'
    note: null
    pmcid: null
    pmid: '24927477'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 0
  version:
  - 1.2.2
  - 1.3.0
- accessibility: Open access
  additionDate: '2025-03-31T08:50:20.800385Z'
  biotoolsCURIE: biotools:csdr
  biotoolsID: csdr
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: Differential gene coexpression analysis based on the Conserved, Specific,
    and Differentiated (CSD) method
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://almaaslab.github.io/csdR/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-31T09:00:57.202555Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://www.bioconductor.org/packages/release/bioc/html/csdR.html
  - note: null
    type:
    - Repository
    url: https://github.com/AlmaasLab/csdR
  maturity: Mature
  name: csdR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: japet
  publication:
  - doi: 10.1186/s12859-022-04605-1
    metadata:
      abstract: 'Background: Differential co-expression network analysis has become
        an important tool to gain understanding of biological phenotypes and diseases.
        The CSD algorithm is a method to generate differential co-expression networks
        by comparing gene co-expressions from two different conditions. Each of the
        gene pairs is assigned conserved (C), specific (S) and differentiated (D)
        scores based on the co-expression of the gene pair between the two conditions.
        The result of the procedure is a network where the nodes are genes and the
        links are the gene pairs with the highest C-, S-, and D-scores. However, the
        existing CSD-implementations suffer from poor computational performance, difficult
        user procedures and lack of documentation. Results: We created the R-package
        csdR aimed at reaching good performance together with ease of use, sufficient
        documentation, and with the ability to play well with other tools for data
        analysis. csdR was benchmarked on a realistic dataset with 20,645 genes. After
        verifying that the chosen number of iterations gave sufficient robustness,
        we tested the performance against the two existing CSD implementations. csdR
        was superior in performance to one of the implementations, whereas the other
        did not run. Our implementation can utilize multiple processing cores. However,
        we were unable to achieve more than ∼ 2.7 parallel speedup with saturation
        reached at about 10 cores. Conclusion: The results suggest that csdR is a
        useful tool for differential co-expression analysis and is able to generate
        robust results within a workday on datasets of realistic sizes when run on
        a workstation or compute server.'
      authors:
      - name: Pettersen J.P.
      - name: Almaas E.
      citationCount: 3
      date: '2022-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: csdR, an R package for differential co-expression analysis
    note: null
    pmcid: PMC8858518
    pmid: '35183100'
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene regulation
    uri: http://edamontology.org/topic_0204
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:13:12.813456Z'
  biotoolsCURIE: biotools:we_sa
  biotoolsID: we_sa
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: 'WeSA, (weighted socioaffinity), is a statistical score which can rank
    results from protein experiments of the bait-prey type, e.g. affinity purification,
    immunoprecipitation or proximity labelling.


    You can enter lists of interacting proteins according to the results of your experiment.
    Input your raw data before any filtering is done.


    As a result you will get a re-ordered list of scores which allows one to understand
    which interactions are more likely than others based on what previous experiments
    can add to your data.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://wesa.russelllab.org/help
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://wesa.russelllab.org/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-28T12:14:32.901305Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/russelllab/wesa
  maturity: null
  name: WeSA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/nar/gkae423
    metadata:
      abstract: 'Protein-protein interaction experiments still yield many false positive
        interactions. The socioaffinity metric can distinguish true protein-protein
        interactions from noise based on available data. Here, we present WeSA (Weighted
        SocioAffinity), which considers large datasets of interaction proteomics data
        (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in
        a statistically robust way, flag those (even from a single experiment) that
        are likely to be false positives. ROC analysis (using CORUM-PDB positives
        and Negatome negatives) shows that WeSA improves over other measures of interaction
        confidence. WeSA shows consistently good results over all datasets (up to:
        AUC = 0.93 and at best threshold: TPR = 0.84, FPR = 0.11, Precision = 0.98).
        WeSA is freely available without login (wesa.russelllab.org). Users can submit
        their own data or look for organized information on human protein interactions
        using the web server. Users can either retrieve available information for
        a list of proteins of interest or calculate scores for new experiments. The
        server outputs either pre-computed or updated WeSA scores for the input enriched
        with information from databases. The summary is presented as a table and a
        network-based visualization allowing the user to remove those nodes/edges
        that the method considers spurious.'
      authors:
      - name: Shtetinska M.M.
      - name: Gonzalez-Sanchez J.-C.
      - name: Beyer T.
      - name: Boldt K.
      - name: Ueffing M.
      - name: Russell R.B.
      citationCount: 1
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'WeSA: A web server for improving analysis of affinity proteomics data'
    note: null
    pmcid: PMC11223876
    pmid: '38795065'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Protein interaction experiment
    uri: http://edamontology.org/topic_3957
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:01:52.929561Z'
  biotoolsCURIE: biotools:chemfh
  biotoolsID: chemfh
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: yjc@nudt.edu.cn
    fundrefid: null
    gridid: null
    name: Jiacai Yi
    note: National University of Defense Technology, Changsha, Hunan
    orcidid: https://orcid.org/0000-0001-6823-1882
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://github.com/antwiser
  description: 'An integrated online platform developed for the screening and prediction
    of potential frequent drug hitters, thus improving the efficiency of drug R&D
    in colloidal aggregate, firefly luciferase reporter enzyme inhibition, fluorescence,
    chemical reactivity, and promiscuity. ChemFH: an integrated tool for screening
    frequent false positives in chemical biology and drug discovery.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/antwiser/ChemFH/blob/main/README.md
  - note: null
    type:
    - Quick start guide
    url: https://chemfh.scbdd.com/documentation/#/
  download:
  - note: null
    type: Source code
    url: https://github.com/antwiser/ChemFH/releases/tag/ChemFH-1.0.0
    version: 1.0.0
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: SMILES string
        uri: http://edamontology.org/data_2301
      format: []
    note: null
    operation:
    - term: Virtual screening
      uri: http://edamontology.org/operation_3938
    output:
    - data:
        term: Drug report
        uri: http://edamontology.org/data_1696
      format: []
    - data:
        term: Chemical structure image
        uri: http://edamontology.org/data_1712
      format: []
  homepage: https://chemfh.scbdd.com/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-03-28T12:04:07.529430Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/antwiser/ChemFH
  - note: Webpage of the tool
    type:
    - Other
    url: https://chemfh.scbdd.com/
  maturity: Emerging
  name: ChemFH
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: etepf22
  publication:
  - doi: 10.1093/nar/gkae424
    metadata:
      abstract: High-throughput screening rapidly tests an extensive array of chemical
        compounds to identify hit compounds for specific biological targets in drug
        discovery. However, false-positive results disrupt hit compound screening,
        leading to wastage of time and resources. To address this, we propose ChemFH,
        an integrated online platform facilitating rapid virtual evaluation of potential
        false positives, including colloidal aggregators, spectroscopic interference
        compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous
        compounds, and other assay interferences. By leveraging a dataset containing
        823 391 compounds, we constructed high-quality prediction models using multi-task
        directed message-passing network (DMPNN) architectures combining uncertainty
        estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated
        1441 representative alert substructures derived from the collected data and
        ten commonly used frequent hitter screening rules. ChemFH was validated with
        an external set of 75 compounds. Subsequently, the virtual screening capability
        of ChemFH was successfully confirmed through its application to five virtual
        screening libraries. Furthermore, ChemFH underwent additional validation on
        two natural products and FDA-approved drugs, yielding reliable and accurate
        results. ChemFH is a comprehensive, reliable, and computationally efficient
        screening pipeline that facilitates the identification of true positive results
        in assays, contributing to enhanced efficiency and success rates in drug discovery.
        ChemFH is freely available via https://chemfh.scbdd.com/.
      authors:
      - name: Shi S.
      - name: Fu L.
      - name: Yi J.
      - name: Yang Z.
      - name: Zhang X.
      - name: Deng Y.
      - name: Wang W.
      - name: Wu C.
      - name: Zhao W.
      - name: Hou T.
      - name: Zeng X.
      - name: Lyu A.
      - name: Cao D.
      citationCount: 0
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ChemFH: An integrated tool for screening frequent false positives in
        chemical biology and drug discovery'
    note: High-throughput screening rapidly tests an extensive array of chemical compounds
      to identify hit compounds for specific biological targets in drug discovery.
      However, false-positive results disrupt hit compound screening, leading to wastage
      of time and resources. To address this, we propose ChemFH, an integrated online
      platform facilitating rapid virtual evaluation of potential false positives,
      including colloidal aggregators, spectroscopic interference compounds, firefly
      luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and
      other assay interferences. ChemFH is freely available via https://chemfh.scbdd.com/.
    pmcid: PMC11223804
    pmid: '38783035'
    type:
    - Method
    version: 1.0.0
  relation: []
  toolType:
  - Script
  - Command-line tool
  topic:
  - term: Medicinal chemistry
    uri: http://edamontology.org/topic_0209
  validated: 0
  version:
  - 1.0.0
- accessibility: Open access
  additionDate: '2025-03-28T11:01:27.681477Z'
  biotoolsCURIE: biotools:lncrnaway
  biotoolsID: lncrnaway
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: qlyu@cqu.edu.cn
    fundrefid: null
    gridid: null
    name: Chongqing General Hospital, Chongqing University, Lyu Lab
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A web-based sgRNA design tool for precise and effective suppression
    of long noncoding RNAs
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Ensembl gene ID
        uri: http://edamontology.org/data_1033
      format: []
    - data:
        term: Gene symbol
        uri: http://edamontology.org/data_1026
      format: []
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
    note: Design of sgRNA for lncRNA knockout; Primer design for PCR and qPCR to knockout
      detection.
    operation:
    - term: Sequence generation
      uri: http://edamontology.org/operation_0230
    - term: PCR primer design
      uri: http://edamontology.org/operation_0308
    output:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Text mining report
        uri: http://edamontology.org/data_0972
      format: []
  homepage: https://www.lncrnaway.com
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-28T11:54:36.402981Z'
  license: null
  link:
  - note: null
    type:
    - Service
    url: https://www.lncrnaway.com
  maturity: Mature
  name: LncRNAway
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: inespinheiro
  publication:
  - doi: 10.1093/nar/gkae383
    metadata:
      abstract: Thousands of long noncoding RNAs (lncRNAs) have been annotated via
        high-throughput RNA sequencing, yet only a small fraction have been functionally
        investigated. Genomic knockout is the mainstream strategy for studying the
        biological function of protein-coding genes and lncRNAs, whereas the complexity
        of the lncRNA locus, especially the natural antisense lncRNAs (NAT-lncRNAs),
        presents great challenges. Knocking out lncRNAs often results in unintended
        disruptions of neighboring protein-coding genes and small RNAs, leading to
        ambiguity in observing phenotypes and interpreting biological function. To
        address this issue, we launched LncRNAway, a user-friendly web tool based
        on the BESST (branchpoint to 3' splicing site targeting) method, to design
        sgRNAs for lncRNA knockout. LncRNAway not only provides specific and effective
        lncRNA knockout guidelines but also integrates genotyping primers and quantitative
        PCR primers designing, thereby streamlining experimental procedures of lncRNA
        function study. LncRNAway is freely available at https://www.lncrnaway.com.
      authors:
      - name: Zhang S.
      - name: Wang S.
      - name: Lu F.
      - name: Bie L.
      - name: Luo Y.
      - name: Sun J.
      - name: Zhang Y.
      - name: Wang Y.
      - name: Zhang Y.
      - name: Lyu Q.R.
      citationCount: 0
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'LncRNAway: A web-based sgRNA design tool for precise and effective suppression
        of long noncoding RNAs'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:20:32.972026Z'
  biotoolsCURIE: biotools:isovis
  biotoolsID: isovis
  collectionID:
  - IsoVis
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A webserver for visualization and annotation of alternative RNA isoforms
  documentation:
  - note: null
    type:
    - User manual
    url: https://isovis.gandallab.org/tutorial/
  download:
  - note: null
    type: Source code
    url: https://github.com/ClarkLaboratory/IsoVis.git
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - UK
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: --
    input:
    - data:
        term: Protein complex
        uri: http://edamontology.org/data_2877
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
      - term: GTF
        uri: http://edamontology.org/format_2306
      - term: BED
        uri: http://edamontology.org/format_3003
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: isoform stack
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format: []
  homepage: https://isomix.org/isovis/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-03-28T11:41:10.718643Z'
  license: MPL-2.0
  link:
  - note: a webserver
    type:
    - Other
    url: https://isomix.org/isovis/
  - note: null
    type:
    - Repository
    url: https://github.com/ClarkLaboratory/IsoVis?tab=MPL-2.0-1-ov-file
  - note: Nucleic Acids Research
    type:
    - Other
    url: https://academic.oup.com/nar/article/52/W1/W341/7665639
  maturity: Mature
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  owner: Kvetoslava_Mahutova
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  toolType:
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  topic:
  - term: Biosciences
    uri: http://edamontology.org/topic_4019
  validated: 0
  version:
  - v1.10
- accessibility: Open access
  additionDate: '2025-02-28T15:04:33.594183Z'
  biotoolsCURIE: biotools:prolfquapp
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  cost: Free of charge
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  description: A command-line tool for differential expression analysis in quantitative
    proteomics
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/prolfqua/prolfquapp/blob/master/README.md
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  function:
  - cmd: './prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis
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      # and again you run the version within the docker container with

      # ./prolfquapp_docker.sh prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y
      config.yaml -w NameOfAnalysis -s DIANN'
    input:
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        term: Expression data
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      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Sequence
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      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Differential protein expression profiling
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    output:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
      - term: HTML
        uri: http://edamontology.org/format_2331
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    input:
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        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
    note: null
    operation:
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output:
    - data:
        term: Quality control report
        uri: http://edamontology.org/data_3914
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
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      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://github.com/prolfqua/prolfquapp
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-28T10:18:25.715685Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/prolfqua/prolfquapp
  - note: null
    type:
    - Issue tracker
    url: https://github.com/prolfqua/prolfquapp/issues
  maturity: Emerging
  name: prolfquapp
  operatingSystem:
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  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1021/acs.jproteome.4c00911
    metadata:
      abstract: Mass spectrometry is a cornerstone of quantitative proteomics, enabling
        relative protein quantification and differential expression analysis (DEA)
        of proteins. As experiments grow in complexity, involving more samples, groups,
        and identified proteins, interactive differential expression analysis tools
        become impractical. The prolfquapp addresses this challenge by providing a
        command-line interface that simplifies DEA, making it accessible to nonprogrammers
        and seamlessly integrating it into workflow management systems. Prolfquapp
        streamlines data processing and result visualization by generating dynamic
        HTML reports that facilitate the exploration of differential expression results.
        These reports allow for investigating complex experiments, such as those involving
        repeated measurements or multiple explanatory variables. Additionally, prolfquapp
        supports various output formats, including XLSX files, SummarizedExperiment
        objects and rank files, for further interactive analysis using spreadsheet
        software, the exploreDE Shiny application, or gene set enrichment analysis
        software, respectively. By leveraging advanced statistical models from the
        prolfqua R package, prolfquapp offers a user-friendly, integrated solution
        for large-scale quantitative proteomics studies, combining efficient data
        processing with insightful, publication-ready outputs.
      authors:
      - name: Wolski W.E.
      - name: Grossmann J.
      - name: Schwarz L.
      - name: Leary P.
      - name: Turker C.
      - name: Nanni P.
      - name: Schlapbach R.
      - name: Panse C.
      citationCount: 0
      date: '2025-02-07T00:00:00Z'
      journal: Journal of Proteome Research
      title: prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential
        Expression Analysis in Quantitative Proteomics
    note: null
    pmcid: null
    pmid: null
    type: []
    version: 0.0.6
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version:
  - 0.1.6
- accessibility: Open access
  additionDate: '2025-03-25T09:59:44.309666Z'
  biotoolsCURIE: biotools:rocrate-validator
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    note: null
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  description: rocrate-validator is a tool and Python package to validate RO-Crates
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  documentation:
  - note: null
    type:
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    - Command-line options
    - Installation instructions
    - Quick start guide
    url: https://rocrate-validator.readthedocs.io/
  download:
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    url: https://github.com/crs4/rocrate-validator/releases
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  function:
  - cmd: '`rocrate-validator validate <path_to_rocrate>`'
    input: []
    note: Validate compliance of RO-Crate objects with specifications
    operation:
    - term: Format validation
      uri: http://edamontology.org/operation_0336
    output: []
  homepage: https://rocrate-validator.readthedocs.io/
  homepage_status: 0
  language:
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  lastUpdate: '2025-03-25T12:19:18.196376Z'
  license: Apache-2.0
  link:
  - note: null
    type:
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    url: https://github.com/crs4/rocrate-validator
  - note: null
    type:
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  name: rocrate-validator
  operatingSystem:
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  owner: mep
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    type: usedBy
  toolType:
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  - Library
  topic:
  - term: Data quality management
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  - term: FAIR data
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  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-25T12:17:42.278603Z'
  biotoolsCURIE: biotools:lifemonitor
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  collectionID: []
  confidence_flag: null
  cost: Free of charge
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    name: Marco Enrico Piras
    note: null
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  - email: simone.leo@crs4.it
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    orcidid: https://orcid.org/0000-0001-8271-5429
    rorid: null
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    note: null
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    rorid: null
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    note: null
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    rorid: null
    typeEntity: Institute
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    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
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    url: https://www.eosc-life.eu/
  description: 'LifeMonitor is a service to support the sustainability and reusability
    of published computational workflows. It provides:

    * periodic testing;

    * test monitoring and notification;

    * automated checks and templates for workflow best practices.'
  documentation:
  - note: null
    type:
    - User manual
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  - note: null
    type:
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  homepage: https://lifemonitor.eu
  homepage_status: 0
  language:
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  - JavaScript
  lastUpdate: '2025-03-25T12:17:42.289116Z'
  license: MIT
  link:
  - note: null
    type:
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    url: https://github.com/crs4/life_monitor
  - note: REST API (production instance)
    type:
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  - note: REST API (dev instance)
    type:
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    url: https://api.dev/lifemonitor.eu
  - note: Web App (production instance)
    type:
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    url: https://app.lifemonitor.eu
  - note: Web App (dev instance)
    type:
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    url: https://app.dev.lifemonitor.eu
  - note: null
    type:
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    url: https://github.com/crs4/life_monitor/issues
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  maturity: Mature
  name: LifeMonitor
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  otherID: []
  owner: mep
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  relation:
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    type: uses
  toolType:
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  - Web API
  - Web service
  topic:
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    uri: http://edamontology.org/topic_3372
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Open science
    uri: http://edamontology.org/topic_4010
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-22T00:35:21.183948Z'
  biotoolsCURIE: biotools:deepannotation
  biotoolsID: deepannotation
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: The python package 'DeepAnnotation' can be used to perform genomic
    selection (GS), which is a promising breeding strategy for agricultural breeding.
    DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations
    with interpretable deep learning framework.
  documentation: []
  download:
  - note: GitHub repository
    type: Downloads page
    url: https://github.com/mawenlong2016/DeepAnnotation
    version: null
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  homepage: https://github.com/mawenlong2016/DeepAnnotation
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  lastUpdate: '2025-03-22T00:35:21.186342Z'
  license: GPL-3.0
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    url: https://github.com/mawenlong2016/DeepAnnotation
  maturity: null
  name: DeepAnnotation
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  owner: mawenlong2025
  publication: []
  relation: []
  toolType:
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  - Script
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-11T14:55:53.492167Z'
  biotoolsCURIE: biotools:molgenis_european_health_research_data_and_sample_catalogue
  biotoolsID: molgenis_european_health_research_data_and_sample_catalogue
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A collaborative effort to integrate the catalogues of diverse EU research
    projects and networks to accelerate reuse and improve citizens health.
  documentation: []
  download: []
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    - mswertz
    - EleanorHyde
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  homepage_status: 0
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  license: null
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  name: European Health Research Data and Sample Catalogue
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  publication:
  - doi: 10.1055/s-0042-1742522
    metadata:
      abstract: 'Objectives: Existing individual-level human data cover large populations
        on many dimensions such as lifestyle, demography, laboratory measures, clinical
        parameters, etc. Recent years have seen large investments in data catalogues
        to FAIRify data descriptions to capitalise on this great promise, i.e. make
        catalogue contents more Findable, Accessible, Interoperable and Reusable.
        However, their valuable diversity also created heterogeneity, which poses
        challenges to optimally exploit their richness. Methods: In this opinion review,
        we analyse catalogues for human subject research ranging from cohort studies
        to surveillance, administrative and healthcare records. Results: We observe
        that while these catalogues are heterogeneous, have various scopes, and use
        different terminologies, still the underlying concepts seem potentially harmonizable.
        We propose a unified framework to enable catalogue data sharing, with catalogues
        of multi-center cohorts nested as a special case in catalogues of real-world
        data sources. Moreover, we list recommendations to create an integrated community
        of metadata catalogues and an open catalogue ecosystem to sustain these efforts
        and maximise impact. Conclusions: We propose to embrace the autonomy of motivated
        catalogue teams and invest in their collaboration via minimal standardisation
        efforts such as clear data licensing, persistent identifiers for linking same
        records between catalogues, minimal metadata ''common data elements'' using
        shared ontologies, symmetric architectures for data sharing (push/pull) with
        clear provenance tracks to process updates and acknowledge original contributors.
        And most importantly, we encourage the creation of environments for collaboration
        and resource sharing between catalogue developers, building on international
        networks such as OpenAIRE and research data alliance, as well as domain specific
        ESFRIs such as BBMRI and ELIXIR.'
      authors:
      - name: Swertz M.
      - name: Van Enckevort E.
      - name: Oliveira J.L.
      - name: Fortier I.
      - name: Bergeron J.
      - name: Thurin N.H.
      - name: Hyde E.
      - name: Kellmann A.
      - name: Pahoueshnja R.
      - name: Sturkenboom M.
      - name: Cunnington M.
      - name: Nybo Andersen A.-M.
      - name: Marcon Y.
      - name: Goncalves G.
      - name: Gini R.
      citationCount: 9
      date: '2022-12-04T00:00:00Z'
      journal: Yearbook of Medical Informatics
      title: Towards an Interoperable Ecosystem of Research Cohort and Real-world
        Data Catalogues Enabling Multi-center Studies
    note: null
    pmcid: PMC9719789
    pmid: '36463884'
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- accessibility: Open access
  additionDate: '2025-03-12T22:55:02.224894Z'
  biotoolsCURIE: biotools:matheat
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  cost: Free of charge
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    fundrefid: null
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    name: Seyyed Amin Seyyed Rezaei
    note: null
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    typeRole:
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    url: https://matheat.streamlit.app/
  description: MatHeat transforms your gene expression data into stunning interactive
    heatmaps with advanced clustering, UMAP/KMeans, and Reactome pathway analysis.
  documentation: []
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  license: null
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  - note: Landing page of the MatHeat
    type:
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    type:
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    url: https://matheat.org/
  maturity: null
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  owner: Aminn
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  toolType: []
  topic:
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  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
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  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps-md
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    note: null
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    url: https://www.biocomp.unibo.it
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  description: Predicting the impact of mutations on protein stability from sequence
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  documentation:
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    type:
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  function:
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    input:
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    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
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    operation: []
    output:
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    input:
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    - data:
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    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
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    output:
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  - doi: 10.1093/bioinformatics/btv291
    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 111
      date: '2015-02-06T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS: Predicting the impact of non-synonymous variations on protein
        stability from sequence'
    note: null
    pmcid: null
    pmid: '25957347'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 189
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
        sequence and structure'
    note: null
    pmcid: null
    pmid: '27153629'
    type:
    - Primary
    version: null
  relation: []
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  topic:
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  - term: Genetic variation
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  - term: Rare diseases
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      abstract: Gene-by-gene approaches are becoming increasingly popular in bacterial
        genomic epidemiology and outbreak detection. However, there is a lack of open-source
        scalable software for schema definition and allele calling for these methodologies.
        The chewBBACA suite was designed to assist users in the creation and evaluation
        of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent
        allele calling in bacterial strains of interest. chewBBACA performs the schema
        creation and allele calls on complete or draft genomes resulting from de novo
        assemblers. The chewBBACA software uses Python 3.4 or higher and can run on
        a laptop or in high performance clusters making it useful for both small laboratories
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      - name: Silva M.
      - name: Machado M.P.
      - name: Silva D.N.
      - name: Rossi M.
      - name: Moran-Gilad J.
      - name: Santos S.
      - name: Ramirez M.
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      citationCount: 276
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    note: null
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      abstract: Core genome multilocus sequence typing (cgMLST) has gained in popularity
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        We introduce here pyMLST, a new complete, stand-alone, free and open source
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        For each gene, the first allele is aligned against the bacterial genome of
        interest using BLAT. Incomplete genes are aligned using MAFT. All data are
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        genome collections of major bacterial pathogens (Escherichia coli, Pseudomonas
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        ChewBBACA, SeqSphere and the variant calling approach. We compared the sensitivity,
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        We found that pyMLST-KMA is a convenient screening method to avoid assembling
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      authors:
      - name: Biguenet A.
      - name: Bordy A.
      - name: Atchon A.
      - name: Hocquet D.
      - name: Valot B.
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  description: Predicting the impact of mutations on protein stability from sequence
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    input:
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      abstract: 'Motivation: The knowledge of protein stability upon residue variation
        is an important step for functional protein design and for understanding how
        protein variants can promote disease onset. Computational methods are important
        to complement experimental approaches and allow a fast screening of large
        datasets of variations. Results: In this work, we present DDGemb, a novel
        method combining protein language model embeddings and transformer architectures
        to predict protein ΔΔG upon both single- and multi-point variations. DDGemb
        has been trained on a high-quality dataset derived from literature and tested
        on available benchmark datasets of single- and multi-point variations. DDGemb
        performs at the state of the art in both single- and multi-point variations.'
      authors:
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      - name: Manfredi M.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
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    note: null
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  topic:
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  - term: Genetic variation
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  - term: Rare diseases
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  version:
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  description: Predicting the impact of mutations on protein stability from sequence
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  function:
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    input:
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  publication:
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    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
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  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
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      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
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  description: This Python script provides functions to process raster files in chunks
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  description: The Arabidopsis Coexpression Tool (ACT) is based on the coexpression
    analysis of 21273 Arabidopsis thaliana genes from gene pair correlation data of
    3500 Affymetrix Arabidopsis ATH1 Genome Array Chip microarray samples.
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  publication:
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    metadata: null
    note: null
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    type:
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  - doi: 10.1016/j.xpro.2022.101208
    metadata:
      abstract: Coexpressed genes tend to participate in related biological processes.
        Gene coexpression analysis allows the discovery of functional gene partners
        or the assignment of biological roles to genes of unknown function. In this
        protocol, we describe the steps necessary to create a gene coexpression tree
        for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray
        data. Because the computational analysis described here is highly dependent
        on sample quality, we detail an automatic quality control approach. For complete
        details on the use and execution of this protocol, please refer to Zogopoulos
        et al. (2021).
      authors:
      - name: Zogopoulos V.L.
      - name: Malatras A.
      - name: Michalopoulos I.
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      title: Gene coexpression analysis in Arabidopsis thaliana based on public microarray
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    note: null
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    metadata:
      abstract: Gene coexpression analysis constitutes a widely used practice for
        gene partner identification and gene function prediction, consisting of many
        intricate procedures. The analysis begins with the collection of primary transcriptomic
        data and their preprocessing, continues with the calculation of the similarity
        between genes based on their expression values in the selected sample dataset
        and results in the construction and visualisation of a gene coexpression network
        (GCN) and its evaluation using biological term enrichment analysis. As gene
        coexpression analysis has been studied ex-tensively, we present most parts
        of the methodology in a clear manner and the reasoning behind the selection
        of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble
        account of the steps required for performing a complete gene coexpression
        analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays,
        as well as the best practices for GCN construction. Furthermore, we recount
        the most popular webtools and standalone applications performing gene coexpression
        analysis, with details on their methods, features and outputs.
      authors:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Papadopoulos K.
      - name: Tsotra I.
      - name: Iconomidou V.A.
      - name: Michalopoulos I.
      citationCount: 9
      date: '2022-07-01T00:00:00Z'
      journal: Biology
      title: Approaches in Gene Coexpression Analysis in Eukaryotes
    note: null
    pmcid: PMC9312353
    pmid: '36101400'
    type:
    - Review
    version: null
  - doi: 10.1093/nar/gkl204
    metadata:
      abstract: The Arabidopsis Co-expression Tool, ACT, ranks the genes across a
        large microarray dataset according to how closely their expression follows
        the expression of a query gene. A database stores pre-calculated co-expression
        results for ∼21 800 genes based on data from over 300 arrays. These results
        can be corroborated by calculation of co-expression results for user-defined
        sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique
        Finder (CF) identifies groups of genes which are consistently co-expressed
        with each other across a user-defined co-expression list. The parameters can
        be altered easily to adjust cluster size and the output examined for optimal
        inclusion of genes with known biological roles. Alternatively, a Scatter Plot
        tool displays the correlation coefficients for all genes against two user-selected
        queries on a scatter plot which can be useful for visual identification of
        clusters of genes with similar r-values. User-input groups of genes can be
        highlighted on the scatter plots. Inclusion of genes with known biology in
        sets of genes identified using CF and Scatter Plot tools allows inferences
        to be made about the roles of the other genes in the set and both tools can
        therefore be used to generate short lists of genes for further characterization.
        ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT. © The Author 2006.
        Published by Oxford University Press. All rights reserved.
      authors:
      - name: Manfield I.W.
      - name: Jen C.-H.
      - name: Pinney J.W.
      - name: Michalopoulos I.
      - name: Bradford J.R.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 130
      date: '2006-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based
        gene expression analysis'
    note: null
    pmcid: PMC1538833
    pmid: '16845059'
    type:
    - Other
    version: null
  - doi: 10.1111/j.1365-313x.2006.02681.x
    metadata:
      abstract: 'We present a new WWW-based tool for plant gene analysis, the Arabidopsis
        Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray
        data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression
        analysis tool allows users to identify genes whose expression patterns are
        correlated across selected experiments or the complete data set. Results are
        accompanied by estimates of the statistical significance of the correlation
        relationships, expressed as probability (P) and expectation (E) values. Additionally,
        highly ranked genes on a correlation list can be examined using the novel
        CLIQUE FINDER tool to determine the sets of genes most likely to be regulated
        in a similar manner. In combination, these tools offer three levels of analysis:
        creation of correlation lists of co-expressed genes, refinement of these lists
        using two-dimensional scatter plots, and dissection into cliques of co-regulated
        genes. We illustrate the applications of the software by analysing genes encoding
        functionally related proteins, as well as pathways involved in plant responses
        to environmental stimuli. These analyses demonstrate novel biological relationships
        underlying the observed gene co-expression patterns. To demonstrate the ability
        of the software to develop testable hypotheses on gene function within a defined
        biological process we have used the example of cell wall biosynthesis genes.
        The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/.
        © 2006 The Authors.'
      authors:
      - name: Jen C.-H.
      - name: Manfield I.W.
      - name: Michalopoulos I.
      - name: Pinney J.W.
      - name: Willats W.G.T.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 65
      date: '2006-04-01T00:00:00Z'
      journal: Plant Journal
      title: 'The Arabidopsis co-expression tool (ACT): A WWW-based tool and database
        for microarray-based gene expression analysis'
    note: null
    pmcid: null
    pmid: '16623895'
    type:
    - Other
    version: null
  - doi: 10.1016/j.isci.2021.102848
    metadata:
      abstract: Gene coexpression analysis refers to the discovery of sets of genes
        which exhibit similar expression patterns across multiple transcriptomic data
        sets, such as microarray experiment data of public repositories. Arabidopsis
        Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis
        thaliana, identifies genes which are correlated to a driver gene. Primary
        microarray data from ATH1 Affymetrix platform were processed with Single-Channel
        Array Normalization algorithm and combined to produce a coexpression tree
        which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades
        of coexpressed genes, as well as to perform gene set enrichment analysis,
        being unique in revealing enriched transcription factors targeting coexpressed
        genes. ACT offers a simple and user-friendly interface producing working hypotheses
        which can be experimentally verified for the discovery of gene partnership,
        pathway membership, and transcriptional regulation. ACT analyses have been
        successful in identifying not only genes with coordinated ubiquitous expressions
        but also genes with tissue-specific expressions.
      authors:
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      - name: Saxami G.
      - name: Malatras A.
      - name: Angelopoulou A.
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      - name: Duddy W.J.
      - name: Daras G.
      - name: Hatzopoulos P.
      - name: Westhead D.R.
      - name: Michalopoulos I.
      citationCount: 14
      date: '2021-08-20T00:00:00Z'
      journal: iScience
      title: 'Arabidopsis Coexpression Tool: a tool for gene coexpression analysis
        in Arabidopsis thaliana'
    note: null
    pmcid: PMC8334378
    pmid: '34381973'
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    version: null
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  description: PEP-FOLD4 is a fast and accurate structure prediction tool for peptides
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      - name: Murail S.
      - name: De Vries S.
      - name: Derreumaux P.
      - name: Tuffery P.
      citationCount: 39
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    note: null
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  validated: 0
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        three structural levels of lipids: classes, molecular species, and lipid building
        blocks (acyl, alkyl, or alkenes chains). The tool''s flexibility empowers
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    note: null
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  description: The Coxmos (Cox MultiBlock Survival) R package is an end-to-end pipeline
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    - Maintainer
    url: null
  description: MORE (Multi-Omics REgulation) is an R package for the application of
    Partial Least Squares (PLS) or Multiple Linear Regression (MLR) models with Elastic
    Net or Iterative Sparse Group Lasso (ISGL) regularizations to multi-omics data.
    The MORE method applies MLRs or PLS to model a target omic expression as a function
    of experimental variables, such as diseases or treatments, and the potential regulators
    of that given target feature. The aim is to obtain specific candidate regulators
    for the biological system under study.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    output: []
  homepage: https://github.com/BiostatOmics/MORE
  homepage_status: 0
  language:
  - Other
  - R
  lastUpdate: '2025-03-04T15:19:11.815695Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/BiostatOmics/MORE
  maturity: Emerging
  name: MORE
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication:
  - doi: 10.1101/2024.01.25.577162
    metadata: null
    note: This is a preprint available at bioRxiv. When the paper is published the
      link wil be updated.
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 1.0.0
- accessibility: Open access
  additionDate: '2025-03-03T15:06:10.867996Z'
  biotoolsCURIE: biotools:multipower
  biotoolsID: multipower
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sotacam@eio.upv.es
    fundrefid: null
    gridid: null
    name: Sonia Tarazona
    note: null
    orcidid: https://orcid.org/0000-0001-5346-1407
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    url: null
  description: The MultiPower R method performs statistical power studies for multi-omics
    experiments, and is designed to assist users in experimental design as well as
    in the evaluation of already-generated multi-omics datasets.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/ConesaLab/MultiPower/blob/master/MultiPowerUsersGuide_v2.pdf
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Power test
      uri: http://edamontology.org/operation_4031
    output: []
  homepage: https://github.com/ConesaLab/MultiPower
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-04T15:16:47.048391Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ConesaLab/MultiPower
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ConesaLab/MultiPower/issues
  maturity: Emerging
  name: MultiPower
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication:
  - doi: 10.1038/s41467-020-16937-8
    metadata:
      abstract: Multi-omic studies combine measurements at different molecular levels
        to build comprehensive models of cellular systems. The success of a multi-omic
        data analysis strategy depends largely on the adoption of adequate experimental
        designs, and on the quality of the measurements provided by the different
        omic platforms. However, the field lacks a comparative description of performance
        parameters across omic technologies and a formulation for experimental design
        in multi-omic data scenarios. Here, we propose a set of harmonized Figures
        of Merit (FoM) as quality descriptors applicable to different omic data types.
        Employing this information, we formulate the MultiPower method to estimate
        and assess the optimal sample size in a multi-omics experiment. MultiPower
        supports different experimental settings, data types and sample sizes, and
        includes graphical for experimental design decision-making. MultiPower is
        complemented with MultiML, an algorithm to estimate sample size for machine
        learning classification problems based on multi-omic data.
      authors:
      - name: Tarazona S.
      - name: Balzano-Nogueira L.
      - name: Gomez-Cabrero D.
      - name: Schmidt A.
      - name: Imhof A.
      - name: Hankemeier T.
      - name: Tegner J.
      - name: Westerhuis J.A.
      - name: Conesa A.
      citationCount: 54
      date: '2020-12-01T00:00:00Z'
      journal: Nature Communications
      title: Harmonization of quality metrics and power calculation in multi-omic
        studies
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-03-03T13:46:56.613465Z'
  biotoolsCURIE: biotools:mosim
  biotoolsID: mosim
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sotacam@eio.upv.es
    fundrefid: null
    gridid: null
    name: Sonia Tarazona
    note: null
    orcidid: https://orcid.org/0000-0001-5346-1407
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    url: null
  description: MOSim (Multi-Omics Simulation) package simulates multi-omic experiments
    that mimic regulatory mechanisms within the cell, allowing flexible experimental
    design including time course and multiple groups.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output: []
  homepage: https://bioconductor.org/packages/release/bioc/html/MOSim.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-04T15:12:34.696650Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ConesaLab/MOSim
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ConesaLab/MOSim/issues
  maturity: Emerging
  name: MOSim
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication:
  - doi: 10.1101/421834
    metadata: null
    note: This is a preprint accesible at bioRxiv. When the paper is published it
      will be updated
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Simulation experiment
    uri: http://edamontology.org/topic_3524
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 2.2.0
- accessibility: Open access
  additionDate: '2021-01-18T09:08:27Z'
  biotoolsCURIE: biotools:metidfyr
  biotoolsID: metidfyr
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: 'Open-Source R Package to Decipher Small-Molecule Drugs Metabolism
    Through High Resolution Mass Spectrometry.


    MetIDfyR is an open-source, cross-platform and versatile R script to predict and
    detect metabolites in mass spectrometry data (mzML) based on the raw formula of
    the drug of interest.'
  documentation:
  - note: null
    type:
    - Quick start guide
    - Installation instructions
    - Citation instructions
    url: https://github.com/agnesbrnb/MetIDfyR
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: mzML
        uri: http://edamontology.org/format_3244
    - data:
        term: Chemical formula
        uri: http://edamontology.org/data_0846
      format: []
    note: null
    operation:
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
  homepage: https://github.com/agnesbrnb/MetIDfyR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-02-27T15:46:01.807140Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: MetIDfyR
  operatingSystem: []
  otherID: []
  owner: agnesbarnabe
  publication:
  - doi: 10.1021/acs.analchem.0c02281
    metadata:
      abstract: With recent advances in analytical chemistry, liquid chromatography
        high-resolution tandem mass spectrometry (LC-HRMS/MS) has become an essential
        tool for metabolite discovery and detection. Even if most of the common drug
        transformations have already been extensively described, manual search of
        drug metabolites in LC-HRMS/MS datasets is still a common practice in toxicology
        laboratories, complicating metabolite discovery. Furthermore, the availability
        of free open-source software for metabolite discovery is still limited. In
        this article, we present MetIDfyR, an open-source and cross-platform R package
        for in silico drug phase I/II biotransformation prediction and mass-spectrometric
        data mining. MetIDfyR has proven its efficacy for advanced metabolite identification
        in semi-complex and complex mixtures in in vitro or in vivo drug studies and
        is freely available at github.com/agnesblch/MetIDfyR.
      authors:
      - name: Delcourt V.
      - name: Barnabe A.
      - name: Loup B.
      - name: Garcia P.
      - name: Andre F.
      - name: Chabot B.
      - name: Trevisiol S.
      - name: Moulard Y.
      - name: Popot M.-A.
      - name: Bailly-Chouriberry L.
      citationCount: 10
      date: '2020-10-06T00:00:00Z'
      journal: Analytical Chemistry
      title: 'MetIDfyR: An Open-Source R Package to Decipher Small-Molecule Drug Metabolism
        through High-Resolution Mass Spectrometry'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Drug metabolism
    uri: http://edamontology.org/topic_3375
  - term: Analytical chemistry
    uri: http://edamontology.org/topic_3370
  validated: 0
  version: []
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