count: 3469
list:
- accessibility: null
  additionDate: '2025-05-09T13:44:22.905859Z'
  biotoolsCURIE: biotools:buffercalc
  biotoolsID: buffercalc
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: Web application for designing thermodynamically corrected monoprotic
    buffers used in biological systems, with corrections for the temperature and ionic
    strength.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Calculation
      uri: http://edamontology.org/operation_3438
    output:
    - data:
        term: Protocol
        uri: http://edamontology.org/data_2531
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://phbuffers.org/BuffferCalc/Buffer.html
  homepage_status: 0
  language:
  - Other
  lastUpdate: '2025-05-09T13:48:04.641670Z'
  license: null
  link: []
  maturity: Mature
  name: BufferCalc
  operatingSystem: []
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1093/bioinformatics/4.4.487
    metadata:
      abstract: This paper describes a stack for the Apple MacintoshlTM, HypercardTM
        environment that facilitates the calculation of a set of thermodynamically-corrected
        pH buffers. Presented in the familiar Macintosh mouse-based environment, the
        program allows comprehensive buffer design and gives the user full control
        of buffer species, temperature, ionic strength, pH and choice of counter-ion
        to maintain the ionic strength at a fixed value. Addition of new buffers to
        the stack is straightforward and the recipes that are generated, including
        the weights of the various components, can be displayed on the screen or saved
        to a disk file for subsequent printing. © 1988 IRL Press Limited.
      authors:
      - name: Beynon R.J.
      citationCount: 7
      date: '1988-11-01T00:00:00Z'
      journal: Bioinformatics
      title: A Macintosh hypercard stack for calculation of thermodynamically-corrected
        buffer recipes
    note: null
    pmcid: null
    pmid: '3208185'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Chemistry
    uri: http://edamontology.org/topic_3314
  validated: 0
  version: []
- 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: null
  additionDate: '2016-01-19T16:39:56Z'
  biotoolsCURIE: biotools:openms
  biotoolsID: openms
  collectionID:
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  - de.NBI
  - Proteomics
  - OpenMS
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ETH Zürich
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Eberhard-Karls-Universität Tübingen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Freie Universität Berlin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Center for Integrative Bioinformatics (CiBi)
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: cibi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: open-ms-general@lists.sourceforge.net
    fundrefid: null
    gridid: null
    name: General Mailinglist
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Support
    url: null
  - email: open-ms-general@lists.sourceforge.net
    fundrefid: null
    gridid: null
    name: General Mailinglist
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Hannes Röst
    note: null
    orcidid: http://orcid.org/0000-0003-0990-7488
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: Open source library and a collection of tools and interfaces for the
    analysis of mass spectrometry data. Includes over 200 standalone (TOPP) tools
    that can be combined to a workflow with the integrated workflow editor TOPPAS.
    Raw and intermediate mass spectrometry data can be visualised with the included
    viewer TOPPView.
  documentation:
  - note: null
    type:
    - General
    url: http://ftp.mi.fu-berlin.de/pub/OpenMS/release-documentation/html/index.html
  download:
  - note: null
    type: Source code
    url: https://github.com/OpenMS/OpenMS
    version: null
  - note: null
    type: Binaries
    url: http://sourceforge.net/projects/open-ms/files/OpenMS/OpenMS-2.0/
    version: null
  - note: null
    type: Source code
    url: https://github.com/OpenMS/OpenMS
    version: null
  editPermission:
    authors:
    - proteomics.bio.tools
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: pepXML
        uri: http://edamontology.org/format_3655
      - term: mzQuantML
        uri: http://edamontology.org/format_3248
      - term: dta
        uri: http://edamontology.org/format_3652
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: protXML
        uri: http://edamontology.org/format_3747
      - term: idXML
        uri: http://edamontology.org/format_3764
      - term: OBO
        uri: http://edamontology.org/format_2549
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: TraML
        uri: http://edamontology.org/format_3246
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: mzIdentML
        uri: http://edamontology.org/format_3247
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    - term: Prediction and recognition (protein)
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    - term: Comparison
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  homepage: http://www.openms.de
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-04-30T11:39:30.251890Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/OpenMS/OpenMS
  maturity: Mature
  name: OpenMS
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  publication:
  - doi: 10.1038/nmeth.3959
    metadata:
      abstract: © 2016 Nature America, Inc. All rights reserved.High-resolution mass
        spectrometry (MS) has become an important tool in the life sciences, contributing
        to the diagnosis and understanding of human diseases, elucidating biomolecular
        structural information and characterizing cellular signaling networks. However,
        the rapid growth in the volume and complexity of MS data makes transparent,
        accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de),
        a robust, open-source, cross-platform software specifically designed for the
        flexible and reproducible analysis of high-throughput MS data. The extensible
        OpenMS software implements common mass spectrometric data processing tasks
        through a well-defined application programming interface in C++ and Python
        and through standardized open data formats. OpenMS additionally provides a
        set of 185 tools and ready-made workflows for common mass spectrometric data
        processing tasks, which enable users to perform complex quantitative mass
        spectrometric analyses with ease.
      authors:
      - name: Rost H.L.
      - name: Sachsenberg T.
      - name: Aiche S.
      - name: Bielow C.
      - name: Weisser H.
      - name: Aicheler F.
      - name: Andreotti S.
      - name: Ehrlich H.-C.
      - name: Gutenbrunner P.
      - name: Kenar E.
      - name: Liang X.
      - name: Nahnsen S.
      - name: Nilse L.
      - name: Pfeuffer J.
      - name: Rosenberger G.
      - name: Rurik M.
      - name: Schmitt U.
      - name: Veit J.
      - name: Walzer M.
      - name: Wojnar D.
      - name: Wolski W.E.
      - name: Schilling O.
      - name: Choudhary J.S.
      - name: Malmstrom L.
      - name: Aebersold R.
      - name: Reinert K.
      - name: Kohlbacher O.
      citationCount: 246
      date: '2016-08-30T00:00:00Z'
      journal: Nature Methods
      title: 'OpenMS: A flexible open-source software platform for mass spectrometry
        data analysis'
    note: null
    pmcid: null
    pmid: '27575624'
    type:
    - Primary
    version: null
  - doi: 10.1007/978-1-60761-987-1_23
    metadata:
      abstract: Proteomics experiments based on state-of-the-art mass spectrometry
        produce vast amounts of data, which cannot be analyzed manually. Hence, software
        is needed which is able to analyze the data in an automated fashion. The need
        for robust and reusable software tools triggered the development of libraries
        implementing different algorithms for the various analysis steps. OpenMS is
        such a software library and provides a wealth of data structures and algorithms
        for the analysis of mass spectrometric data. For users unfamiliar with programming,
        TOPP ("The OpenMS Proteomics Pipeline") offers a wide range of already implemented
        tools sharing the same interface and designed for a specific analysis task
        each. TOPP thus makes the sophisticated algorithms of OpenMS accessible to
        nonprogrammers. The individual TOPP tools can be strung together into pipelines
        for analyzing mass spectrometry-based experiments starting from the raw output
        of the mass spectrometer. These analysis pipelines can be constructed using
        a graphical editor. Even complex analytical workflows can thus be analyzed
        with ease.
      authors:
      - name: Bertsch A.
      - name: Gropl C.
      - name: Reinert K.
      - name: Kohlbacher O.
      citationCount: 65
      date: '2011-01-01T00:00:00Z'
      journal: Methods in molecular biology (Clifton, N.J.)
      title: 'OpenMS and TOPP: open source software for LC-MS data analysis.'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
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  - term: Proteomics
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  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 1
  version:
  - 2.0.0
- accessibility: Open access
  additionDate: '2019-08-09T13:16:33Z'
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    fundrefid: null
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    name: Hannes L. Rost
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Python library for mass spectrometry, specifically for the analysis
    of proteomics and metabolomics data.
  documentation:
  - note: null
    type:
    - User manual
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  language:
  - Python
  lastUpdate: '2025-04-30T11:39:29.549782Z'
  license: BSD-3-Clause
  link:
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    - Repository
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  name: pyOpenMS
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  owner: samwein
  publication:
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    metadata: null
    note: null
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    type:
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  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-04-29T17:07:56.642694Z'
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    fundrefid: null
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    name: Jorge S. Oliveira
    note: null
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    name: Ana Teresa Freitas
    note: null
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    name: Ana Tereza Ribeiro de Vasconcelos
    note: null
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    - data:
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  publication:
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    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:
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  - term: Microbiology
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  - term: Enzymes
    uri: http://edamontology.org/topic_0821
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2018-07-09T23:06:59Z'
  biotoolsCURIE: biotools:metexplore
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    fundrefid: null
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    name: Fabien JOURDAN
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  - email: ludovic.cottret@inra.fr
    fundrefid: null
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    name: null
    note: null
    orcidid: null
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    typeEntity: Person
    typeRole:
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  - email: metexplore@oulouse.inra.fr
    fundrefid: null
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    note: null
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    typeEntity: Person
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    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.
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    type:
    - User manual
    url: https://metexplore.toulouse.inra.fr/metexplore-doc/index.php
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    type:
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    url: https://metexplore.toulouse.inra.fr/metexploreViz/doc/documentation.php
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    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/
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    input:
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    note: null
    operation:
    - term: Pathway or network analysis
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  publication:
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    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
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      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.
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      citationCount: 92
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    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: null
  additionDate: '2025-04-23T10:31:12.521148Z'
  biotoolsCURIE: biotools:longitools_exposome_toolbox
  biotoolsID: longitools_exposome_toolbox
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: karim.lekadir@ub.edu
    fundrefid: null
    gridid: null
    name: Karim Lekadir
    note: null
    orcidid: https://orcid.org/0000-0002-9456-1612
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  description: The LongITools toolbox is an open-source platform developed to support
    exposome research analysis. This cloud-based computational environment enables
    researchers to construct customized analytical platforms suited to their specific
    research requirements. The system provides a web-based interface that integrates
    various analytical resources, including statistical tools, computational pipelines,
    connections to external data repositories, and data visualization capabilities.
  documentation:
  - note: null
    type:
    - Quick start guide
    url: https://github.com/inab/openVRE/tree/longitools
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://longitools.bsc.es/vre/
  homepage_status: 0
  language:
  - R
  - Python
  - PHP
  lastUpdate: '2025-04-23T10:31:21.719823Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://longitools.bsc.es/vre/
  maturity: Mature
  name: Longitools Exposome Toolbox
  operatingSystem: []
  otherID: []
  owner: noussair
  publication: []
  relation: []
  toolType:
  - Web application
  - Command-line tool
  - Web service
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Medicines research and development
    uri: http://edamontology.org/topic_3376
  validated: 0
  version:
  - '2.0'
- 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:
    authors:
    - artrymix
    - paulpavlidis
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      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
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  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: null
  additionDate: '2016-04-15T11:52:42Z'
  biotoolsCURIE: biotools:comparems2
  biotoolsID: comparems2
  collectionID:
  - ms-utils
  - Proteomics
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: lumc.nl
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Support
    url: https://www.lumc.nl
  - email: magnus.palmblad@gmail.com
    fundrefid: null
    gridid: null
    name: Magnus Palmblad
    note: null
    orcidid: http://orcid.org/0000-0002-5865-8994
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    - Documentor
    url: https://github.com/magnuspalmblad
  - email: null
    fundrefid: null
    gridid: null
    name: Rob Marissen
    note: null
    orcidid: https://orcid.org/0000-0002-1220-9173
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://github.com/524D
  description: compareMS2 is a tool for comparing sets of (tandem) mass spectra for
    clustering samples, molecular phylogenetics, identification of biological species
    or tissues, and quality control. compareMS2 currently consumes Mascot Generic
    Format, or MGF, and produces output in a variety of common image and distance
    matrix formats.
  documentation:
  - note: null
    type:
    - General
    - Command-line options
    url: http://www.ms-utils.org/compareMS2.html
  - note: null
    type:
    - General
    - User manual
    - Command-line options
    - Installation instructions
    url: https://github.com/524D/compareMS2
  download:
  - note: null
    type: Source code
    url: http://www.ms-utils.org/compareMS2.c
    version: '1.0'
  - note: null
    type: Binaries
    url: http://www.ms-utils.org/compareMS2.html
    version: '1.0'
  - note: null
    type: Source code
    url: http://www.ms-utils.org/compareMS2.c
    version: '1.0'
  - note: null
    type: Source code
    url: https://github.com/524D/compareMS2/tree/main/src
    version: '2.0'
  - note: null
    type: Binaries
    url: https://github.com/524D/compareMS2/tree/main
    version: '2.0'
  editPermission:
    authors:
    - proteomics.bio.tools
    type: group
  elixirCommunity:
  - Proteomics
  elixirNode:
  - Netherlands
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: MGF
        uri: http://edamontology.org/format_3651
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    - term: Phylogenetic tree visualisation
      uri: http://edamontology.org/operation_0567
    output:
    - data:
        term: Species tree
        uri: http://edamontology.org/data_3272
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Distance matrix
        uri: http://edamontology.org/data_2855
      format:
      - term: mega
        uri: http://edamontology.org/format_1991
      - term: Nexus format
        uri: http://edamontology.org/format_1912
      - term: newick
        uri: http://edamontology.org/format_1910
  homepage: https://github.com/524D/compareMS2
  homepage_status: 0
  language:
  - C
  - JavaScript
  lastUpdate: '2025-04-16T14:22:04.957317Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/524D/compareMS2
  - note: null
    type:
    - Software catalogue
    url: https://www.ms-utils.org/compareMS2.html
  - note: null
    type:
    - Software catalogue
    url: https://research-software-directory.org/software/comparems2
  maturity: Mature
  name: compareMS2
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1002/rcm.6162
    metadata:
      abstract: 'Rationale: Molecular phylogenetics is the study of evolution and
        relatedness of organisms or genes. Mass spectrometry is used routinely for
        bacterial identification and has also been used for phylogenetic analysis,
        for instance from bone material. Unfortunately, only a small fraction of the
        acquired tandem mass spectra allow direct interpretation. Methods: We describe
        a new algorithm and software for molecular phylogenetics using pairwise comparisons
        of tandem mass spectra from enzymatically digested proteins. The spectra need
        not be annotated and all acquired data is used in the analysis. To demonstrate
        the method, we analyzed tryptic digests of sera from four great apes and two
        other primates. Results: The distribution of spectra dot products for thousands
        of tandem mass spectra collected from two samples provides a measure on the
        fraction of shared peptides between the two samples. When inverted, this becomes
        a distance metric. By pairwise comparison between species and averaging over
        four individuals per species, it was possible to reconstruct the unique correct
        phylogenetic tree for the great apes and other primates. Conclusions: The
        new method described here has several attractive features compared with existing
        methods, among them simplicity, the unbiased use of all acquired data rather
        than a small subset of spectra, and the potential use of heavily degraded
        proteins or proteins with a priori unknown modifications. © 2012 John Wiley
        & Sons, Ltd.'
      authors:
      - name: Palmblad M.
      - name: Deelder A.M.
      citationCount: 30
      date: '2012-04-15T00:00:00Z'
      journal: Rapid Communications in Mass Spectrometry
      title: Molecular phylogenetics by direct comparison of tandem mass spectra
    note: null
    pmcid: null
    pmid: '22368051'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1021/acs.jproteome.2c00457
    metadata:
      abstract: It has long been known that biological species can be identified from
        mass spectrometry data alone. Ten years ago, we described a method and software
        tool, compareMS2, for calculating a distance between sets of tandem mass spectra,
        as routinely collected in proteomics. This method has seen use in species
        identification and mixture characterization in food and feed products, as
        well as other applications. Here, we present the first major update of this
        software, including a new metric, a graphical user interface and additional
        functionality. The data have been deposited to ProteomeXchange with dataset
        identifier PXD034932.
      authors:
      - name: Marissen R.
      - name: Varunjikar M.S.
      - name: Laros J.F.J.
      - name: Rasinger J.D.
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 7
      date: '2023-02-03T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry
        Datasets'
    note: null
    pmcid: PMC9903320
    pmid: '36173614'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1021/acs.jproteome.1c00528
    metadata:
      abstract: Science is full of overlooked and undervalued research waiting to
        be rediscovered. Proteomics is no exception. In this perspective, we follow
        the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing
        tryptic peptides across animal species. This pioneering work directly led
        to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics.
        In the decades following, proteins continued to provide essential clues on
        evolutionary history. While technology has continued to improve, contemporary
        proteomics has strayed from this larger biological context, rarely comparing
        species or asking how protein structure, function, and interactions have evolved.
        Here we recombine proteomics with molecular phylogenetics, highlighting the
        value of framing proteomic results in a larger biological context and how
        almost forgotten research, though technologically surpassed, can still generate
        new ideas and illuminate our work from a different perspective. Though it
        is infeasible to read all research published on a large topic, looking up
        older papers can be surprisingly rewarding when rediscovering a "gem"at the
        end of a long citation chain, aided by digital collections and perpetually
        helpful librarians. Proper literature study reduces unnecessary repetition
        and allows research to be more insightful and impactful by truly standing
        on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/)
        as dataset MSV000087993.
      authors:
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 1
      date: '2021-10-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics
        Experiments in the Light of Proteomics'
    note: null
    pmcid: PMC8491155
    pmid: '34523928'
    type:
    - Review
    version: '2.0'
  relation: []
  toolType:
  - Command-line tool
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - '1.0'
  - '2.0'
- accessibility: Open access
  additionDate: '2025-04-16T08:53:57.270825Z'
  biotoolsCURIE: biotools:openvre
  biotoolsID: openvre
  collectionID:
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  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:
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    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
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  - note: null
    type: Source code
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    version: '1.0'
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  - Tools
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  function:
  - cmd: null
    input: []
    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: Restricted access
  additionDate: '2025-03-17T14:01:59.601345Z'
  biotoolsCURIE: biotools:exposome_maps
  biotoolsID: exposome_maps
  collectionID:
  - EXPANSE
  - EHEN
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: R.C.H.Vermeulen@uu.nl
    fundrefid: null
    gridid: null
    name: Roel Vermeulen
    note: null
    orcidid: https://orcid.org/0000-0003-4082-8163
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: z.s.r.m.boudewijns@uu.nl
    fundrefid: null
    gridid: null
    name: Zimbo Boudewijns
    note: null
    orcidid: https://orcid.org/0000-0002-9383-9571
    rorid: null
    typeEntity: null
    typeRole:
    - Maintainer
    url: null
  - email: l.q.roga@uu.nl
    fundrefid: null
    gridid: null
    name: Lloyd Roga
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: "The Exposome Maps contain geospatially resolved data on environmental\
    \ exposures (Exposome Surfaces), categorized into the following Exposome dimensions:\n\
    Built Environment28 exposures related to:\n•\tGreen space\n•\tBlue space\n•\t\
    Grey space\nFood Environment4 exposures related to:\n•\tFood landscape / healthy\
    \ food index\nPhysico-Chemical Environment99 exposures related to:\n•\tAir pollution\n\
    •\tBiodiversity\n•\tWeather conditions\n•\tLight intensity at night\n•\tNoise\n\
    •\tPesticides\n•\tElectromagnetic fields\nSocial Environment70 exposures related\
    \ to:\n•\tNeighbourhood socio-economic position\n•\tNeighbourhood Demographic\
    \ surfaces and population characteristics\n•\tSocial capital\n•\tSecurity\n•\t\
    Mental health\n•\tProximity to facilities\n•\tEnergy use\n•\tLabour market"
  documentation:
  - note: Description of Environmental variables available through Exposome Maps
    type:
    - User manual
    url: https://surfdrive.surf.nl/files/index.php/s/uqUORDrd428H2F9
  - note: Exposome-NL data platform Data Access and Publication Policy
    type:
    - Terms of use
    url: https://surfdrive.surf.nl/files/index.php/s/fI0Gmuu5O1MJAI9
  download: []
  editPermission:
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    - hkucukali
    type: group
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  elixir_badge: 0
  function: []
  homepage: https://exposome.dataplatform.nl/
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-15T08:35:02.159681Z'
  license: Other
  link:
  - note: Project information page
    type:
    - Other
    url: https://expanseproject.eu/
  maturity: Mature
  name: Exposome Maps
  operatingSystem: []
  otherID: []
  owner: Channel2291
  publication:
  - doi: 10.1126/science.aay3164
    metadata:
      abstract: Despite extensive evidence showing that exposure to specific chemicals
        can lead to disease, current research approaches and regulatory policies fail
        to address the chemical complexity of our world. To safeguard current and
        future generations from the increasing number of chemicals polluting our environment,
        a systematic and agnostic approach is needed. The “exposome” concept strives
        to capture the diversity and range of exposures to synthetic chemicals, dietary
        constituents, psychosocial stressors, and physical factors, as well as their
        corresponding biological responses. Technological advances such as high-resolution
        mass spectrometry and network science have allowed us to take the first steps
        toward a comprehensive assessment of the exposome. Given the increased recognition
        of the dominant role that nongenetic factors play in disease, an effort to
        characterize the exposome at a scale comparable to that of the human genome
        is warranted.
      authors:
      - name: Vermeulen R.
      - name: Schymanski E.L.
      - name: Barabasi A.-L.
      - name: Miller G.W.
      citationCount: 597
      date: '2020-01-24T00:00:00Z'
      journal: Science
      title: 'The exposome and health: Where chemistry meets biology'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1097/EE9.0000000000000162
    metadata:
      abstract: 'By 2030, more than 80% of Europe''s population will live in an urban
        environment. The urban exposome, consisting of factors such as where we live
        and work, where and what we eat, our social network, and what chemical and
        physical hazards we are exposed to, provides important targets to improve
        population health. The EXPANSE (EXposome Powered tools for healthy living
        in urbAN SEttings) project will study the impact of the urban exposome on
        the major contributors to Europe''s burden of disease: Cardio-Metabolic and
        Pulmonary Disease. EXPANSE will address one of the most pertinent questions
        for urban planners, policy makers, and European citizens: "How to maximize
        one''s health in a modern urban environment?" EXPANSE will take the next step
        in exposome research by (1) bringing together exposome and health data of
        more than 55 million adult Europeans and OMICS information for more than 2
        million Europeans; (2) perform personalized exposome assessment for 5,000
        individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry
        to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution
        of the exposome and health through the life course; and (5) evaluating the
        impact of changes in the urban exposome on the burden of cardiometabolic and
        pulmonary disease. EXPANSE will translate its insights and innovations into
        research and dissemination tools that will be openly accessible via the EXPANSE
        toolbox. By applying innovative ethics-by-design throughout the project, the
        social and ethical acceptability of these tools will be safeguarded. EXPANSE
        is part of the European Human Exposome Network.'
      authors:
      - name: Vlaanderen J.
      - name: De Hoogh K.
      - name: Hoek G.
      - name: Peters A.
      - name: Probst-Hensch N.
      - name: Scalbert A.
      - name: Melen E.
      - name: Tonne C.
      - name: De Wit G.A.
      - name: Chadeau-Hyam M.
      - name: Katsouyanni K.
      - name: Esko T.
      - name: Jongsma K.R.
      - name: Vermeulen R.
      citationCount: 38
      date: '2021-08-01T00:00:00Z'
      journal: Environmental Epidemiology
      title: 'Developing the building blocks to elucidate the impact of the urban
        exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: FAIR data
    uri: http://edamontology.org/topic_4012
  - term: Environmental sciences
    uri: http://edamontology.org/topic_3855
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T08:31:46.459964Z'
  biotoolsCURIE: biotools:hictkpy
  biotoolsID: hictkpy
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: 'Python bindings for hictk: read and write .cool and .hic files directly
    from Python'
  documentation:
  - note: null
    type:
    - General
    url: https://hictkpy.readthedocs.io/en/stable/
  - note: null
    type:
    - Installation instructions
    url: https://hictkpy.readthedocs.io/en/stable/installation.html
  - note: null
    type:
    - Quick start guide
    url: https://hictkpy.readthedocs.io/en/stable/quickstart.html
  - note: null
    type:
    - API documentation
    url: https://hictkpy.readthedocs.io/en/stable/api/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictkpy/releases
    version: null
  - note: null
    type: Binaries
    url: https://pypi.org/project/hictkpy/
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictkpy
    version: null
  editPermission:
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    type: private
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  homepage: https://github.com/paulsengroup/hictkpy
  homepage_status: 0
  language:
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  lastUpdate: '2025-04-14T22:54:26.266145Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictkpy
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictkpy/issues
  - note: null
    type:
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    url: https://github.com/paulsengroup/hictkpy/discussions
  maturity: Mature
  name: hictkpy
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: hictk
    type: uses
  toolType:
  - Library
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 0.0.1
  - 0.0.2
  - 0.0.3
  - 0.0.4
  - 0.0.5
  - 1.0.0
  - 1.1.0
  - 1.2.0
- accessibility: null
  additionDate: '2024-02-02T14:42:46.030561Z'
  biotoolsCURIE: biotools:hictk
  biotoolsID: hictk
  collectionID: []
  confidence_flag: null
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  credit: []
  description: Blazing fast toolkit to work with .hic and .cool files
  documentation:
  - note: null
    type:
    - User manual
    url: https://hictk.readthedocs.io/en/stable/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictk/releases
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictk
    version: null
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    type: Container file
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  lastUpdate: '2025-04-14T22:54:16.319235Z'
  license: MIT
  link:
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    type:
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    type:
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  - note: null
    type:
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  maturity: Mature
  name: hictk
  operatingSystem:
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  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  relation:
  - biotoolsID: hictkpy
    type: usedBy
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    type: usedBy
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
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  - 0.0.2
  - 0.0.3
  - 0.0.4
  - 0.0.5
  - 0.0.6
  - 0.0.7
  - 0.0.8
  - 0.0.9
  - 0.0.10
  - 0.0.11
  - 0.0.12
  - 1.0.0
  - 2.0.0
  - 2.0.1
  - 2.0.2
  - 2.1.0
  - 2.1.1
- 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/
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  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
  - biotoolsID: sbgn
    type: uses
  - biotoolsID: libsbml
    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: '2025-04-11T13:56:02.037873Z'
  biotoolsCURIE: biotools:pr2-reference
  biotoolsID: pr2-reference
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  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
  download:
  - note: null
    type: Downloads page
    url: https://github.com/pr2database/pr2database/releases
    version: 5.1.0
  editPermission:
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  homepage: https://app.pr2-database.org/
  homepage_status: 0
  language:
  - R
  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 (with restrictions)
  additionDate: '2025-04-09T13:19:17.715967Z'
  biotoolsCURIE: biotools:clowm
  biotoolsID: clowm
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: dgoebel@techfak.uni-bielefeld.de
    fundrefid: null
    gridid: null
    name: Daniel Göbel
    note: null
    orcidid: https://orcid.org/0009-0009-9985-3823
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    - 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:
    - Primary contact
    - Documentor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Bielefeld University
    note: null
    orcidid: null
    rorid: 02hpadn98
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.uni-bielefeld.de
  - email: null
    fundrefid: null
    gridid: null
    name: Genome Informatics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://gi.cebitec.uni-bielefeld.de
  - email: null
    fundrefid: null
    gridid: null
    name: NFDI4Microbiota
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole: []
    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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    type: private
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  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://clowm.bi.denbi.de
  homepage_status: 0
  language:
  - Python
  - JavaScript
  lastUpdate: '2025-04-09T14:21:50.981521Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://gitlab.ub.uni-bielefeld.de/cmg/clowm
  maturity: Mature
  name: Cloud-based Workflow Manager (CloWM)
  operatingSystem: []
  otherID: []
  owner: dgoebel
  publication:
  - doi: 10.5281/zenodo.14039069
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation:
  - biotoolsID: sans
    type: includes
  toolType:
  - Web API
  - Web service
  - Bioinformatics portal
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-04-08T11:23:25.060891Z'
  biotoolsCURIE: biotools:checkm2
  biotoolsID: checkm2
  collectionID:
  - NFDI4Microbiota
  confidence_flag: null
  cost: null
  credit: []
  description: 'Rapid assessment of genome bin quality using machine learning.


    CheckM2 uses two distinct machine learning models to predict genome completeness.
    The ''general'' gradient boost model is able to generalize well and is intended
    to be used on organisms not well represented in GenBank or RefSeq (roughly, when
    an organism is novel at the level of order, class or phylum). The ''specific''
    neural network model is more accurate when predicting completeness of organisms
    more closely related to the reference training set (roughly, when an organism
    belongs to a known species, genus or family). CheckM2 uses a cosine similarity
    calculation to automatically determine the appropriate completeness model for
    each input genome, but you can also force the use of a particular completeness
    model, or get the prediction outputs for both. There is only one contamination
    model (based on gradient boost) which is applied regardless of taxonomic novelty
    and works well across all cases.'
  documentation:
  - note: null
    type:
    - Quick start guide
    url: https://github.com/chklovski/CheckM2
  download:
  - note: null
    type: Biological data
    url: https://doi.org/10.5281/zenodo.14897628
    version: null
  editPermission:
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  homepage: https://github.com/chklovski/CheckM2
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-08T11:23:25.063938Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Other
    url: https://www.nature.com/articles/s41592-023-01940-w
  maturity: Mature
  name: CheckM2
  operatingSystem:
  - Linux
  otherID: []
  owner: Kasmanas
  publication:
  - doi: 10.1038/s41592-023-01940-w
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Phylogenomics
    uri: http://edamontology.org/topic_0194
  - term: Data quality management
    uri: http://edamontology.org/topic_3572
  validated: 0
  version: []
- 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
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  elixirNode: []
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  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:
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    uri: http://edamontology.org/topic_0204
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version: []
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    name: Chongqing General Hospital, Chongqing University, Lyu Lab
    note: null
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    note: Design of sgRNA for lncRNA knockout; Primer design for PCR and qPCR to knockout
      detection.
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    - term: PCR primer design
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    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
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- accessibility: Open access
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  biotoolsCURIE: biotools:inps-md
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    note: null
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    typeRole:
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    url: https://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
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    name: Castrense Savojardo
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  description: Predicting the impact of mutations on protein stability from sequence
    and structure.
  documentation:
  - note: null
    type:
    - General
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  download: []
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  function:
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    input:
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        term: Protein sequence (raw)
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      format: []
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from sequence.
    operation: []
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
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    input:
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        term: Protein structure
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      format: []
    - data:
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      format: []
    - data:
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      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from structure.
    operation: []
    output:
    - data:
        term: Protein report
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      format: []
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        term: Protein structure
        uri: http://edamontology.org/data_1460
      format: []
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  license: null
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  maturity: Mature
  name: INPS-MD
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  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - 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:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
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  - email: castrense.savojardo2@unibo.it
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  description: Predicting the impact of mutations on protein stability from sequence
    using protein language models
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    input:
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      format: []
    - data:
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        uri: http://edamontology.org/data_3498
      format: []
    note: null
    operation: []
    output:
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        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
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  publication:
  - doi: 10.1093/bioinformatics/btaf019
    metadata:
      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:
      - name: Savojardo C.
      - name: Manfredi M.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'DDGemb: predicting protein stability change upon single- and multi-point
        variations with embeddings and deep learning'
    note: null
    pmcid: PMC11783275
    pmid: '39799516'
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version:
  - '1'
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps
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  - email: castrense.savojardo2@unibo.it
    fundrefid: null
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    name: Castrense Savojardo
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  - email: piero.fariselli@unito.it
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    name: Piero Fariselli
    note: null
    orcidid: null
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    url: null
  description: Predicting the impact of mutations on protein stability from sequence
  documentation:
  - note: null
    type:
    - General
    url: https://inpsmd.biocomp.unibo.it/help
  download: []
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  function:
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    input:
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      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
    operation: []
    output:
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      format: []
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  homepage_status: 0
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  lastUpdate: '2025-03-17T14:54:19.762674Z'
  license: null
  link: []
  maturity: Mature
  name: INPS
  operatingSystem: []
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - 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: []
  toolType: []
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-03-17T14:11:03.252329Z'
  biotoolsCURIE: biotools:raster_extraction
  biotoolsID: raster_extraction
  collectionID: []
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  cost: Free of charge
  credit:
  - email: l.q.roga@uu.nl
    fundrefid: null
    gridid: null
    name: Lloyd Roga
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    - Maintainer
    url: null
  description: This Python script provides functions to process raster files in chunks
    and extract values at specified coordinates using multiprocessing. The script
    is designed to handle large raster datasets efficiently by dividing the workload
    across multiple CPU cores.
  documentation: []
  download: []
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    note: null
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  license: Not licensed
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  name: Raster extraction
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  owner: Channel2291
  publication: []
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  - biotoolsID: exposome_maps
    type: uses
  toolType: []
  topic:
  - term: Environmental sciences
    uri: http://edamontology.org/topic_3855
  - term: Data management
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  validated: 0
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2025-03-16T20:15:23.278615Z'
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  biotoolsID: hdn
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
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    data sharing & analytics that emphasizes patient privacy and security, provides
    transparent and speedy access, and simplifies data discovery and analysis.
  documentation: []
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  lastUpdate: '2025-03-16T20:26:12.603608Z'
  license: MIT
  link:
  - note: null
    type:
    - Other
    url: https://tcairem.utoronto.ca/infrastructure-overview
  - note: null
    type:
    - Repository
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  maturity: Mature
  name: Health Data Nexus
  operatingSystem: []
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  owner: rutvikrj26
  publication: []
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  toolType: []
  topic:
  - term: Data management
    uri: http://edamontology.org/topic_3071
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-12T15:38:32.218141Z'
  biotoolsCURIE: biotools:gtestimate
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  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: GTestimate is a scRNA-seq normalization method. In contrast to other
    methods it uses the Simple Good-Turing estimator for the per cell relative gene
    expression estimation.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/Martin-Fahrenberger/GTestimate
  download: []
  editPermission:
    authors: []
    type: private
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  elixir_badge: 0
  function:
  - cmd: GTestimate()
    input:
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format: []
    note: GTestimate is provided as an R-package containing the GTestimate() function.
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format: []
  homepage: https://github.com/Martin-Fahrenberger/GTestimate
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-12T15:38:32.221353Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/Martin-Fahrenberger/GTestimate
  maturity: Mature
  name: GTestimate
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: martin_fahrenberger
  publication:
  - doi: 10.1101/2024.07.02.601501
    metadata: null
    note: preprint version 2
    pmcid: null
    pmid: null
    type:
    - Method
    version: '2'
  relation: []
  toolType:
  - Library
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2017-02-10T14:14:47Z'
  biotoolsCURIE: biotools:act
  biotoolsID: act
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: D.R.Westhead@leeds.ac.uk
    fundrefid: null
    gridid: null
    name: David R Westhead
    note: null
    orcidid: https://orcid.org/0000-0002-0519-3820
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/154/professor-david-r-westhead
  - email: imichalop@bioacademy.gr
    fundrefid: null
    gridid: null
    name: Ioannis Michalopoulos
    note: null
    orcidid: https://orcid.org/0000-0001-8991-8712
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.michalopoulos.net/
  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.
  documentation:
  - note: null
    type:
    - FAQ
    url: https://www.michalopoulos.net/act2.6/help.php
  download: []
  editPermission:
    authors:
    - imichalop@bioacademy.gr
    type: private
  elixirCommunity:
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  elixirNode:
  - Greece
  elixirPlatform:
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format: []
    note: null
    operation:
    - term: Dendrogram visualisation
      uri: http://edamontology.org/operation_2938
    output:
    - data:
        term: Gene tree
        uri: http://edamontology.org/data_3271
      format: []
  homepage: https://www.michalopoulos.net/act/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-10T11:30:02.403040Z'
  license: null
  link: []
  maturity: Mature
  name: Arabidopsis Co-expression Tool (ACT)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: imichalop@bioacademy.gr
  publication:
  - doi: 10.3390/genes16030258
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - 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.
      citationCount: 5
      date: '2022-03-18T00:00:00Z'
      journal: STAR Protocols
      title: Gene coexpression analysis in Arabidopsis thaliana based on public microarray
        data
    note: null
    pmcid: PMC8885756
    pmid: '35243384'
    type:
    - Method
    version: null
  - doi: 10.3390/biology11071019
    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:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Angelopoulou A.
      - name: Jen C.-H.
      - 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'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - '2.6'
- accessibility: Open access
  additionDate: '2025-03-04T16:25:28.372680Z'
  biotoolsCURIE: biotools:pep-fold4
  biotoolsID: pep-fold4
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Julien Rey
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    - Developer
    - Primary contact
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  - email: samuel.muriail@u-paris.fr
    fundrefid: null
    gridid: null
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    note: null
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    rorid: null
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    url: null
  - email: null
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    note: null
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    rorid: null
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  - email: philippe.derreumaux@ibpc.fr
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    note: null
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    rorid: null
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  - email: pierre.tuffery@inserm.fr
    fundrefid: null
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    name: Pierre Tufféry
    note: null
    orcidid: https://orcid.org/0000-0003-1033-9895
    rorid: null
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  description: PEP-FOLD4 is a fast and accurate structure prediction tool for peptides
    of up to 40 amino acids in aqueous solutions. Unlike many machine-learning approaches
    (e.g., AlphaFold2, TrRosetta, RaptorX), it integrates the Debye-Hückel formalism
    for charged side-chain interactions with a Mie potential for intramolecular forces.
    Based on a coarse-grained representation, PEP-FOLD4 performs competitively on
    well-structured peptides and shows significant improvements for poly-charged peptides,
    making it particularly useful for studying pH- and salt-dependent conformations.
  documentation:
  - note: null
    type:
    - User manual
    url: https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4/
  download:
  - note: Source code and recipe for docker images allowing to run PEP-FOLD4 locally.
      All repository content is covered by a non-commercial license agreement and
      may be used for non-commercial and internal research purposes only.
    type: Source code
    url: https://owncloud.rpbs.univ-paris-diderot.fr/owncloud/index.php/s/7NHDSJXiQA0V2zy
    version: null
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  function:
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    input:
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        term: Protein sequence
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      format: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_2426
    - term: Ab initio structure prediction
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    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
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        uri: http://edamontology.org/format_1475
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format: []
  homepage: https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-05T16:21:19.399517Z'
  license: null
  link:
  - note: Link to the PEP-FOLD4 web page service
    type:
    - Service
    url: https://mobyle2.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::PEP-FOLD4
  maturity: Mature
  name: PEP-FOLD4
  operatingSystem: []
  otherID: []
  owner: nchevrollier
  publication:
  - doi: 10.1093/nar/gkad376
    metadata:
      abstract: Accurate and fast structure prediction of peptides of less 40 amino
        acids in aqueous solution has many biological applications, but their conformations
        are pH-and salt concentration-dependent. In this work, we present PEP-FOLD4
        which goes one step beyond many machine-learning approaches, such as AlphaFold2,
        TrRosetta and RaptorX. Adding the Debye-Hueckel formalism for charged-charged
        side chain interactions to a Mie formalism for all intramolecular (backbone
        and side chain) interactions, PEP-FOLD4, based on a coarse-grained representation
        of the peptides, performs as well as machine-learning methods on well-structured
        peptides, but displays significant improvements for poly-charged peptides.
        PEP-FOLD4 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4.
        This server is free and there is no login requirement.
      authors:
      - name: Rey J.
      - name: Murail S.
      - name: De Vries S.
      - name: Derreumaux P.
      - name: Tuffery P.
      citationCount: 39
      date: '2023-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'PEP-FOLD4: A pH-dependent force field for peptide structure prediction
        in aqueous solution'
    note: null
    pmcid: PMC10320157
    pmid: '37166962'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Computational chemistry
    uri: http://edamontology.org/topic_3332
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-05T11:34:50.555785Z'
  biotoolsCURIE: biotools:lipidone_2.2
  biotoolsID: lipidone_2.2
  collectionID: []
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  cost: Free of charge
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  - email: roberto.pellegrino@unipg.it
    fundrefid: null
    gridid: null
    name: Roberto Maria Pellegrino
    note: null
    orcidid: https://orcid.org/0000-0001-5594-383X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: https://lipidone.eu/
  description: User-friendly lipidomic data analysis tool for a deeper interpretation
    in a systems biology scenario
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  download: []
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    note: null
    operation:
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    output: []
  homepage: https://lipidone.eu/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-05T11:34:50.558190Z'
  license: Freeware
  link: []
  maturity: Mature
  name: LipidOne 2.2
  operatingSystem: []
  otherID: []
  owner: RobertoMaria
  publication:
  - doi: 10.1002/cpz1.70009
    metadata:
      abstract: 'LipidOne 2.0 (https://lipidone.eu) is a new web bioinformatic tool
        for the analysis of lipidomic data. It facilitates the exploration of the
        three structural levels of lipids: classes, molecular species, and lipid building
        blocks (acyl, alkyl, or alkenes chains). The tool''s flexibility empowers
        users to seamlessly include or exclude experimental groups and lipid classes
        at any stage of the analysis. LipidOne 2.0 offers a range of mono- and multivariate
        statistical analyses, specifically tailored to each structural level. This
        includes a novel lipid biomarker identification function, integrating four
        diverse statistical parameters. LipidOne 2.0 incorporates Lipid Pathway analysis
        across all three structural levels of lipids. Users can identify lipid-involved
        reactions through case-control comparisons, generating lists of genes/enzymes
        and their activation states based on Z scores. Accessible without the need
        for registration, LipidOne 2.0 provides a user-friendly and efficient platform
        for exploring and analyzing lipidomic data. © 2024 The Author(s). Current
        Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Dataset preparation
        for LipidOne 2.0. Support Protocol: Lipid nomenclature from spectrometric
        experiments. Basic Protocol 2: Uploading a dataset into LipidOne 2.0. Basic
        Protocol 3: Data mining of lipidomic dataset by LipidOne 2.0.'
      authors:
      - name: Alabed H.B.R.
      - name: Mancini D.F.
      - name: Buratta S.
      - name: Calzoni E.
      - name: Giacomo D.D.
      - name: Emiliani C.
      - name: Martino S.
      - name: Urbanelli L.
      - name: Pellegrino R.M.
      citationCount: 1
      date: '2024-09-01T00:00:00Z'
      journal: Current Protocols
      title: 'LipidOne 2.0: A Web Tool for Discovering Biological Meanings Hidden
        in Lipidomic Data'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: lipidone
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  topic:
  - term: Lipidomics
    uri: http://edamontology.org/topic_0153
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Biomarkers
    uri: http://edamontology.org/topic_3360
  validated: 0
  version:
  - '2.2'
- accessibility: Open access
  additionDate: '2025-02-26T14:07:27.855714Z'
  biotoolsCURIE: biotools:basico
  biotoolsID: basico
  collectionID:
  - de.NBI
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: frank.bergmann@bioquant.uni-heidelberg.de
    fundrefid: null
    gridid: null
    name: Frank T. Bergmann
    note: null
    orcidid: https://orcid.org/0000-0001-5553-4702
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: null
  description: "BASICO, a simplified Python interface to COPASI. It provides a set\
    \ of functions\n that allow to create, edit, simulate and analyze models in an\
    \ automated way"
  documentation: []
  download:
  - note: null
    type: Source code
    url: https://pypi.org/project/copasi-basico/
    version: null
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    input:
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      format:
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: SED-ML
        uri: http://edamontology.org/format_3685
    note: null
    operation:
    - term: Metabolic network modelling
      uri: http://edamontology.org/operation_3660
    output:
    - data:
        term: Kinetic model
        uri: http://edamontology.org/data_3241
      format:
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: SED-ML
        uri: http://edamontology.org/format_3685
  homepage: https://basico.readthedocs.io/en/latest/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-02-26T14:09:14.108566Z'
  license: Artistic-2.0
  link: []
  maturity: Mature
  name: basico
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: frankbergmann
  publication:
  - doi: 10.21105/joss.05553
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: copasi
    type: uses
  toolType:
  - Library
  - Script
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2017-01-17T15:07:47Z'
  biotoolsCURIE: biotools:copasi
  biotoolsID: copasi
  collectionID:
  - de.NBI
  - EBI Training Tools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://copasi.org/About/Team/
  - email: frank.bergmann@bioquant.uni-heidelberg.de
    fundrefid: null
    gridid: null
    name: Frank T. Bergmann
    note: null
    orcidid: https://orcid.org/0000-0001-5553-4702
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Open-source software application for creating and solving mathematical
    models of biological processes such as metabolic networks, cell-signaling pathways,
    regulatory networks, infectious diseases, and many others. It includes features
    to define models of biological processes, simulate and analyze these models, generate
    analysis reports, and import/export models in SBML format.
  documentation:
  - note: null
    type:
    - User manual
    url: http://copasi.org/Support/User_Manual/
  download:
  - note: Source and binary packages are available for download.
    type: Binaries
    url: http://copasi.org/Download/
    version: null
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  - Germany
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    input:
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        term: Pathway or network
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      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
    note: null
    operation:
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Metabolic network modelling
      uri: http://edamontology.org/operation_3660
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
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      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
  homepage: http://copasi.org/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-02-26T13:59:11.356816Z'
  license: Artistic-2.0
  link:
  - note: User Forum
    type:
    - Discussion forum
    url: https://groups.google.com/g/copasi-user-forum
  - note: Issue tracker
    type:
    - Issue tracker
    url: http://tracker.copasi.org/
  - note: Github Repo
    type:
    - Repository
    url: https://github.com/copasi/COPASI
  - note: null
    type:
    - Social media
    url: https://fosstodon.org/@copasi
  maturity: Mature
  name: COPASI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: frankbergmann
  publication:
  - doi: 10.1093/bioinformatics/btl485
    metadata:
      abstract: 'Motivation: Simulation and modeling is becoming a standard approach
        to understand complex biochemical processes. Therefore, there is a big need
        for software tools that allow access to diverse simulation and modeling methods
        as well as support for the usage of these methods. Results: Here, we present
        COPASI, a platform-independent and user-friendly biochemical simulator that
        offers several unique features. We discuss numerical issues with these features;
        in particular, the criteria to switch between stochastic and deterministic
        simulation methods, hybrid deterministic-stochastic methods, and the importance
        of random number generator numerical resolution in stochastic simulation.
        © 2006 Oxford University Press.'
      authors:
      - name: Hoops S.
      - name: Gauges R.
      - name: Lee C.
      - name: Pahle J.
      - name: Simus N.
      - name: Singhal M.
      - name: Xu L.
      - name: Mendes P.
      - name: Kummer U.
      citationCount: 1960
      date: '2006-12-15T00:00:00Z'
      journal: Bioinformatics
      title: COPASI - A COmplex PAthway SImulator
    note: null
    pmcid: null
    pmid: '17032683'
    type: []
    version: null
  - doi: 10.1007/978-1-59745-525-1_2
    metadata:
      abstract: Computational modeling and simulation of biochemical networks is at
        the core of systems biology and this includes many types of analyses that
        can aid understanding of how these systems work. COPASI is a generic software
        package for modeling and simulation of biochemical networks which provides
        many of these analyses in convenient ways that do not require the user to
        program or to have deep knowledge of the numerical algorithms. Here we provide
        a description of how these modeling techniques can be applied to biochemical
        models using COPASI. The focus is both on practical aspects of software usage
        as well as on the utility of these analyses in aiding biological understanding.
        Practical examples are described for steady-state and time-course simulations,
        stoichiometric analyses, parameter scanning, sensitivity analysis (including
        metabolic control analysis), global optimization, parameter estimation, and
        stochastic simulation. The examples used are all published models that are
        available in the BioModels database in SBML format. © 2009 Humana Press.
      authors:
      - name: Mendes P.
      - name: Hoops S.
      - name: Sahle S.
      - name: Gauges R.
      - name: Dada J.
      - name: Kummer U.
      citationCount: 168
      date: '2009-12-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Computational modeling of biochemical networks using COPASI
    note: null
    pmcid: null
    pmid: '19399433'
    type: []
    version: null
  - doi: 10.1016/j.jbiotec.2017.06.1200
    metadata:
      abstract: COPASI is software used for the creation, modification, simulation
        and computational analysis of kinetic models in various fields. It is open-source,
        available for all major platforms and provides a user-friendly graphical user
        interface, but is also controllable via the command line and scripting languages.
        These are likely reasons for its wide acceptance. We begin this review with
        a short introduction describing the general approaches and techniques used
        in computational modeling in the biosciences. Next we introduce the COPASI
        package, and its capabilities, before looking at typical applications of COPASI
        in biotechnology.
      authors:
      - name: Bergmann F.T.
      - name: Hoops S.
      - name: Klahn B.
      - name: Kummer U.
      - name: Mendes P.
      - name: Pahle J.
      - name: Sahle S.
      citationCount: 75
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: COPASI and its applications in biotechnology
    note: null
    pmcid: PMC5623632
    pmid: '28655634'
    type: []
    version: null
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    type: usedBy
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    type: usedBy
  - biotoolsID: biosimulations
    type: includedIn
  - biotoolsID: sbmlwebapp
    type: usedBy
  toolType:
  - Command-line tool
  - Library
  - Desktop application
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-02-11T08:17:30.706907Z'
  biotoolsCURIE: biotools:pvga
  biotoolsID: pvga
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit:
  - email: zhisong3-c@my.cityu.edu.hk
    fundrefid: null
    gridid: null
    name: Zhi Song
    note: null
    orcidid: https://orcid.org/0000-0001-9720-1084
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: PVGA is a novel viral genome assembler that seamlessly integrates assembly
    and polishing into a unified workflow. Its design prioritizes high accuracy, enabling
    the detection of subtle genomic variations that can significantly impact viral
    function and pathogenicity. By addressing the unique challenges of viral genome
    assembly, PVGA provides a reliable and precise solution for advancing our understanding
    of viral evolution and behavior.
  documentation:
  - note: null
    type:
    - Code of conduct
    url: https://github.com/SoSongzhi/PVGA
  download:
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    type: Downloads page
    url: https://github.com/SoSongzhi/PVGA
    version: null
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  - Python
  lastUpdate: '2025-02-11T08:17:30.709291Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/SoSongzhi/PVGA
  maturity: Mature
  name: PVGA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: zhisong
  publication: []
  relation:
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    type: uses
  toolType:
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  topic:
  - term: Biosciences
    uri: http://edamontology.org/topic_4019
  - term: Computer science
    uri: http://edamontology.org/topic_3316
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-09-27T14:20:23.943662Z'
  biotoolsCURIE: biotools:ensembleflex
  biotoolsID: ensembleflex
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: "EnsembleFlex: Protein Structure Ensemble Analysis made easy \n- \n\
    EnsembleFlex is a tool designed to perform dual-scale flexibility analysis of\
    \ protein structure ensembles, encompassing both backbone and side-chain dynamics,\
    \ in an easy, streamlined and reproducible way. It offers a user-friendly, no-coding\
    \ graphical interface as well as a code-based option. It  complements experimental\
    \ ensemble data analysis with predictive methods based on elastic network models."
  documentation:
  - note: null
    type:
    - General
    - Quick start guide
    url: https://github.com/chembl/EnsembleFlex
  - note: null
    type:
    - Quick start guide
    - User manual
    url: https://github.com/chembl/EnsembleFlex/blob/master/documentation/user_guide.md
  - note: null
    type:
    - General
    - Quick start guide
    url: https://gitlab.ebi.ac.uk/melanie/ensembleflex
  download:
  - note: null
    type: Source code
    url: https://github.com/chembl/EnsembleFlex
    version: null
  - note: null
    type: Source code
    url: https://gitlab.ebi.ac.uk/melanie/ensembleflex
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    version: null
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    input:
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    - data:
        term: Protein-ligand complex
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      format:
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    note: null
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    - term: Structure clustering
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    - term: Dimensionality reduction
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    - term: Principal component analysis
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    - term: Trajectory visualization
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    - term: Structure alignment
      uri: http://edamontology.org/operation_0295
    output:
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        term: Plot
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      format: []
    - data:
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        uri: http://edamontology.org/data_3905
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    - data:
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    - data:
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      format: []
    - data:
        term: Heat map
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      format: []
    - data:
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      format: []
    - data:
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      format: []
    - data:
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      format: []
    - data:
        term: Protein solvent accessibility
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      format: []
    - data:
        term: Protein interaction data
        uri: http://edamontology.org/data_0906
      format: []
    - data:
        term: Protein-ligand interaction report
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      format: []
  homepage: https://github.com/chembl/EnsembleFlex
  homepage_status: 0
  language:
  - Python
  - R
  lastUpdate: '2025-02-10T16:23:46.060188Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/chembl/EnsembleFlex
  - note: null
    type:
    - Repository
    url: https://gitlab.ebi.ac.uk/melanie/ensembleflex
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  name: EnsembleFlex
  operatingSystem:
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  - Linux
  - Windows
  otherID: []
  owner: MelanieS
  publication: []
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  toolType:
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  - Command-line tool
  - Workflow
  topic:
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    uri: http://edamontology.org/topic_2814
  - term: Drug discovery
    uri: http://edamontology.org/topic_3336
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  validated: 0
  version: []
- accessibility: null
  additionDate: '2016-02-05T06:57:46Z'
  biotoolsCURIE: biotools:pnu
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  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
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    name: Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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  description: Compilation of all names of Bacteria and Archaea which have been validly
    published according to the Bacteriological Code since 1 January 1980, and nomenclatural
    proposals that have been validly published since then. Was merged with LPSN and
    replaced by the new LPSN when LPSN was taken over by DSMZ.
  documentation:
  - note: null
    type:
    - FAQ
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  download: []
  editPermission:
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      format: []
    - data:
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        uri: http://edamontology.org/data_1870
      format: []
    - data:
        term: Family name
        uri: http://edamontology.org/data_2732
      format: []
    - data:
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      format: []
    - data:
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    - data:
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      format: []
    note: class culture collection number reclassified taxonomy
    operation:
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      uri: http://edamontology.org/operation_2422
    output:
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      format:
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://lpsn.dsmz.de/
  homepage_status: 0
  language:
  - R
  - Python
  lastUpdate: '2025-02-03T19:54:19.762596Z'
  license: null
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      - name: Carbasse J.S.
      - name: Meier-Kolthoff J.P.
      - name: Reimer L.C.
      - name: Goker M.
      citationCount: 1192
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      journal: International Journal of Systematic and Evolutionary Microbiology
      title: List of prokaryotic names with standing in nomenclature (LPSN) moves
        to the DSMZ
    note: null
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  - term: Microbiology
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  - term: Biodiversity
    uri: http://edamontology.org/topic_3050
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  description: The EnzymeDetector database offers a comparative and integrative approach
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    UniProt, KEGG, PATRIC, and NCBI's RefSeq, are integrated to have a vast view on
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    pattern recognition.
  documentation:
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    metadata:
      abstract: 'Background: The ability to accurately predict enzymatic functions
        is an essential prerequisite for the interpretation of cellular functions,
        and the reconstruction and analysis of metabolic models. Several biological
        databases exist that provide such information. However, in many cases these
        databases provide partly different and inconsistent genome annotations.Description:
        We analysed nine prokaryotic genomes and found about 70% inconsistencies in
        the enzyme predictions of the main annotation resources. Therefore, we implemented
        the annotation pipeline EnzymeDetector. This tool automatically compares and
        evaluates the assigned enzyme functions from the main annotation databases
        and supplements them with its own function prediction. This is based on a
        sequence similarity analysis, on manually created organism-specific enzyme
        information from BRENDA (Braunschweig Enzyme Database), and on sequence pattern
        searches.Conclusions: EnzymeDetector provides a fast and comprehensive overview
        of the available enzyme function annotations for a genome of interest. The
        web interface allows the user to work with customisable weighting schemes
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        Annotation errors that occur in only one of the databases can be recognised
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        and can be accessed at http://enzymedetector.tu-bs.de. © 2011 Quester and
        Schomburg; licensee BioMed Central Ltd.'
      authors:
      - name: Quester S.
      - name: Schomburg D.
      citationCount: 49
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    note: null
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  - doi: 10.1093/nar/gkaa1025
    metadata:
      abstract: The BRENDA enzyme database (https://www.brendaenzymes.org), established
        in 1987, has evolved into the main collection of functional enzyme and metabolism
        data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA
        provides reliable data, continuous curation and updates of classified enzymes,
        and the integration of newly discovered enzymes. The main part contains >5
        million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted
        from ∼157 000 primary literature references, combined with information of
        text and data mining, data integration, and prediction algorithms. Supplements
        comprise disease-related data, protein sequences, 3D structures, genome annotations,
        lig- and information, taxonomic, bibliographic, and kinetic data. BRENDA offers
        an easy access to enzyme information from quick to advanced searches, text-
        and structured-based queries for enzyme-ligand interactions, word maps, and
        visualization of enzyme data. The BRENDA Pathway Maps are completely revised
        and updated for an enhanced interactive and intuitive usability. The new design
        of the Enzyme Summary Page provides an improved access to each individual
        enzyme. A new protein structure 3D viewer was integrated. The prediction of
        the intracellular localization of eukaryotic enzymes has been implemented.
        The new EnzymeDetector combines BRENDA enzyme annotations with protein and
        genome databases for the detection of eukaryotic and prokaryotic enzymes.
      authors:
      - name: Chang A.
      - name: Jeske L.
      - name: Ulbrich S.
      - name: Hofmann J.
      - name: Koblitz J.
      - name: Schomburg I.
      - name: Neumann-Schaal M.
      - name: Jahn D.
      - name: Schomburg D.
      citationCount: 392
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    note: null
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  validated: 0
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  description: a method for topology-independent superposition of RNA 3D structures
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    note: null
    operation:
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    metadata:
      abstract: Non-coding RNAs play a major role in diverse processes in living cells
        with their sequence and spatial structure serving as the principal determinants
        of their function. Superposition of RNA 3D structures is the most accurate
        method for comparative analysis of RNA molecules and for inferring structure-based
        sequence alignments. Topology-independent superposition is particularly relevant,
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        and topology-independent RNA 3D structure superposition. Using ARTEMIS we
        discovered a helical packing motif to be preserved within different backbone
        topology contexts across various non-coding RNAs, including multiple ribozymes
        and riboswitches. We anticipate that ARTEMIS will be essential for elucidating
        the landscape of RNA 3D folds and motifs featuring sequence permutations that
        thus far remained unexplored due to limitations in previous computational
        approaches.
      authors:
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    note: null
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  description: PhageDive is a specific database for bacteriophages and archaeal viruses.
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        databases or internal files of culture collections. PhageDive allows to link
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      - name: Dudek C.-A.
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      - name: Bunk B.
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  description: Internationally leading information system on all aspects of enzymes,
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    note: null
    operation:
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      uri: http://edamontology.org/operation_0305
    - term: Sequence database search
      uri: http://edamontology.org/operation_0338
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    - term: Structural similarity search
      uri: http://edamontology.org/operation_0360
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    - term: Transmembrane protein visualisation
      uri: http://edamontology.org/operation_2241
    output:
    - data:
        term: UniProt ID
        uri: http://edamontology.org/data_2291
      format: []
    - data:
        term: PDB ID
        uri: http://edamontology.org/data_1127
      format: []
    - data:
        term: UniProt accession
        uri: http://edamontology.org/data_3021
      format: []
    - data:
        term: Reaction ID
        uri: http://edamontology.org/data_2108
      format: []
    - data:
        term: Pathway or network name
        uri: http://edamontology.org/data_2342
      format: []
    - data:
        term: Pathway or network accession
        uri: http://edamontology.org/data_2365
      format: []
    - data:
        term: Strain name
        uri: http://edamontology.org/data_1046
      format: []
    - data:
        term: Organism name
        uri: http://edamontology.org/data_2909
      format: []
    - data:
        term: NCBI taxonomy ID
        uri: http://edamontology.org/data_1179
      format: []
    - data:
        term: SCOP domain identifier
        uri: http://edamontology.org/data_1039
      format: []
    - data:
        term: CATH identifier
        uri: http://edamontology.org/data_2700
      format: []
    - data:
        term: Enzyme name
        uri: http://edamontology.org/data_1012
      format: []
    - data:
        term: EC number
        uri: http://edamontology.org/data_1011
      format: []
    - data:
        term: Enzyme kinetics data
        uri: http://edamontology.org/data_2024
      format: []
  homepage: http://www.brenda-enzymes.org
  homepage_status: 0
  language: []
  lastUpdate: '2025-01-27T14:31:05.585156Z'
  license: CC-BY-4.0
  link:
  - note: null
    type:
    - Other
    url: https://www.dsmz.de/
  - note: null
    type:
    - Service
    url: https://hub.dsmz.de/#/
  - note: null
    type:
    - Service
    url: https://www.brenda-enzymes.org/
  - note: null
    type:
    - Helpdesk
    url: https://brenda-enzymes.org/support.php
  maturity: Mature
  name: BRENDA
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: matkrull
  publication:
  - doi: 10.1093/nar/gkaa1025
    metadata:
      abstract: The BRENDA enzyme database (https://www.brendaenzymes.org), established
        in 1987, has evolved into the main collection of functional enzyme and metabolism
        data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA
        provides reliable data, continuous curation and updates of classified enzymes,
        and the integration of newly discovered enzymes. The main part contains >5
        million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted
        from ∼157 000 primary literature references, combined with information of
        text and data mining, data integration, and prediction algorithms. Supplements
        comprise disease-related data, protein sequences, 3D structures, genome annotations,
        lig- and information, taxonomic, bibliographic, and kinetic data. BRENDA offers
        an easy access to enzyme information from quick to advanced searches, text-
        and structured-based queries for enzyme-ligand interactions, word maps, and
        visualization of enzyme data. The BRENDA Pathway Maps are completely revised
        and updated for an enhanced interactive and intuitive usability. The new design
        of the Enzyme Summary Page provides an improved access to each individual
        enzyme. A new protein structure 3D viewer was integrated. The prediction of
        the intracellular localization of eukaryotic enzymes has been implemented.
        The new EnzymeDetector combines BRENDA enzyme annotations with protein and
        genome databases for the detection of eukaryotic and prokaryotic enzymes.
      authors:
      - name: Chang A.
      - name: Jeske L.
      - name: Ulbrich S.
      - name: Hofmann J.
      - name: Koblitz J.
      - name: Schomburg I.
      - name: Neumann-Schaal M.
      - name: Jahn D.
      - name: Schomburg D.
      citationCount: 393
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA, the ELIXIR core data resource in 2021: New developments and
        updates'
    note: null
    pmcid: PMC7779020
    pmid: '33211880'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gky1048
    metadata:
      abstract: The BRENDA enzyme database (www.brenda-enzymes.org), recently appointed
        ELIXIR Core Data Resource, is the main enzyme and enzyme-ligand information
        system. The core database provides a comprehensive overview on enzymes. A
        collection of 4.3 million data for ∼84 000 enzymes manually evaluated and
        extracted from ∼140 000 primary literature references is combined with information
        obtained by text and data mining, data integration and prediction algorithms.
        Supplements comprise disease-related data, protein sequences, 3D structures,
        predicted enzyme locations and genome annotations. Major developments are
        a revised ligand summary page and the structure search now including a similarity
        and isomer search. BKMS-react, an integrated database containing known enzyme-catalyzed
        reactions, is supplemented with further reactions and improved access to pathway
        connections. In addition to existing enzyme word maps with graphical information
        of enzyme specific terms, plant word maps have been developed. They show a
        graphical overview of terms, e.g. enzyme or plant pathogen information, connected
        to specific plants. An organism summary page showing all relevant information,
        e.g. taxonomy and synonyms linked to enzyme data, was implemented. Based on
        a decision by the IUBMB enzyme task force the enzyme class EC 7 has been established
        for 'translocases-, enzymes that catalyze a transport of ions or metabolites
        across cellular membranes.
      authors:
      - name: Jeske L.
      - name: Placzek S.
      - name: Schomburg I.
      - name: Chang A.
      - name: Schomburg D.
      citationCount: 296
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA in 2019: A European ELIXIR core data resource'
    note: null
    pmcid: PMC6323942
    pmid: '30395242'
    type: []
    version: null
  - doi: 10.1016/j.jbiotec.2017.04.020
    metadata:
      abstract: Enzymes, representing the largest and by far most complex group of
        proteins, play an essential role in all processes of life, including metabolism,
        gene expression, cell division, the immune system, and others. Their function,
        also connected to most diseases or stress control makes them interesting targets
        for research and applications in biotechnology, medical treatments, or diagnosis.
        Their functional parameters and other properties are collected, integrated,
        and made available to the scientific community in the BRaunschweig ENzyme
        DAtabase (BRENDA). In the last 30 years BRENDA has developed into one of the
        most highly used biological databases worldwide. The data contents, the process
        of data acquisition, data integration and control, the ways to access the
        data, and visualizations provided by the website are described and discussed.
      authors:
      - name: Schomburg I.
      - name: Jeske L.
      - name: Ulbrich M.
      - name: Placzek S.
      - name: Chang A.
      - name: Schomburg D.
      citationCount: 133
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: The BRENDA enzyme information system–From a database to an expert system
    note: null
    pmcid: null
    pmid: '28438579'
    type: []
    version: null
  - doi: 10.1093/nar/gku1068
    metadata:
      abstract: The BRENDA enzyme information system (http://www.brenda-enzymes.org/)
        has developed into an elaborate system of enzyme and enzyme-ligand information
        obtained from different sources, combined with flexible query systems and
        evaluation tools. The information is obtained by manual extraction from primary
        literature, text and data mining, data integration, and prediction algorithms.
        Approximately 300 million data include enzyme function and molecular data
        from more than 30 000 organisms. The manually derived core contains 3 million
        data from 77 000 enzymes annotated from 135 000 literature references. Each
        entry is connected to the literature reference and the source organism. They
        are complemented by information on occurrence, enzyme/disease relationships
        from text mining, sequences and 3D structures from other databases, and predicted
        enzyme location and genome annotation. Functional and structural data of more
        than 190 000 enzyme ligands are stored in BRENDA. New features improving the
        functionality and analysis tools were implemented. The human anatomy atlas
        CAVEman is linked to the BRENDA Tissue Ontology terms providing a connection
        between anatomical and functional enzyme data. Word Maps for enzymes obtained
        from PubMed.
      authors:
      - name: Chang A.
      - name: Schomburg I.
      - name: Placzek S.
      - name: Jeske L.
      - name: Ulbrich M.
      - name: Xiao M.
      - name: Sensen C.W.
      - name: Schomburg D.
      citationCount: 161
      date: '2015-01-28T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA in 2015: Exciting developments in its 25th year of existence'
    note: null
    pmcid: PMC4383907
    pmid: '25378310'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkn820
    metadata:
      abstract: The BRENDA (BRaunschweig ENzyme DAtabase) (http://www.brenda-enzymes.org)
        represents the largest freely available information system containing a huge
        amount of biochemical and molecular information on all classified enzymes
        as well as software tools for querying the database and calculating molecular
        properties. The database covers information on classification and nomenclature,
        reaction and specificity, functional parameters, occurrence, enzyme structure
        and stability, mutants and enzyme engineering, preparation and isolation,
        the application of enzymes, and ligand-related data. The data in BRENDA are
        manually curated from more than 79 000 primary literature references. Each
        entry is clearly linked to a literature reference, the origin organism and,
        where available, to the protein sequence of the enzyme protein. A new search
        option provides the access to protein-specific data. FRENDA (Full Reference
        ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are additional databases
        created by continuously improved text-mining procedures. These databases ought
        to provide a complete survey on enzyme data of the literature collection of
        PubMed. The web service via a SOAP (Simple Object Access Protocol) interface
        for access to the BRENDA data has been further enhanced. © 2008 The Author(s).
      authors:
      - name: Chang A.
      - name: Scheer M.
      - name: Grote A.
      - name: Schomburg I.
      - name: Schomburg D.
      citationCount: 314
      date: '2009-01-09T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA, AMENDA and FRENDA the enzyme information system: New content
        and tools in 2009'
    note: null
    pmcid: PMC2686525
    pmid: '18984617'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkq1089
    metadata:
      abstract: The BRENDA (BRaunschweig ENzyme Database, http://www.brenda-enzymes.org)
        enzyme information system is the main collection of enzyme functional and
        property data for the scientific community. The majority of the data are manually
        extracted from the primary literature. The content covers information on function,
        structure, occurrence, preparation and application of enzymes as well as properties
        of mutants and engineered variants. The number of manually annotated references
        increased by 30% to more than 100 000, the number of ligand structures by
        45% to almost 100 000. New query, analysis and data management tools were
        implemented to improve data processing, data presentation, data input and
        data access. BRENDA now provides new viewing options such as the display of
        the statistics of functional parameters and the 3D view of protein sequence
        and structure features. Furthermore a ligand summary shows comprehensive information
        on the BRENDA ligands. The enzymes are linked to their respective pathways
        and can be viewed in pathway maps. The disease text mining part is strongly
        enhanced. It is possible to submit new, not yet classified enzymes to BRENDA,
        which then are reviewed and classified by the International Union of Biochemistry
        and Molecular Biology. A new SBML output format of BRENDA kinetic data allows
        the construction of organism-specific metabolic models. © The Author(s) 2010.
      authors:
      - name: Scheer M.
      - name: Grote A.
      - name: Chang A.
      - name: Schomburg I.
      - name: Munaretto C.
      - name: Rother M.
      - name: Sohngen C.
      - name: Stelzer M.
      - name: Thiele J.
      - name: Schomburg D.
      citationCount: 353
      date: '2011-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BRENDA, the enzyme information system in 2011
    note: null
    pmcid: PMC3013686
    pmid: '21062828'
    type:
    - Other
    version: null
  - doi: 10.1186/1471-2105-12-329
    metadata:
      abstract: 'Background: BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org)
        is a major resource for enzyme related information. First and foremost, it
        provides data which are manually curated from the primary literature. DRENDA
        (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus
        on the automatic search and categorization of enzyme and disease related information
        from title and abstracts of primary publications. In a two-step procedure
        DRENDA makes use of text mining and machine learning methods.Results: Currently
        enzyme and disease related references are biannually updated as part of the
        standard BRENDA update. 910,897 relations of EC-numbers and diseases were
        extracted from titles or abstracts and are included in the second release
        in 2010. The enzyme and disease entity recognition has been successfully enhanced
        by a further relation classification via machine learning. The classification
        step has been evaluated by a 5-fold cross validation and achieves an F1 score
        between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing
        procedures. In the eventual DRENDA content every category reaches a classification
        specificity of at least 96.7% and a precision that ranges from 86-98% in the
        highest confidence level, and 64-83% for the smallest confidence level associated
        with higher recall.Conclusions: The DRENDA processing chain analyses PubMed,
        locates references with disease-related information on enzymes and categorises
        their focus according to the categories causal interaction, therapeutic application,
        diagnostic usage and ongoing research. The categorisation gives an impression
        on the focus of the located references. Thus, the relation categorisation
        can facilitate orientation within the rapidly growing number of references
        with impact on diseases and enzymes. The DRENDA information is available as
        additional information in BRENDA. © 2011 Söhngen et al; licensee BioMed Central
        Ltd.'
      authors:
      - name: Sohngen C.
      - name: Chang A.
      - name: Schomburg D.
      citationCount: 16
      date: '2011-08-09T00:00:00Z'
      journal: BMC Bioinformatics
      title: Development of a classification scheme for disease-related enzyme information
    note: null
    pmcid: PMC3166944
    pmid: '21827651'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkh081
    metadata: null
    note: null
    pmcid: PMC308815
    pmid: '14681450'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gks1049
    metadata:
      abstract: The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org)
        is the main information system of functional biochemical and molecular enzyme
        data and provides access to seven interconnected databases. BRENDA contains
        2.7 million manually annotated data on enzyme occurrence, function, kinetics
        and molecular properties. Each entry is connected to a reference and the source
        organism. Enzyme ligands are stored with their structures and can be accessed
        via their names, synonyms or via a structure search. FRENDA (Full Reference
        ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text
        mining methods and represent a complete survey of PubMed abstracts with information
        on enzymes in different organisms, tissues or organelles. The supplemental
        database DRENDA provides more than 910000 new EC number-disease relations
        in more than 510 000 references from automatic search and a classification
        of enzyme-disease-related information. KENDA (Kinetic ENzyme DAta), a new
        amendment extracts and displays kinetic values from PubMed abstracts. The
        integration of the EnzymeDetector offers an automatic comparison, evaluation
        and prediction of enzyme function annotations for prokaryotic genomes. The
        biochemical reaction database BKM-react contains non-redundant enzyme-catalysed
        and spontaneous reactions and was developed to facilitate and accelerate the
        construction of biochemical models. © The Author(s) 2012.
      authors:
      - name: Schomburg I.
      - name: Chang A.
      - name: Placzek S.
      - name: Sohngen C.
      - name: Rother M.
      - name: Lang M.
      - name: Munaretto C.
      - name: Ulas S.
      - name: Stelzer M.
      - name: Grote A.
      - name: Scheer M.
      - name: Schomburg D.
      citationCount: 347
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA in 2013: Integrated reactions, kinetic data, enzyme function
        data, improved disease classification: New options and contents in BRENDA'
    note: null
    pmcid: PMC3531171
    pmid: '23203881'
    type:
    - Other
    version: null
  - doi: 10.1016/s0968-0004(01)02027-8
    metadata:
      abstract: BRENDA (BRaunschweig ENzyme DAtabase), founded in 1987 by Dietmar
        Schomburg, is a comprehensive protein function database, containing enzymatic
        and metabolic information extracted from the primary literature. Presently,
        the database holds data on more than 40 000 enzymes and 4460 different organisms,
        and includes information about enzyme -ligand relationships with numerous
        chemical compounds. The collection of molecular and biochemical information
        in BRENDA provides a fundamental resource for research in biotechnology, pharmacology,
        medicinal diagnostics, enzyme mechanics, and metabolism. BRENDA is accessible
        free of charge to the academic community at http://www.brenda.uni-koeln.de/;
        commercial users need a license available from http://www.science-factory.com/.
      authors:
      - name: Schomburg I.
      - name: Chang A.
      - name: Hofmann O.
      - name: Ebeling C.
      - name: Ehrentreich F.
      - name: Schomburg D.
      citationCount: 159
      date: '2002-01-01T00:00:00Z'
      journal: Trends in Biochemical Sciences
      title: 'BRENDA: A resource for enzyme data and metabolic information'
    note: null
    pmcid: null
    pmid: '11796225'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkl972
    metadata:
      abstract: The BRENDA (BRaunschweig ENzyme DAtabase) enzyme information system
        (http://www.brenda.uni-koeln.de) is the largest publicly available enzyme
        information system worldwide. The major parts of its contents are manually
        extracted from primary literature. It is not restricted to specific groups
        of enzymes, but includes information on all identified enzymes irrespective
        of the enzyme's source. The range of data encompasses functional, structural,
        sequence, localisation, disease-related, isolation, stability information
        on enzyme and ligand-related data. Each single entry is linked to the enzyme
        source and to a literature reference. Recently the data repository was complemented
        by text-mining data in AMENDA (Automatic Mining of ENzyme DAta) and FRENDA
        (Full Reference ENzyme DAta). A genome browser, membrane protein prediction
        and full-text search capacities were added. The newly implemented web service
        provides instant access to the data for programmers via a SOAP (Simple Object
        Access Protocol) interface. The BRENDA data can be downloaded in the form
        of a text file from the beginning of 2007. © 2007 Oxford University Press.
      authors:
      - name: Barthelmes J.
      - name: Ebeling C.
      - name: Chang A.
      - name: Schomburg I.
      - name: Schomburg D.
      citationCount: 137
      date: '2007-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BRENDA, AMENDA and FRENDA: The enzyme information system in 2007'
    note: null
    pmcid: PMC1899097
    pmid: '17202167'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/30.1.47
    metadata:
      abstract: BRENDA is a comprehensive relational database on functional and molecular
        information of enzymes, based on primary literature. The database contains
        information extracted and evaluated from approximately 46 000 references,
        holding data of at least 40 000 different enzymes from more than 6900 different
        organisms, classified in approximately 3900 EC numbers. BRENDA is an important
        tool for biochemical and medical research covering information on properties
        of all classified enzymes, including data on the occurrence, catalyzed reaction,
        kinetics, substrates/products, inhibitors, cofactors, activators, structure
        and stability. All data are connected to literature references which in turn
        are linked to PubMed. The data and information provide a fundamental tool
        for research of enzyme mechanisms, metabolic pathways, the evolution of metabolism
        and, furthermore, for medicinal diagnostics and pharmaceutical research. The
        database is a resource for data of enzymes, classified according to the EC
        system of the IUBMB Enzyme Nomenclature Committee, and the entries are cross-referenced
        to other databases, i.e. organism classification, protein sequence, protein
        structure and literature references. BRENDA provides an academic web access
        at http://www.brenda.uni-koeln.de.
      authors:
      - name: Schomburg I.
      - name: Chang A.
      - name: Schomburg D.
      citationCount: 384
      date: '2002-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BRENDA, enzyme data and metabolic information
    note: null
    pmcid: PMC99121
    pmid: '11752250'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkq968
    metadata:
      abstract: BTO, the BRENDA Tissue Ontology (http://www.BTO.brenda-enzymes.org)
        represents a comprehensive structured encyclopedia of tissue terms. The project
        started in 2003 to create a connection between the enzyme data collection
        of the BRENDA enzyme database and a structured network of source tissues and
        cell types. Currently, BTO contains more than 4600 different anatomical structures,
        tissues, cell types and cell lines, classified under generic categories corresponding
        to the rules and formats of the Gene Ontology Consortium and organized as
        a directed acyclic graph (DAG). Most of the terms are endowed with comments
        on their derivation or definitions. The content of the ontology is constantly
        curated with ̃1000 new terms each year. Four different types of relationships
        between the terms are implemented. A versatile web interface with several
        search and navigation functionalities allows convenient online access to the
        BTO and to the enzymes isolated from the tissues. Important areas of applications
        of the BTO terms are the detection of enzymes in tissues and the provision
        of a solid basis for text-mining approaches in this field. It is widely used
        by lab scientists, curators of genomic and biochemical databases and bioinformaticians.
        The BTO is freely available at http://www.obofoundry.org. © The Author(s)
        2010.
      authors:
      - name: Gremse M.
      - name: Chang A.
      - name: Schomburg I.
      - name: Grote A.
      - name: Scheer M.
      - name: Ebeling C.
      - name: Schomburg D.
      citationCount: 141
      date: '2011-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The BRENDA Tissue Ontology (BTO): The first all-integrating ontology
        of all organisms for enzyme sources'
    note: null
    pmcid: PMC3013802
    pmid: '21030441'
    type:
    - Other
    version: null
  relation:
  - biotoolsID: bto
    type: includes
  toolType:
  - Database portal
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  - term: Enzymes
    uri: http://edamontology.org/topic_0821
  - term: Biology
    uri: http://edamontology.org/topic_3070
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Biomedical science
    uri: http://edamontology.org/topic_3344
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-08-09T13:13:30Z'
  biotoolsCURIE: biotools:TYGS
  biotoolsID: TYGS
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: jan.meier-kolthoff@dsmz.de
    fundrefid: null
    gridid: null
    name: Jan P. Meier-Kolthoff
    note: null
    orcidid: https://orcid.org/0000-0001-9105-9814
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Automated high-throughput platform for state-of-the-art genome-based
    taxonomy.
  documentation:
  - note: null
    type:
    - FAQ
    url: https://tygs.dsmz.de/faqs
  download: []
  editPermission:
    authors:
    - mgoeker
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    - term: Phylogenetic species tree construction
      uri: http://edamontology.org/operation_0544
    output: []
  homepage: https://tygs.dsmz.de
  homepage_status: 0
  language: []
  lastUpdate: '2025-01-23T15:00:45.502376Z'
  license: Unlicense
  link: []
  maturity: Mature
  name: TYGS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: mgoeker
  publication:
  - doi: 10.1038/S41467-019-10210-3
    metadata:
      abstract: © 2019, The Author(s).Microbial taxonomy is increasingly influenced
        by genome-based computational methods. Yet such analyses can be complex and
        require expert knowledge. Here we introduce TYGS, the Type (Strain) Genome
        Server, a user-friendly high-throughput web server for genome-based prokaryote
        taxonomy, connected to a large, continuously growing database of genomic,
        taxonomic and nomenclatural information. It infers genome-scale phylogenies
        and state-of-the-art estimates for species and subspecies boundaries from
        user-defined and automatically determined closest type genome sequences. TYGS
        also provides comprehensive access to nomenclature, synonymy and associated
        taxonomic literature. Clinically important examples demonstrate how TYGS can
        yield new insights into microbial classification, such as evidence for a species-level
        separation of previously proposed subspecies of Salmonella enterica. TYGS
        is an integrated approach for the classification of microbes that unlocks
        novel scientific approaches to microbiologists worldwide and is particularly
        helpful for the rapidly expanding field of genome-based taxonomic descriptions
        of new genera, species or subspecies.
      authors:
      - name: Meier-Kolthoff J.P.
      - name: Goker M.
      citationCount: 409
      date: '2019-12-01T00:00:00Z'
      journal: Nature Communications
      title: TYGS is an automated high-throughput platform for state-of-the-art genome-based
        taxonomy
    note: null
    pmcid: PMC6522516
    pmid: '31097708'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Web service
  topic:
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2016-10-10T12:39:00Z'
  biotoolsCURIE: biotools:bacdive
  biotoolsID: bacdive
  collectionID:
  - de.NBI
  - DSMZ Digital Diversity
  - Core Data Resources
  - de.NBI-biodata
  - ELIXIR-DE
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen
      GmbH
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.dsmz.de/
  - email: contact@bacdive.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://bacdive.dsmz.de/contact
  description: 'BacDive (The Bacterial Diversity Database) is the worldwide largest
    database for standardized bacterial and archaeal strain-level information. Its
    mission is to mobilize and integrate research data on strain level from diverse
    sources and make it freely accessible.


    BacDive is a comprehensive resource containing diverse data on bacterial and archaeal
    strains, including taxonomy, morphology, physiology, sampling and environmental
    data and sequence information.'
  documentation:
  - note: null
    type:
    - Terms of use
    - Citation instructions
    url: https://bacdive.dsmz.de/about
  - note: null
    type:
    - Training material
    url: https://bacdive.dsmz.de/tutorials
  download: []
  editPermission:
    authors:
    - JKoblitz
    - Joaquim
    - isabelschober
    type: group
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Species name
        uri: http://edamontology.org/data_1045
      format: []
    - data:
        term: Strain accession
        uri: http://edamontology.org/data_2912
      format: []
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format: []
    - data:
        term: NCBI taxonomy ID
        uri: http://edamontology.org/data_1179
      format: []
    - data:
        term: Strain identifier
        uri: http://edamontology.org/data_2379
      format: []
    note: "The BacDive portal offers several search options:\nSimple Search / Smart\
      \ Search \nAdvanced Search\nIsolation Source Search\nAPI test finder\nTAXplorer\n\
      Special collections"
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Image
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      format: []
    - data:
        term: Data reference
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      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Taxonomic classification
        uri: http://edamontology.org/data_1872
      format:
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        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Sequence accession (nucleic acid)
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      format:
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        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
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    - data:
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      - term: Textual format
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      - term: JSON
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    - data:
        term: NCBI taxonomy ID
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      format:
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      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Molecular property
        uri: http://edamontology.org/data_2087
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Nucleic acid property
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      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
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      format:
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      - term: Textual format
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      - term: JSON
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    - data:
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      format:
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        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Isolation source
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      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Geographic location
        uri: http://edamontology.org/data_3720
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Biosafety classification
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      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
    - data:
        term: Pathogenicity report
        uri: http://edamontology.org/data_3718
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: JSON
        uri: http://edamontology.org/format_3464
  homepage: https://bacdive.dsmz.de
  homepage_status: 0
  language:
  - PHP
  lastUpdate: '2025-01-23T07:43:33.828764Z'
  license: CC-BY-4.0
  link:
  - note: null
    type:
    - Mailing list
    url: https://bacdive.dsmz.de/mailinglist/subscribe
  - note: null
    type:
    - Helpdesk
    url: https://bacdive.dsmz.de/contact
  - note: null
    type:
    - Social media
    url: https://bsky.app/profile/bacdive.bsky.social
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/dsmzd3/
  maturity: Mature
  name: BacDive
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: lorenz.reimer
  publication:
  - doi: 10.1093/nar/gkae959
    metadata:
      abstract: In 2025, the bacterial diversity database BacDive is the leading database
        for strain-level bacterial and archaeal information. It has been selected
        as an ELIXIR Core Data Resource as well as a Global Core Biodata Resource.
        Since its initial release more than ten years ago, BacDive (https://bacdive.dsmz.de)
        has grown tremendously in content and functionalities, and is a comprehensive
        resource covering the phenotypic diversity of prokaryotes with data on taxonomy,
        morphology, physiology, cultivation, and more. The current release (2023.2)
        contains 2.6 million data points on 97 334 strains, reflecting an increase
        by 52% since the previous publication in 2021. This remarkable growth can
        largely be attributed to the integration of the world-wide largest collection
        of Analytical Profile Index (API) test results, which are now fully integrated
        into the database and searchable. A novel BacDive knowledge graph provides
        powerful search options through a SPARQL endpoint, including the possibility
        for federated searches across multiple data sources. The high-quality data
        provided by BacDive is increasingly being used for the training of artificial
        intelligence models and resulting genome-based predictions with high confidence
        are now used to fill content gaps in the database.
      authors:
      - name: Schober I.
      - name: Koblitz J.
      - name: Carbasse J.S.
      - name: Ebeling C.
      - name: Schmidt M.L.
      - name: Podstawka A.
      - name: Gupta R.
      - name: Ilangovan V.
      - name: Chamanara J.
      - name: Overmann J.
      - name: Reimer L.C.
      citationCount: 1
      date: '2025-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2025: the core database for prokaryotic strain data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkab961
    metadata:
      abstract: The bacterial metadatabase BacDive (https://bacdive.dsmz.de) has developed
        into a leading database for standardized prokaryotic data on strain level.
        With its current release (07/2021) the database offers information for 82
        892 bacterial and archaeal strains covering taxonomy, morphology, cultivation,
        metabolism, origin, and sequence information within 1048 data fields. By integrating
        high-quality data from additional culture collections as well as detailed
        information from species descriptions, the amount of data provided has increased
        by 30% over the past three years. A newly developed query builder tool in
        the advanced search now allows complex database queries. Thereby bacterial
        strains can be systematically searched based on combinations of their attributes,
        e.g. growth and metabolic features for biotechnological applications or to
        identify gaps in the present knowledge about bacteria. A new interactive dashboard
        provides a statistic overview over the most important data fields. Additional
        new features are improved genomic sequence data, integrated NCBI TaxIDs and
        links to BacMedia, the new sister database on cultivation media. To improve
        the findability and interpretation of data through search engines, data in
        BacDive are annotated with bioschemas.org terms.
      authors:
      - name: Reimer L.C.
      - name: Sarda Carbasse J.
      - name: Koblitz J.
      - name: Ebeling C.
      - name: Podstawka A.
      - name: Overmann J.
      citationCount: 106
      date: '2022-01-07T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2022: The knowledge base for standardized bacterial and archaeal
        data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gky879
    metadata:
      abstract: The bacterial metadatabase BacDive (http://bacdive.dsmz.de) has become
        a comprehensive resource for structured data on the taxonomy, morphology,
        physiology, cultivation, isolation and molecular data of prokaryotes. With
        its current release (7/2018) the database offers information for 63 669 bacterial
        and archaeal strains including 12 715 type strains. During recent developments
        of BacDive, the enrichment of information on existing strains was prioritized.
        This has resulted in a 146% increase of database content over the past three
        years. Especially rich datasets were integrated from 4782 manual annotated
        species descriptions in the International Journal of Systematic and Evolutionary
        Microbiology which yielded standardized phenotypic data for 5468 type strains.
        Another important improvement of content was achieved through the mobilization
        of 8977 Analytical Profile Index (API ® ) test results that constitute physiological
        data for the identification of 5237 strains. BacDive offers a unique API ®
        data collection with respect to size and diversity. In addition, data on fatty
        acid profiles and antibiotic susceptibility tests were integrated. A revised
        graphical user interface and new search tools such as the API ® test finder,
        the TAXplorer, or the Microbial Isolation Source Search significantly improve
        the user experience.
      authors:
      - name: Reimer L.C.
      - name: Vetcininova A.
      - name: Carbasse J.S.
      - name: Sohngen C.
      - name: Gleim D.
      - name: Ebeling C.
      - name: Overmann J.
      citationCount: 132
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2019: Bacterial phenotypic data for High-throughput biodiversity
        analysis'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1016/j.jbiotec.2017.05.004
    metadata:
      abstract: Microbial data and metadata are scattered throughout the scientific
        literature, databases and unpublished lab notes and thereby often are difficult
        to access. Hot spots of (meta)data are internal descriptions of culture collections
        and initial descriptions of novel taxa in primary literature. Here we describe
        three exemplary mobilization projects which yielded metadata published through
        the prokaryotic metadatabase BacDive. The Reichenbach collection of myxobacteria
        includes information on 12,535 typewritten index cards which were digitized.
        A total of 37,156 data points were extracted by text mining. In the second
        mobilization project, Analytical Profile Index (API) tests on paper forms
        were targeted. Overall 6820 API tests were digitized, which provide physiological
        data of 4524 microbial strains. Thirdly, the extraction of metadata from 523
        new species descriptions of the International Journal of Systematic and Evolutionary
        Microbiology, yielding 35,651 data points, is described. All data sets were
        integrated and published in BacDive. Thereby these metadata not only became
        accessible and searchable but were also linked to strain taxonomy, isolation
        source, cultivation condition, and molecular biology data.
      authors:
      - name: Reimer L.C.
      - name: Sohngen C.
      - name: Vetcininova A.
      - name: Overmann J.
      citationCount: 6
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: Mobilization and integration of bacterial phenotypic data—Enabling next
        generation biodiversity analysis through the BacDive metadatabase
    note: null
    pmcid: null
    pmid: '28487186'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkv983
    metadata:
      abstract: BacDive-the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de)
        provides strain-linked information about bacterial and archaeal biodiversity.
        The range of data encompasses taxonomy, morphology, physiology, sampling and
        concomitant environmental conditions as well as molecular biology. The majority
        of data is manually annotated and curated. Currently (with release 9/2015),
        BacDive covers 53 978 strains. Newly implemented RESTful web services provide
        instant access to the content in machinereadable XML and JSON format. Besides
        an overall increase of data content, BacDive offers new data fields and features,
        e.g. the search for gene names, plasmids or 16S rRNA in the advanced search,
        as well as improved linkage of entries to external life science web resources.
      authors:
      - name: Sohngen C.
      - name: Podstawka A.
      - name: Bunk B.
      - name: Gleim D.
      - name: Vetcininova A.
      - name: Reimer L.C.
      - name: Ebeling C.
      - name: Pendarovski C.
      - name: Leibniz J.O.
      citationCount: 44
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BacDive - The Bacterial Diversity Metadatabase in 2016
    note: null
    pmcid: PMC4702946
    pmid: '26424852'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkt1058
    metadata:
      abstract: BacDive - the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de)
        merges detailed strain-linked information on the different aspects of bacterial
        and archaeal biodiversity. Currently (release 9/2013), BacDive contains entries
        for 23 458 strains and provides information on their taxonomy, morphology,
        physiology, sampling and concomitant environmental conditions as well as molecular
        biology. Where available, links to access the respective biological resources
        are given. The majority of the BacDive data is manually annotated and curated.
        The BacDive portal offers an easy-to-use simple search and in addition powerful
        advanced search functionalities allowing to combine more than 30 search fields
        for text and numerical data. The user can compile individual sets of strains
        to a download selection that can easily be imported into nearly all spreadsheet
        applications. © 2013 The Author(s). Published by Oxford University Press.
      authors:
      - name: Sohngen C.
      - name: Bunk B.
      - name: Podstawka A.
      - name: Gleim D.
      - name: Overmann J.
      citationCount: 33
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BacDive - The Bacterial Diversity Metadatabase
    note: null
    pmcid: PMC3965005
    pmid: '24214959'
    type:
    - Other
    version: null
  relation:
  - biotoolsID: bacdive_ws
    type: includes
  - biotoolsID: d3hub
    type: includedIn
  toolType:
  - Database portal
  topic:
  - term: Biodiversity
    uri: http://edamontology.org/topic_3050
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Microbial collection
    uri: http://edamontology.org/topic_3339
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  validated: 1
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2016-01-28T09:14:11Z'
  biotoolsCURIE: biotools:bacdive_ws
  biotoolsID: bacdive_ws
  collectionID:
  - de.NBI
  - DSMZ Digital Diversity
  - Core Data Resources
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: hub@dsmz.de
    fundrefid: null
    gridid: null
    name: Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen
      GmbH
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://dsmz.de
  - email: contact@bacdive.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The API for retrieving BacDive data in machine-readable JSON format.
    BacDive is the Bacterial Diversity Database and provides strain-linked information
    about bacterial and archaeal biodiversity. The API offers several possibilities
    to query detailed strain information.
  documentation:
  - note: null
    type:
    - API documentation
    - Citation instructions
    url: https://api.bacdive.dsmz.de/
  - note: null
    type:
    - Training material
    url: https://bacdive.dsmz.de/tutorials
  download: []
  editPermission:
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    - Joaquim
    type: group
  elixirCommunity: []
  elixirNode:
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  elixirPlatform:
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  elixir_badge: 0
  function:
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    input:
    - data:
        term: Database ID
        uri: http://edamontology.org/data_1048
      format: []
    note: curl "https://api.bacdive.dsmz.de/example/fetch/{bacdive_id}"
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
  - cmd: null
    input:
    - data:
        term: Species name
        uri: http://edamontology.org/data_1045
      format: []
    - data:
        term: Genus name
        uri: http://edamontology.org/data_1870
      format: []
    - data:
        term: Strain identifier
        uri: http://edamontology.org/data_2379
      format: []
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format: []
    - data:
        term: NCBI genome accession
        uri: http://edamontology.org/data_2787
      format: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Database ID
        uri: http://edamontology.org/data_1048
      format: []
  homepage: https://api.bacdive.dsmz.de/
  homepage_status: 0
  language: []
  lastUpdate: '2025-01-22T12:15:27.195571Z'
  license: CC-BY-4.0
  link:
  - note: null
    type:
    - Service
    url: https://bacdive.dsmz.de/
  - note: null
    type:
    - Social media
    url: https://bsky.app/profile/bacdive.bsky.social
  maturity: Mature
  name: BacDive API
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: lorenz.reimer
  publication:
  - doi: 10.1093/nar/gkae959
    metadata:
      abstract: In 2025, the bacterial diversity database BacDive is the leading database
        for strain-level bacterial and archaeal information. It has been selected
        as an ELIXIR Core Data Resource as well as a Global Core Biodata Resource.
        Since its initial release more than ten years ago, BacDive (https://bacdive.dsmz.de)
        has grown tremendously in content and functionalities, and is a comprehensive
        resource covering the phenotypic diversity of prokaryotes with data on taxonomy,
        morphology, physiology, cultivation, and more. The current release (2023.2)
        contains 2.6 million data points on 97 334 strains, reflecting an increase
        by 52% since the previous publication in 2021. This remarkable growth can
        largely be attributed to the integration of the world-wide largest collection
        of Analytical Profile Index (API) test results, which are now fully integrated
        into the database and searchable. A novel BacDive knowledge graph provides
        powerful search options through a SPARQL endpoint, including the possibility
        for federated searches across multiple data sources. The high-quality data
        provided by BacDive is increasingly being used for the training of artificial
        intelligence models and resulting genome-based predictions with high confidence
        are now used to fill content gaps in the database.
      authors:
      - name: Schober I.
      - name: Koblitz J.
      - name: Carbasse J.S.
      - name: Ebeling C.
      - name: Schmidt M.L.
      - name: Podstawka A.
      - name: Gupta R.
      - name: Ilangovan V.
      - name: Chamanara J.
      - name: Overmann J.
      - name: Reimer L.C.
      citationCount: 1
      date: '2025-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2025: the core database for prokaryotic strain data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkab961
    metadata:
      abstract: The bacterial metadatabase BacDive (https://bacdive.dsmz.de) has developed
        into a leading database for standardized prokaryotic data on strain level.
        With its current release (07/2021) the database offers information for 82
        892 bacterial and archaeal strains covering taxonomy, morphology, cultivation,
        metabolism, origin, and sequence information within 1048 data fields. By integrating
        high-quality data from additional culture collections as well as detailed
        information from species descriptions, the amount of data provided has increased
        by 30% over the past three years. A newly developed query builder tool in
        the advanced search now allows complex database queries. Thereby bacterial
        strains can be systematically searched based on combinations of their attributes,
        e.g. growth and metabolic features for biotechnological applications or to
        identify gaps in the present knowledge about bacteria. A new interactive dashboard
        provides a statistic overview over the most important data fields. Additional
        new features are improved genomic sequence data, integrated NCBI TaxIDs and
        links to BacMedia, the new sister database on cultivation media. To improve
        the findability and interpretation of data through search engines, data in
        BacDive are annotated with bioschemas.org terms.
      authors:
      - name: Reimer L.C.
      - name: Sarda Carbasse J.
      - name: Koblitz J.
      - name: Ebeling C.
      - name: Podstawka A.
      - name: Overmann J.
      citationCount: 106
      date: '2022-01-07T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2022: The knowledge base for standardized bacterial and archaeal
        data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gky879
    metadata:
      abstract: The bacterial metadatabase BacDive (http://bacdive.dsmz.de) has become
        a comprehensive resource for structured data on the taxonomy, morphology,
        physiology, cultivation, isolation and molecular data of prokaryotes. With
        its current release (7/2018) the database offers information for 63 669 bacterial
        and archaeal strains including 12 715 type strains. During recent developments
        of BacDive, the enrichment of information on existing strains was prioritized.
        This has resulted in a 146% increase of database content over the past three
        years. Especially rich datasets were integrated from 4782 manual annotated
        species descriptions in the International Journal of Systematic and Evolutionary
        Microbiology which yielded standardized phenotypic data for 5468 type strains.
        Another important improvement of content was achieved through the mobilization
        of 8977 Analytical Profile Index (API ® ) test results that constitute physiological
        data for the identification of 5237 strains. BacDive offers a unique API ®
        data collection with respect to size and diversity. In addition, data on fatty
        acid profiles and antibiotic susceptibility tests were integrated. A revised
        graphical user interface and new search tools such as the API ® test finder,
        the TAXplorer, or the Microbial Isolation Source Search significantly improve
        the user experience.
      authors:
      - name: Reimer L.C.
      - name: Vetcininova A.
      - name: Carbasse J.S.
      - name: Sohngen C.
      - name: Gleim D.
      - name: Ebeling C.
      - name: Overmann J.
      citationCount: 132
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BacDive in 2019: Bacterial phenotypic data for High-throughput biodiversity
        analysis'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1016/j.jbiotec.2017.05.004
    metadata:
      abstract: Microbial data and metadata are scattered throughout the scientific
        literature, databases and unpublished lab notes and thereby often are difficult
        to access. Hot spots of (meta)data are internal descriptions of culture collections
        and initial descriptions of novel taxa in primary literature. Here we describe
        three exemplary mobilization projects which yielded metadata published through
        the prokaryotic metadatabase BacDive. The Reichenbach collection of myxobacteria
        includes information on 12,535 typewritten index cards which were digitized.
        A total of 37,156 data points were extracted by text mining. In the second
        mobilization project, Analytical Profile Index (API) tests on paper forms
        were targeted. Overall 6820 API tests were digitized, which provide physiological
        data of 4524 microbial strains. Thirdly, the extraction of metadata from 523
        new species descriptions of the International Journal of Systematic and Evolutionary
        Microbiology, yielding 35,651 data points, is described. All data sets were
        integrated and published in BacDive. Thereby these metadata not only became
        accessible and searchable but were also linked to strain taxonomy, isolation
        source, cultivation condition, and molecular biology data.
      authors:
      - name: Reimer L.C.
      - name: Sohngen C.
      - name: Vetcininova A.
      - name: Overmann J.
      citationCount: 6
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: Mobilization and integration of bacterial phenotypic data—Enabling next
        generation biodiversity analysis through the BacDive metadatabase
    note: null
    pmcid: null
    pmid: '28487186'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkv983
    metadata:
      abstract: BacDive-the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de)
        provides strain-linked information about bacterial and archaeal biodiversity.
        The range of data encompasses taxonomy, morphology, physiology, sampling and
        concomitant environmental conditions as well as molecular biology. The majority
        of data is manually annotated and curated. Currently (with release 9/2015),
        BacDive covers 53 978 strains. Newly implemented RESTful web services provide
        instant access to the content in machinereadable XML and JSON format. Besides
        an overall increase of data content, BacDive offers new data fields and features,
        e.g. the search for gene names, plasmids or 16S rRNA in the advanced search,
        as well as improved linkage of entries to external life science web resources.
      authors:
      - name: Sohngen C.
      - name: Podstawka A.
      - name: Bunk B.
      - name: Gleim D.
      - name: Vetcininova A.
      - name: Reimer L.C.
      - name: Ebeling C.
      - name: Pendarovski C.
      - name: Leibniz J.O.
      citationCount: 44
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BacDive - The Bacterial Diversity Metadatabase in 2016
    note: null
    pmcid: PMC4702946
    pmid: '26424852'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkt1058
    metadata:
      abstract: BacDive - the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de)
        merges detailed strain-linked information on the different aspects of bacterial
        and archaeal biodiversity. Currently (release 9/2013), BacDive contains entries
        for 23 458 strains and provides information on their taxonomy, morphology,
        physiology, sampling and concomitant environmental conditions as well as molecular
        biology. Where available, links to access the respective biological resources
        are given. The majority of the BacDive data is manually annotated and curated.
        The BacDive portal offers an easy-to-use simple search and in addition powerful
        advanced search functionalities allowing to combine more than 30 search fields
        for text and numerical data. The user can compile individual sets of strains
        to a download selection that can easily be imported into nearly all spreadsheet
        applications. © 2013 The Author(s). Published by Oxford University Press.
      authors:
      - name: Sohngen C.
      - name: Bunk B.
      - name: Podstawka A.
      - name: Gleim D.
      - name: Overmann J.
      citationCount: 33
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: BacDive - The Bacterial Diversity Metadatabase
    note: null
    pmcid: PMC3965005
    pmid: '24214959'
    type:
    - Other
    version: null
  relation:
  - biotoolsID: bacdive
    type: includedIn
  - biotoolsID: d3hub
    type: includedIn
  toolType:
  - Web API
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Microbial collection
    uri: http://edamontology.org/topic_3339
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-01-22T10:00:56.561701Z'
  biotoolsCURIE: biotools:mmtb
  biotoolsID: mmtb
  collectionID:
  - DSMZ Digital Diversity
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: julia.koblitz@dsmz.de
    fundrefid: null
    gridid: null
    name: Julia Koblitz
    note: null
    orcidid: https://orcid.org/0000-0002-7260-2129
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: https://research.dsmz.de/person/651cecd8b3c97f11cc28cfc2
  - email: hub@dsmz.de
    fundrefid: null
    gridid: null
    name: Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen
      GmbH
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://dsmz.de
  description: The Metano Metabolic Modeling Toolbox.
  documentation:
  - note: null
    type:
    - User manual
    url: https://mmtb.brenda-enzymes.org/help
  download:
  - note: null
    type: Downloads page
    url: https://mmtb.brenda-enzymes.org/metano/install
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format: []
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output:
    - data:
        term: Simulation
        uri: http://edamontology.org/data_3869
      format: []
  homepage: https://mmtb.brenda-enzymes.org/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-01-22T10:09:15.144513Z'
  license: CC-BY-4.0
  link: []
  maturity: Mature
  name: MMTB
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: JKoblitz
  publication:
  - doi: 10.3390/metabo11020113
    metadata:
      abstract: Genome-scale metabolic models are of high interest in a number of
        different research fields. Flux balance analysis (FBA) and other mathematical
        methods allow the prediction of the steady-state behavior of metabolic networks
        under different environmental conditions. However, many existing applications
        for flux optimizations do not provide a metabolite-centric view on fluxes.
        Metano is a standalone, open-source toolbox for the analysis and refinement
        of metabolic models. While flux distributions in metabolic networks are predominantly
        analyzed from a reaction-centric point of view, the Metano methods of split-ratio
        analysis and metabolite flux minimization also allow a metabolite-centric
        view on flux distributions. In addition, we present MMTB (mmtb.brendaenzymes.org),
        a web-based toolbox for metabolic modeling including a user-friendly interface
        to Metano methods. MMTB assists during bottom-up construction of metabolic
        models by integrating reaction and enzymatic annotation data from different
        databases. Furthermore, MMTB is especially designed for non-experienced users
        by providing an intuitive interface to the most commonly used modeling methods
        and offering novel visualizations. Additionally, MMTB allows users to upload
        their models, which can in turn be explored and analyzed by the community.
        We introduce MMTB by two use cases, involving a published model of Corynebacterium
        glutamicum and a newly created model of Phaeobacter inhibens.
      authors:
      - name: Koblitz J.
      - name: Will S.E.
      - name: Riemer S.A.
      - name: Ulas T.
      - name: Neumann-Schaal M.
      - name: Schomburg D.
      citationCount: 2
      date: '2021-02-01T00:00:00Z'
      journal: Metabolites
      title: 'The metano modeling toolbox MMTB: An intuitive, web-based toolbox introduced
        by two use cases'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: enzymedetector
    type: includes
  - biotoolsID: bkmsreact
    type: includes
  - biotoolsID: metano
    type: includes
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Omics
    uri: http://edamontology.org/topic_3391
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2016-09-30T15:59:05Z'
  biotoolsCURIE: biotools:silva
  biotoolsID: silva
  collectionID:
  - de.NBI
  - de.NBI-biodata
  - DSMZ Digital Diversity
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: hub@dsmz.de
    fundrefid: null
    gridid: grid.420081.f
    name: Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.dsmz.de
  - email: contact@arb-silva.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Primary contact
    url: null
  description: SILVA provides comprehensive, quality checked and regularly updated
    datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal
    RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukaryota).
  documentation:
  - note: null
    type:
    - Terms of use
    url: https://www.arb-silva.de/silva-license-information/
  - note: null
    type:
    - Citation instructions
    url: https://www.arb-silva.de/contact/
  - note: null
    type:
    - General
    url: http://www.arb-silva.de/documentation/
  - note: null
    type:
    - FAQ
    url: https://www.arb-silva.de/documentation/faqs/
  download:
  - note: SILVA dataset archive
    type: Downloads page
    url: https://www.arb-silva.de/download/archive/
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format:
      - term: plain text format (unformatted)
        uri: http://edamontology.org/format_1964
    - data:
        term: Taxon
        uri: http://edamontology.org/data_1868
      format:
      - term: plain text format (unformatted)
        uri: http://edamontology.org/format_1964
    - data:
        term: Strain name
        uri: http://edamontology.org/data_1046
      format:
      - term: plain text format (unformatted)
        uri: http://edamontology.org/format_1964
    - data:
        term: Article ID
        uri: http://edamontology.org/data_1088
      format:
      - term: plain text format (unformatted)
        uri: http://edamontology.org/format_1964
    - data:
        term: Organism name
        uri: http://edamontology.org/data_2909
      format:
      - term: plain text format (unformatted)
        uri: http://edamontology.org/format_1964
    note: The search and retrieval functions of the SILVA website can be used to build
      custom subsets of sequences. In addition to simple searches e.g. for accession
      numbers, organism names, taxonomic entities, or publication DOI/PubMed IDs,
      complex queries over several database fields using constraints such as sequence
      length or quality values are possible. The results can be sorted according to
      accession numbers, organism names, sequence length, sequence and alignment quality
      and Pintail values. The search results show accession number, organism name,
      sequence length, sequence quality values, taxonomic classifications, and links
      to view the full sequence record on SILVA and on ENA. Sequences found via search
      and added to download cart can be downloaded as FASTA and ARB files.
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Sequence alignment (nucleic acid)
        uri: http://edamontology.org/data_1383
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: SILVA Incremental Aligner (SINA) is used to align the rRNA gene databases
      provided by SILVA, as well as user submitted sequences. SINA uses a combination
      of k-mer searching and partial order alignment (POA) to maintain very high alignment
      accuracy while satisfying high throughput performance demands. Aligned sequences
      can be dowloaded as FASTA or ARB files
    operation:
    - term: Sequence alignment
      uri: http://edamontology.org/operation_0292
    output:
    - data:
        term: Sequence alignment (nucleic acid)
        uri: http://edamontology.org/data_1383
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: SINA can also operate as a taxonomic classification tool. It uses the search
      result to derive a classification with the LCA (lowest common ancestor) method.
      Each query sequence is assigned the shared part of the classifications of the
      search results. Aligned sequences can be dowloaded as FASTA or ARB files Taxonomic
      classification results can be downloaded as csv files.
    operation:
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Sequence alignment (nucleic acid)
        uri: http://edamontology.org/data_1383
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
    - data:
        term: Taxonomic classification
        uri: http://edamontology.org/data_1872
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: nucleotide
        uri: http://edamontology.org/format_1207
    note: The SILVA Probe Match and Evaluation Tool detects and displays all occurrences
      of a given probe or primer sequence in the SILVA datasets. TestPrime allows
      you to evaluate the performance of primer pairs by running an in silico PCR
      on the SILVA databases. From the results of the PCR, TestPrime computes coverages
      for each taxonomic group in all of the taxonomies offered by SILVA.
    operation:
    - term: Primer and probe design
      uri: http://edamontology.org/operation_2419
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://www.arb-silva.de
  homepage_status: 0
  language: []
  lastUpdate: '2025-01-22T09:59:03.064970Z'
  license: CC-BY-4.0
  link:
  - note: SILVA Taxonomy Browser
    type:
    - Service
    url: https://www.arb-silva.de/browser/
  - note: SILVA metadata search
    type:
    - Service
    url: https://www.arb-silva.de/search/
  - note: 'ACT: Alignment, Classification and Tree Service'
    type:
    - Service
    url: https://www.arb-silva.de/aligner/
  - note: SILVA Probe Match and Evaluation Tool
    type:
    - Service
    url: https://www.arb-silva.de/search/testprobe/
  - note: SILVA Primer Evaluation Tool
    type:
    - Service
    url: https://www.arb-silva.de/search/testprime/
  - note: Web-based viewer for the SILVA guide trees
    type:
    - Service
    url: https://treeviewer.arb-silva.de/
  maturity: Mature
  name: SILVA rRNA database
  operatingSystem: []
  otherID:
  - type: rrid
    value: RRID:SCR_006423
    version: null
  owner: silva
  publication:
  - doi: 10.1093/nar/gks1219
    metadata:
      abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive
        web resource for up to date, quality-controlled databases of aligned ribosomal
        RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains
        and supplementary online services. The referred database release 111 (July
        2012) contains 3194 778 small subunit and 288717 large subunit rRNA gene sequences.
        Since the initial description of the project, substantial new features have
        been introduced, including advanced quality control procedures, an improved
        rRNA gene aligner, online tools for probe and primer evaluation and optimized
        browsing, searching and downloading on the website. Furthermore, the extensively
        curated SILVA taxonomy and the new non-redundant SILVA datasets provide an
        ideal reference for high-throughput classification of data from next-generation
        sequencing approaches. © The Author(s) 2012.
      authors:
      - name: Quast C.
      - name: Pruesse E.
      - name: Yilmaz P.
      - name: Gerken J.
      - name: Schweer T.
      - name: Yarza P.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 20831
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The SILVA ribosomal RNA gene database project: Improved data processing
        and web-based tools'
    note: null
    pmcid: PMC3531112
    pmid: '23193283'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkt1209
    metadata:
      abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive
        resource for up-to-date quality-controlled databases of aligned ribosomal
        RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains
        and supplementary online services. SILVA provides a manually curated taxonomy
        for all three domains of life, based on representative phylogenetic trees
        for the small- and large-subunit rRNA genes. This article describes the improvements
        the SILVA taxonomy has undergone in the last 3 years. Specifically we are
        focusing on the curation process, the various resources used for curation
        and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies.
        Our comparisons not only revealed a reasonable overlap between the taxa names,
        but also points to significant differences in both names and numbers of taxa
        between the three resources. © 2013 The Author(s). Published by Oxford University
        Press.
      authors:
      - name: Yilmaz P.
      - name: Parfrey L.W.
      - name: Yarza P.
      - name: Gerken J.
      - name: Pruesse E.
      - name: Quast C.
      - name: Schweer T.
      - name: Peplies J.
      - name: Ludwig W.
      - name: Glockner F.O.
      citationCount: 2372
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The SILVA and "all-species Living Tree Project (LTP)" taxonomic frameworks
    note: null
    pmcid: PMC3965112
    pmid: '24293649'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkm864
    metadata:
      abstract: 'Sequencing ribosomal RNA (rRNA) genes is currently the method of
        choice for phylogenetic reconstruction, nucleic acid based detection and quantification
        of microbial diversity. The ARB software suite with its corresponding rRNA
        datasets has been accepted by researchers worldwide as a standard tool for
        large scale rRNA analysis. However, the rapid increase of publicly available
        rRNA sequence data has recently hampered the maintenance of comprehensive
        and curated rRNA knowledge databases. A new system, SILVA (from Latin silva,
        forest), was implemented to provide a central comprehensive web resource for
        up to date, quality controlled databases of aligned rRNA sequences from the
        Bacteria, Archaea and Eukarya domains. All sequences are checked for anomalies,
        carry a rich set of sequence associated contextual information, have multiple
        taxonomic classifications, and the latest validly described nomenclature.
        Furthermore, two precompiled sequence datasets compatible with ARB are offered
        for download on the SILVA website: (i) the reference (Ref) datasets, comprising
        only high quality, nearly full length sequences suitable for in-depth phylogenetic
        analysis and probe design and (ii) the comprehensive Parc datasets with all
        publicly available rRNA sequences longer than 300 nucleotides suitable for
        biodiversity analyses. The latest publicly available database release 91 (August
        2007) hosts 547 521 sequences split into 461 823 small subunit and 85 689
        large subunit rRNAs. © 2007 The Author(s).'
      authors:
      - name: Pruesse E.
      - name: Quast C.
      - name: Knittel K.
      - name: Fuchs B.M.
      - name: Ludwig W.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 5145
      date: '2007-12-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'SILVA: A comprehensive online resource for quality checked and aligned
        ribosomal RNA sequence data compatible with ARB'
    note: null
    pmcid: PMC2175337
    pmid: '17947321'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/bts252
    metadata:
      abstract: 'Motivation: In the analysis of homologous sequences, computation
        of multiple sequence alignments (MSAs) has become a bottleneck. This is especially
        troublesome for marker genes like the ribosomal RNA (rRNA) where already millions
        of sequences are publicly available and individual studies can easily produce
        hundreds of thousands of new sequences. Methods have been developed to cope
        with such numbers, but further improvements are needed to meet accuracy requirements.Results:
        In this study, we present the SILVA Incremental Aligner (SINA) used to align
        the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA
        uses a combination of k-mer searching and partial order alignment (POA) to
        maintain very high alignment accuracy while satisfying high throughput performance
        demands. SINA was evaluated in comparison with the commonly used high throughput
        MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could
        be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising
        38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference
        MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy
        than PyNAST and mothur in all performed benchmarks. © The Author(s) 2012.
        Published by Oxford University Press.'
      authors:
      - name: Pruesse E.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 2352
      date: '2012-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'SINA: Accurate high-throughput multiple sequence alignment of ribosomal
        RNA genes'
    note: null
    pmcid: PMC3389763
    pmid: '22556368'
    type:
    - Other
    version: null
  - doi: 10.1016/j.jbiotec.2017.06.1198
    metadata:
      abstract: SILVA (lat. forest) is a comprehensive web resource, providing services
        around up to date, high-quality datasets of aligned ribosomal RNA gene (rDNA)
        sequences from the Bacteria, Archaea, and Eukaryota domains. SILVA dates back
        to the year 1991 when Dr. Wolfgang Ludwig from the Technical University Munich
        started the integrated software workbench ARB (lat. tree) to support high-quality
        phylogenetic inference and taxonomy based on the SSU and LSU rDNA marker genes.
        At that time, the ARB project maintained both, the sequence reference datasets
        and the software package for data analysis. In 2005, with the massive increase
        of DNA sequence data, the maintenance of the software system ARB and the corresponding
        rRNA databases SILVA was split between Munich and the Microbial Genomics and
        Bioinformatics Research Group in Bremen. ARB has been continuously developed
        to include new features and improve the usability of the workbench. Thousands
        of users worldwide appreciate the seamless integration of common analysis
        tools under a central graphical user interface, in combination with its versatility.
        The first SILVA release was deployed in February 2007 based on the EMBL-EBI/ENA
        release 89. Since then, full SILVA releases offering the database content
        in various flavours are published at least annually, complemented by intermediate
        web-releases where only the SILVA web dataset is updated. SILVA is the only
        rDNA database project worldwide where special emphasis is given to the consistent
        naming of clades of uncultivated (environmental) sequences, where no validly
        described cultivated representatives are available. Also exclusive for SILVA
        is the maintenance of both comprehensive aligned 16S/18S rDNA and 23S/28S
        rDNA sequence datasets. Furthermore, the SILVA alignments and trees were designed
        to include Eukaryota, another unique feature among rDNA databases. With the
        termination of the European Ribosomal RNA Database Project in 2007, the SILVA
        database has become the authoritative rDNA database project for Europe. The
        application spectrum of ARB and SILVA ranges from biodiversity analysis, medical
        diagnostics, to biotechnology and quality control for academia and industry.
      authors:
      - name: Glockner F.O.
      - name: Yilmaz P.
      - name: Quast C.
      - name: Gerken J.
      - name: Beccati A.
      - name: Ciuprina A.
      - name: Bruns G.
      - name: Yarza P.
      - name: Peplies J.
      - name: Westram R.
      - name: Ludwig W.
      citationCount: 578
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: 25 years of serving the community with ribosomal RNA gene reference databases
        and tools
    note: null
    pmcid: null
    pmid: '28648396'
    type:
    - Review
    version: null
  - doi: 10.1186/s12859-017-1841-3
    metadata:
      abstract: 'Background: Phylogenetic trees are an important tool to study the
        evolutionary relationships among organisms. The huge amount of available taxa
        poses difficulties in their interactive visualization. This hampers the interaction
        with the users to provide feedback for the further improvement of the taxonomic
        framework. Results: The SILVA Tree Viewer is a web application designed for
        visualizing large phylogenetic trees without requiring the download of any
        software tool or data files. The SILVA Tree Viewer is based on Web Geographic
        Information Systems (Web-GIS) technology with a PostgreSQL backend. It enables
        zoom and pan functionalities similar to Google Maps. The SILVA Tree Viewer
        enables access to two phylogenetic (guide) trees provided by the SILVA database:
        the SSU Ref NR99 inferred from high-quality, full-length small subunit sequences,
        clustered at 99% sequence identity and the LSU Ref inferred from high-quality,
        full-length large subunit sequences. Conclusions: The Tree Viewer provides
        tree navigation, search and browse tools as well as an interactive feedback
        system to collect any kinds of requests ranging from taxonomy to data curation
        and improving the tool itself.'
      authors:
      - name: Beccati A.
      - name: Gerken J.
      - name: Quast C.
      - name: Yilmaz P.
      - name: Glockner F.O.
      citationCount: 28
      date: '2017-09-30T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'SILVA tree viewer: Interactive web browsing of the SILVA phylogenetic
        guide trees'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation:
  - biotoolsID: silvangs
    type: usedBy
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    type: includedIn
  toolType:
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  - Database portal
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Biodiversity
    uri: http://edamontology.org/topic_3050
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Probes and primers
    uri: http://edamontology.org/topic_0632
  validated: 1
  version: []
- accessibility: null
  additionDate: '2016-09-30T07:30:11Z'
  biotoolsCURIE: biotools:silvangs
  biotoolsID: silvangs
  collectionID:
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  - de.NBI-biodata
  - DSMZ Digital Diversity
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
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    typeEntity: Project
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    url: null
  - email: hub@dsmz.de
    fundrefid: null
    gridid: grid.420081.f
    name: Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
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    url: https://www.dsmz.de
  description: SILVAngs is a data analysis service for ribosomal RNA gene (rDNA) amplicon
    reads from high-throughput sequencing (next-generation sequencing (NGS)) approaches
    based on an automatic software pipeline. It uses the SILVA rDNA databases, taxonomies,
    and alignments as a reference. It facilitates the classification of rDNA reads
    and provides a wealth of results (tables, graphs and sequence files) for download.
  documentation:
  - note: null
    type:
    - General
    url: https://www.arb-silva.de/ngs/service/file/?file=SILVAngs_User_Guide_15_12_15.pdf
  download: []
  editPermission:
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    - noha_elkayal
    type: group
  elixirCommunity: []
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  function:
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: The pipeline accepts input data in Multi-Fasta format with each input file
      representing one sample. Samples that belong to one project (a transect, timeseries
      etc.) should be uploaded as a single SILVAngs project.
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    - term: Nucleic acid sequence analysis
      uri: http://edamontology.org/operation_2478
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    - term: Validation
      uri: http://edamontology.org/operation_2428
    - term: Sequence clustering
      uri: http://edamontology.org/operation_0291
    - term: Sequence alignment
      uri: http://edamontology.org/operation_0292
    output:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Matrix
        uri: http://edamontology.org/data_2082
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format: []
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Sequence cluster (nucleic acid)
        uri: http://edamontology.org/data_1246
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
  homepage: https://www.arb-silva.de/ngs/
  homepage_status: 0
  language: []
  lastUpdate: '2025-01-22T09:55:10.038132Z'
  license: null
  link: []
  maturity: Mature
  name: SILVAngs
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: silva
  publication:
  - doi: 10.1093/nar/gks1219
    metadata:
      abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive
        web resource for up to date, quality-controlled databases of aligned ribosomal
        RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains
        and supplementary online services. The referred database release 111 (July
        2012) contains 3194 778 small subunit and 288717 large subunit rRNA gene sequences.
        Since the initial description of the project, substantial new features have
        been introduced, including advanced quality control procedures, an improved
        rRNA gene aligner, online tools for probe and primer evaluation and optimized
        browsing, searching and downloading on the website. Furthermore, the extensively
        curated SILVA taxonomy and the new non-redundant SILVA datasets provide an
        ideal reference for high-throughput classification of data from next-generation
        sequencing approaches. © The Author(s) 2012.
      authors:
      - name: Quast C.
      - name: Pruesse E.
      - name: Yilmaz P.
      - name: Gerken J.
      - name: Schweer T.
      - name: Yarza P.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 20831
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The SILVA ribosomal RNA gene database project: Improved data processing
        and web-based tools'
    note: null
    pmcid: PMC3531112
    pmid: '23193283'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkt1209
    metadata:
      abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive
        resource for up-to-date quality-controlled databases of aligned ribosomal
        RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains
        and supplementary online services. SILVA provides a manually curated taxonomy
        for all three domains of life, based on representative phylogenetic trees
        for the small- and large-subunit rRNA genes. This article describes the improvements
        the SILVA taxonomy has undergone in the last 3 years. Specifically we are
        focusing on the curation process, the various resources used for curation
        and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies.
        Our comparisons not only revealed a reasonable overlap between the taxa names,
        but also points to significant differences in both names and numbers of taxa
        between the three resources. © 2013 The Author(s). Published by Oxford University
        Press.
      authors:
      - name: Yilmaz P.
      - name: Parfrey L.W.
      - name: Yarza P.
      - name: Gerken J.
      - name: Pruesse E.
      - name: Quast C.
      - name: Schweer T.
      - name: Peplies J.
      - name: Ludwig W.
      - name: Glockner F.O.
      citationCount: 2372
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The SILVA and "all-species Living Tree Project (LTP)" taxonomic frameworks
    note: null
    pmcid: PMC3965112
    pmid: '24293649'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/bts252
    metadata:
      abstract: 'Motivation: In the analysis of homologous sequences, computation
        of multiple sequence alignments (MSAs) has become a bottleneck. This is especially
        troublesome for marker genes like the ribosomal RNA (rRNA) where already millions
        of sequences are publicly available and individual studies can easily produce
        hundreds of thousands of new sequences. Methods have been developed to cope
        with such numbers, but further improvements are needed to meet accuracy requirements.Results:
        In this study, we present the SILVA Incremental Aligner (SINA) used to align
        the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA
        uses a combination of k-mer searching and partial order alignment (POA) to
        maintain very high alignment accuracy while satisfying high throughput performance
        demands. SINA was evaluated in comparison with the commonly used high throughput
        MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could
        be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising
        38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference
        MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy
        than PyNAST and mothur in all performed benchmarks. © The Author(s) 2012.
        Published by Oxford University Press.'
      authors:
      - name: Pruesse E.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 2352
      date: '2012-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'SINA: Accurate high-throughput multiple sequence alignment of ribosomal
        RNA genes'
    note: null
    pmcid: PMC3389763
    pmid: '22556368'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gks808
    metadata:
      abstract: 16S ribosomal RNA gene (rDNA) amplicon analysis remains the standard
        approach for the cultivation-independent investigation of microbial diversity.
        The accuracy of these analyses depends strongly on the choice of primers.
        The overall coverage and phylum spectrum of 175 primers and 512 primer pairs
        were evaluated in silico with respect to the SILVA 16S/18S rDNA non-redundant
        reference dataset (SSURef 108 NR). Based on this evaluation a selection of
        'best available' primer pairs for Bacteria and Archaea for three amplicon
        size classes (100-400, 400-1000, ≥1000 bp) is provided. The most promising
        bacterial primer pair (S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21), with an
        amplicon size of 464 bp, was experimentally evaluated by comparing the taxonomic
        distribution of the 16S rDNA amplicons with 16S rDNA fragments from directly
        sequenced metagenomes. The results of this study may be used as a guideline
        for selecting primer pairs with the best overall coverage and phylum spectrum
        for specific applications, therefore reducing the bias in PCR-based microbial
        diversity studies. © 2012 The Author(s). Published by Oxford University Press.
      authors:
      - name: Klindworth A.
      - name: Pruesse E.
      - name: Schweer T.
      - name: Peplies J.
      - name: Quast C.
      - name: Horn M.
      - name: Glockner F.O.
      citationCount: 5992
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: Evaluation of general 16S ribosomal RNA gene PCR primers for classical
        and next-generation sequencing-based diversity studies
    note: null
    pmcid: PMC3592464
    pmid: '22933715'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Biodiversity
    uri: http://edamontology.org/topic_3050
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  validated: 1
  version:
  - 1.6.2
- accessibility: Open access
  additionDate: '2022-11-09T17:22:40.898557Z'
  biotoolsCURIE: biotools:mediadive
  biotoolsID: mediadive
  collectionID:
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  - de.NBI
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: julia.koblitz@dsmz.de
    fundrefid: null
    gridid: null
    name: Julia Koblitz
    note: null
    orcidid: https://orcid.org/0000-0002-7260-2129
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    - Primary contact
    - Developer
    url: https://research.dsmz.de/person/651cecd8b3c97f11cc28cfc2
  - email: hub@dsmz.de
    fundrefid: null
    gridid: null
    name: Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen
      GmbH
    note: null
    orcidid: null
    rorid: 02tyer376
    typeEntity: Institute
    typeRole:
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    url: https://dsmz.de
  description: Comprehensive database on cultivation media and growth conditions for
    all domains of life. Data derived from culture collections and individuals.
  documentation:
  - note: General introduction to the web interface
    type:
    - User manual
    url: https://mediadive.dsmz.de/docs/website
  - note: null
    type:
    - API documentation
    url: https://mediadive.dsmz.de/doc/index.html
  - note: Medium Builder
    type:
    - User manual
    url: https://mediadive.dsmz.de/docs/medium-builder
  download: []
  editPermission:
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    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_2421
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  - cmd: null
    input:
    - data:
        term: Protocol
        uri: http://edamontology.org/data_2531
      format: []
    note: Medium Builder
    operation:
    - term: Data deposition
      uri: http://edamontology.org/operation_3431
    output:
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        term: Accession
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      format: []
    - data:
        term: Protocol
        uri: http://edamontology.org/data_2531
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: CSV
        uri: http://edamontology.org/format_3752
  - cmd: null
    input:
    - data:
        term: Query script
        uri: http://edamontology.org/data_3786
      format:
      - term: SPARQL
        uri: http://edamontology.org/format_3790
    note: SPARQL end point
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: RDF format
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  homepage: https://mediadive.dsmz.de
  homepage_status: 0
  language:
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  lastUpdate: '2025-01-22T09:37:58.121773Z'
  license: CC-BY-4.0
  link: []
  maturity: Mature
  name: MediaDive
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  - Windows
  otherID: []
  owner: JKoblitz
  publication:
  - doi: 10.1093/NAR/GKAC803
    metadata:
      abstract: We present MediaDive (https://mediadive.dsmz.de), a comprehensive
        and expert-curated cultivation media database, which comprises recipes, instructions
        and molecular compositions of >3200 standardized cultivation media for >40
        000 microbial strains from all domains of life. MediaDive is designed to enable
        broad range applications from every-day-use in research and diagnostic laboratories
        to knowledge-driven support of new media design and artificial intelligence-driven
        data mining. It offers a number of intuitive search functions and comparison
        tools, for example to identify media for related taxonomic groups and to integrate
        strain-specific modifications. Besides classical PDF archiving and printing,
        the state-of-the-art website allows paperless use of media recipes on mobile
        devices for convenient wet-lab use. In addition, data can be retrieved using
        a RESTful web service for large-scale data analyses. An internal editor interface
        ensures continuous extension and curation of media by cultivation experts
        from the Leibniz Institute DSMZ, which is interlinked with the growing microbial
        collections at DSMZ. External user engagement is covered by a dedicated media
        builder tool. The standardized and programmatically accessible data will foster
        new approaches for the design of cultivation media to target the vast majority
        of uncultured microorganisms.
      authors:
      - name: Koblitz J.
      - name: Halama P.
      - name: Spring S.
      - name: Thiel V.
      - name: Baschien C.
      - name: Hahnke R.L.
      - name: Pester M.
      - name: Overmann J.
      - name: Reimer L.C.
      citationCount: 22
      date: '2023-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MediaDive: the expert-curated cultivation media database'
    note: null
    pmcid: null
    pmid: '36134710'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: bacdive
    type: uses
  - biotoolsID: mediadive
    type: includedIn
  toolType:
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  - Web application
  topic:
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
  - term: Experimental design and studies
    uri: http://edamontology.org/topic_3678
  - term: Laboratory information management
    uri: http://edamontology.org/topic_0607
  validated: 1
  version: []
next: ?page=2
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