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      abstract: A thorough analysis of the protein interaction partners of the yeast
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  description: Make2D-DB II is an environment to create, convert, publish, interconnect
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      abstract: The Make 2D-DB tool has been previously developed to help build federated
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      - name: Hoogland C.
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        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
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  description: A Database to Quickly Access All Tryptic Peptides of the UniProtKB
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  elixir_badge: 0
  function: []
  homepage: https://macpepdb.cubimed.rub.de/
  homepage_status: 0
  language:
  - Rust
  - JavaScript
  - Other
  lastUpdate: '2025-07-31T09:03:29.192901Z'
  license: null
  link:
  - note: Backend implementation written in Rust / Axum
    type:
    - Repository
    url: https://github.com/medbioinf/macpepdb
  - note: Frontend implementation written in Rust / Dioxus
    type:
    - Repository
    url: https://github.com/medbioinf/macpepdb-frontend
  maturity: Mature
  name: MaCPepDB - Mass Centric Peptide Database
  operatingSystem: []
  otherID: []
  owner: di_hardt
  publication:
  - doi: 10.1021/acs.jproteome.0c00967
    metadata:
      abstract: Protein sequence databases play a crucial role in the majority of
        the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB
        serves as one of the major sources, as it combines the information of several
        smaller databases and enriches the entries with additional biological information.
        For the identification of peptides in a sample by tandem mass spectra, as
        generated by data-dependent acquisition, protein sequence databases provide
        the basis for most spectrum identification search engines. In addition, for
        targeted proteomics approaches like selected reaction monitoring (SRM) and
        parallel reaction monitoring (PRM), knowledge of the peptide sequences, their
        masses, and whether they are unique for a protein is essential. Because most
        bottom-up proteomics approaches use trypsin to cleave the proteins in a sample,
        the tryptic peptides contained in a protein database are of great interest.
        We present a database, called MaCPepDB (mass-centric peptide database), that
        consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts
        of UniProtKB. This database is especially designed to not only allow queries
        of peptide sequences and return the respective information about connected
        proteins and thus whether a peptide is unique but also allow queries of specific
        masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational
        modifications can be considered in a query as well as different mass deviations
        for posttranslational modifications. Hence the database can be used by a sequence
        query not only to, for example, check in which proteins of the UniProt database
        a tryptic peptide can be found but also to find possibly interfering peptides
        in PRM/SRM experiments using the mass query. The complete database contains
        currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version
        2020_03), for which a single query usually takes less than 1 s. For easy exploration
        of the data, a web interface was developed. A REST application programming
        interface (API) for programmatic and workflow access is also available at
        https://macpepdb.mpc.rub.de.
      authors:
      - name: Uszkoreit J.
      - name: Winkelhardt D.
      - name: Barkovits K.
      - name: Wulf M.
      - name: Roocke S.
      - name: Marcus K.
      - name: Eisenacher M.
      citationCount: 9
      date: '2021-04-02T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB'
    note: null
    pmcid: null
    pmid: '33724838'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version:
  - 3.0.0
- accessibility: Open access
  additionDate: '2019-11-05T10:40:37Z'
  biotoolsCURIE: biotools:WheatIS
  biotoolsID: WheatIS
  collectionID:
  - elixir-fr-sdp-2019
  - URGI
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: urgi-support@inrae.fr
    fundrefid: null
    gridid: null
    name: Support service
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    - Support
    url: null
  description: "This project aims at building an International Wheat Information System\
    \ (WheatIS) to support the wheat research community. The main objective is to\
    \ provide a single-access web based system to access to the available data resources\
    \ and bioinformatics tools. The web portal indexes and makes findable any kind\
    \ of data from wheat related species. \nThe WheatIS search is an implementation\
    \ of DataDiscovery."
  documentation:
  - note: null
    type:
    - General
    url: https://urgi.versailles.inra.fr/wheatis/about
  - note: null
    type:
    - Contributions policy
    url: https://urgi.versailles.inra.fr/wheatis/join
  - note: null
    type:
    - Terms of use
    url: https://urgi.versailles.inra.fr/wheatis/legal
  - note: null
    type:
    - User manual
    url: https://urgi.versailles.inra.fr/wheatis/help
  download: []
  editPermission:
    authors:
    - nfrancillon
    - raphael.flores
    - cmichotey
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform:
  - Tools
  - Data
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Keyword
        uri: http://edamontology.org/data_0968
      format: []
    - data:
        term: Ontology data
        uri: http://edamontology.org/data_2353
      format: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Relation extraction
      uri: http://edamontology.org/operation_3625
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Information retrieval
      uri: http://edamontology.org/operation_3908
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format: []
    - data:
        term: Data reference
        uri: http://edamontology.org/data_2093
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
  homepage: https://urgi.versailles.inrae.fr/wheatis/
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-29T13:42:41.391722Z'
  license: BSD-3-Clause
  link:
  - note: WheatIS website
    type:
    - Other
    url: http://www.wheatis.org/
  maturity: Mature
  name: WheatIS
  operatingSystem: []
  otherID: []
  owner: jison
  publication:
  - doi: 10.1186/s13059-018-1491-4
    metadata:
      abstract: © 2018 The Author(s).The Wheat@URGI portal has been developed to provide
        the international community of researchers and breeders with access to the
        bread wheat reference genome sequence produced by the International Wheat
        Genome Sequencing Consortium. Genome browsers, BLAST, and InterMine tools
        have been established for in-depth exploration of the genome sequence together
        with additional linked datasets including physical maps, sequence variations,
        gene expression, and genetic and phenomic data from other international collaborative
        projects already stored in the GnpIS information system. The portal provides
        enhanced search and browser features that will facilitate the deployment of
        the latest genomics resources in wheat improvement.
      authors:
      - name: Alaux M.
      - name: Rogers J.
      - name: Letellier T.
      - name: Flores R.
      - name: Alfama F.
      - name: Pommier C.
      - name: Mohellibi N.
      - name: Durand S.
      - name: Kimmel E.
      - name: Michotey C.
      - name: Guerche C.
      - name: Loaec M.
      - name: Laine M.
      - name: Steinbach D.
      - name: Choulet F.
      - name: Rimbert H.
      - name: Leroy P.
      - name: Guilhot N.
      - name: Salse J.
      - name: Feuillet C.
      - name: Paux E.
      - name: Eversole K.
      - name: Adam-Blondon A.-F.
      - name: Quesneville H.
      citationCount: 88
      date: '2018-08-17T00:00:00Z'
      journal: Genome Biology
      title: Linking the International Wheat Genome Sequencing Consortium bread wheat
        reference genome sequence to wheat genetic and phenomic data
    note: Alaux M, Rogers J, Letellier T, Flores R, Alfama  F, Pommier C, Mohellibi
      N, Durand S, Kimmel E, Michotey C, Guerche C, Loaec M, Lainé M, Steinbach D,
      Choulet F, Rimbert H, Leroy P, Guilhot N, Salse J, Feuillet C, International
      Wheat Genome Sequencing Consortium, Paux E, Eversole K, Adam-Blondon A-F, Quesneville
      H (2018) Linking the International Wheat Genome Sequencing Consortium bread
      wheat reference genome sequence to wheat genetic and phenomic data. Genome Biology,
      19:111
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: gnpis
    type: uses
  - biotoolsID: DataDiscovery
    type: uses
  toolType:
  - Web API
  - Web application
  - Web service
  - Database portal
  topic:
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Ecology
    uri: http://edamontology.org/topic_0610
  - term: Biological databases
    uri: http://edamontology.org/topic_3071
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Agricultural science
    uri: http://edamontology.org/topic_3810
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2020-01-21T16:40:38Z'
  biotoolsCURIE: biotools:DataDiscovery
  biotoolsID: DataDiscovery
  collectionID:
  - elixir-fr-sdp-2019
  - URGI
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: urgi-support@inrae.fr
    fundrefid: null
    gridid: null
    name: urgi-support
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    - Support
    url: null
  description: 'DataDiscovery aims at providing researchers a simple and fast access
    to relevant biological data using specific keywords and easy to use filters.


    This tool is expected to be easily customizable for specific filters, environments,
    or data schemas. Its current implementations supported by URGI are: WheatIS, Plant,
    RARe.'
  documentation:
  - note: General terms of use
    type:
    - Terms of use
    url: https://forgemia.inra.fr/urgi-is/data-discovery/blob/master/LEGAL-MENTIONS.md
  - note: How to contribute
    type:
    - Contributions policy
    url: https://forgemia.inra.fr/urgi-is/data-discovery/blob/master/README.md#contribute
  - note: How to use
    type:
    - User manual
    url: https://forgemia.inra.fr/urgi-is/data-discovery/blob/master/HELP.md
  - note: How to install
    type:
    - Installation instructions
    url: https://forgemia.inra.fr/urgi-is/data-discovery/blob/master/README.md#setup
  download: []
  editPermission:
    authors:
    - urgi-contact
    - cpommier
    - cmichotey
    - erlefloch
    - raphael.flores
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform:
  - Data
  - Tools
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Keyword
        uri: http://edamontology.org/data_0968
      format: []
    - data:
        term: Ontology data
        uri: http://edamontology.org/data_2353
      format: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Relation extraction
      uri: http://edamontology.org/operation_3625
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Information retrieval
      uri: http://edamontology.org/operation_3908
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format: []
    - data:
        term: Data reference
        uri: http://edamontology.org/data_2093
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
  homepage: https://urgi.versailles.inrae.fr/data-discovery/
  homepage_status: 0
  language:
  - JavaScript
  - Java
  - Bash
  lastUpdate: '2025-07-29T13:42:39.375595Z'
  license: BSD-3-Clause
  link:
  - note: GitLab code repository for DataDiscovery
    type:
    - Repository
    url: https://forgemia.inra.fr/urgi-is/data-discovery
  - note: Code repository mirror link for DataDiscovery
    type:
    - Mirror
    url: https://github.com/gnpis/DataDiscovery/
  maturity: Emerging
  name: DataDiscovery
  operatingSystem:
  - Linux
  otherID: []
  owner: nfrancillon
  publication:
  - doi: 10.3835/plantgenome2015.06.0038
    metadata:
      abstract: The genome sequences of many important Triticeae species, including
        bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), remained
        uncharacterized for a long time because their high repeat content, large sizes,
        and polyploidy. As a result of improvements in sequencing technologies and
        novel analyses strategies, several of these have recently been deciphered.
        These efforts have generated new insights into Triticeae biology and genome
        organization and have important implications for downstream usage by breeders,
        experimental biologists, and comparative genomicists. transPLANT (http://www.transplantdb.eu)
        is an EU-funded project aimed at constructing hardware, software, and data
        infrastructure for genome-scale research in the life sciences. Since the Triticeae
        data are intrinsically complex, heterogenous, and distributed, the transPLANT
        consortium has undertaken efforts to develop common data formats and tools
        that enable the exchange and integration of data from distributed resources.
        Here we present an overview of the individual Triticeae genome resources hosted
        by transPLANT partners, introduce the objectives of transPLANT, and outline
        common developments and interfaces supporting integrated data access.
      authors:
      - name: Spannagl M.
      - name: Alaux M.
      - name: Lange M.
      - name: Bolser D.M.
      - name: Bader K.C.
      - name: Letellier T.
      - name: Kimmel E.
      - name: Flores R.
      - name: Pommier C.
      - name: Kerhornou A.
      - name: Walts B.
      - name: Nussbaumer T.
      - name: Grabmuller C.
      - name: Chen J.
      - name: Colmsee C.
      - name: Beier S.
      - name: Mascher M.
      - name: Schmutzer T.
      - name: Arend D.
      - name: Thanki A.
      - name: Ramirez-Gonzalez R.
      - name: Ayling M.
      - name: Ayling S.
      - name: Caccamo M.
      - name: Mayer K.F.X.
      - name: Scholz U.
      - name: Steinbach D.
      - name: Quesneville H.
      - name: Kersey P.J.
      citationCount: 6
      date: '2016-03-01T00:00:00Z'
      journal: Plant Genome
      title: TransPLANT resources for triticeae genomic data
    note: "1-\tSpannagl, M., Alaux, M., Lange, M., Bolser, D. M., Bader, K. C., Letellier,\
      \ T., Kimmel, E., Flores, R.-G., Pommier, C., Kerhornou, A., Walts, B., Nussbaumer,\
      \ T., Grabmuller, C., Chen, J., Colmsee, C., Beier, S., Mascher, M., Schmutzer,\
      \ T., Arend, D., Thanki, A., Ramirez-Gonzalez, R., Ayling, M., Ayling, S., Caccamo,\
      \ M., Mayer, K. F. X., Scholz, U., Steinbach, D., Quesneville, H., Kersey, P.\
      \ (2016). TransPLANT resources for triticeae genomic data. Plant Genome, 9 (1),\
      \ 13 p."
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1038/hortres.2016.56
    metadata:
      abstract: Viticulture, like other fields of agriculture, is currently facing
        important challenges that will be addressed only through sustained, dedicated
        and coordinated research. Although the methods used in biology have evolved
        tremendously in recent years and now involve the routine production of large
        data sets of varied nature, in many domains of study, including grapevine
        research, there is a need to improve the findability, accessibility, interoperability
        and reusability (FAIR-ness) of these data. Considering the heterogeneous nature
        of the data produced, the transnational nature of the scientific community
        and the experience gained elsewhere, we have formed an open working group,
        in the framework of the International Grapevine Genome Program (www.vitaceae.org),
        to construct a coordinated federation of information systems holding grapevine
        data distributed around the world, providing an integrated set of interfaces
        supporting advanced data modeling, rich semantic integration and the next
        generation of data mining tools. To achieve this goal, it will be critical
        to develop, implement and adopt appropriate standards for data annotation
        and formatting. The development of this system, the GrapeIS, linking genotypes
        to phenotypes, and scientific research to agronomical and oeneological data,
        should provide new insights into grape biology, and allow the development
        of new varieties to meet the challenges of biotic and abiotic stress, environmental
        change, and consumer demand.
      authors:
      - name: Adam-Blondon A.-F.
      - name: Alaux M.
      - name: Pommier C.
      - name: Cantu D.
      - name: Cheng Z.-M.
      - name: Cramer G.
      - name: Davies C.
      - name: Delrot S.
      - name: Deluc L.
      - name: Di Gaspero G.
      - name: Grimplet J.
      - name: Fennell A.
      - name: Londo J.
      - name: Kersey P.
      - name: Mattivi F.
      - name: Naithani S.
      - name: Neveu P.
      - name: Nikolski M.
      - name: Pezzotti M.
      - name: Reisch B.
      - name: Topfer R.
      - name: Vivier M.
      - name: Ware D.
      - name: Quesneville H.
      citationCount: 37
      date: '2016-11-23T00:00:00Z'
      journal: Horticulture Research
      title: Towards an open grapevine information system
    note: "2-\tAdam-Blondon, A.-F., Alaux, M., Pommier, C., Cantu, D., Cheng, Z.-M.,\
      \ Cramer, G. R., Davies, C., Delrot, S., Deluc, L., di Gaspero, G., Grimplet,\
      \ J., Fennell, A., Londo, J. P., Kersey, P., Mattivi, F., Naithani, S., Neveu,\
      \ P., Nikolski, M., Pezzotti, M., Reisch, B. I., Topfer, R., Vivier, M., Ware,\
      \ D., Quesneville, H. (2016). Towards an open grapevine information system.\
      \ Horticulture Research, 3, 8 p. , DOI : 10.1038/hortres.2016.56"
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1186/s13059-018-1491-4
    metadata:
      abstract: The Wheat@URGI portal has been developed to provide the international
        community of researchers and breeders with access to the bread wheat reference
        genome sequence produced by the International Wheat Genome Sequencing Consortium.
        Genome browsers, BLAST, and InterMine tools have been established for in-depth
        exploration of the genome sequence together with additional linked datasets
        including physical maps, sequence variations, gene expression, and genetic
        and phenomic data from other international collaborative projects already
        stored in the GnpIS information system. The portal provides enhanced search
        and browser features that will facilitate the deployment of the latest genomics
        resources in wheat improvement.
      authors:
      - name: Alaux M.
      - name: Rogers J.
      - name: Letellier T.
      - name: Flores R.
      - name: Alfama F.
      - name: Pommier C.
      - name: Mohellibi N.
      - name: Durand S.
      - name: Kimmel E.
      - name: Michotey C.
      - name: Guerche C.
      - name: Loaec M.
      - name: Laine M.
      - name: Steinbach D.
      - name: Choulet F.
      - name: Rimbert H.
      - name: Leroy P.
      - name: Guilhot N.
      - name: Salse J.
      - name: Feuillet C.
      - name: Paux E.
      - name: Eversole K.
      - name: Adam-Blondon A.-F.
      - name: Quesneville H.
      citationCount: 181
      date: '2018-08-17T00:00:00Z'
      journal: Genome Biology
      title: Linking the International Wheat Genome Sequencing Consortium bread wheat
        reference genome sequence to wheat genetic and phenomic data
    note: "3-\tAlaux M, Rogers J, Letellier T, Flores R, Alfama  F, Pommier C, Mohellibi\
      \ N, Durand S, Kimmel E, Michotey C, Guerche C, Loaec M, Lainé M, Steinbach\
      \ D, Choulet F, Rimbert H, Leroy P, Guilhot N, Salse J, Feuillet C, International\
      \ Wheat Genome Sequencing Consortium, Paux E, Eversole K, Adam-Blondon A-F,\
      \ Quesneville H (2018) Linking the International Wheat Genome Sequencing Consortium\
      \ bread wheat reference genome sequence to wheat genetic and phenomic data.\
      \ Genome Biology, 19:111."
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: wheatis
    type: usedBy
  - biotoolsID: RARe
    type: usedBy
  - biotoolsID: Plant_DataDiscovery
    type: usedBy
  toolType:
  - Web API
  - Web application
  - Web service
  - Database portal
  topic:
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Ecology
    uri: http://edamontology.org/topic_0610
  - term: Biological databases
    uri: http://edamontology.org/topic_3071
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Agricultural science
    uri: http://edamontology.org/topic_3810
  validated: 1
  version: []
- accessibility: Restricted access
  additionDate: '2017-03-23T14:24:24Z'
  biotoolsCURIE: biotools:gnpis
  biotoolsID: gnpis
  collectionID:
  - elixir-fr-sdp-2019
  - Animal and Crop Genomics
  confidence_flag: null
  cost: null
  credit:
  - email: urgi-contact@versailles.inra.fr
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A multispecies integrative information system dedicated to plant and
    fungi pests.. It allows researchers to access genetic, phenotypic and genomic
    data. It is used by both large international projects and the French National
    Research Institute for Agriculture, Food and Environment
  documentation:
  - note: null
    type:
    - General
    url: https://urgi.versailles.inra.fr/gnpis/
  download: []
  editPermission:
    authors:
    - urgi-contact
    - animalandcropgenomics
    - nfrancillon
    - cmichotey
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome visualisation
      uri: http://edamontology.org/operation_3208
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Ontology visualisation
      uri: http://edamontology.org/operation_3559
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format: []
    - data:
        term: Sequence features
        uri: http://edamontology.org/data_1255
      format: []
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format: []
    - data:
        term: Ontology data
        uri: http://edamontology.org/data_2353
      format: []
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format: []
    - data:
        term: Genotype/phenotype report
        uri: http://edamontology.org/data_0920
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
  homepage: https://urgi.versailles.inrae.fr/gnpis
  homepage_status: 0
  language:
  - JavaScript
  - C#
  lastUpdate: '2025-07-29T13:42:38.871501Z'
  license: CC-BY-4.0
  link: []
  maturity: Mature
  name: Genetic and Genomic Information System (GnpIS)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: cpommier
  publication:
  - doi: 10.1093/database/bat058
    metadata:
      abstract: Data integration is a key challenge for modern bioinformatics. It
        aims to provide biologists with tools to explore relevant data produced by
        different studies. Large-scale international projects can generate lots of
        heterogeneous and unrelated data. The challenge is to integrate this information
        with other publicly available data. Nucleotide sequencing throughput has been
        improved with new technologies; this increases the need for powerful information
        systems able to store, manage and explore data. GnpIS is a multispecies integrative
        information system dedicated to plant and fungi pests. It bridges genetic
        and genomic data, allowing researchers access to both genetic information
        (e.g. genetic maps, quantitative trait loci, markers, single nucleotide polymorphisms,
        germplasms and genotypes) and genomic data (e.g. genomic sequences, physical
        maps, genome annotation and expression data) for species of agronomical interest.
        GnpIS is used by both large international projects and plant science departments
        at the French National Institute for Agricultural Research. Here, we illustrate
        its use. © The Author(s) 2013. Published by Oxford University Press.
      authors:
      - name: Steinbach D.
      - name: Alaux M.
      - name: Amselem J.
      - name: Choisne N.
      - name: Durand S.
      - name: Flores R.
      - name: Keliet A.-O.
      - name: Kimmel E.
      - name: Lapalu N.
      - name: Luyten I.
      - name: Michotey C.
      - name: Mohellibi N.
      - name: Pommier C.
      - name: Reboux S.
      - name: Valdenaire D.
      - name: Verdelet D.
      - name: Quesneville H.
      citationCount: 42
      date: '2013-12-01T00:00:00Z'
      journal: Database
      title: 'GnpIS: An information system to integrate genetic and genomic data from
        plants and fungi'
    note: null
    pmcid: PMC3746681
    pmid: '23959375'
    type:
    - Primary
    version: null
  - doi: 10.1007/978-1-4939-6658-5_5
    metadata:
      abstract: GnpIS is an information system designed to help scientists working
        on plants and fungi to decipher the molecular and genetic architecture of
        trait variations by facilitating the navigation through genetic, genomic,
        and phenotypic information. The purpose of the present chapter is to illustrate
        how users can (1) explore datasets from phenotyping experiments in order to
        build new datasets for studying genotype × environment interactions in traits,
        (2) browse into the results of other genetic analysis data such as GWAS to
        generate or check working hypothesis about candidate genes or to identify
        important alleles and germplasms for breeding programs, and (3) explore the
        polymorphism in specific area of the genome using InterMine, JBrowse tools
        embedded in the GnpIS information system.
      authors:
      - name: Adam-Blondon A.-F.
      - name: Alaux M.
      - name: Durand S.
      - name: Letellier T.
      - name: Merceron G.
      - name: Mohellibi N.
      - name: Pommier C.
      - name: Steinbach D.
      - name: Alfama F.
      - name: Amselem J.
      - name: Charruaud D.
      - name: Choisne N.
      - name: Flores R.
      - name: Guerche C.
      - name: Jamilloux V.
      - name: Kimmel E.
      - name: Lapalu N.
      - name: Loaec M.
      - name: Michotey C.
      - name: Quesneville H.
      citationCount: 1
      date: '2017-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Mining plant genomic and genetic data using the gnpis information system
    note: null
    pmcid: null
    pmid: '27987166'
    type:
    - Primary
    version: null
  - doi: 10.1038/hortres.2016.56
    metadata:
      abstract: Viticulture, like other fields of agriculture, is currently facing
        important challenges that will be addressed only through sustained, dedicated
        and coordinated research. Although the methods used in biology have evolved
        tremendously in recent years and now involve the routine production of large
        data sets of varied nature, in many domains of study, including grapevine
        research, there is a need to improve the findability, accessibility, interoperability
        and reusability (FAIR-ness) of these data. Considering the heterogeneous nature
        of the data produced, the transnational nature of the scientific community
        and the experience gained elsewhere, we have formed an open working group,
        in the framework of the International Grapevine Genome Program (www.vitaceae.org),
        to construct a coordinated federation of information systems holding grapevine
        data distributed around the world, providing an integrated set of interfaces
        supporting advanced data modeling, rich semantic integration and the next
        generation of data mining tools. To achieve this goal, it will be critical
        to develop, implement and adopt appropriate standards for data annotation
        and formatting. The development of this system, the GrapeIS, linking genotypes
        to phenotypes, and scientific research to agronomical and oeneological data,
        should provide new insights into grape biology, and allow the development
        of new varieties to meet the challenges of biotic and abiotic stress, environmental
        change, and consumer demand.
      authors:
      - name: Adam-Blondon A.-F.
      - name: Alaux M.
      - name: Pommier C.
      - name: Cantu D.
      - name: Cheng Z.-M.
      - name: Cramer G.
      - name: Davies C.
      - name: Delrot S.
      - name: Deluc L.
      - name: Di Gaspero G.
      - name: Grimplet J.
      - name: Fennell A.
      - name: Londo J.
      - name: Kersey P.
      - name: Mattivi F.
      - name: Naithani S.
      - name: Neveu P.
      - name: Nikolski M.
      - name: Pezzotti M.
      - name: Reisch B.
      - name: Topfer R.
      - name: Vivier M.
      - name: Ware D.
      - name: Quesneville H.
      citationCount: 37
      date: '2016-11-23T00:00:00Z'
      journal: Horticulture Research
      title: Towards an open grapevine information system
    note: null
    pmcid: PMC5120350
    pmid: '27917288'
    type:
    - Other
    version: null
  - doi: 10.3835/plantgenome2015.06.0038
    metadata:
      abstract: The genome sequences of many important Triticeae species, including
        bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), remained
        uncharacterized for a long time because their high repeat content, large sizes,
        and polyploidy. As a result of improvements in sequencing technologies and
        novel analyses strategies, several of these have recently been deciphered.
        These efforts have generated new insights into Triticeae biology and genome
        organization and have important implications for downstream usage by breeders,
        experimental biologists, and comparative genomicists. transPLANT (http://www.transplantdb.eu)
        is an EU-funded project aimed at constructing hardware, software, and data
        infrastructure for genome-scale research in the life sciences. Since the Triticeae
        data are intrinsically complex, heterogenous, and distributed, the transPLANT
        consortium has undertaken efforts to develop common data formats and tools
        that enable the exchange and integration of data from distributed resources.
        Here we present an overview of the individual Triticeae genome resources hosted
        by transPLANT partners, introduce the objectives of transPLANT, and outline
        common developments and interfaces supporting integrated data access.
      authors:
      - name: Spannagl M.
      - name: Alaux M.
      - name: Lange M.
      - name: Bolser D.M.
      - name: Bader K.C.
      - name: Letellier T.
      - name: Kimmel E.
      - name: Flores R.
      - name: Pommier C.
      - name: Kerhornou A.
      - name: Walts B.
      - name: Nussbaumer T.
      - name: Grabmuller C.
      - name: Chen J.
      - name: Colmsee C.
      - name: Beier S.
      - name: Mascher M.
      - name: Schmutzer T.
      - name: Arend D.
      - name: Thanki A.
      - name: Ramirez-Gonzalez R.
      - name: Ayling M.
      - name: Ayling S.
      - name: Caccamo M.
      - name: Mayer K.F.X.
      - name: Scholz U.
      - name: Steinbach D.
      - name: Quesneville H.
      - name: Kersey P.J.
      citationCount: 6
      date: '2016-03-01T00:00:00Z'
      journal: Plant Genome
      title: TransPLANT resources for triticeae genomic data
    note: null
    pmcid: null
    pmid: '27898761'
    type:
    - Primary
    version: null
  relation: []
  toolType:
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  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 1
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- accessibility: null
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    fundrefid: null
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    name: Erwin Vast
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
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    url: http://xnat.bigr.nl
  - email: s.klein@erasmusmc.nl
    fundrefid: null
    gridid: null
    name: Stefan Klein
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Imaging platform based on the open source XNAT software. It is used
    for sharing de-identified medical imaging data,  and for data exchange in collaborative
    research projects. The data is served in a standardized way, with proper access
    control.
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  - note: null
    type:
    - General
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      - term: xls
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      - term: DICOM format
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      - term: nii
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    - data:
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      format:
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        uri: http://edamontology.org/format_2330
      - term: xls
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      - term: DICOM format
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      - term: nii
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    note: null
    operation:
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      uri: http://edamontology.org/operation_3431
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    output:
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        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: xls
        uri: http://edamontology.org/format_3468
      - term: mhd
        uri: http://edamontology.org/format_3550
      - term: nrrd
        uri: http://edamontology.org/format_3551
      - term: DICOM format
        uri: http://edamontology.org/format_3548
      - term: nii
        uri: http://edamontology.org/format_3549
    - data:
        term: Image metadata
        uri: http://edamontology.org/data_3546
      format:
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        uri: http://edamontology.org/format_2330
      - term: xls
        uri: http://edamontology.org/format_3468
      - term: mhd
        uri: http://edamontology.org/format_3550
      - term: nrrd
        uri: http://edamontology.org/format_3551
      - term: DICOM format
        uri: http://edamontology.org/format_3548
      - term: nii
        uri: http://edamontology.org/format_3549
  homepage: https://bigr-xnat.erasmusmc.nl
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-24T22:46:15.794823Z'
  license: null
  link:
  - note: null
    type:
    - Helpdesk
    url: http://xnat.bigr.nl
  maturity: null
  name: BIGR XNAT
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: hachterberg
  publication:
  - doi: 10.1186/2043-9113-5-s1-s18
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web API
  topic:
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  validated: 1
  version:
  - '1'
- accessibility: Open access
  additionDate: '2025-07-24T08:48:17.637773Z'
  biotoolsCURIE: biotools:xnat
  biotoolsID: xnat
  collectionID:
  - EUCAIM
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: dmarcus@wustl.edu
    fundrefid: null
    gridid: null
    name: Daniel Marcus
    note: null
    orcidid: https://orcid.org/0000-0001-5662-5358
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://profiles.wustl.edu/en/persons/daniel-marcus
  description: XNAT is an open source imaging informatics platform developed by the
    Neuroinformatics Research Group at Washington University. XNAT was originally
    developed at Washington University in the Buckner Lab, which is now located at
    Harvard University. It facilitates common management, productivity, and quality
    assurance tasks for imaging and associated data. Thanks to its extensibility,
    XNAT can be used to support a wide range of imaging-based projects.
  documentation:
  - note: null
    type:
    - User manual
    url: https://wiki.xnat.org/documentation/
  - note: null
    type:
    - API documentation
    url: https://wiki.xnat.org/xnat-api/xnat-rest-api-directory
  download:
  - note: null
    type: Downloads page
    url: https://xnat.org/download/
    version: null
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  function:
  - cmd: null
    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_3431
    output: []
  - cmd: null
    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_3802
    output: []
  - cmd: null
    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_0224
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Anonymisation
      uri: http://edamontology.org/operation_3283
    output: []
  homepage: https://xnat.org
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2025-07-24T08:53:51.609723Z'
  license: BSD-2-Clause
  link:
  - note: null
    type:
    - Discussion forum
    url: https://groups.google.com/g/xnat_discussion/
  - note: null
    type:
    - Repository
    url: https://bitbucket.org/xnatdev/workspace/repositories/
  maturity: Mature
  name: XNAT
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: hachterberg
  publication:
  - doi: 10.1385/ni:5:1:11
    metadata:
      abstract: The Extensible Neuroimaging Archive Toolkit (XNAT) is a software platform
        designed to facilitate common management and productivity tasks for neuroimaging
        and associated data. In particular, XNAT enables qualitycontrol procedures
        and provides secure access to and storage of data. XNAT follows a threetiered
        architecture that includes a data archive, user interface, and middleware
        engine. Data can be entered into the archive as XML or through data entry
        forms. Newly added data are stored in a virtual quarantine until an authorized
        user has validated it. XNAT subsequently maintains a history profile to track
        all changes made to the managed data. User access to the archive is provided
        by a secure web application. The web application provides a number of quality
        control and productivity features, including data entry forms, data-type-specific
        searches, searches that combine across data types, detailed reports, and listings
        of experimental data, upload/download tools, access to standard laboratory
        workflows, and administration and security tools. XNAT also includes an online
        image viewer that supports a number of common neuroimaging formats, including
        DICOM and Analyze. The viewer can be extended to support additional formats
        and to generate custom displays. By managing data with XNAT, laboratories
        are prepared to better maintain the long-term integrity of their data, to
        explore emergent relations across data types, and to share their data with
        the broader neuroimaging community. © Copyright 2007 by Humana Press Inc.
        All rights of any nature whatsoever are reserved.
      authors:
      - name: Marcus D.S.
      - name: Olsen T.R.
      - name: Ramaratnam M.
      - name: Buckner R.L.
      citationCount: 378
      date: '2007-03-01T00:00:00Z'
      journal: Neuroinformatics
      title: 'The extensible neuroimaging archive toolkit: An informatics platform
        for managing, exploring, and sharing neuroimaging data'
    note: null
    pmcid: null
    pmid: '17426351'
    type:
    - Primary
    version: null
  - doi: 10.1162/jocn.2009.21407
    metadata:
      abstract: The Open Access Series of Imaging Studies is a series of neuroimaging
        data sets that are publicly available for study and analysis. The present
        MRI data set consists of a longitudinal collection of 150 subjects aged 60
        to 96 years all acquired on the same scanner using identical sequences. Each
        subject was scanned on two or more visits, separated by at least 1 year for
        a total of 373 imaging sessions. Subjects were characterized using the Clinical
        Dementia Rating (CDR) as either nondemented or with very mild tomild Alzheimer's
        disease. Seventy-two of the subjects were characterized as nondemented throughout
        the study. Sixty-four of the included subjects were characterized as demented
        at the time of their initial visits and remained so for subsequent scans,
        including 51 individuals with CDR 0.5 similar level of impairment to individuals
        elsewhere considered to have "mild cognitive impairment." Another 14 subjects
        were characterized as nondemented at the time of their initial visit (CDR
        0) and were subsequently characterized as demented at a later visit (CDR >
        0). The subjects were all right-handed and include bothmen (n=62) and women
        (n = 88). For each scanning session, three or four individual T1-weighted
        MRI scans were obtained. Multiple withinsession acquisitions provide extremely
        high contrast to noise, making the data amenable to a wide range of analytic
        approaches including automated computational analysis. Automated calculation
        of whole-brain volume is presented to demonstrate use of the data for measuring
        differences associated with normal aging and Alzheimer's disease. © 2010 Massachusetts
        Institute of Technology.
      authors:
      - name: Marcus D.S.
      - name: Fotenos A.F.
      - name: Csernansky J.G.
      - name: Morris J.C.
      - name: Buckner R.L.
      citationCount: 461
      date: '2010-12-01T00:00:00Z'
      journal: Journal of Cognitive Neuroscience
      title: 'Open access series of imaging studies: Longitudinal MRI data in nondemented
        and demented older adults'
    note: null
    pmcid: PMC2895005
    pmid: '19929323'
    type:
    - Usage
    version: null
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    type: usedBy
  toolType:
  - Web application
  - Web API
  topic:
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  - term: MRI
    uri: http://edamontology.org/topic_3444
  - term: Bioimaging
    uri: http://edamontology.org/topic_3383
  - term: Tomography
    uri: http://edamontology.org/topic_3452
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-07-23T10:27:57.572501Z'
  biotoolsCURIE: biotools:etenlnc
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  cost: Free of charge
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  - email: barah@tezu.ernet.in
    fundrefid: null
    gridid: null
    name: Pankaj Barah
    note: Assistant Professor at Department of Molecular Biology and Biotechnology,
      Tezpur University.
    orcidid: https://orcid.org/0000-0001-7039-7996
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  - email: prangannathofficial@gmai.com
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  description: ETENLNC (End-To-End-Novel-NonCoding) is a lncRNA identification and
    analysis framework
  documentation:
  - note: A detailed guide on running ETENLNC using the demo data can be found in
      the ETENLNC manual (supplementary to our publication)
    type:
    - Command-line options
    url: https://github.com/EvolOMICS-TU/ETENLNC
  download:
  - note: Sample/demo data for ETENLNC. A detailed guide on running ETENLNC using
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    type: Biological data
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  function:
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    input:
    - data:
        term: Expression data
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      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Nucleic acid sequence
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      format:
      - term: FASTA
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    - data:
        term: Genome map
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      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: GTF
        uri: http://edamontology.org/format_2306
      - term: GFF
        uri: http://edamontology.org/format_2305
    note: Run ETENLNC on paired-end raw RNA-Seq (.fastq) data
    operation:
    - term: Read pre-processing
      uri: http://edamontology.org/operation_3219
    - term: Sequencing quality control
      uri: http://edamontology.org/operation_3218
    - term: Transcriptome assembly
      uri: http://edamontology.org/operation_3258
    - term: Nucleic acid feature detection
      uri: http://edamontology.org/operation_0415
    - term: RNA-Seq quantification
      uri: http://edamontology.org/operation_3800
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Heat map generation
      uri: http://edamontology.org/operation_0531
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: k-mer counting
      uri: http://edamontology.org/operation_3472
    - term: Protein-nucleic acid interaction analysis
      uri: http://edamontology.org/operation_0389
    - term: RNA-binding protein prediction
      uri: http://edamontology.org/operation_3901
    - term: Gene regulatory network prediction
      uri: http://edamontology.org/operation_2437
    output:
    - data:
        term: Nucleic acid sequence record
        uri: http://edamontology.org/data_2887
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Protein interaction data
        uri: http://edamontology.org/data_0906
      format: []
  homepage: https://github.com/EvolOMICS-TU/ETENLNC
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-23T10:27:57.574879Z'
  license: GPL-3.0
  link:
  - note: GitHub Repo
    type:
    - Repository
    url: https://github.com/EvolOMICS-TU/ETENLNC
  maturity: Emerging
  name: ETENLNC
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: prangannath
  publication:
  - doi: 10.1016/j.compbiolchem.2024.108140
    metadata:
      abstract: 'Long non-coding RNAs (lncRNAs) play crucial roles in the regulation
        of gene expression and maintenance of genomic integrity through various interactions
        with DNA, RNA, and proteins. The availability of large-scale sequence data
        from various high-throughput platforms has opened possibilities to identify,
        predict, and functionally annotate lncRNAs. As a result, there is a growing
        demand for an integrative computational framework capable of identifying known
        lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory
        interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding),
        a user-friendly, integrative, open-source, scalable, and modular computational
        framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC
        employs six stringent filtration steps to identify novel lncRNAs, performs
        differential expression analysis of mRNA and lncRNA transcripts, and predicts
        regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked
        ETENLNC against six existing tools and optimized it for desktop workstations
        and high-performance computing environments using data from three different
        species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.'
      authors:
      - name: Nath P.
      - name: Bhuyan K.
      - name: Bhattacharyya D.K.
      - name: Barah P.
      citationCount: 2
      date: '2024-10-01T00:00:00Z'
      journal: Computational Biology and Chemistry
      title: 'ETENLNC: An end to end lncRNA identification and analysis framework
        to facilitate construction of known and novel lncRNA regulatory networks'
    note: null
    pmcid: null
    pmid: '38996755'
    type:
    - Primary
    version: '.0'
  relation:
  - biotoolsID: hisat2
    type: uses
  - biotoolsID: salmon
    type: uses
  - biotoolsID: fastqc
    type: uses
  - biotoolsID: fastp
    type: uses
  - biotoolsID: stringtie
    type: uses
  - biotoolsID: gffcompare
    type: uses
  - biotoolsID: cpc2
    type: uses
  - biotoolsID: ncbi_blast_plus
    type: uses
  - biotoolsID: tximport
    type: uses
  - biotoolsID: deseq2
    type: uses
  - biotoolsID: lnctar
    type: uses
  - biotoolsID: capsule-lpi
    type: uses
  - biotoolsID: seekr
    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2022-03-13T11:27:48.265908Z'
  biotoolsCURIE: biotools:subtiwiki
  biotoolsID: subtiwiki
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: jstuelk@gwdg.de
    fundrefid: null
    gridid: null
    name: Jörg Stülke
    note: null
    orcidid: https://orcid.org/0000-0001-5881-5390
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: christoph.elfmann@uni-goettingen.de
    fundrefid: null
    gridid: null
    name: Christoph Elfmann
    note: PhD student in charge of SubtiWiki.
    orcidid: https://orcid.org/0000-0002-7872-8232
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: SubtiWiki is a database for the model organism Bacillus subtilis.
  documentation: []
  download: []
  editPermission:
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome visualisation
      uri: http://edamontology.org/operation_3208
    - term: Operon prediction
      uri: http://edamontology.org/operation_0435
    - term: Gene regulatory network analysis
      uri: http://edamontology.org/operation_1781
    output: []
  homepage: https://subtiwiki.uni-goettingen.de/
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-16T13:05:53.746014Z'
  license: null
  link: []
  maturity: null
  name: SubtiWiki
  operatingSystem:
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  - Linux
  - Windows
  otherID: []
  owner: ChristophElfmann
  publication:
  - doi: 10.1093/nar/gkae957
    metadata:
      abstract: Bacillus subtilis is a Gram-positive model bacterium and one of the
        most-studied and best understood organisms. The complex information resulting
        from its investigation is compiled in the database SubtiWiki (https://subtiwiki.uni-goettingen.de/v5)
        in an integrated and intuitive manner. To enhance the utility of SubtiWiki,
        we have added novel features such as a viewer to interrogate conserved genomic
        organization, a widget that shows mutant fitness data for all non-essential
        genes, and a widget showing protein structures, structure predictions and
        complex structures. Moreover, we have integrated metabolites as new entities.
        The new framework also includes a documented API, enabling programmatic access
        to data for computational tasks. Here we present the recent developments of
        SubtiWiki and the current state of the data for this organism.
      authors:
      - name: Elfmann C.
      - name: Dumann V.
      - name: van den Berg T.
      - name: Stulke J.
      citationCount: 4
      date: '2025-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: A new framework for SubtiWiki, the database for the model organism Bacillus
        subtilis
    note: null
    pmcid: PMC11701700
    pmid: '39441067'
    type:
    - Primary
    version: '5.0'
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Endocrinology and metabolism
    uri: http://edamontology.org/topic_3407
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 1
  version: []
- accessibility: null
  additionDate: '2025-07-10T12:32:00.838316Z'
  biotoolsCURIE: biotools:inmoose
  biotoolsID: inmoose
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Maximilien Colange
    note: null
    orcidid: https://orcid.org/0000-0003-4769-3302
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Epigene Labs
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://www.epigenelabs.com/
  description: InMoose, the INtegrated Multi-Omic Open-Source environment, ports to
    Python state-of-the-art R tools for the analysis of transcriptomic data.
  documentation:
  - note: null
    type:
    - General
    url: https://inmoose.readthedocs.io/en/stable/
  - note: null
    type:
    - Quick start guide
    url: https://colab.research.google.com/drive/1bzmNDZeaw1A6tSgfx61O6PFtaQDve6SV?usp=sharing
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    output: []
  homepage: https://github.com/epigenelabs/inmoose
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-10T13:00:37.310588Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/epigenelabs/inmoose
  - note: null
    type:
    - Issue tracker
    url: https://github.com/epigenelabs/inmoose/issues
  maturity: Mature
  name: InMoose
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: maximilien.colange
  publication:
  - doi: 10.1038/s41598-025-03376-y
    metadata:
      abstract: We introduce InMoose, an open-source Python environment aimed at omic
        data analysis. We illustrate its capabilities for bulk transcriptomic data
        analysis. Due to its wide adoption, Python has grown as a de facto standard
        in fields increasingly important for bioinformatic pipelines, such as data
        science, machine learning, or artificial intelligence (AI). As a general-purpose
        language, Python is also recognized for its versatility and scalability. InMoose
        aims at bringing state-of-the-art tools, historically written in R, to the
        Python ecosystem. InMoose focuses on providing drop-in replacements for R
        tools, to ensure consistency and reproducibility between R-based and Python-based
        pipelines. The first development phase has focused on bulk transcriptomic
        data, with current capabilities encompassing data simulation, batch effect
        correction, and differential analysis and meta-analysis.
      authors:
      - name: Colange M.
      - name: Appe G.
      - name: Meunier L.
      - name: Weill S.
      - name: Johnson W.E.
      - name: Nordor A.
      - name: Behdenna A.
      citationCount: 1
      date: '2025-12-01T00:00:00Z'
      journal: Scientific Reports
      title: Bridging the gap between R and Python in bulk transcriptomic data analysis
        with InMoose
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    - Usage
    version: null
  - doi: 10.1186/s12859-023-05578-5
    metadata:
      abstract: 'Background: Variability in datasets is not only the product of biological
        processes: they are also the product of technical biases. ComBat and ComBat-Seq
        are among the most widely used tools for correcting those technical biases,
        called batch effects, in, respectively, microarray and RNA-Seq expression
        data. Results: In this technical note, we present a new Python implementation
        of ComBat and ComBat-Seq. While the mathematical framework is strictly the
        same, we show here that our implementations: (i) have similar results in terms
        of batch effects correction; (ii) are as fast or faster than the original
        implementations in R and; (iii) offer new tools for the bioinformatics community
        to participate in its development. pyComBat is implemented in the Python language
        and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html)
        license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose
        and Python package at https://pypi.org/project/inmoose . Conclusions: We present
        a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq
        for the correction of batch effects in microarray and RNA-Seq data. This new
        implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq,
        offers similar power for batch effect correction, at reduced computational
        cost.'
      authors:
      - name: Behdenna A.
      - name: Colange M.
      - name: Haziza J.
      - name: Gema A.
      - name: Appe G.
      - name: Azencott C.-A.
      - name: Nordor A.
      citationCount: 30
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: pyComBat, a Python tool for batch effects correction in high-throughput
        molecular data using empirical Bayes methods
    note: null
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    - Method
    version: null
  - doi: 10.1186/s12859-025-06180-7
    metadata:
      abstract: 'Background: Differential gene expression analysis is a prominent
        technique for the analysis of biomolecular data to identify genetic features
        associated with phenotypes. Limma—for microarray data –, and edgeR and DESeq2—for
        RNA-Seq data–, are the most widely used tools for differential gene expression
        analysis of bulk transcriptomic data. Results: We present the differential
        expression features of InMoose, a Python implementation of R tools limma,
        edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in
        replacement for those tools, with nearly identical results. This ensures reproducibility
        when interfacing both languages in bioinformatic pipelines. InMoose is an
        open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose
        and https://inmoose.readthedocs.io. Conclusions: We present a new Python implementation
        of state-of-the-art tools limma, edgeR, and DESeq2, to perform differential
        gene expression analysis of bulk transcriptomic data. This new implementation
        exhibits results nearly identical to the original tools, improving interoperability
        and reproducibility between Python and R bioinformatics pipelines.'
      authors:
      - name: Colange M.
      - name: Appe G.
      - name: Meunier L.
      - name: Weill S.
      - name: Nordor A.
      - name: Behdenna A.
      citationCount: 0
      date: '2025-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: Differential expression analysis with inmoose, the integrated multi-omic
        open-source environment in Python
    note: null
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    - Method
    version: null
  relation:
  - biotoolsID: deseq2
    type: isNewVersionOf
  - biotoolsID: limma
    type: isNewVersionOf
  - biotoolsID: edger
    type: isNewVersionOf
  toolType:
  - Library
  topic: []
  validated: 0
  version:
  - 0.8.0
- accessibility: null
  additionDate: '2025-07-07T09:04:03.787249Z'
  biotoolsCURIE: biotools:sqanti3
  biotoolsID: sqanti3
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: ana.conesa@csic.es
    fundrefid: null
    gridid: null
    name: Ana Conesa
    note: null
    orcidid: https://orcid.org/0000-0001-9597-311X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: pablo.atienza@csic.es
    fundrefid: null
    gridid: null
    name: Pablo Atienza
    note: Maintainer of SQANTI3
    orcidid: https://orcid.org/0009-0002-1093-693X
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  - email: fabian.robledo@csic.es
    fundrefid: null
    gridid: null
    name: Fabián Robledo
    note: Maintainer of SQANTI3 and ELIXIR-related contact
    orcidid: https://orcid.org/0009-0005-9047-3315
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  description: "SQANTI3 constitutes the first module of the Functional IsoTranscriptomics\
    \ (FIT) pipeline, which is an end-to-end strategy to perform isoform-level bioinformatics\
    \ analyses. \n\nThe SQANTI3 tool is designed to enable quality control and filtering\
    \ of long read-defined transcriptomes, which are often rich in artifacts and false-positive\
    \ isoforms. \n\nTherefore, a good curation of the transcriptome is indispensable\
    \ to proceed with FIT analysis and produce valid, biologically sound conclusions/hypothesis."
  documentation:
  - note: SQANTI3 main documentation resource
    type:
    - General
    url: https://github.com/ConesaLab/SQANTI3/wiki
  - note: null
    type:
    - Code of conduct
    url: https://github.com/ConesaLab/SQANTI3/blob/master/CODE_OF_CONDUCT.md
  - note: Command-line options and instructions for the QC submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-Quality-Control
  - note: Command-line options and instructions for the filter submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-filter
  - note: Command-line options and instructions for the rescue submodule
    type:
    - Command-line options
    url: https://github.com/ConesaLab/SQANTI3/wiki/Running-SQANTI3-rescue
  - note: Instructions to install and use sqanti, either in docker or apptainer containers
      or in a linux system
    type:
    - Installation instructions
    url: https://github.com/ConesaLab/SQANTI3/wiki/Dependencies-and-installation
  - note: Basic tutorial with examples to start using SQANTI3
    type:
    - Quick start guide
    url: https://github.com/ConesaLab/SQANTI3/wiki/Tutorial:-running-SQANTI3-on-an-example-dataset
  - note: Changelog and release notes for every version
    type:
    - Release notes
    url: https://github.com/ConesaLab/SQANTI3/releases
  - note: Citation instructions are on the end of the Github repository's main page
    type:
    - Citation instructions
    url: https://github.com/ConesaLab/SQANTI3?tab=readme-ov-file#how-to-cite-sqanti3
  - note: Benchmarking about the resources needed to run sqanti3 with multiple cores
    type:
    - Other
    url: https://github.com/ConesaLab/SQANTI3/wiki/SQANTI3-memory-requeriments-and-paralellization
  download:
  - note: null
    type: Downloads page
    url: https://github.com/ConesaLab/SQANTI3/releases
    version: null
  - note: Version 5.5 download link from github
    type: Binaries
    url: https://github.com/ConesaLab/SQANTI3/releases/download/v5.5/SQANTI3_v5.5.zip
    version: '5.5'
  editPermission:
    authors:
    - Fabian-RY
    type: group
  elixirCommunity: []
  elixirNode:
  - Spain
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/ConesaLab/SQANTI3
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-07T09:35:33.280160Z'
  license: GPL-3.0
  link:
  - note: Github repository for source code access and download
    type:
    - Repository
    url: https://github.com/ConesaLab/SQANTI3
  maturity: Mature
  name: SQANTI3
  operatingSystem:
  - Linux
  otherID: []
  owner: Fabian-RY
  publication:
  - doi: 10.1038/s41592-024-02229-2
    metadata:
      abstract: SQANTI3 offers a flexible tool for quality control, curation and annotation
        of long-read RNA sequencing data. SQANTI3 is a tool designed for the quality
        control, curation and annotation of long-read transcript models obtained with
        third-generation sequencing technologies. Leveraging its annotation framework,
        SQANTI3 calculates quality descriptors of transcript models, junctions and
        transcript ends. With this information, potential artifacts can be identified
        and replaced with reliable sequences. Furthermore, the integrated functional
        annotation feature enables subsequent functional iso-transcriptomics analyses.
      authors:
      - name: Pardo-Palacios F.J.
      - name: Arzalluz-Luque A.
      - name: Kondratova L.
      - name: Salguero P.
      - name: Mestre-Tomas J.
      - name: Amorin R.
      - name: Estevan-Morio E.
      - name: Liu T.
      - name: Nanni A.
      - name: McIntyre L.
      - name: Tseng E.
      - name: Conesa A.
      citationCount: 41
      date: '2024-05-01T00:00:00Z'
      journal: Nature Methods
      title: 'SQANTI3: curation of long-read transcriptomes for accurate identification
        of known and novel isoforms'
    note: Publication of SQANTI3
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '5.1'
  - doi: 10.1101/gr.222976.117
    metadata:
      abstract: High-throughput sequencing of full-length transcripts using long reads
        has paved the way for the discovery of thousands of novel transcripts, even
        in well-annotated mammalian species. The advances in sequencing technology
        have created a need for studies and tools that can characterize these novel
        variants. Here, we present SQANTI, an automated pipeline for the classification
        of long-read transcripts that can assess the quality of data and the preprocessing
        pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse
        transcriptome using Pacific Biosciences (PacBio) long reads and illustrate
        how the tool is effective in characterizing and describing the composition
        of the full-length transcriptome. We perform extensive evaluation of ToFU
        PacBio transcripts by PCR to reveal that an important number of the novel
        transcripts are technical artifacts of the sequencing approach and that SQANTI
        quality descriptors can be used to engineer a filtering strategy to remove
        them. Most novel transcripts in this curated transcriptome are novel combinations
        of existing splice sites, resulting more frequently in novel ORFs than novel
        UTRs, and are enriched in both general metabolic and neural-specific functions.
        We show that these new transcripts have a major impact in the correct quantification
        of transcript levels by state-of-the-art short-read-based quantification algorithms.
        By comparing our iso-transcriptome with public proteomics databases, we find
        that alternative isoforms are elusive to proteogenomics detection. SQANTI
        allows the user to maximize the analytical outcome of long-read technologies
        by providing the tools to deliver quality-evaluated and curated full-length
        transcriptomes.
      authors:
      - name: Tardaguila M.
      - name: De La Fuente L.
      - name: Marti C.
      - name: Pereira C.
      - name: Pardo-Palacios F.J.
      - name: Del Risco H.
      - name: Ferrell M.
      - name: Mellado M.
      - name: Macchietto M.
      - name: Verheggen K.
      - name: Edelmann M.
      - name: Ezkurdia I.
      - name: Vazquez J.
      - name: Tress M.
      - name: Mortazavi A.
      - name: Martens L.
      - name: Rodriguez-Navarro S.
      - name: Moreno-Manzano V.
      - name: Conesa A.
      citationCount: 256
      date: '2018-03-01T00:00:00Z'
      journal: Genome Research
      title: 'SQANTI: Extensive characterization of long-read transcript sequences
        for quality control in full-length transcriptome identification and quantification'
    note: Original SQANTI publication
    pmcid: null
    pmid: null
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  validated: 0
  version:
  - '5.5'
- accessibility: Open access
  additionDate: '2016-02-09T13:19:44Z'
  biotoolsCURIE: biotools:mirgen
  biotoolsID: mirgen
  collectionID:
  - UiO tools
  - ELIXIR-NO
  - ELIXIR-Norway
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: BastianFromm@gmail.com
    fundrefid: null
    gridid: null
    name: Bastian Fromm
    note: null
    orcidid: https://orcid.org/0000-0003-0352-3037
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    - Support
    url: null
  - email: kevin.j.peterson@dartmouth.edu
    fundrefid: null
    gridid: null
    name: Kevin J. Peterson
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Maintainer
    url: null
  - email: support@elixir.no
    fundrefid: null
    gridid: null
    name: The Norwegian Bioinformatics Platform (ELIXIR-Norway) Helpdesk
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole:
    - Support
    url: https://elixir.no/helpdesk
  - email: null
    fundrefid: 10.13039/501100005366
    gridid: grid.5510.1
    name: University of Oslo
    note: null
    orcidid: null
    rorid: 01xtthb56
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.uio.no/english/index.html
  description: MirGeneDB is a database of manually curated microRNA genes that have
    been validated and annotated as initially described in Fromm et al. 2015 , Fromm
    et al. 2020 and Fromm et al 2022. MirGeneDB 3.0 (Clarke and Hoye et al. 2024 )
    includes more than 21,000 microRNA gene entries representing more than 1,700 microRNA
    families from 114 metazoan species. All microRNAs can be browsed, searched and
    downloaded.
  documentation:
  - note: null
    type:
    - General
    url: https://www.mirgenedb.org/information
  download:
  - note: Sequence downloads for 75 species
    type: Biological data
    url: https://www.mirgenedb.org/download
    version: '3.0'
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  - cmd: null
    input:
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format:
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        uri: http://edamontology.org/format_1929
    - data:
        term: Organism identifier
        uri: http://edamontology.org/data_1869
      format: []
    note: 'Data retrieval: curated miRNA. Organism identifier: a specific miRNA identifier
      or a species  for all miRNAs for that species. Gene transcript report: with
      metadata and visualization. RNA secondary structure: the hairpin loop of the
      miRNA with bases.'
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    output:
    - data:
        term: Gene transcript report
        uri: http://edamontology.org/data_3134
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: RNA secondary structure
        uri: http://edamontology.org/data_0880
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
      - term: BED
        uri: http://edamontology.org/format_3003
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://mirgenedb.org/
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-05T22:33:30.082420Z'
  license: CC0-1.0
  link:
  - note: null
    type:
    - Helpdesk
    url: https://elixir.no/helpdesk
  maturity: Mature
  name: MirGeneDB
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: doi
    value: doi:10.25504/FAIRsharing.QXSgvF
    version: '2.0'
  owner: UiO
  publication:
  - doi: 10.1093/nar/gkab1101
    metadata:
      abstract: We describe an update of MirGeneDB, the manually curated microRNA
        gene database. Adhering to uniform and consistent criteria for microRNA annotation
        and nomenclature, we substantially expanded MirGeneDB with 30 additional species
        representing previously missing metazoan phyla such as sponges, jellyfish,
        rotifers and flatworms. MirGeneDB 2.1 now consists of 75 species spanning
        over ∼800 million years of animal evolution, and contains a total number of
        16 670 microRNAs from 1549 families. Over 6000 microRNAs were added in this
        update using ∼550 datasets with ∼7.5 billion sequencing reads. By adding new
        phylogenetically important species, especially those relevant for the study
        of whole genome duplication events, and through updating evolutionary nodes
        of origin for many families and genes, we were able to substantially refine
        our nomenclature system. All changes are traceable in the specifically developed
        MirGeneDB version tracker. The performance of read-pages is improved and microRNA
        expression matrices for all tissues and species are now also downloadable.
        Altogether, this update represents a significant step toward a complete sampling
        of all major metazoan phyla, and a widely needed foundation for comparative
        microRNA genomics and transcriptomics studies. MirGeneDB 2.1 is part of RNAcentral
        and Elixir Norway, publicly and freely available at http://www.mirgenedb.org/.
      authors:
      - name: Fromm B.
      - name: Hoye E.
      - name: Domanska D.
      - name: Zhong X.
      - name: Aparicio-Puerta E.
      - name: Ovchinnikov V.
      - name: Umu S.U.
      - name: Chabot P.J.
      - name: Kang W.
      - name: Aslanzadeh M.
      - name: Tarbier M.
      - name: Marmol-Sanchez E.
      - name: Urgese G.
      - name: Johansen M.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Friedlander M.R.
      - name: Peterson K.J.
      citationCount: 95
      date: '2022-01-07T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 2.1: Toward a complete sampling of all major animal phyla'
    note: null
    pmcid: PMC8728216
    pmid: '34850127'
    type:
    - Primary
    version: '2.1'
  - doi: 10.1093/nar/gkz885
    metadata:
      abstract: Small non-coding RNAs have gained substantial attention due to their
        roles in animal development and human disorders. Among them, microRNAs are
        special because individual gene sequences are conserved across the animal
        kingdom. In addition, unique and mechanistically well understood features
        can clearly distinguish bona fide miRNAs from the myriad other small RNAs
        generated by cells. However, making this distinction is not a common practice
        and, thus, not surprisingly, the heterogeneous quality of available miRNA
        complements has become a major concern in microRNA research. We addressed
        this by extensively expanding our curated microRNA gene database-MirGeneDB-to
        45 organisms, encompassing a wide phylogenetic swath of animal evolution.
        By consistently annotating and naming 10,899 microRNA genes in these organisms,
        we show that previous microRNA annotations contained not only many false positives,
        but surprisingly lacked >2000 bona fide microRNAs. Indeed, curated microRNA
        complements of closely related organisms are very similar and can be used
        to reconstruct ancestral miRNA repertoires. MirGeneDB represents a robust
        platform for microRNA-based research, providing deeper and more significant
        insights into the biology and evolution of miRNAs as well as biomedical and
        biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.
      authors:
      - name: Fromm B.
      - name: Domanska D.
      - name: Hoye E.
      - name: Ovchinnikov V.
      - name: Kang W.
      - name: Aparicio-Puerta E.
      - name: Johansen M.
      - name: Flatmark K.
      - name: Mathelier A.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Friedlander M.R.
      - name: Peterson K.J.
      citationCount: 178
      date: '2020-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 2.0: The metazoan microRNA complement'
    note: null
    pmcid: PMC6943042
    pmid: '31598695'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1146/annurev-genet-120213-092023
    metadata:
      abstract: Although microRNAs (miRNAs) are among the most intensively studied
        molecules of the past 20 years, determining what is and what is not a miRNA
        has not been straightforward. Here, we present a uniform system for the annotation
        and nomenclature of miRNA genes. We show that less than a third of the 1,881
        human miRBase entries, and only approximately 16% of the 7,095 metazoan miRBase
        entries, are robustly supported as miRNA genes. Furthermore, we show that
        the human repertoire of miRNAs has been shaped by periods of intense miRNA
        innovation and that mature gene products show a very different tempo and mode
        of sequence evolution than star products. We establish a new open access database-MirGeneDB
        (http://mirgenedb.org)-to catalog this set of miRNAs, which complements the
        efforts of miRBase but differs from it by annotating the mature versus star
        products and by imposing an evolutionary hierarchy upon this curated and consistently
        named repertoire.
      authors:
      - name: Fromm B.
      - name: Billipp T.
      - name: Peck L.E.
      - name: Johansen M.
      - name: Tarver J.E.
      - name: King B.L.
      - name: Newcomb J.M.
      - name: Sempere L.F.
      - name: Flatmark K.
      - name: Hovig E.
      - name: Peterson K.J.
      citationCount: 435
      date: '2015-11-23T00:00:00Z'
      journal: Annual Review of Genetics
      title: A Uniform System for the Annotation of Vertebrate microRNA Genes and
        the Evolution of the Human microRNAome
    note: null
    pmcid: PMC4743252
    pmid: '26473382'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1093/nar/gkae1094
    metadata:
      abstract: We present a major update of MirGeneDB (3.0), the manually curated
        animal microRNA gene database. Beyond moving to a new server and the creation
        of a computational mirror, we have expanded the database with the addition
        of 33 invertebrate species, including representatives of 5 previously unsampled
        phyla, and 6 mammal species. MirGeneDB now contains entries for 21 822 microRNA
        genes (5160 of these from the new species) belonging to 1743 microRNA families.
        The inclusion of these new species allowed us to refine both the evolutionary
        node of appearance of a number of microRNA genes/families, as well as MirGeneDB's
        phylogenetically informed nomenclature system. Updated covariance models of
        all microRNA families, along with all smallRNA read data are now downloadable.
        These enhanced annotations will allow researchers to analyze microRNA properties
        such as secondary structure and features of their biogenesis within a robust
        phylogenetic context and without the database plagued with numerous false
        positives and false negatives. In light of these improvements, MirGeneDB 3.0
        will assume the responsibility for naming conserved novel metazoan microRNAs.
        MirGeneDB is part of RNAcentral and Elixir Norway and is publicly and freely
        available at mirgenedb.org.
      authors:
      - name: Clarke A.W.
      - name: Hoye E.
      - name: Hembrom A.A.
      - name: Paynter V.M.
      - name: Vinther J.
      - name: Wyrozemski L.
      - name: Biryukova I.
      - name: Formaggioni A.
      - name: Ovchinnikov V.
      - name: Herlyn H.
      - name: Pierce A.
      - name: Wu C.
      - name: Aslanzadeh M.
      - name: Cheneby J.
      - name: Martinez P.
      - name: Friedlander M.R.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Umu S.U.
      - name: Johansen M.
      - name: Peterson K.J.
      - name: Fromm B.
      citationCount: 6
      date: '2025-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 3.0: Improved taxonomic sampling, uniform nomenclature of
        novel conserved microRNA families and updated covariance models'
    note: null
    pmcid: PMC11701709
    pmid: '39673268'
    type:
    - Primary
    version: '3.0'
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Gene regulation
    uri: http://edamontology.org/topic_0204
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  - term: Zoology
    uri: http://edamontology.org/topic_3500
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  validated: 1
  version:
  - '3.0'
- accessibility: null
  additionDate: '2017-01-17T14:50:32Z'
  biotoolsCURIE: biotools:pyteomics
  biotoolsID: pyteomics
  collectionID:
  - Proteomics
  confidence_flag: null
  cost: null
  credit:
  - email: pyteomics@googlegroups.com
    fundrefid: null
    gridid: null
    name: Pyteomics Google Groups
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Issue tracker
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://github.com/levitsky/pyteomics/issues
  description: Framework for proteomics data analysis, supporting mzML, MGF, pepXML
    and more.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pyteomics.readthedocs.io/
  download:
  - note: null
    type: Software package
    url: https://pypi.org/project/pyteomics/
    version: null
  editPermission:
    authors:
    - proteomics.bio.tools
    - levitsky
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: MGF
        uri: http://edamontology.org/format_3651
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: mzXML
        uri: http://edamontology.org/format_3654
    note: null
    operation:
    - term: Protein identification
      uri: http://edamontology.org/operation_3767
    output: []
  homepage: https://pyteomics.readthedocs.io/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-04T15:07:26.685242Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/levitsky/pyteomics
  maturity: null
  name: Pyteomics
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: msutils_import
  publication:
  - doi: 10.1007/s13361-012-0516-6
    metadata:
      abstract: 'Pyteomics is a cross-platform, open-source Python library providing
        a rich set of tools for MS-based proteomics. It provides modules for reading
        LC-MS/MS data, search engine output, protein sequence databases, theoretical
        prediction of retention times, electrochemical properties of polypeptides,
        mass and m/z calculations, and sequence parsing. Pyteomics is available under
        Apache license; release versions are available at the Python Package Index
        http://pypi.python. org/pyteomics, the source code repository at http://hg.theorchromo.ru/pyteomics,
        documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation
        is available at http://packages.python.org/pyteomics.biolccc/. Questions on
        installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com
        [Figure not available: see fulltext.] © 2013 American Society for Mass Spectrometry.'
      authors:
      - name: Goloborodko A.A.
      - name: Levitsky L.I.
      - name: Ivanov M.V.
      - name: Gorshkov M.V.
      citationCount: 141
      date: '2013-02-01T00:00:00Z'
      journal: Journal of the American Society for Mass Spectrometry
      title: Pyteomics - A python framework for exploratory data analysis and rapid
        software prototyping in proteomics
    note: null
    pmcid: null
    pmid: '23292976'
    type:
    - Primary
    version: null
  - doi: 10.1021/acs.jproteome.8b00717
    metadata:
      abstract: Many of the novel ideas that drive today's proteomic technologies
        are focused essentially on experimental or data-processing workflows. The
        latter are implemented and published in a number of ways, from custom scripts
        and programs, to projects built using general-purpose or specialized workflow
        engines; a large part of routine data processing is performed manually or
        with custom scripts that remain unpublished. Facilitating the development
        of reproducible data-processing workflows becomes essential for increasing
        the efficiency of proteomic research. To assist in overcoming the bioinformatics
        challenges in the daily practice of proteomic laboratories, 5 years ago we
        developed and announced Pyteomics, a freely available open-source library
        providing Python interfaces to proteomic data. We summarize the new functionality
        of Pyteomics developed during the time since its introduction.
      authors:
      - name: Levitsky L.I.
      - name: Klein J.A.
      - name: Ivanov M.V.
      - name: Gorshkov M.V.
      citationCount: 117
      date: '2019-02-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - 4.7.5
- accessibility: Open access
  additionDate: '2025-06-18T09:20:19.874306Z'
  biotoolsCURIE: biotools:chopper
  biotoolsID: chopper
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: decosterwouter@gmail.com
    fundrefid: null
    gridid: null
    name: Wouter de Coster
    note: null
    orcidid: https://orcid.org/0000-0002-5248-8197
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: https://gigabaseorgigabyte.wordpress.com
  description: Rust implementation of NanoFilt+NanoLyse, both originally written in
    Python. This tool, intended for long read sequencing such as PacBio or ONT, filters
    and trims a fastq file.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: 'gunzip -c reads.fastq.gz | chopper -q 10 -l 500 | gzip > filtered_reads.fastq.gz

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

      chopper -q 10 -l 500 -i reads.fastq.gz | gzip > filtered_reads.fastq.gz'
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Scatter plot plotting
      uri: http://edamontology.org/operation_2940
    - term: Box-Whisker plot plotting
      uri: http://edamontology.org/operation_2943
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: JPG
        uri: http://edamontology.org/format_3579
  homepage: https://github.com/wdecoster/chopper
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-30T15:46:01.095401Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/wdecoster/chopper
  maturity: Mature
  name: Chopper
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Keiler_Collier
  publication:
  - doi: 10.1093/bioinformatics/btad311
    metadata:
      abstract: 'Increases in the cohort size in long-read sequencing projects necessitate
        more efficient software for quality assessment and processing of sequencing
        data from Oxford Nanopore Technologies and Pacific Biosciences. Here, we describe
        novel tools for summarizing experiments, filtering datasets, visualizing phased
        alignments results, and updates to the NanoPack software suite. Availability
        and implementation: The cramino, chopper, kyber, and phasius tools are written
        in Rust and available as executable binaries without requiring installation
        or managing dependencies. Binaries build on musl are available for broad compatibility.
        NanoPlot and NanoComp are written in Python3. Links to the separate tools
        and their documentation can be found at https://github.com/wdecoster/nanopack.
        All tools are compatible with Linux, Mac OS, and the MS Windows Subsystem
        for Linux and are released under the MIT license. The repositories include
        test data, and the tools are continuously tested using GitHub Actions and
        can be installed with the conda dependency manager.'
      authors:
      - name: De Coster W.
      - name: Rademakers R.
      citationCount: 211
      date: '2023-05-01T00:00:00Z'
      journal: Bioinformatics
      title: 'NanoPack2: population-scale evaluation of long-read sequencing data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: nanopack
    type: isNewVersionOf
  toolType:
  - Command-line tool
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - v0.10.0b
- accessibility: Open access
  additionDate: '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: grid.507782.f
    name: SILVA Team
    note: null
    orcidid: null
    rorid: 027z9pz32
    typeEntity: Division
    typeRole:
    - Primary contact
    url: https://www.arb-silva.de/contact/team/
  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: https://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:
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        uri: http://edamontology.org/format_1964
    - data:
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      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: ARB
        uri: http://edamontology.org/format_3830
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: ARB
        uri: http://edamontology.org/format_3830
      - 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: nucleotide
        uri: http://edamontology.org/format_1207
    - data:
        term: PCR primers
        uri: http://edamontology.org/data_1240
      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. \n\nTestPrime 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: CSV
        uri: http://edamontology.org/format_3752
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
    - data:
        term: Nucleic acid sequence alignment
        uri: http://edamontology.org/data_1383
      format:
      - term: ARB
        uri: http://edamontology.org/format_3830
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: ARB
        uri: http://edamontology.org/format_3830
      - 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-aln
        uri: http://edamontology.org/format_1984
    note: The Alignment, Classification and Tree Service (ACT) of SILVA allows to
      align and optionally classify sequences using the SILVA Incremental Aligner
      (SINA). From aligned sequences, (optionally) phylogenetic trees can be inferred
      using FastTree or RAxML.
    operation:
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    - term: Global alignment
      uri: http://edamontology.org/operation_0496
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    - term: Phylogenetic inference (maximum likelihood and Bayesian methods)
      uri: http://edamontology.org/operation_0547
    output:
    - data:
        term: Nucleic acid sequence alignment
        uri: http://edamontology.org/data_1383
      format:
      - term: ARB
        uri: http://edamontology.org/format_3830
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
    - data:
        term: Sequence alignment report
        uri: http://edamontology.org/data_0867
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Phylogenetic tree
        uri: http://edamontology.org/data_0872
      format:
      - term: newick
        uri: http://edamontology.org/format_1910
    - data:
        term: Taxonomic classification
        uri: http://edamontology.org/data_1872
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: https://www.arb-silva.de
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-30T13:26:42.882897Z'
  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: 22633
      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: 2590
      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: 5291
      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: 2408
      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: 635
      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: 29
      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
  - biotoolsID: d3hub
    type: includedIn
  toolType:
  - Web application
  - 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:
  - de.NBI
  - de.NBI-biodata
  - DSMZ Digital Diversity
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: ngs-contact@arb-silva.de
    fundrefid: null
    gridid: grid.507782.f
    name: SILVA Team
    note: null
    orcidid: null
    rorid: 027z9pz32
    typeEntity: Division
    typeRole:
    - Primary contact
    url: https://www.arb-silva.de/contact/team/
  - 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
  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:
    - User manual
    url: https://www.arb-silva.de/fileadmin/silva_databases/sngs/SILVAngs_User_Guide.pdf
  - note: null
    type:
    - FAQ
    url: https://www.arb-silva.de/documentation/silvangs/userfaq/
  - note: null
    type:
    - Terms of use
    url: https://www.arb-silva.de/footer/sngs-termsofuse
  download: []
  editPermission:
    authors: []
    type: group
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform:
  - Data
  elixir_badge: 0
  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: 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-aln
        uri: http://edamontology.org/format_1984
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: ARB
        uri: http://edamontology.org/format_3830
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
  homepage: https://ngs.arb-silva.de
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-30T13:26:39.251875Z'
  license: null
  link: []
  maturity: Mature
  name: SILVAngs
  operatingSystem: []
  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: 22633
      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: 2590
      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:
    - Primary
    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: 2408
      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:
    - Primary
    version: null
  relation:
  - biotoolsID: silva
    type: uses
  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
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  validated: 1
  version:
  - 1.9.10
- accessibility: Open access
  additionDate: '2019-08-26T14:34:55Z'
  biotoolsCURIE: biotools:MINERVA_Platform
  biotoolsID: MINERVA_Platform
  collectionID:
  - ELIXIR-LU
  - LCSB
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'The MINERVA (Molecular Interaction NEtwoRk VisuAlization) platform
    is a standalone webserver for visualization, exploration and management of molecular
    networks encoded in SBGN-compliant format, including files produced using CellDesigner
    or SBGN editors. Visualization of uploaded networks generated by the platform
    is accessible via a web browser to all viewers with the weblink to the resource.


    The MINERVA Platform is a webservice using the Java Server Faces 2 technology.
    The server side, including data parsing, integration, annotation and verification,
    is implemented in Java. The platform uses the Postgres SQL database for data storage
    and the Hibernate framework as a middle layer between web server and database.
    The user web-interface is generated using React.js. The displayed content is visualized
    by OpenLayers API, dedicated JavaScript and CSS.'
  documentation:
  - note: null
    type:
    - Quick start guide
    - Release notes
    - User manual
    - API documentation
    - Citation instructions
    - Terms of use
    url: https://minerva.uni.lu
  download:
  - note: Installation instructions, including debian package, virtual machine images
      and docker containers.
    type: Other
    url: https://minerva.pages.uni.lu/doc/install/
    version: 13.1.3 - 18.1.1
  editPermission:
    authors:
    - sascha.herzinger
    type: group
  elixirCommunity: []
  elixirNode:
  - Luxembourg
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: Visualise systems biology diagrams online, on a standalone web server
    operation:
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output: []
  - cmd: null
    input: []
    note: Visualise omics data from multiple datasets on top of the diagrams
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output: []
  - cmd: null
    input: []
    note: 'Convert between main systems biology layout formats: CellDeslgners SBML,
      SBML layout+render, SBGN-ML, GPML'
    operation:
    - term: Conversion
      uri: http://edamontology.org/operation_3434
    output: []
  - cmd: null
    input: []
    note: Use MINERVA API to access systems biology formats for modelling
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output: []
  homepage: https://minerva.uni.lu
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-26T13:15:59.673112Z'
  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: 65
      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: 25
      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: 17
      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: 39
      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
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  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: null
  additionDate: '2017-12-18T00:03:45Z'
  biotoolsCURIE: biotools:autodock_vina
  biotoolsID: autodock_vina
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://olegtrott.com/
  description: AutoDock Vina is a new open-source program for drug discovery, molecular
    docking and virtual screening, offering multi-core capability, high performance
    and enhanced accuracy and ease of use.
  documentation:
  - note: null
    type:
    - User manual
    url: http://vina.scripps.edu/manual.html
  download: []
  editPermission:
    authors:
    - ragibsiddique
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Molecular docking
      uri: http://edamontology.org/operation_0478
    output: []
  homepage: http://vina.scripps.edu/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-06-22T11:35:04.906923Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/autodock-vina-1-1-2-molecular-docking-virtual-screening-program.html
  maturity: null
  name: AutoDock Vina
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_011958
    version: null
  owner: mbs_import
  publication:
  - doi: 10.1002/jcc.21334
    metadata: null
    note: null
    pmcid: PMC3041641
    pmid: '19499576'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Drug discovery
    uri: http://edamontology.org/topic_3336
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Molecular docking
    uri: http://edamontology.org/topic_2275
  validated: 1
  version:
  - 1.1.2
- accessibility: null
  additionDate: '2017-04-21T12:12:09Z'
  biotoolsCURIE: biotools:cytoscape_web
  biotoolsID: cytoscape_web
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: gary.bader@utoronto.ca
    fundrefid: null
    gridid: null
    name: Gary D. Bader
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: An online implementation of Cytoscape Desktop that captures its interface
    and key visualization functionality while providing integration with web tools
    and databases.
  documentation:
  - note: null
    type:
    - User manual
    url: https://cytoscape-web.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Loading
      uri: http://edamontology.org/operation_1812
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    - term: Pathway or network comparison
      uri: http://edamontology.org/operation_0277
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    - term: Pathway or network prediction
      uri: http://edamontology.org/operation_3439
    output: []
  homepage: https://web.cytoscape.org/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-06-19T13:28:13.117454Z'
  license: null
  link: []
  maturity: null
  name: Cytoscape Web
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: garybader
  publication:
  - doi: 10.1093/nar/gkaf365
    metadata: null
    note: null
    pmcid: null
    pmid: '40308211'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2018-05-28T14:50:09Z'
  biotoolsCURIE: biotools:deepsig
  biotoolsID: deepsig
  collectionID:
  - Bologna Biocomputing Group
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it
  description: Prediction of secretory signal peptides in protein sequences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/deepsig
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/deepsig
    version: 1.2.5
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/deepsig
    version: 1.2.5
  editPermission:
    authors:
    - savo
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://busca.biocomp.unibo.it/deepsig/
  homepage_status: 0
  language:
  - Python
  - C++
  lastUpdate: '2025-06-19T11:55:09.017105Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: DeepSig
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btx818
    metadata:
      abstract: Motivation The identification of signal peptides in protein sequences
        is an important step toward protein localization and function characterization.
        Results Here, we present DeepSig, an improved approach for signal peptide
        detection and cleavage-site prediction based on deep learning methods. Comparative
        benchmarks performed on an updated independent dataset of proteins show that
        DeepSig is the current best performing method, scoring better than other available
        state-of-the-art approaches on both signal peptide detection and precise cleavage-site
        identification. Availability and implementation DeepSig is available as both
        standalone program and web server at https://deepsig.biocomp.unibo.it. All
        datasets used in this study can be obtained from the same website.
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 96
      date: '2018-05-15T00:00:00Z'
      journal: Bioinformatics
      title: 'DeepSig: Deep learning improves signal peptide detection in proteins'
    note: null
    pmcid: PMC5946842
    pmid: '29280997'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - 1.2.5
- accessibility: Open access
  additionDate: '2021-05-27T09:02:10Z'
  biotoolsCURIE: biotools:qiime2
  biotoolsID: qiime2
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: greg.caporaso@nau.edu
    fundrefid: null
    gridid: null
    name: Greg Caporaso
    note: null
    orcidid: https://orcid.org/0000-0002-8865-1670
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: https://cap-lab.bio
  description: QIIME 2 is an AI-ready microbiome multi-omics data science platform
    that is trusted, free, open source, extensible, and community developed and supported
    bioinformatics.
  documentation:
  - note: User documentation for amplicon analysis, oriented to new users.
    type:
    - General
    url: https://amplicon-docs.qiime2.org
  - note: User documentation for metagenome analysis, oriented to new users.
    type:
    - Installation instructions
    url: https://moshpit.qiime2.org
  - note: User documentation, oriented toward power users.
    type:
    - User manual
    url: https://use.qiime2.org
  - note: Developer documentation, including a plugin development tutorial.
    type:
    - API documentation
    url: https://develop.qiime2.org
  - note: 'The QIIME 2 Library Stacks: a resource for discovering relevant documentation.'
    type:
    - Other
    url: https://library.qiime2.org/books
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Galaxy
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Demultiplexing
      uri: http://edamontology.org/operation_3933
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output: []
  homepage: https://qiime2.org
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-18T21:00:46.185472Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/qiime2
  - note: null
    type:
    - Software catalogue
    url: https://library.qiime2.org
  - note: null
    type:
    - Discussion forum
    url: https://forum.qiime2.org
  - note: Site for viewing and interacting with QIIME 2 results.
    type:
    - Other
    url: https://view.qiime2.org
  maturity: Mature
  name: QIIME 2
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: gregcaporaso
  publication:
  - doi: 10.1038/s41587-019-0209-9
    metadata:
      abstract: ''
      authors:
      - name: Bolyen E.
      - name: Rideout J.R.
      - name: Dillon M.R.
      - name: Bokulich N.A.
      - name: Abnet C.C.
      - name: Al-Ghalith G.A.
      - name: Alexander H.
      - name: Alm E.J.
      - name: Arumugam M.
      - name: Asnicar F.
      - name: Bai Y.
      - name: Bisanz J.E.
      - name: Bittinger K.
      - name: Brejnrod A.
      - name: Brislawn C.J.
      - name: Brown C.T.
      - name: Callahan B.J.
      - name: Caraballo-Rodriguez A.M.
      - name: Chase J.
      - name: Cope E.K.
      - name: Da Silva R.
      - name: Diener C.
      - name: Dorrestein P.C.
      - name: Douglas G.M.
      - name: Durall D.M.
      - name: Duvallet C.
      - name: Edwardson C.F.
      - name: Ernst M.
      - name: Estaki M.
      - name: Fouquier J.
      - name: Gauglitz J.M.
      - name: Gibbons S.M.
      - name: Gibson D.L.
      - name: Gonzalez A.
      - name: Gorlick K.
      - name: Guo J.
      - name: Hillmann B.
      - name: Holmes S.
      - name: Holste H.
      - name: Huttenhower C.
      - name: Huttley G.A.
      - name: Janssen S.
      - name: Jarmusch A.K.
      - name: Jiang L.
      - name: Kaehler B.D.
      - name: Kang K.B.
      - name: Keefe C.R.
      - name: Keim P.
      - name: Kelley S.T.
      - name: Knights D.
      - name: Koester I.
      - name: Kosciolek T.
      - name: Kreps J.
      - name: Langille M.G.I.
      - name: Lee J.
      - name: Ley R.
      - name: Liu Y.-X.
      - name: Loftfield E.
      - name: Lozupone C.
      - name: Maher M.
      - name: Marotz C.
      - name: Martin B.D.
      - name: McDonald D.
      - name: McIver L.J.
      - name: Melnik A.V.
      - name: Metcalf J.L.
      - name: Morgan S.C.
      - name: Morton J.T.
      - name: Naimey A.T.
      - name: Navas-Molina J.A.
      - name: Nothias L.F.
      - name: Orchanian S.B.
      - name: Pearson T.
      - name: Peoples S.L.
      - name: Petras D.
      - name: Preuss M.L.
      - name: Pruesse E.
      - name: Rasmussen L.B.
      - name: Rivers A.
      - name: Robeson M.S.
      - name: Rosenthal P.
      - name: Segata N.
      - name: Shaffer M.
      - name: Shiffer A.
      - name: Sinha R.
      - name: Song S.J.
      - name: Spear J.R.
      - name: Swafford A.D.
      - name: Thompson L.R.
      - name: Torres P.J.
      - name: Trinh P.
      - name: Tripathi A.
      - name: Turnbaugh P.J.
      - name: Ul-Hasan S.
      - name: van der Hooft J.J.J.
      - name: Vargas F.
      - name: Vazquez-Baeza Y.
      - name: Vogtmann E.
      - name: von Hippel M.
      - name: Walters W.
      - name: Wan Y.
      - name: Wang M.
      - name: Warren J.
      - name: Weber K.C.
      - name: Williamson C.H.D.
      - name: Willis A.D.
      - name: Xu Z.Z.
      - name: Zaneveld J.R.
      - name: Zhang Y.
      - name: Zhu Q.
      - name: Knight R.
      - name: Caporaso J.G.
      citationCount: 14289
      date: '2019-08-01T00:00:00Z'
      journal: Nature Biotechnology
      title: Reproducible, interactive, scalable and extensible microbiome data science
        using QIIME 2
    note: null
    pmcid: PMC7015180
    pmid: '31341288'
    type:
    - Primary
    version: '2.0'
  relation:
  - biotoolsID: qiime
    type: isNewVersionOf
  toolType:
  - Suite
  - Command-line tool
  - Library
  topic:
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2021-05-27T09:04:45Z'
  biotoolsCURIE: biotools:quickmerge
  biotoolsID: quickmerge
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Mahul Chakraborty
    note: null
    orcidid: https://orcid.org/0000-0003-2414-9187
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://mahulchakraborty.wordpress.com/
  description: Quickmerge is a program that uses complementary information from genomes
    assembled with long reads in order to improve contiguity, and works with assemblies
    derived from both Pacific Biosciences or Oxford Nanopore. Quickmerge will even
    work with hybrid assemblies made by combining long reads and Illumina short reads.
  documentation: []
  download: []
  editPermission:
    authors:
    - ELIXIR-CZ
    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: merge_wrapper.py -pre output_prefix assembly_1.fa assembly_2.fa
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: 'Runs whole merge process on an input assembly.

      Assembly 2 will be used to fill gaps in assembly 1.'
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/mahulchak/quickmerge
  homepage_status: 0
  language:
  - C++
  - C
  lastUpdate: '2025-06-18T12:17:20.983962Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/mahulchak/quickmerge/issues
  - note: null
    type:
    - Repository
    url: https://github.com/mahulchak/quickmerge
  maturity: null
  name: quickmerge
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1534/g3.118.200162
    metadata:
      abstract: Accurate and comprehensive characterization of genetic variation is
        essential for deciphering the genetic basis of diseases and other phenotypes.
        A vast amount of genetic variation stems from largescale sequence changes
        arising from the duplication, deletion, inversion, and translocation of sequences.
        In the past 10 years, high-throughput short reads have greatly expanded our
        ability to assay sequence variation due to single nucleotide polymorphisms.
        However, a recent de novo assembly of a second Drosophila melanogaster reference
        genome has revealed that short read genotyping methods miss hundreds of structural
        variants, including those affecting phenotypes. While genomes assembled using
        highcoverage long reads can achieve high levels of contiguity and completeness,
        concerns about cost, errors, and low yield have limited widespread adoption
        of such sequencing approaches. Here we resequenced the reference strain of
        D. melanogaster (ISO1) on a single Oxford Nanopore MinION flow cell run for
        24 hr. Using only reads longer than 1 kb or with at least 30x coverage, we
        assembled a highly contiguous de novo genome. The addition of inexpensive
        paired reads and subsequent scaffolding using an optical map technology achieved
        an assembly with completeness and contiguity comparable to the D. melanogaster
        reference assembly. Comparison of our assembly to the reference assembly of
        ISO1 uncovered a number of structural variants (SVs), including novel LTR
        transposable element insertions and duplications affecting genes with developmental,
        behavioral, and metabolic functions. Collectively, these SVs provide a snapshot
        of the dynamics of genome evolution. Furthermore, our assembly and comparison
        to the D. melanogaster reference genome demonstrates that high-quality de
        novo assembly of reference genomes and comprehensive variant discovery using
        such assemblies are now possible by a single lab for under $1,000 (USD).
      authors:
      - name: Solares E.A.
      - name: Chakraborty M.
      - name: Miller D.E.
      - name: Kalsow S.
      - name: Hall K.
      - name: Perera A.G.
      - name: Emerson J.J.
      - name: Scott Hawley R.
      citationCount: 70
      date: '2018-10-01T00:00:00Z'
      journal: 'G3: Genes, Genomes, Genetics'
      title: Rapid low-cost assembly of the drosophila melanogaster reference genome
        using low-coverage, long-read sequencing
    note: null
    pmcid: PMC6169397
    pmid: '30018084'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version:
  - v.0.3
- accessibility: Open access
  additionDate: '2022-05-16T13:26:15.720803Z'
  biotoolsCURIE: biotools:skm
  biotoolsID: skm
  collectionID:
  - ELIXIR-SI
  - Plant Systems Biology
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: National Institute of Biology, Department of Biotechnology and Systems Biology
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://www.nib.si/
  - email: carissarobyn.bleker@nib.si
    fundrefid: null
    gridid: null
    name: Carissa Bleker
    note: null
    orcidid: https://orcid.org/0000-0003-1617-7145
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Kristina Gruden
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Živa Ramšak
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Vid Podpečan
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: 'Stress Knowledge Map:  A compilation of knowledge on mechanisms underlying
    responses of plants to stress, the so called stress signalling network.'
  documentation:
  - note: null
    type:
    - General
    url: https://skm.nib.si/documentation
  download: []
  editPermission:
    authors:
    - zagor
    type: group
  elixirCommunity:
  - Plant Sciences
  elixirNode:
  - Slovenia
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene name
        uri: http://edamontology.org/data_2299
      format: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: sif
        uri: http://edamontology.org/format_3619
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Mathematical model
        uri: http://edamontology.org/data_0950
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: SBGN-ML
        uri: http://edamontology.org/format_3692
      - term: sif
        uri: http://edamontology.org/format_3619
  homepage: https://skm.nib.si/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-18T12:10:26.465410Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://skm.nib.si/contact
  maturity: Emerging
  name: SKM
  operatingSystem: []
  otherID: []
  owner: carissa
  publication:
  - doi: 10.1016/j.xplc.2024.100920
    metadata:
      abstract: 'Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available
        resource containing two complementary knowledge graphs that describe the current
        knowledge of biochemical, signaling, and regulatory molecular interactions
        in plants: a highly curated model of plant stress signaling (PSS; 543 reactions)
        and a large comprehensive knowledge network (488 390 interactions). Both were
        constructed by domain experts through systematic curation of diverse literature
        and database resources. SKM provides a single entry point for investigations
        of plant stress response and related growth trade-offs, as well as interactive
        explorations of current knowledge. PSS is also formulated as a qualitative
        and quantitative model for systems biology and thus represents a starting
        point for a plant digital twin. Here, we describe the features of SKM and
        show, through two case studies, how it can be used for complex analyses, including
        systematic hypothesis generation and design of validation experiments, or
        to gain new insights into experimental observations in plant biology.'
      authors:
      - name: Bleker C.
      - name: Ramsak
      - name: Bittner A.
      - name: Podpecan V.
      - name: Zagorscak M.
      - name: Wurzinger B.
      - name: Baebler
      - name: Petek M.
      - name: Kriznik M.
      - name: van Dieren A.
      - name: Gruber J.
      - name: Afjehi-Sadat L.
      - name: Weckwerth W.
      - name: Zupanic A.
      - name: Teige M.
      - name: Vothknecht U.C.
      - name: Gruden K.
      citationCount: 8
      date: '2024-06-10T00:00:00Z'
      journal: Plant Communications
      title: 'Stress Knowledge Map: A knowledge graph resource for systems biology
        analysis of plant stress responses'
    note: 'Stress Knowledge Map: A knowledge graph resource for systems biology analysis
      of plant stress responses'
    pmcid: null
    pmid: '38616489'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: gomapman
    type: uses
  - biotoolsID: newt-ve
    type: uses
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2019-11-14T20:08:20Z'
  biotoolsCURIE: biotools:Flye
  biotoolsID: Flye
  collectionID:
  - ONTeater
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fenderglass@gmail.com
    fundrefid: null
    gridid: null
    name: Mikhail Kolmogorov
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Yu Lin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jeffrey Yuan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'Flye is a de novo assembler for single molecule sequencing reads,
    such as those produced by PacBio and Oxford Nanopore Technologies. It is designed
    for a wide range of datasets, from small bacterial projects to large mammalian-scale
    assemblies. The package represents a complete pipeline: it takes raw PB / ONT
    reads as input and outputs polished contigs.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Mapping assembly
      uri: http://edamontology.org/operation_0523
    - term: Cross-assembly
      uri: http://edamontology.org/operation_3730
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/fenderglass/Flye
  homepage_status: 0
  language:
  - C++
  - Python
  - C
  lastUpdate: '2025-06-18T10:20:03.775603Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/fenderglass/Flye/issues
  - note: null
    type:
    - Repository
    url: https://github.com/mikolmogorov/Flye
  maturity: null
  name: Flye
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1099/mgen.0.000294
    metadata:
      abstract: Illumina sequencing allows rapid, cheap and accurate whole genome
        bacterial analyses, but short reads (<300 bp) do not usually enable complete
        genome assembly. Long-read sequencing greatly assists with resolving complex
        bacterial genomes, particularly when combined with short-read Illumina data
        (hybrid assembly). However, it is not clear how different long-read sequencing
        methods affect hybrid assembly accuracy. Relative automation of the assembly
        process is also crucial to facilitating high-throughput complete bacterial
        genome reconstruction, avoiding multiple bespoke filtering and data manipulation
        steps. In this study, we compared hybrid assemblies for 20 bacterial isolates,
        including two reference strains, using Illumina sequencing and long reads
        from either Oxford Nanopore Technologies (ONT) or SMRT Pacific Biosciences
        (PacBio) sequencing platforms. We chose isolates from the family Enterobacteriaceae,
        as these frequently have highly plastic, repetitive genetic structures, and
        complete genome reconstruction for these species is relevant for a precise
        understanding of the epidemiology of antimicrobial resistance. We de novo
        assembled genomes using the hybrid assembler Unicycler and compared different
        read processing strategies, as well as comparing to long-read-only assembly
        with Flye followed by short-read polishing with Pilon. Hybrid assembly with
        either PacBio or ONT reads facilitated high-quality genome reconstruction,
        and was superior to the longread assembly and polishing approach evaluated
        with respect to accuracy and completeness. Combining ONT and Illumina reads
        fully resolved most genomes without additional manual steps, and at a lower
        consumables cost per isolate in our setting. Automated hybrid assembly is
        a powerful tool for complete and accurate bacterial genome assembly.
      authors:
      - name: De Maio N.
      - name: Shaw L.P.
      - name: Hubbard A.
      - name: George S.
      - name: Sanderson N.D.
      - name: Swann J.
      - name: Wick R.
      - name: Oun M.A.
      - name: Stubberfield E.
      - name: Hoosdally S.J.
      - name: Crook D.W.
      - name: Peto T.E.A.
      - name: Sheppard A.E.
      - name: Bailey M.J.
      - name: Read D.S.
      - name: Anjum M.F.
      - name: Sarah Walker A.
      - name: Stoesser N.
      citationCount: 166
      date: '2019-01-01T00:00:00Z'
      journal: Microbial Genomics
      title: Comparison of long-read sequencing technologies in the hybrid assembly
        of complex bacterial genomes
    note: null
    pmcid: PMC6807382
    pmid: '31483244'
    type:
    - Usage
    version: null
  - doi: 10.1038/s41587-019-0072-8
    metadata:
      abstract: Accurate genome assembly is hampered by repetitive regions. Although
        long single molecule sequencing reads are better able to resolve genomic repeats
        than short-read data, most long-read assembly algorithms do not provide the
        repeat characterization necessary for producing optimal assemblies. Here,
        we present Flye, a long-read assembly algorithm that generates arbitrary paths
        in an unknown repeat graph, called disjointigs, and constructs an accurate
        repeat graph from these error-riddled disjointigs. We benchmark Flye against
        five state-of-the-art assemblers and show that it generates better or comparable
        assemblies, while being an order of magnitude faster. Flye nearly doubled
        the contiguity of the human genome assembly (as measured by the NGA50 assembly
        quality metric) compared with existing assemblers.
      authors:
      - name: Kolmogorov M.
      - name: Yuan J.
      - name: Lin Y.
      - name: Pevzner P.A.
      citationCount: 3077
      date: '2019-05-01T00:00:00Z'
      journal: Nature Biotechnology
      title: Assembly of long, error-prone reads using repeat graphs
    note: null
    pmcid: null
    pmid: '30936562'
    type:
    - Primary
    version: null
  - doi: 10.1038/s41592-020-00971-x
    metadata:
      abstract: Long-read sequencing technologies have substantially improved the
        assemblies of many isolate bacterial genomes as compared to fragmented short-read
        assemblies. However, assembling complex metagenomic datasets remains difficult
        even for state-of-the-art long-read assemblers. Here we present metaFlye,
        which addresses important long-read metagenomic assembly challenges, such
        as uneven bacterial composition and intra-species heterogeneity. First, we
        benchmarked metaFlye using simulated and mock bacterial communities and show
        that it consistently produces assemblies with better completeness and contiguity
        than state-of-the-art long-read assemblers. Second, we performed long-read
        sequencing of the sheep microbiome and applied metaFlye to reconstruct 63
        complete or nearly complete bacterial genomes within single contigs. Finally,
        we show that long-read assembly of human microbiomes enables the discovery
        of full-length biosynthetic gene clusters that encode biomedically important
        natural products.
      authors:
      - name: Kolmogorov M.
      - name: Bickhart D.M.
      - name: Behsaz B.
      - name: Gurevich A.
      - name: Rayko M.
      - name: Shin S.B.
      - name: Kuhn K.
      - name: Yuan J.
      - name: Polevikov E.
      - name: Smith T.P.L.
      - name: Pevzner P.A.
      citationCount: 511
      date: '2020-11-01T00:00:00Z'
      journal: Nature Methods
      title: 'metaFlye: scalable long-read metagenome assembly using repeat graphs'
    note: null
    pmcid: PMC10699202
    pmid: '33020656'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Workflow
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - 2.9.6
- accessibility: null
  additionDate: '2017-12-18T00:11:28Z'
  biotoolsCURIE: biotools:pyrx
  biotoolsID: pyrx
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
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    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://mgl.scripps.edu/
  - email: null
    fundrefid: null
    gridid: null
    name: Alfiya Iksanova
    note: null
    orcidid: https://orcid.org/0000-0003-0474-6638
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Virtual Screening software for Computational Drug Discovery that can
    be used to screen libraries of compounds against potential drug targets.
  documentation: []
  download: []
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    operation:
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  homepage: http://pyrx.sourceforge.net/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-17T16:32:33.365776Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/pyrx-0-8-virtual-screening-software-computer-aided-drug-design.html
  maturity: null
  name: PyRx
  operatingSystem:
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  - Windows
  - Mac
  otherID: []
  owner: Bioinfo
  publication:
  - doi: null
    metadata:
      abstract: © 2017 The Author(s).Glucokinase is one of the promising targets for
        glucose-lowering agents, and the development of GK activators are now considered
        as one of the most promising strategies for the treatment of type 2 diabetes
        mellitus. In this work, a series of novel symmetric molecular constructs,
        in which two pyridoxine moieties are connected via sulfur-containing linkers,
        have been synthesized and tested in vitro for glucokinase activation potential.
        The enzyme activation rates by two most active compounds at 100 μM (~150%
        and 130%) were comparable to that of the reference agent PF-04937319 (~154%).
        Both leading compounds demonstrated low cytotoxicity and excellent safety
        profile in acute toxicity experiment in rats after oral administration with
        LD50 exceeding 2000 mg/kg of body weight. Binding mode of the active compounds
        in comparison with the reference agent was studied using molecular docking.
        The leading compounds represent viable preclinical candidates for the treatment
        of type 2 diabetes mellitus, as well as a promising starting point for the
        design of structural analogs with improved activity.
      authors:
      - name: Dzyurkevich M.S.
      - name: Babkov D.A.
      - name: Shtyrlin N.V.
      - name: Mayka O.Y.
      - name: Iksanova A.G.
      - name: Vassiliev P.M.
      - name: Balakin K.V.
      - name: Spasov A.A.
      - name: Tarasov V.V.
      - name: Barreto G.
      - name: Shtyrlin Y.G.
      - name: Aliev G.
      citationCount: 7
      date: '2017-12-01T00:00:00Z'
      journal: Scientific Reports
      title: Pyridoxine dipharmacophore derivatives as potent glucokinase activators
        for the treatment of type 2 diabetes mellitus
    note: null
    pmcid: null
    pmid: '29167582'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Drug discovery
    uri: http://edamontology.org/topic_3336
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Drug targets
    uri: http://edamontology.org/topic_0154
  - term: Targets
    uri: http://edamontology.org/topic_0154
  - term: Drug design
    uri: http://edamontology.org/topic_0209
  validated: 1
  version:
  - 0.9.2
- accessibility: Open access
  additionDate: '2017-08-20T16:34:27Z'
  biotoolsCURIE: biotools:pavian
  biotoolsID: pavian
  collectionID:
  - Animal and Crop Genomics
  - Biodiversity
  - microbiome
  confidence_flag: null
  cost: null
  credit:
  - email: fbreitw1@jhmi.edu
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Web application for exploring metagenomics classification results,
    with a special focus on infectious disease diagnosis. Pinpointing pathogens in
    metagenomics classification results is often complicated by host and laboratory
    contaminants as well as many non-pathogenic microbiota. Researchers can analyze,
    display and transform results from the Kraken and Centrifuge classifiers using
    interactive tables, heatmaps and flow diagrams.
  documentation:
  - note: null
    type:
    - User manual
    url: https://ccb.jhu.edu/software/pavian/index.shtml?t=manual
  download: []
  editPermission:
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
    note: null
    operation:
    - term: Annotation
      uri: http://edamontology.org/operation_0226
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format: []
  homepage: https://ccb.jhu.edu/software/pavian/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-06-17T15:12:54.095225Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/fbreitwieser/pavian/tree/master
  maturity: null
  name: Pavian
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: admin
  publication:
  - doi: 10.1101/084715
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-01-17T15:04:54Z'
  biotoolsCURIE: biotools:xcms
  biotoolsID: xcms
  collectionID:
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  - Bioconda
  - Galaxy Tools
  confidence_flag: null
  cost: null
  credit:
  - email: sneumann@ipb-halle.de
    fundrefid: null
    gridid: null
    name: Steffen Neumann
    note: null
    orcidid: https://orcid.org/0000-0002-7899-7192
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Framework for processing and visualization of chromatographically separated
    and single-spectra mass spectral data.  The packages enables imports from AIA/ANDI
    NetCDF, mzXML, mzData and mzML files and preprocesses data for high-throughput,
    untargeted analyte profiling.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/html/xcms.html
  - note: Galaxy training material for LC-MS data preprocessing with XCMS
    type:
    - Training material
    url: https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/tutorial.html
  - note: Galaxy training for GC-MS data preprocessing with XCMS
    type:
    - Training material
    url: https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gc_ms_with_xcms/tutorial.html
  - note: Tutorial on using the XCMS R library
    type:
    - Training material
    url: https://jorainer.github.io/xcmsTutorials/
  download:
  - note: null
    type: Source code
    url: http://bioconductor/packages/release/bioc/src/contrib/xcms_1.50.1.tar.gz
    version: null
  - note: null
    type: Source code
    url: https://github.com/sneumann/xcms
    version: null
  editPermission:
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    - ELIXIR-EE
    - n.m.palmblad@lumc.nl
    - recetox-specdatri
    - sneumann
    type: group
  elixirCommunity:
  - Metabolomics
  elixirNode:
  - Germany
  - France
  - Czech Republic
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: NetCDF
        uri: http://edamontology.org/format_3650
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: mzXML
        uri: http://edamontology.org/format_3654
    - data:
        term: Mass spectrometry spectra
        uri: http://edamontology.org/data_0943
      format:
      - term: NetCDF
        uri: http://edamontology.org/format_3650
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: mzXML
        uri: http://edamontology.org/format_3654
    note: null
    operation:
    - term: Mass spectrum visualisation
      uri: http://edamontology.org/operation_3694
    - term: Label-free quantification
      uri: http://edamontology.org/operation_3634
    - term: Validation
      uri: http://edamontology.org/operation_2428
    - term: Chromatographic alignment
      uri: http://edamontology.org/operation_3628
    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    - term: Chromatogram visualisation
      uri: http://edamontology.org/operation_3203
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: NetCDF
        uri: http://edamontology.org/format_3650
      - term: mzQuantML
        uri: http://edamontology.org/format_3248
  homepage: http://bioconductor.org/packages/release/bioc/html/xcms.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-06-17T13:07:05.749022Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Mirror
    url: http://metlin.scripps.edu/download/
  - note: Link to the repository hosting the R code.
    type:
    - Repository
    url: https://github.com/sneumann/xcms
  - note: null
    type:
    - Galaxy service
    url: https://usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/lecorguille/xcms_xcmsset/abims_xcms_xcmsSet/3.12.0+galaxy1
  maturity: Mature
  name: xcms
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: bioconductor_import
  publication:
  - doi: 10.1093/bioinformatics/btq441
    metadata:
      abstract: 'Motivation: High mass accuracy is an important goal in liquid chromatography-mass
        spectrometry experiments. Some manufacturers employ a mass calibration system
        that regularly switches between the analyte and a standard reference compound,
        and leads to gaps in the analyte data. We present a method for correction
        of such gaps in global molecular profiling applications such as metabolomics.
        We demonstrate that it improves peak detection and quantification, successfully
        recovering the expected number of peaks and intensity distribution in an example
        metabolomics dataset. © The Author 2010. Published by Oxford University Press.
        All rights reserved.'
      authors:
      - name: Benton H.P.
      - name: Want E.J.
      - name: Ebbels T.M.D.
      citationCount: 159
      date: '2010-07-29T00:00:00Z'
      journal: Bioinformatics
      title: Correction of mass calibration gaps in liquid chromatography-mass spectrometry
        metabolomics data
    note: null
    pmcid: null
    pmid: '20671148'
    type:
    - Primary
    version: null
  relation:
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    type: usedBy
  - biotoolsID: camera
    type: usedBy
  - biotoolsID: workflow4metabolomics
    type: usedBy
  toolType:
  - Command-line tool
  - Library
  - Web application
  topic:
  - term: Biological imaging
    uri: http://edamontology.org/topic_3383
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 1
  version:
  - 3.0.2
- accessibility: Open access
  additionDate: '2025-06-12T11:47:48.641345Z'
  biotoolsCURIE: biotools:mzspeclib
  biotoolsID: mzspeclib
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: A python reference implementation of the mzSpecLib spectral library
    format.
  documentation:
  - note: null
    type:
    - API documentation
    url: https://hupo-psi.github.io/mzSpecLib/
  download:
  - note: null
    type: Source code
    url: https://github.com/HUPO-PSI/mzspeclib-py
    version: null
  - note: null
    type: Software package
    url: https://pypi.org/project/mzspeclib/
    version: null
  editPermission:
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  - Metabolomics
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  function:
  - cmd: mzspeclib convert
    input:
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        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format:
      - term: MSP
        uri: http://edamontology.org/format_4039
      - term: MGF
        uri: http://edamontology.org/format_3651
    note: null
    operation:
    - term: Conversion
      uri: http://edamontology.org/operation_3434
    output:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format: []
  - cmd: null
    input:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format: []
    note: null
    operation:
    - term: Format validation
      uri: http://edamontology.org/operation_0336
    output:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format: []
  homepage: https://github.com/HUPO-PSI/mzspeclib-py
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-12T11:57:26.484553Z'
  license: APSL-2.0
  link:
  - note: null
    type:
    - Discussion forum
    url: https://www.psidev.info/
  - note: null
    type:
    - Repository
    url: https://github.com/HUPO-PSI/mzspeclib-py
  maturity: Mature
  name: mzspeclib
  operatingSystem: []
  otherID: []
  owner: recetox-specdatri
  publication:
  - doi: 10.1021/acs.analchem.4c04091
    metadata:
      abstract: Mass spectral libraries are collections of reference spectra, usually
        associated with specific analytes from which the spectra were generated, that
        are used for further downstream analysis of new spectra. There are many different
        formats used for encoding spectral libraries, but none have undergone a standardization
        process to ensure broad applicability to many applications. As part of the
        Human Proteome Organization Proteomics Standards Initiative (PSI), we have
        developed a standardized format for encoding spectral libraries, called mzSpecLib
        (https://psidev.info/mzSpecLib). It is primarily a data model that flexibly
        encodes metadata about the library entries using the extensible PSI-MS controlled
        vocabulary and can be encoded in and converted between different serialization
        formats. We have also developed a standardized data model and serialization
        for fragment ion peak annotations, called mzPAF (https://psidev.info/mzPAF).
        It is defined as a separate standard, since it may be used for other applications
        besides spectral libraries. The mzSpecLib and mzPAF standards are compatible
        with existing PSI standards such as ProForma 2.0 and the Universal Spectrum
        Identifier. The mzSpecLib and mzPAF standards have been primarily defined
        for peptides in proteomics applications with basic small molecule support.
        They could be extended in the future to other fields that need to encode spectral
        libraries for nonpeptidic analytes.
      authors:
      - name: Klein J.
      - name: Lam H.
      - name: Mak T.D.
      - name: Bittremieux W.
      - name: Perez-Riverol Y.
      - name: Gabriels R.
      - name: Shofstahl J.
      - name: Hecht H.
      - name: Binz P.-A.
      - name: Kawano S.
      - name: Van Den Bossche T.
      - name: Carver J.
      - name: Neely B.A.
      - name: Mendoza L.
      - name: Suomi T.
      - name: Claeys T.
      - name: Payne T.
      - name: Schulte D.
      - name: Sun Z.
      - name: Hoffmann N.
      - name: Zhu Y.
      - name: Neumann S.
      - name: Jones A.R.
      - name: Bandeira N.
      - name: Vizcaino J.A.
      - name: Deutsch E.W.
      citationCount: 1
      date: '2024-11-19T00:00:00Z'
      journal: Analytical Chemistry
      title: 'The Proteomics Standards Initiative Standardized Formats for Spectral
        Libraries and Fragment Ion Peak Annotations: mzSpecLib and mzPAF'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 0
  version:
  - 1.0.5
- accessibility: null
  additionDate: '2025-06-02T12:24:30.535794Z'
  biotoolsCURIE: biotools:wtv
  biotoolsID: wtv
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: "A CLI/python library that selects characteristic qualitative ions\
    \ based on \nhttps://github.com/yuanhonglun/WTV_2.0"
  documentation:
  - note: null
    type:
    - User manual
    url: https://recetox.github.io/wtv/
  download:
  - note: null
    type: Source code
    url: https://github.com/RECETOX/wtv
    version: null
  - note: null
    type: Software package
    url: https://pypi.org/project/wtv/
    version: null
  - note: null
    type: Tool wrapper (Galaxy)
    url: https://github.com/RECETOX/galaxytools/tree/master/tools/wtv
    version: null
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity:
  - Metabolomics
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: wtv-cli --msp_path input.msp --outpath output --mz_min 35 --mz_max 400 ...
    input:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format:
      - term: MSP
        uri: http://edamontology.org/format_4039
    note: null
    operation:
    - term: Feature selection
      uri: http://edamontology.org/operation_3936
    output:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format:
      - term: MSP
        uri: http://edamontology.org/format_4039
  homepage: https://recetox.github.io/wtv/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-12T09:26:29.793603Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/RECETOX/wtv
  maturity: Emerging
  name: wtv
  operatingSystem: []
  otherID: []
  owner: acquayefrank
  publication:
  - doi: 10.1016/j.molp.2024.04.012
    metadata:
      abstract: Volatilomics is essential for understanding the biological functions
        and fragrance contributions of plant volatiles. However, the annotation coverage
        achieved using current untargeted and widely targeted volatomics (WTV) methods
        has been limited by low sensitivity and/or low acquisition coverage. Here,
        we introduce WTV 2.0, which enabled the construction of a high-coverage library
        containing 2111 plant volatiles, and report the development of a comprehensive
        selective ion monitoring (cSIM) acquisition method, including the selection
        of characteristic qualitative ions with the minimal ion number for each compound
        and an optimized segmentation method, that can acquire the smallest but sufficient
        number of ions for most plant volatiles, as well as the automatic qualitative
        and semi-quantitative analysis of cSIM data. Importantly, the library and
        acquisition method we developed can be self-expanded by incorporating compounds
        not present in the library, utilizing the obtained cSIM data. We showed that
        WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with
        the untargeted method, doubled the annotation coverage compared with the untargeted
        and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran
        as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with
        a user-friendly interface and is applicable to profiling of volatiles and
        primary metabolites in any species.
      authors:
      - name: Yuan H.
      - name: Jiangfang Y.
      - name: Liu Z.
      - name: Su R.
      - name: Li Q.
      - name: Fang C.
      - name: Huang S.
      - name: Liu X.
      - name: Fernie A.R.
      - name: Luo J.
      citationCount: 12
      date: '2024-06-03T00:00:00Z'
      journal: Molecular Plant
      title: 'WTV2.0: A high-coverage plant volatilomics method with a comprehensive
        selective ion monitoring acquisition mode'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 0
  version:
  - 0.0.2
- accessibility: null
  additionDate: '2017-08-20T15:17:20Z'
  biotoolsCURIE: biotools:bracken
  biotoolsID: bracken
  collectionID:
  - Animal and Crop Genomics
  confidence_flag: null
  cost: null
  credit:
  - email: jlu26@jhmi.edu
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Bracken is a companion program to Kraken 1, KrakenUniq, or Kraken 2
    While Kraken classifies reads to multiple levels in the taxonomic tree, Bracken
    allows estimation of abundance at a single level using those classifications (e.g.
    Bracken can estimate abundance of species within a sample).
  documentation:
  - note: null
    type:
    - User manual
    url: https://ccb.jhu.edu/software/bracken/index.shtml?t=manual
  download: []
  editPermission:
    authors:
    - animalandcropgenomics
    - ELIXIR-CZ
    - bebatut
    - vashokan
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    output: []
  homepage: https://ccb.jhu.edu/software/bracken/
  homepage_status: 0
  language:
  - Perl
  - Python
  lastUpdate: '2025-06-11T12:25:29.118910Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/jenniferlu717/Bracken
  - note: null
    type:
    - Issue tracker
    url: https://github.com/jenniferlu717/Bracken/issues
  maturity: null
  name: Bracken
  operatingSystem:
  - Linux
  otherID: []
  owner: admin
  publication:
  - doi: 10.7717/peerj-cs.104
    metadata:
      abstract: Metagenomic experiments attempt to characterize microbial communities
        using high-throughput DNA sequencing. Identification of the microorganisms
        in a sample provides information about the genetic profile, population structure,
        and role of microorganisms within an environment. Until recently, most metagenomics
        studies focused on high-level characterization at the level of phyla, or alternatively
        sequenced the 16S ribosomalRNAgene that is present in bacterial species. As
        the cost of sequencing has fallen, though, metagenomics experiments have increasingly
        used unbiased shotgun sequencing to capture all the organisms in a sample.
        This approach requires a method for estimating abundance directly from the
        raw read data. Here we describe a fast, accurate new method that computes
        the abundance at the species level using the reads collected in a metagenomics
        experiment. Bracken (Bayesian Reestimation of Abundance after Classification
        with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level
        classifier, along with information about the genomes themselves to estimate
        abundance at the species level, the genus level, or above. We demonstrate
        that Bracken can produce accurate species- and genus-level abundance estimates
        even when a sample contains multiple near-identical species.
      authors:
      - name: Lu J.
      - name: Breitwieser F.P.
      - name: Thielen P.
      - name: Salzberg S.L.
      citationCount: 1084
      date: '2017-01-01T00:00:00Z'
      journal: PeerJ Computer Science
      title: 'Bracken: Estimating species abundance in metagenomics data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: kraken2
    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  validated: 1
  version: []
- accessibility: null
  additionDate: '2025-06-06T11:29:48.238556Z'
  biotoolsCURIE: biotools:flumut
  biotoolsID: flumut
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: FluMut is an open-source tool designed to search for molecular markers
    with potential impact on the biological characteristics of Influenza A viruses
    of the A(H5N1) subtype, starting from complete or partial nucleotide genome sequences.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://izsvenezie-virology.github.io/FluMut/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-06T12:05:28.550810Z'
  license: gSOAP-1.3b
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/izsvenezie-virology/FluMut
  - note: Website of the tool.
    type:
    - Repository
    url: https://izsvenezie-virology.github.io/FluMut/
  - note: Publication link.
    type:
    - Other
    url: https://doi.org/10.1093/ve/veaf011
  maturity: null
  name: flumut
  operatingSystem: []
  otherID: []
  owner: evezeyl
  publication:
  - doi: 10.1093/ve/veaf011
    metadata:
      abstract: Over the past century, Influenza A virus (IAV) has caused four of
        the five reported pandemics, all of which originated from viruses possessing
        genome segments of avian origin. The recent spread of highly pathogenic avian
        influenza (HPAI) viruses, particularly the clade 2.3.4.4b A(H5N1) subtype,
        has led to an alarming increase in mammalian infections, raising concerns
        about the potential for future pandemics. In response to this, we developed
        FluMut, an open-source, cross-platform tool designed to identify molecular
        markers with potential impacts on H5N1 virus phenotypes. FluMut leverages
        an up-To-date database, FluMutDB, to rapidly analyze thousands of nucleotide
        sequences, identifying mutations associated with host adaptation, increased
        virulence, and antiviral resistance. The tool is available both as a command-line
        interface and a user-friendly graphical interface, making it accessible to
        researchers with varying levels of computational expertise. FluMut provides
        comprehensive outputs, including tables of detected markers, their biological
        effects, and corresponding literature references. This tool fills a critical
        gap in the genomic surveillance of HPAI H5N1, facilitating real-Time monitoring
        of viral evolution and aiding in the identification of mutations that may
        signal increased pandemic potential. Future updates will extend FluMut's capabilities
        to other influenza subtypes.
      authors:
      - name: Giussani E.
      - name: Sartori A.
      - name: Salomoni A.
      - name: Cavicchio L.
      - name: De Battisti C.
      - name: Pastori A.
      - name: Varotto M.
      - name: Zecchin B.
      - name: Hughes J.
      - name: Monne I.
      - name: Fusaro A.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
      journal: Virus Evolution
      title: 'FluMut: A tool for mutation surveillance in highly pathogenic H5N1 genomes'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 0.6.4
- accessibility: Open access
  additionDate: '2021-05-26T20:42:18Z'
  biotoolsCURIE: biotools:maldi_quant
  biotoolsID: maldi_quant
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: mail@sebastiangibb.de
    fundrefid: null
    gridid: null
    name: Sebastian Gibb
    note: null
    orcidid: https://orcid.org/0000-0001-7406-4443
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Korbinian Strimmer
    note: null
    orcidid: https://orcid.org/0000-0001-7917-2056
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: MALDIquant is a complete analysis pipeline for matrix-assisted laser
    desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass
    spectrometry data. In addition to commonly used plotting and processing methods
    it includes distinctive features, namely baseline subtraction methods such as
    morphological filters (TopHat) or the statistics-sensitive non-linear iterative
    peak-clipping algorithm (SNIP), peak alignment using warping functions, handling
    of replicated measurements as well as allowing spectra with different resolutions.
  documentation:
  - note: null
    type:
    - General
    url: https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf
  download: []
  editPermission:
    authors:
    - sizhengZhao
    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: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Spectrum calculation
      uri: http://edamontology.org/operation_3860
    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Mass spectra calibration
      uri: http://edamontology.org/operation_3627
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://cran.r-project.org/package=MALDIquant
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-06-05T11:28:57.497366Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/sgibb/MALDIquant/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/sgibb/MALDIquant/issues
  - note: null
    type:
    - Other
    url: http://strimmerlab.org/software/maldiquant/
  maturity: null
  name: MALDIquant
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1093/bioinformatics/bts447
    metadata:
      abstract: MALDIquant is an R package providing a complete and modular analysis
        pipeline for quantitative analysis of mass spectrometry data. MALDIquant is
        specifically designed with application in clinical diagnostics in mind and
        implements sophisticated routines for importing raw data, preprocessing, non-linear
        peak alignment and calibration. It also handles technical replicates as well
        as spectra with unequal resolution. © The Author 2012. Published by Oxford
        University Press. All rights reserved.
      authors:
      - name: Gibb S.
      - name: Strimmer K.
      citationCount: 499
      date: '2012-09-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Maldiquant: A versatile R package for the analysis of mass spectrometry
        data'
    note: null
    pmcid: null
    pmid: '22796955'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  - Workflow
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-12-23T12:44:47.964606Z'
  biotoolsCURIE: biotools:gibi230_radiomic_features_extraction
  biotoolsID: gibi230_radiomic_features_extraction
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit: []
  description: "Implemented by GIBI230, this tool is a Docker-based software designed\
    \ for extracting radiomic features from 3D medical images in NIfTI format using\
    \ the PyRadiomics library (if DICOM images, the DICOM to NIFTI converter must\
    \ be run before using this tool). It streamlines the radiomics calculation process\
    \ by generating a structured CSV file containing all extracted variables from\
    \ medical images.\nThe dockerized software enables users to configure parameters\
    \ like filters, bin width, resampling spacing, and normalization settings can\
    \ be specified. The output radiomic variables provide quantitative information\
    \ for further analysis in medical imaging research and machine learning applications.\
    \ \nSpecially important the parameter selection of the band width. For robust\
    \ and reproducible results, a bin width of 5 is commonly recommended, but it should\
    \ be adjusted based on image resolution, modality, and noise levels."
  documentation: []
  download: []
  editPermission:
    authors: []
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T11:07:12.219171Z'
  license: null
  link: []
  maturity: null
  name: Radiomic features extraction (EUCAIM-SW-054_T-02-01-003)
  operatingSystem: []
  otherID: []
  owner: pedromiguel_martinez_HULAFE
  publication:
  - doi: 10.1148/ryai.230208
    metadata:
      abstract: 'Purpose: To evaluate the reproducibility of radiomics features extracted
        from T2-weighted MR images in patients with neuroblastoma. Materials and Methods:
        A retrospective study included 419 patients (mean age, 29 months ± 34 [SD];
        220 male, 199 female) with neu-roblastic tumors diagnosed between 2002 and
        2023, within the scope of the PRedictive In-silico Multiscale Analytics to
        support cancer personalized diaGnosis and prognosis, Empowered by imaging
        biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences
        at diagnosis and/or after initial chemotherapy. Images underwent processing
        steps (denoising, inhomogeneity bias field cor-rection, normalization, and
        resampling). Tumors were automatically segmented, and 107 shape, first-order,
        and second-order radiomics features were extracted, considered as the reference
        standard. Subsequently, the previous image processing settings were modified,
        and volumetric masks were applied. New radiomics features were extracted and
        compared with the reference standard. Reproducibility was assessed using the
        concordance correlation coefficient (CCC); intrasubject repeatability was
        measured using the coefficient of variation (CoV). Results: When normalization
        was omitted, only 5% of the radiomics features demonstrated high reproducibility.
        Statistical analysis revealed significant changes in the normalization and
        resampling processes (P < .001). Inhomogeneities removal had the least impact
        on radiomics (83% of parameters remained stable). Shape features remained
        stable after mask modifications, with a CCC greater than 0.90. Mask modifications
        were the most favorable changes for achieving high CCC values, with a radiomics
        features stability of 70%. Only 7% of second-order radiomics features showed
        an excellent CoV of less than 0.10. Conclusion: Modifications in the T2-weighted
        MRI preparation process in patients with neuroblastoma resulted in changes
        in radiomics features, with normalization identified as the most influential
        factor for reproducibility. Inhomogeneities removal had the least impact on
        radiomics features.'
      authors:
      - name: Veiga-Canuto D.
      - name: Fernandez-Paton M.
      - name: Alberich L.C.
      - name: Pastor A.J.
      - name: Maya A.G.
      - name: Sierra J.M.C.
      - name: Nebot C.S.
      - name: de las Heras B.M.
      - name: Potschger U.
      - name: Taschner-Mandl S.
      - name: Neri E.
      - name: Canete A.
      - name: Ladenstein R.
      - name: Hero B.
      - name: Alberich-Bayarri A.
      - name: Marti-Bonmati L.
      citationCount: 1
      date: '2024-07-01T00:00:00Z'
      journal: 'Radiology: Artificial Intelligence'
      title: Reproducibility Analysis of Radiomic Features on T2-weighted MR Images
        after Processing and Segmentation Alterations in Neuroblastoma Tumors
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T12:09:16.704255Z'
  biotoolsCURIE: biotools:mr-based_glioblastoma_tumour_detection_and_segmentation
  biotoolsID: mr-based_glioblastoma_tumour_detection_and_segmentation
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: GIBI230 - HULAFE
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: The tool performs an automatic segmentation of the possible glioblastoma
    tumours on MRI images and its subregions
  documentation: []
  download: []
  editPermission:
    authors:
    - pedromiguel_martinez_HULAFE
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-05T08:54:02.757932Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: MR-based glioblastoma tumour detection and segmentation (EUCAIM-SW-021_T-01-02-004)
  operatingSystem: []
  otherID: []
  owner: casopon
  publication:
  - doi: 10.3390/cancers14153648
    metadata:
      abstract: 'Tumor segmentation is one of the key steps in imaging processing.
        The goals of this study were to assess the inter-observer variability in manual
        segmentation of neuroblastic tumors and to analyze whether the state-of-the-art
        deep learning architecture nnU-Net can provide a robust solution to detect
        and segment tumors on MR images. A retrospective multicenter study of 132
        patients with neuroblastic tumors was performed. Dice Similarity Coefficient
        (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC)
        were used to compare segmentation sets. Two more metrics were elaborated to
        understand the direction of the errors: the modified version of False Positive
        (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented
        46 tumors and a comparative study was performed. nnU-Net was trained-tuned
        with 106 cases divided into five balanced folds to perform cross-validation.
        The five resulting models were used as an ensemble solution to measure training
        (n = 106) and validation (n = 26) performance, independently. The time needed
        by the model to automatically segment 20 cases was compared to the time required
        for manual segmentation. The median DSC for manual segmentation sets was 0.969
        (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR).
        The automatic segmentation model achieved a better performance regarding the
        FPRm. MR images segmentation variability is similar between radiologists and
        nnU-Net. Time leverage when using the automatic model with posterior visual
        validation and manual adjustment corresponds to 92.8%.'
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Sanguesa Nebot C.
      - name: Martinez de las Heras B.
      - name: Potschger U.
      - name: Gabelloni M.
      - name: Carot Sierra J.M.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Canete A.
      - name: Ladenstein R.
      - name: Neri E.
      - name: Marti-Bonmati L.
      citationCount: 25
      date: '2022-08-01T00:00:00Z'
      journal: Cancers
      title: Comparative Multicentric Evaluation of Inter-Observer Variability in
        Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance
        Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.3390/cancers15051622
    metadata:
      abstract: Objectives. To externally validate and assess the accuracy of a previously
        trained fully automatic nnU-Net CNN algorithm to identify and segment primary
        neuroblastoma tumors in MR images in a large children cohort. Methods. An
        international multicenter, multivendor imaging repository of patients with
        neuroblastic tumors was used to validate the performance of a trained Machine
        Learning (ML) tool to identify and delineate primary neuroblastoma tumors.
        The dataset was heterogeneous and completely independent from the one used
        to train and tune the model, consisting of 300 children with neuroblastic
        tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and
        49 after finalization of the first phase of chemotherapy). The automatic segmentation
        algorithm was based on a nnU-Net architecture developed within the PRIMAGE
        project. For comparison, the segmentation masks were manually edited by an
        expert radiologist, and the time for the manual editing was recorded. Different
        overlaps and spatial metrics were calculated to compare both masks. Results.
        The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median;
        Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify
        nor segment the tumor. No differences were found regarding the MR magnetic
        field, type of T2 sequence, or tumor location. No significant differences
        in the performance of the net were found in patients with an MR performed
        after chemotherapy. The time for visual inspection of the generated masks
        was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where
        manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The
        automatic CNN was able to locate and segment the primary tumor on the T2-weighted
        images in 94% of cases. There was an extremely high agreement between the
        automatic tool and the manually edited masks. This is the first study to validate
        an automatic segmentation model for neuroblastic tumor identification and
        segmentation with body MR images. The semi-automatic approach with minor manual
        editing of the deep learning segmentation increases the radiologist’s confidence
        in the solution with a minor workload for the radiologist.
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Jimenez-Pastor A.
      - name: Carot Sierra J.M.
      - name: Gomis-Maya A.
      - name: Sanguesa-Nebot C.
      - name: Fernandez-Paton M.
      - name: Martinez de las Heras B.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Potschger U.
      - name: Simon T.
      - name: Neri E.
      - name: Alberich-Bayarri A.
      - name: Canete A.
      - name: Hero B.
      - name: Ladenstein R.
      - name: Marti-Bonmati L.
      citationCount: 11
      date: '2023-03-01T00:00:00Z'
      journal: Cancers
      title: Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma
        Detection and Segmentation in MR Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 1.0.0
next: ?page=2
previous: null
