count: 247
list:
- accessibility: Open access
  additionDate: '2017-01-13T13:16:12Z'
  biotoolsCURIE: biotools:samtools
  biotoolsID: samtools
  collectionID:
  - Rare Disease
  - Animal and Crop Genomics
  - SAMtools
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Richard Durbin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    - Primary contact
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: SAMtools and BCFtools are widely used programs for processing and analysing
    high-throughput sequencing data. They include tools for file format conversion
    and manipulation, sorting, querying, statistics, variant calling, and effect analysis
    amongst other methods.
  documentation:
  - note: HowTos for samtools
    type:
    - Other
    url: http://www.htslib.org/doc/#howtos
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  - note: null
    type:
    - Installation instructions
    url: http://www.htslib.org/download/
  download:
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    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
  editPermission:
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    - animalandcropgenomics
    - alice
    - awhitwham
    - sergitobara
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
    note: null
    operation:
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Data editing
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    - term: Data parsing
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    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Data formatting
      uri: http://edamontology.org/operation_0335
    - term: Data sorting
      uri: http://edamontology.org/operation_3802
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T09:08:10.199446Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/samtools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/samtools/issues
  maturity: Mature
  name: SAMtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 42239
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 6224
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btr509
    metadata:
      abstract: 'Motivation: Most existing methods for DNA sequence analysis rely
        on accurate sequences or genotypes. However, in applications of the next-generation
        sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample
        low-coverage sequencing or somatic mutation discovery). These applications
        press for the development of new methods for analyzing sequence data with
        uncertainty. Results: We present a statistical framework for calling SNPs,
        discovering somatic mutations, inferring population genetical parameters and
        performing association tests directly based on sequencing data without explicit
        genotyping or linkage-based imputation. On real data, we demonstrate that
        our method achieves comparable accuracy to alternative methods for estimating
        site allele count, for inferring allele frequency spectrum and for association
        mapping. We also highlight the necessity of using symmetric datasets for finding
        somatic mutations and confirm that for discovering rare events, mismapping
        is frequently the leading source of errors. © The Author 2011. Published by
        Oxford University Press. All rights reserved.'
      authors:
      - name: Li H.
      citationCount: 4405
      date: '2011-11-01T00:00:00Z'
      journal: Bioinformatics
      title: A statistical framework for SNP calling, mutation discovery, association
        mapping and population genetical parameter estimation from sequencing data
    note: null
    pmcid: PMC3198575
    pmid: '21903627'
    type: []
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Suite
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
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  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - 1.16.1
  - '1.17'
  - '1.18'
  - '1.19'
  - 1.19.1
  - 1.19.2
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: Open access
  additionDate: '2024-08-02T20:40:51.771278Z'
  biotoolsCURIE: biotools:kvfinder-web
  biotoolsID: kvfinder-web
  collectionID:
  - COVID-19
  - Rare Disease
  - KVFinder suite
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: joao.guerra@lnbio.cnpem.br
    fundrefid: null
    gridid: null
    name: João V. S. Guerra
    note: null
    orcidid: https://orcid.org/0000-0002-6800-4425
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    - Primary contact
    url: https://github.com/jvsguerra
  - email: null
    fundrefid: null
    gridid: null
    name: Helder V. Ribeiro-Filho
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: José G. C. Pereira
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Paulo S. Lopes-de-Oliveira
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  description: "KVFinder-web is an open-source web-based application of an updated\
    \ version of parKVFinder software (v1.2.0) for cavity detection and characterization\
    \ of any type of biomolecular structure, including but not limited to proteins\
    \ and nucleic acids. \n\nKVFinder-web employs a geometric grid-and-sphere-based\
    \ method with a dual-probe system for efficient cavity detection. The web-based\
    \ application provides comprehensive characterizations, including shape, volume,\
    \ area, depth, constitutional, and hydropathy information."
  documentation:
  - note: null
    type:
    - API documentation
    url: https://lbc-lnbio.github.io/KVFinder-web/
  download: []
  editPermission:
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    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Binding site prediction
      uri: http://edamontology.org/operation_2575
    output:
    - data:
        term: Protein structural motif
        uri: http://edamontology.org/data_2878
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://kvfinder-web.cnpem.br/
  homepage_status: 0
  language:
  - R
  - C
  - Other
  lastUpdate: '2025-05-14T16:17:24.394544Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Other
    url: https://github.com/LBC-LNBio/KVFinder-web
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/KVFinder-web-service
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/KVFinder-web-portal
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/PyMOL-KVFinder-web-Tools
  maturity: Mature
  name: KVFinder-web
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: jvsguerra
  publication:
  - doi: 10.1093/nar/gkad324
    metadata:
      abstract: 'Molecular interactions that modulate catalytic processes occur mainly
        in cavities throughout the molecular surface. Such interactions occur with
        specific small molecules due to geometric and physicochemical complementarity
        with the receptor. In this scenario, we present KVFinder-web, an open-source
        web-based application of parKVFinder software for cavity detection and characterization
        of biomolecular structures. The KVFinder-web has two independent components:
        A RESTful web service and a web graphical portal. Our web service, KVFinder-web
        service, handles client requests, manages accepted jobs, and performs cavity
        detection and characterization on accepted jobs. Our graphical web portal,
        KVFinder-web portal, provides a simple and straightforward page for cavity
        analysis, which customizes detection parameters, submits jobs to the web service
        component, and displays cavities and characterizations. We provide a publicly
        available KVFinder-web at https://kvfinder-web.cnpem.br, running in a cloud
        environment as docker containers. Further, this deployment type allows KVFinder-web
        components to be configured locally and customized according to user demand.
        Hence, users may run jobs on a locally configured service or our public KVFinder-web.'
      authors:
      - name: Guerra J.V.S.
      - name: Ribeiro-Filho H.V.
      - name: Pereira J.G.C.
      - name: Lopes-De-Oliveira P.S.
      citationCount: 13
      date: '2023-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'KVFinder-web: A web-based application for detecting and characterizing
        biomolecular cavities'
    note: null
    pmcid: PMC10320092
    pmid: '37140050'
    type:
    - Primary
    version: null
  - doi: 10.1021/acs.jcim.3c00328
    metadata:
      abstract: Confining molecular guests within artificial hosts has provided a
        major driving force in the rational design of supramolecular cages with tailored
        properties. Over the last 30 years, a set of design strategies have been developed
        that enabled the controlled synthesis of a myriad of cages. Recently, there
        has been a growing interest in involving in silico methods in this toolbox.
        Cavity shape and size are important parameters that can be easily accessed
        by inexpensive geometric algorithms. Although these algorithms are well developed
        for the detection of nonartificial cavities (e.g., enzymes), they are not
        routinely used for the rational design of supramolecular cages. In order to
        test the capabilities of this tool, we have evaluated the performance and
        characteristics of seven different cavity characterization software in the
        context of 22 analogues of well-known supramolecular cages. Among the tested
        software, KVFinder project and Fpocket proved to be the most software to characterize
        supramolecular cavities. With the results of this work, we aim to popularize
        this underused technique within the supramolecular community.
      authors:
      - name: Guerra J.V.S.
      - name: Alves L.F.G.
      - name: Bourissou D.
      - name: Lopes-De-Oliveira P.S.
      - name: Szaloki G.
      citationCount: 3
      date: '2023-06-26T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: Cavity Characterization in Supramolecular Cages
    note: null
    pmcid: null
    pmid: '37129917'
    type:
    - Benchmarking study
    version: null
  relation:
  - biotoolsID: parKVFinder
    type: uses
  - biotoolsID: KVFinder
    type: isNewVersionOf
  toolType:
  - Web service
  - Web API
  topic:
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  validated: 0
  version:
  - v1.1.1
- accessibility: null
  additionDate: '2015-01-21T13:30:06Z'
  biotoolsCURIE: biotools:nextprot
  biotoolsID: nextprot
  collectionID:
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  - Proteomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: SIB Swiss Institute of Bioinformatics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: support@nextprot.org
    fundrefid: null
    gridid: null
    name: Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Pascale Gaudet
    note: null
    orcidid: http://orcid.org/0000-0003-1813-6857
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: neXtProt was an innovative knowledge platform dedicated to human proteins.
    This resource contained a wealth of high-quality data on all the human proteins
    that are produced by the 20'000 protein-coding genes found in the human genome.
    The content of neXtProt was continuously extended so as to provide many more carefully
    selected data sets and analysis tools. neXtProt data and tools have been archived.
  documentation: []
  download: []
  editPermission:
    authors:
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    - mzahn
    - LanfearJ
    type: group
  elixirCommunity: []
  elixirNode:
  - Switzerland
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Protein sequence analysis
      uri: http://edamontology.org/operation_2479
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    output: []
  homepage: https://www.expasy.org/archives/nextprot
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-02T08:05:19.163091Z'
  license: CC-BY-4.0
  link:
  - note: neXtProt software repository with issue tracking
    type:
    - Repository
    url: https://github.com/calipho-sib/
  - note: neXtProt release news, publications, presentations, tips and more. DM us
      with feedback!
    type:
    - Social media
    url: https://twitter.com/nextprot_news
  - note: neXtProt entry in Expasy
    type:
    - Software catalogue
    url: https://www.expasy.org/resources/nextprot
  maturity: Legacy
  name: neXtProt
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: SIB
  publication:
  - doi: 10.1093/nar/gkz995
    metadata:
      abstract: The neXtProt knowledgebase (https://www.nextprot.org) is an integrative
        resource providing both data on human protein and the tools to explore these.
        In order to provide comprehensive and up-to-date data, we evaluate and add
        new data sets. We describe the incorporation of three new data sets that provide
        expression, function, protein-protein binary interaction, post-translational
        modifications (PTM) and variant information. New SPARQL query examples illustrating
        uses of the new data were added. neXtProt has continued to develop tools for
        proteomics. We have improved the peptide uniqueness checker and have implemented
        a new protein digestion tool. Together, these tools make it possible to determine
        which proteases can be used to identify trypsin-resistant proteins by mass
        spectrometry. In terms of usability, we have finished revamping our web interface
        and completely rewritten our API. Our SPARQL endpoint now supports federated
        queries. All the neXtProt data are available via our user interface, API,
        SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally,
        the data on our FTP site is now CC BY 4.0 to promote its reuse.
      authors:
      - name: Zahn-Zabal M.
      - name: Michel P.-A.
      - name: Gateau A.
      - name: Nikitin F.
      - name: Schaeffer M.
      - name: Audot E.
      - name: Gaudet P.
      - name: Duek P.D.
      - name: Teixeira D.
      - name: De Laval V.R.
      - name: Samarasinghe K.
      - name: Bairoch A.
      - name: Lane L.
      citationCount: 155
      date: '2020-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The neXtProt knowledgebase in 2020: Data, tools and usability improvements'
    note: null
    pmcid: null
    pmid: '31724716'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkr1179
    metadata:
      abstract: neXtProt (http://www.nextprot.org/) is a new human protein-centric
        knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB),
        it aims to help researchers answer questions relevant to human proteins. To
        achieve this goal, neXtProt is built on a corpus containing both curated knowledge
        originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected
        and filtered high-throughput data pertinent to human proteins. This article
        presents an overview of the database and the data integration process. We
        also lay out the key future directions of neXtProt that we consider the necessary
        steps to make neXtProt the one-stop-shop for all research projects focusing
        on human proteins. © The Author(s) 2011.
      authors:
      - name: Lane L.
      - name: Argoud-Puy G.
      - name: Britan A.
      - name: Cusin I.
      - name: Duek P.D.
      - name: Evalet O.
      - name: Gateau A.
      - name: Gaudet P.
      - name: Gleizes A.
      - name: Masselot A.
      - name: Zwahlen C.
      - name: Bairoch A.
      citationCount: 168
      date: '2012-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'NeXtProt: A knowledge platform for human proteins'
    note: null
    pmcid: PMC3245017
    pmid: '22139911'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Protein variants
    uri: http://edamontology.org/topic_3120
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2024-11-26T07:47:41.489067Z'
  biotoolsCURIE: biotools:gen_seeker
  biotoolsID: gen_seeker
  collectionID:
  - ELIXIR-CZ
  - EATRIS-CZ
  - Palacky University
  - Rare Disease
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Institute of Applied Biotechnologies, a.s.
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Palacky University Olomouc, Czech Republic
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ministry of Industry of the Czech Republic
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole: []
    url: null
  description: "Gen Seeker is the browser of variants detected in the Czech population\
    \ included in the Czech digital genome map. \nAlthough Slavs are the largest European\
    \ ethnolinguistic group, Slavic and especially Czech genomes are underrepresented\
    \ in international genomic databases, which limits the ability to understand and\
    \ interpret specific slavic gene variations."
  documentation:
  - note: null
    type:
    - General
    url: https://czechgenome.cz/data-info
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Human Copy Number Variation
  - Federated Human Data
  - Rare Diseases
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene ID (NCBI)
        uri: http://edamontology.org/data_1027
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence signature data
        uri: http://edamontology.org/data_0860
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Gene transcript report
        uri: http://edamontology.org/data_3134
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Genome analysis
      uri: http://edamontology.org/operation_3918
    output:
    - data:
        term: Gene transcript report
        uri: http://edamontology.org/data_3134
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: https://czechgenome.cz/search
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-11-26T08:03:23.603198Z'
  license: Not licensed
  link: []
  maturity: Emerging
  name: Gen Seeker
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-CZ
  publication: []
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  validated: 0
  version:
  - '1'
- accessibility: null
  additionDate: '2018-09-04T10:24:29Z'
  biotoolsCURIE: biotools:wikipathways_app
  biotoolsID: wikipathways_app
  collectionID:
  - RD-connect
  - Rare Disease
  - WikiPathways
  - Cytoscape
  confidence_flag: null
  cost: null
  credit:
  - email: mkutmon@gmail.com
    fundrefid: null
    gridid: null
    name: Martina Kutmon
    note: null
    orcidid: http://orcid.org/0000-0002-7699-8191
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Easy access to pathway content at WikiPathways.org from Cytoscape.
  documentation:
  - note: null
    type:
    - Training material
    url: http://wikipathways.org/index.php/Help:DataVisualizationInCytoscape
  download: []
  editPermission:
    authors:
    - ChrisEvelo
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pathway or network prediction
      uri: http://edamontology.org/operation_3439
    - term: Pathway or network comparison
      uri: http://edamontology.org/operation_0277
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    output: []
  homepage: http://apps.cytoscape.org/apps/wikipathways
  homepage_status: 0
  language:
  - Shell
  - Java
  lastUpdate: '2024-11-25T14:08:30.617797Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/wikipathways/cytoscape-wikipathways-app
  - note: null
    type:
    - Helpdesk
    url: https://wikipathways.slack.com/?redir=%2Fmessages%2Fwikipathways-helpdesk%2F
  maturity: Mature
  name: WikiPathways App
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: aotamendi.1
  publication:
  - doi: 10.12688/F1000RESEARCH.4254.2
    metadata:
      abstract: 'In this paper we present the open-source WikiPathways app for Cytoscape
        ( http://apps.cytoscape.org/apps/wikipathways) that can be used to import
        biological pathways for data visualization and network analysis. WikiPathways
        is an open, collaborative biological pathway database that provides fully
        annotated pathway diagrams for manual download or through web services. The
        WikiPathways app allows users to load pathways in two different views: as
        an annotated pathway ideal for data visualization and as a simple network
        to perform computational analysis. An example pathway and dataset are used
        to demonstrate the functionality of the WikiPathways app and how they can
        be combined and used together with other apps. More than 3000 downloads in
        the first 12 months following its release in August 2013 highlight the importance
        and adoption of the app in the network biology field.'
      authors:
      - name: Kutmon M.
      - name: Lotia S.
      - name: Evelo C.T.
      - name: Pico A.R.
      citationCount: 47
      date: '2014-09-11T00:00:00Z'
      journal: F1000Research
      title: 'WikiPathways App for Cytoscape: Making biological pathways amenable
        to network analysis and visualization'
    note: null
    pmcid: PMC4168754
    pmid: '25254103'
    type:
    - Primary
    version: null
  - doi: 10.1371/journal.pone.0006447
    metadata:
      abstract: WikiPathways is a platform for creating, updating, and sharing biological
        pathways [1]. Pathways can be edited and downloaded using the wiki-style website.
        Here we present a SOAP web service that provides programmatic access to WikiPathways
        that is complementary to the website. We describe the functionality that this
        web service offers and discuss several use cases in detail. Exposing WikiPathways
        through a web service opens up new ways of utilizing pathway information and
        assisting the community curation process. © 2009 Kelder et al.
      authors:
      - name: Kelder T.
      - name: Pico A.R.
      - name: Hanspers K.
      - name: Van Iersel M.P.
      - name: Evelo C.
      - name: Conklin B.R.
      citationCount: 99
      date: '2009-07-30T00:00:00Z'
      journal: PLoS ONE
      title: Mining biological pathways using WikiPathways web services
    note: null
    pmcid: PMC2714472
    pmid: '19649250'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Plug-in
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version:
  - 3.3.1
- accessibility: null
  additionDate: '2017-03-25T12:41:02Z'
  biotoolsCURIE: biotools:homozygositymapper
  biotoolsID: homozygositymapper
  collectionID:
  - RD-connect
  - Rare Disease
  - ELIXIR-DE
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: dominik.seelow@charite.de
    fundrefid: null
    gridid: null
    name: Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A web base approach to homozygosity mapping. Users upload SNP genotype
    or sequencing files for analysis and detection of long homozygous stretches between
    affected individuals. Human, rodent and other mammals are mappable.
  documentation:
  - note: null
    type:
    - General
    url: http://www.homozygositymapper.org/documentation.html
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Over-representation analysis
      uri: http://edamontology.org/operation_3501
    - term: Genetic mapping
      uri: http://edamontology.org/operation_0282
    - term: SNP detection
      uri: http://edamontology.org/operation_0484
    - term: Mapping
      uri: http://edamontology.org/operation_2429
    output: []
  homepage: http://www.homozygositymapper.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T14:08:26.490635Z'
  license: null
  link: []
  maturity: Mature
  name: HomozygosityMapper
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Dominik
  publication:
  - doi: 10.1093/nar/gkp369
    metadata:
      abstract: Homozygosity mapping is a common method for mapping recessive traits
        in consanguineous families. In most studies, applications for multipoint linkage
        analyses are applied to determine the genomic region linked to the disease.
        Unfortunately, these are neither suited for very large families nor for the
        inclusion of tens of thousands of SNPs. Even if less than 10 000 markers are
        employed, such an analysis may easily last hours if not days. Here we present
        a web-based approach to homozygosity mapping. Our application stores marker
        data in a database into which users can directly upload their own SNP genotype
        files. Within a few minutes, the database analyses the data, detects homozygous
        stretches and provides an intuitive graphical interface to the results. The
        homozygosity in affected individuals is visualized genome-wide with the ability
        to zoom into single chromosomes and user-defined chromosomal regions. The
        software also displays the underlying genotypes in all samples. It is integrated
        with our candidate gene search engine, GeneDistiller, so that users can interactively
        determine the most promising gene. They can at any point restrict access to
        their data or make it public, allowing HomozygosityMapper to be used as a
        data repository for homozygosity-mapping studies. HomozygosityMapper is available
        at http://www.homozygositymapper.org/.
      authors:
      - name: Seelow D.
      - name: Schuelke M.
      - name: Hildebrandt F.
      - name: Nurnberg P.
      citationCount: 326
      date: '2009-08-04T00:00:00Z'
      journal: Nucleic Acids Research
      title: HomozygosityMapper - An interactive approach to homozygosity mapping
    note: null
    pmcid: PMC2703915
    pmid: '19465395'
    type: []
    version: null
  - doi: 10.1093/nar/gks487
    metadata:
      abstract: Homozygosity mapping is a common method to map recessive traits in
        consanguineous families. To facilitate these analyses, we have developed HomozygosityMapper,
        a web-based approach to homozygosity mapping. HomozygosityMapper allows researchers
        to directly upload the genotype files produced by the major genotyping platforms
        as well as deep sequencing data. It detects stretches of homozygosity shared
        by the affected individuals and displays them graphically. Users can interactively
        inspect the underlying genotypes, manually refine these regions and eventually
        submit them to our candidate gene search engine GeneDistiller to identify
        the most promising candidate genes. Here, we present the new version of HomozygosityMapper.
        The most striking new feature is the support of Next Generation Sequencing
        *.vcf files as input. Upon users' requests, we have implemented the analysis
        of common experimental rodents as well as of important farm animals. Furthermore,
        we have extended the options for single families and loss of heterozygosity
        studies. Another new feature is the export of *.bed files for targeted enrichment
        of the potential disease regions for deep sequencing strategies. HomozygosityMapper
        also generates files for conventional linkage analyses which are already restricted
        to the possible disease regions, hence superseding CPU-intensive genome-wide
        analyses. HomozygosityMapper is freely available at http://www.homozygositymapper.org/.
        © 2012 The Author(s).
      authors:
      - name: Seelow D.
      - name: Schuelke M.
      citationCount: 61
      date: '2012-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: HomozygosityMapper2012-bridging the gap between homozygosity mapping
        and deep sequencing
    note: null
    pmcid: PMC3394249
    pmid: '22669902'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Animals
    uri: http://edamontology.org/topic_3500
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2018-12-06T12:56:05Z'
  biotoolsCURIE: biotools:mutationdistiller
  biotoolsID: mutationdistiller
  collectionID:
  - RD-connect
  - Rare Disease
  - ELIXIR-DE
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: daniela.hombach@charite.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: dominik.seelow@charite.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: 'Make the analysis of Next-Generation Sequencing data simple. Depending
    on your background and interest, it offers various modes with optimised settings:
    Clinical (HPO), Clinical (Gene Panels), Gene Function and Basic Search.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://www.mutationdistiller.org/MutationDistiller/info/documentation.html
  - note: null
    type:
    - Training material
    url: https://www.mutationdistiller.org/MutationDistiller/info/tutorial.html
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
    note: null
    operation:
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Expression analysis
      uri: http://edamontology.org/operation_2495
    output: []
  homepage: https://www.mutationdistiller.org/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T14:08:24.923182Z'
  license: null
  link: []
  maturity: Mature
  name: MutationDistiller
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Dominik
  publication:
  - doi: 10.1093/nar/gkz330
    metadata:
      abstract: MutationDistiller is a freely available online tool for user-driven
        analyses of Whole Exome Sequencing data. It offers a user-friendly interface
        aimed at clinicians and researchers, who are not necessarily bioinformaticians.
        MutationDistiller combines MutationTaster's pathogenicity predictions with
        a phenotype-based approach. Phenotypic information is not limited to symptoms
        included in the Human Phenotype Ontology (HPO), but may also comprise clinical
        diagnoses and the suspected mode of inheritance. The search can be restricted
        to lists of candidate genes (e.g. virtual gene panels) and by tissue-specific
        gene expression. The inclusion of GeneOntology (GO) and metabolic pathways
        facilitates the discovery of hitherto unknown disease genes. In a novel approach,
        we trained MutationDistiller's HPO-based prioritization on authentic genotype-phenotype
        sets obtained from ClinVar and found it to match or outcompete current prioritization
        tools in terms of accuracy. In the output, the program provides a list of
        potential disease mutations ordered by the likelihood of the affected genes
        to cause the phenotype. MutationDistiller provides links to gene-related information
        from various resources. It has been extensively tested by clinicians and their
        suggestions have been valued in many iterative cycles of revisions. The tool,
        a comprehensive documentation and examples are freely available at https://www.mutationdistiller.org/
      authors:
      - name: Hombach D.
      - name: Schuelke M.
      - name: Knierim E.
      - name: Ehmke N.
      - name: Schwarz J.M.
      - name: Fischer-Zirnsak B.
      - name: Seelow D.
      citationCount: 31
      date: '2019-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MutationDistiller: User-driven identification of pathogenic DNA variants'
    note: null
    pmcid: PMC6602447
    pmid: '31106342'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-03-05T09:42:12Z'
  biotoolsCURIE: biotools:umd-predictor
  biotoolsID: umd-predictor
  collectionID:
  - Developed_RD-Connect
  - RD-Connect
  - Rare Disease
  - ELIXIR-FR
  confidence_flag: null
  cost: null
  credit:
  - email: christophe.beroud@univ-amu.fr
    fundrefid: null
    gridid: null
    name: Christophe Beroud
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: david.salgado@univ-amu.fr
    fundrefid: null
    gridid: null
    name: David Salgado
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: 'A tool that provides a combinatorial approach to identify potential
    pathogenic variations, that associates the following data: localization within
    the protein, conservation, biochemical properties of the mutant and wild-type
    residues, and the potential impact of the variation on mRNA.'
  documentation: []
  download: []
  editPermission:
    authors:
    - lmatalonga
    - dsalgado
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://umd-predictor.eu
  homepage_status: 0
  language:
  - PHP
  - JavaScript
  lastUpdate: '2024-11-25T14:07:06.419265Z'
  license: null
  link: []
  maturity: Mature
  name: UMD-Predictor
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: dsalgado
  publication:
  - doi: 10.1002/humu.22965
    metadata:
      abstract: Whole-exome sequencing (WES) is increasingly applied to research and
        clinical diagnosis of human diseases. It typically results in large amounts
        of genetic variations. Depending on the mode of inheritance, only one or two
        correspond to pathogenic mutations responsible for the disease and present
        in affected individuals. Therefore, it is crucial to filter out nonpathogenic
        variants and limit downstream analysis to a handful of candidate mutations.
        We have developed a new computational combinatorial system UMD-Predictor (http://umd-predictor.eu)
        to efficiently annotate cDNA substitutions of all human transcripts for their
        potential pathogenicity. It combines biochemical properties, impact on splicing
        signals, localization in protein domains, variation frequency in the global
        population, and conservation through the BLOSUM62 global substitution matrix
        and a protein-specific conservation among 100 species. We compared its accuracy
        with the seven most used and reliable prediction tools, using the largest
        reference variation datasets including more than 140,000 annotated variations.
        This system consistently demonstrated a better accuracy, specificity, Matthews
        correlation coefficient, diagnostic odds ratio, speed, and provided the shortest
        list of candidate mutations for WES. Webservices allow its implementation
        in any bioinformatics pipeline for next-generation sequencing analysis. It
        could benefit to a wide range of users and applications varying from gene
        discovery to clinical diagnosis. UMD-Predictor is a new bionformatics system
        to predict the pathogenicity of human genes mutations. It is today the most
        efficient system and it can be integrated in any NGS analysis pipeline. It
        will benefit to a wide range of users and applications varying from gene discovery
        to clinical diagnosis.
      authors:
      - name: Salgado D.
      - name: Desvignes J.-P.
      - name: Rai G.
      - name: Blanchard A.
      - name: Miltgen M.
      - name: Pinard A.
      - name: Levy N.
      - name: Collod-Beroud G.
      - name: Beroud C.
      citationCount: 97
      date: '2016-05-01T00:00:00Z'
      journal: Human Mutation
      title: 'UMD-Predictor: A High-Throughput Sequencing Compliant System for Pathogenicity
        Prediction of any Human cDNA Substitution'
    note: null
    pmcid: PMC5067603
    pmid: '26842889'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-03-30T21:27:44Z'
  biotoolsCURIE: biotools:genecards
  biotoolsID: genecards
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: support@lifemapsc.com
    fundrefid: null
    gridid: null
    name: Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Database of human genes, their products, and involvement in diseases.
    It automatically integrates gene-centric data from ~150 web sources, including
    genomic, transcriptomic, proteomic, genetic, clinical and functional information.
  documentation:
  - note: null
    type:
    - General
    url: http://www.genecards.org/Guide
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Service discovery
      uri: http://edamontology.org/operation_3761
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: http://www.genecards.org/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T13:46:01.243335Z'
  license: null
  link: []
  maturity: Mature
  name: GeneCards
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-EE
  publication:
  - doi: 10.1016/s0168-9525(97)01103-7
    metadata:
      abstract: ''
      authors:
      - name: Rebhan M.
      - name: Chalifa-Caspi V.
      - name: Prilusky J.
      - name: Lancet D.
      citationCount: 538
      date: '1997-01-01T00:00:00Z'
      journal: Trends in Genetics
      title: 'GeneCards: Integrating information about genes, proteins and diseases'
    note: null
    pmcid: null
    pmid: '9097728'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/18.11.1542
    metadata:
      abstract: 'Motivation: In the post-genomic era, functional analysis of genes
        requires a sophisticated interdisciplinary arsenal. Comprehensive resources
        are challenged to provide consistently improving, state-of-the-art tools.
        Results: GeneCards (Rebhan et al., 1998) has made innovative strides: (a)
        regular updates and enhancements incorporating new genes enriched with sequences,
        genomic locations, cDNA assemblies, orthologies, medical information, 3D protein
        structures, gene expression, and focused SNP summaries; (b) restructured software
        using object-oriented Perl, migration to schema-driven XML, and (c) pilot
        studies, introducing methods to produce cards for novel and predicted genes.'
      authors:
      - name: Safran M.
      - name: Solomon I.
      - name: Shmueli O.
      - name: Lapidot M.
      - name: Shen-Orr S.
      - name: Adato A.
      - name: Ben-Dor U.
      - name: Esterman N.
      - name: Rosen N.
      - name: Peter I.
      - name: Olender T.
      - name: Chalifa-Caspi V.
      - name: Lancet D.
      citationCount: 171
      date: '2002-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'GeneCards™ 2002: Towards a complete, object-oriented, human gene compendium'
    note: null
    pmcid: null
    pmid: '12424129'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/14.8.656
    metadata:
      abstract: 'Motivation: Modern biology is shifting from the ''one gene one postdoc''
        approach to genomic analyses that include the simultaneous monitoring of thousands
        of genes. The importance of efficient access to concise and integrated biomedical
        information to support data analysis and decision making is therefore increasing
        rapidly, in both academic and industrial research. However knowledge discovery
        in the widely scattered resources relevant for biomedical research is often
        a cumbersome and non-trivial task, one that requires a significant amount
        of training and effort. Results: To develop a model for a new type of topic-specific
        overview resource that provides efficient access to distributed information
        we designed a database called ''GeneCards''. It is a freely accessible Web
        resource that offers one hypertext (card) for each of the more than 7000 human
        genes that currently have an approved gene symbol published by the HUGO/GDB
        nomenclature committee. The presented information aims at giving immediate
        insight into current knowledge about the respective gene including a focus
        on its functions in health and disease. It is compiled by Perl scripts that
        automatically extract relevant information from several databases including
        SWISS-PROT, OMIM, Genatlas and GDB. Analyses of the interactions of users
        with the Web interface of GeneCards triggered development of easy-to-scan
        displays optimized for human browsing. Also, we developed algorithms that
        offer ''ready-to-click'' query reformulation support to facilitate information
        retrieval and exploration. Many of the long-term users turn to GeneCards to
        quickly access information about the function of very large sets of genes,
        for example in the realm of large-scale expression studies using ''DNA chip''
        technology or two-dimensional protein ebectrophoresis. Availability: Freely
        available at http://bioinformatics.weizmann.ac.il/cards/ Contact: cards@@@bioinformatics.weizmann.ac.il.'
      authors:
      - name: Rebhan M.
      - name: Chalifa-Caspi V.
      - name: Prilusky J.
      - name: Lancet D.
      citationCount: 347
      date: '1998-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'GeneCards: A novel functional genomics compendium with automated data
        mining and query reformulation support'
    note: null
    pmcid: null
    pmid: '9789091'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Function analysis
    uri: http://edamontology.org/topic_1775
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-07-04T12:34:10Z'
  biotoolsCURIE: biotools:genatlasdb
  biotoolsID: genatlasdb
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: claude.mugnier@univ-paris5.fr
    fundrefid: null
    gridid: null
    name: Claude Mugnier
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: marie-liesse.chauvet@inserm.fr
    fundrefid: null
    gridid: null
    name: Marie-Liesse Chauvet
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: martine.lemerrer@inserm.fr
    fundrefid: null
    gridid: null
    name: Martine Lemerrer
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: alexandra.caude@inserm.fr
    fundrefid: null
    gridid: null
    name: Alexandra Caude
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The Genatlas database provides information on the structure, expression
    and function of genes, gene mutations and their consequences on diseases.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Gene expression analysis
      uri: http://edamontology.org/operation_2495
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: http://www.genatlas.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T13:45:59.920028Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: Genatlas
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-EE
  publication:
  - doi: 10.1016/s0764-4469(99)80021-3
    metadata:
      abstract: This article aims to illustrate the potentialities of the Genatlas
        database, taking, as an example, the developmental genes and their associated
        diseases in man. These genes belong to several categories intervening from
        the first stages of embryonic life. They operate at all steps of developmental
        cascades from extracellular signaling to activation of target genes. Quite
        a number of those genes have been identified in man, which are the orthologs
        of genes previously described in lower species. These genes are mapped and
        an increasing number are associated with developmental anomalies. These studies
        shed light on the mechanisms of congenital malformations. They disclose a
        large array of genetic and phenotypic heterogeneity and a high degree of complexity.
      authors:
      - name: Frezal J.
      citationCount: 56
      date: '1998-10-01T00:00:00Z'
      journal: Comptes Rendus de l'Academie des Sciences - Serie III
      title: Genatlas database, genes and development defects
    note: null
    pmcid: null
    pmid: '9835018'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Function analysis
    uri: http://edamontology.org/topic_1775
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2015-12-01T15:59:44Z'
  biotoolsCURIE: biotools:lovd
  biotoolsID: lovd
  collectionID:
  - NBIC
  - Rare Disease
  - ELIXIR-NL
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-NL
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.lovd.nl/3.0/contact
  description: The Leiden Open Variation Database (LOVD) provides a flexible, freely
    available tool for gene-centered collection and display of DNA variations. LOVD
    3.0 extends this idea to also provide patient-centered data storage and storage
    of NGS data, even of variants outside of genes.
  documentation:
  - note: null
    type:
    - General
    url: http://www.lovd.nl/3.0/docs/
  download:
  - note: null
    type: Downloads page
    url: http://www.lovd.nl/3.0/download
    version: null
  editPermission:
    authors:
    - ELIXIR-EE
    type: group
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://www.lovd.nl/3.0/home
  homepage_status: 0
  language:
  - PHP
  lastUpdate: '2024-11-24T21:08:42.537394Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: LOVD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-NL
  publication:
  - doi: 10.1002/humu.21438
    metadata:
      abstract: Locus-Specific DataBases (LSDBs) store information on gene sequence
        variation associated with human phenotypes and are frequently used as a reference
        by researchers and clinicians. We developed the Leiden Open-source Variation
        Database (LOVD) as a platform-independent Web-based LSDB-in-a-Box package.
        LOVD was designed to be easy to set up and maintain and follows the Human
        Genome Variation Society (HGVS) recommendations. Here we describe LOVD v.2.0,
        which adds enhanced flexibility and functionality and has the capacity to
        store sequence variants in multiple genes per patient. To reduce redundancy,
        patient and sequence variant data are stored in separate tables. Tables are
        linked to generate connections between sequence variant data for each gene
        and every patient. The dynamic structure allows database managers to add custom
        columns. The database structure supports fast queries and allows storage of
        sequence variants from high-throughput sequence analysis, as demonstrated
        by the X-chromosomal Mental Retardation LOVD installation. LOVD contains measures
        to ensure database security from unauthorized access. Currently, the LOVD
        Website () lists 71 public LOVD installations hosting 3,294 gene variant databases
        with 199,000 variants in 84,000 patients. To promote LSDB standardization
        and thereby database interoperability, we offer free server space and help
        to establish an LSDB on our Leiden server. © 2011 Wiley-Liss, Inc.
      authors:
      - name: Fokkema I.F.A.C.
      - name: Taschner P.E.M.
      - name: Schaafsma G.C.P.
      - name: Celli J.
      - name: Laros J.F.J.
      - name: den Dunnen J.T.
      citationCount: 817
      date: '2011-05-01T00:00:00Z'
      journal: Human Mutation
      title: 'LOVD v.2.0: The next generation in gene variant databases'
    note: null
    pmcid: null
    pmid: '21520333'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '3.0'
- accessibility: Open access
  additionDate: '2015-12-02T11:00:49Z'
  biotoolsCURIE: biotools:molgenis
  biotoolsID: molgenis
  collectionID:
  - NBIC
  - Rare Disease
  - BBMRI
  - BioMedBridges Tools
  - ELIXIR-NL
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: UMC Groningen and collaborators
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://www.rug.nl/
  - email: null
    fundrefid: null
    gridid: null
    name: BioMedBridges
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole:
    - Documentor
    url: http://www.biomedbridges.eu
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-NL
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://www.dtls.nl/elixir-nl/elixir-nl-2/
  - email: molgenis-hackers@googlegroups.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: m.a.swertz@rug.nl
    fundrefid: null
    gridid: null
    name: Morris Swertz
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: molgenis-support@umcg.nl
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: null
  description: A framework for scientific data management, exploration, integration
    and analysis. It is adaptable to many types of scientific data and data workflows,
    and there are several pre-configured MOLGENIS systems for specific data types.
  documentation:
  - note: null
    type:
    - General
    url: https://molgenis.gitbooks.io/molgenis/content/
  download:
  - note: null
    type: Source code
    url: https://github.com/molgenis/molgenis
    version: null
  editPermission:
    authors:
    - ELIXIR-EE
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform:
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: https://molgenis.org
  homepage_status: 0
  language:
  - R
  - Java
  - SQL
  lastUpdate: '2024-11-24T21:08:40.375468Z'
  license: LGPL-3.0
  link: []
  maturity: Mature
  name: MOLGENIS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: mswertz
  publication:
  - doi: 10.1093/bioinformatics/btw155
    metadata:
      abstract: 'Motivation: While the size and number of biobanks, patient registries
        and other data collections are increasing, biomedical researchers still often
        need to pool data for statistical power, a task that requires time-intensive
        retrospective integration. Results: To address this challenge, we developed
        MOLGENIS/connect, a semi-automatic system to find, match and pool data from
        different sources. The system shortlists relevant source attributes from thousands
        of candidates using ontology-based query expansion to overcome variations
        in terminology. Then it generates algorithms that transform source attributes
        to a common target DataSchema. These include unit conversion, categorical
        value matching and complex conversion patterns (e.g. calculation of BMI).
        In comparison to human-experts, MOLGENIS/connect was able to auto-generate
        27% of the algorithms perfectly, with an additional 46% needing only minor
        editing, representing a reduction in the human effort and expertise needed
        to pool data. Availability and Implementation: Source code, binaries and documentation
        are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis
        and www.molgenis.org/connect. Contact: Supplementary information: Supplementary
        data are available at Bioinformatics online.'
      authors:
      - name: Pang C.
      - name: Van Enckevort D.
      - name: De Haan M.
      - name: Kelpin F.
      - name: Jetten J.
      - name: Hendriksen D.
      - name: De Boer T.
      - name: Charbon B.
      - name: Winder E.
      - name: Van Der Velde K.J.
      - name: Doiron D.
      - name: Fortier I.
      - name: Hillege H.
      - name: Swertz M.A.
      citationCount: 13
      date: '2016-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'MOLGENIS/connect: A system for semi-automatic integration of heterogeneous
        phenotype data with applications in biobanks'
    note: null
    pmcid: PMC4937195
    pmid: '27153686'
    type:
    - Primary
    version: null
  - doi: 10.1186/1471-2105-11-s12-s12
    metadata:
      abstract: 'Background: There is a huge demand on bioinformaticians to provide
        their biologists with user friendly and scalable software infrastructures
        to capture, exchange, and exploit the unprecedented amounts of new *omics
        data. We here present MOLGENIS, a generic, open source, software toolkit to
        quickly produce the bespoke MOLecular GENetics Information Systems needed.Methods:
        The MOLGENIS toolkit provides bioinformaticians with a simple language to
        model biological data structures and user interfaces. At the push of a button,
        MOLGENIS'' generator suite automatically translates these models into a feature-rich,
        ready-to-use web application including database, user interfaces, exchange
        formats, and scriptable interfaces. Each generator is a template of SQL, JAVA,
        R, or HTML code that would require much effort to write by hand. This ''model-driven''
        method ensures reuse of best practices and improves quality because the modeling
        language and generators are shared between all MOLGENIS applications, so that
        errors are found quickly and improvements are shared easily by a re-generation.
        A plug-in mechanism ensures that both the generator suite and generated product
        can be customized just as much as hand-written software.Results: In recent
        years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping
        of many types of biomedical applications, including next-generation sequencing,
        GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically
        replaces 15,000 lines of hand-written programming code, which allows for quick
        adaptation if the information system is not yet to the biologist''s satisfaction.
        Each application generated with MOLGENIS comes with an optimized database
        back-end, user interfaces for biologists to manage and exploit their data,
        programming interfaces for bioinformaticians to script analysis tools in R,
        Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload
        and exchange of data, and detailed technical documentation. Existing databases
        can be quickly enhanced with MOLGENIS generated interfaces using the ''ExtractModel''
        procedure.Conclusions: The MOLGENIS toolkit provides bioinformaticians with
        a simple model to quickly generate flexible web platforms for all possible
        genomic, molecular and phenotypic experiments with a richness of interfaces
        not provided by other tools. All the software and manuals are available free
        as LGPLv3 open source at http://www.molgenis.org. © 2010 Swertz et al; licensee
        BioMed Central Ltd.'
      authors:
      - name: Swertz M.A.
      - name: Dijkstra M.
      - name: Adamusiak T.
      - name: van der Velde J.K.
      - name: Kanterakis A.
      - name: Roos E.T.
      - name: Lops J.
      - name: Thorisson G.A.
      - name: Arends D.
      - name: Byelas G.
      - name: Muilu J.
      - name: Brookes A.J.
      - name: De Brock E.O.
      - name: Jansen R.C.
      - name: Parkinson H.
      citationCount: 91
      date: '2010-12-21T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'The MOLGENIS toolkit: Rapid prototyping of biosoftware at the push of
        a button'
    note: null
    pmcid: PMC3040526
    pmid: '21210979'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Data management
    uri: http://edamontology.org/topic_3071
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '1'
- accessibility: Restricted access
  additionDate: '2017-09-26T08:01:18Z'
  biotoolsCURIE: biotools:umd
  biotoolsID: umd
  collectionID:
  - RD-connect
  - Rare Disease
  - ELIXIR-FR
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: christophe.beroud@inserm.fr
    fundrefid: null
    gridid: null
    name: Christophe Béroud
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://rd-connect.eu/contacts/christophe-beroud/
  - email: david.salgado@univ-amu.fr
    fundrefid: null
    gridid: null
    name: David Salgado
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Universal Mutation Database (UMD) is a database for mutations. UMD-predictor
    is a tool that enables functional annotation of variants to find the relevant
    ones.
  documentation:
  - note: null
    type:
    - User manual
    url: http://www.umd.be/UMD_Manual.pdf
  download: []
  editPermission:
    authors:
    - lmatalonga
    - dsalgado
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Polymorphism detection
      uri: http://edamontology.org/operation_3202
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: http://www.umd.be/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T21:05:05.832637Z'
  license: Unlicense
  link: []
  maturity: Mature
  name: UMD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: dsalgado
  publication:
  - doi: 10.1002/(sici)1098-1004(200001)15:1<86::aid-humu16>3.0.co;2-4
    metadata: null
    note: null
    pmcid: null
    pmid: '10612827'
    type: []
    version: null
  - doi: 10.1002/humu.20210
    metadata:
      abstract: With the completion of the Human Genome Project, our vision of human
        genetic diseases has changed. The cloning of new disease-causing genes can
        now be performed in silico, and thousands of mutations are being identified
        in diagnostic and research laboratories yearly. Knowledge about these mutations
        and their association with clinical and biological data is essential for clinicians,
        geneticists, and researchers. To collect and analyze these data, we developed
        a generic software called Universal Mutation Databases (UMD ) to create locus-specific
        databases. Here we report the new release (September 2004) of this freely
        available tool (www.umd.be), which allows the creation of LSDBs for virtually
        any gene and includes a large set of new analysis tools. We have implemented
        new features to integrate noncoding sequences, clinical data, pictures, monoclonal
        antibodies, and polymorphic markers (SNPs). Today the UMD retains all specifically
        designed tools to analyze mutations at the molecular level, as well as new
        sets of routines to search for genotype-phenotype correlations. We also created
        specific tools for infrequent mutations such as gross deletions and duplications,
        and deep intronic mutations. A large set of dedicated tools are now available
        for intronic mutations, including methods to calculate the consensus values
        (CVs) of potential splice sites and to search for exonic splicing enhancer
        (ESE) motifs. In addition, we have created specific routines to help researchers
        design new therapeutic strategies, such as exon skipping, aminoglycoside read-through
        of stop codons, or monoclonal antibody selection and epitope scanning for
        gene therapy. © 2005 Wiley-Liss, Inc.
      authors:
      - name: Beroud C.
      - name: Hamroun D.
      - name: Collod-Beroud G.
      - name: Boileau C.
      - name: Soussi T.
      - name: Claustres M.
      citationCount: 92
      date: '2005-09-01T00:00:00Z'
      journal: Human Mutation
      title: 'UMD (Universal Mutation Database): 2005 Update'
    note: null
    pmcid: null
    pmid: '16086365'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Public health and epidemiology
    uri: http://edamontology.org/topic_3305
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-07-19T13:24:41Z'
  biotoolsCURIE: biotools:RegulationSpotter
  biotoolsID: RegulationSpotter
  collectionID:
  - Rare Disease
  - RD-connect
  - ELIXIR-DE
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'RegulationSpotter is a web-based tool for the user-friendly annotation
    and interpretation of ''extratranscriptic'' DNA variants. It annotates individual
    variants with underlying regulatory features in an intuitive way by assessing
    over 100 genome-wide annotations. Additionally, it calculates a score, which reflects
    the regulatory potential of the variant region.

    Phenotypic data can be used to focus on likely disease genes.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output: []
  homepage: https://www.regulationspotter.org/
  homepage_status: 0
  language:
  - SQL
  - Perl
  lastUpdate: '2024-11-24T21:04:08.016505Z'
  license: null
  link: []
  maturity: Mature
  name: RegulationSpotter
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Dominik
  publication:
  - doi: 10.1093/nar/gkz327
    metadata:
      abstract: RegulationSpotter is a web-based tool for the user-friendly annotation
        and interpretation of DNA variants located outside of protein-coding transcripts
        (extratranscriptic variants). It is designed for clinicians and researchers
        who wish to assess the potential impact of the considerable number of non-coding
        variants found in Whole Genome Sequencing runs. It annotates individual variants
        with underlying regulatory features in an intuitive way by assessing over
        100 genome-wide annotations. Additionally, it calculates a score, which reflects
        the regulatory potential of the variant region. Its dichotomous classifications,
        'functional' or 'non-functional', and a human-readable presentation of the
        underlying evidence allow a biologically meaningful interpretation of the
        score. The output shows key aspects of every variant and allows rapid access
        to more detailed information about its possible role in gene regulation. RegulationSpotter
        can either analyse single variants or complete VCF files. Variants located
        within protein-coding transcripts are automatically assessed by MutationTaster
        as well as by RegulationSpotter to account for possible intragenic regulatory
        effects. RegulationSpotter offers the possibility of using phenotypic data
        to focus on known disease genes or genomic elements interacting with them.
        RegulationSpotter is freely available at https://www.regulationspotter.org.
      authors:
      - name: Schwarz J.M.
      - name: Hombach D.
      - name: Kohler S.
      - name: Cooper D.N.
      - name: Schuelke M.
      - name: Seelow D.
      citationCount: 16
      date: '2019-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'RegulationSpotter: Annotation and interpretation of extratranscriptic
        DNA variants'
    note: null
    pmcid: PMC6602480
    pmid: '31106382'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2016-05-17T10:17:53Z'
  biotoolsCURIE: biotools:mendelian
  biotoolsID: mendelian
  collectionID:
  - BIG N2N
  - UGent
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
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    name: ugent.be
    note: null
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    url: null
  - email: Bart.Broeckx@UGent.be
    fundrefid: null
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    name: Bart J. G. Broeckx
    note: null
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    rorid: null
    typeEntity: Person
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    url: null
  - email: Filip.VanNieuwerburgh@UGent.be
    fundrefid: null
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    name: Filip Van Nieuwerburgh
    note: null
    orcidid: null
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    typeEntity: Person
    typeRole:
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    url: null
  description: R-package for heuristic variant filtering.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BartBroeckx/Mendelian
  download: []
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  function:
  - cmd: null
    input:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
    note: null
    operation:
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    - data:
        term: Gene report
        uri: http://edamontology.org/data_0916
      format: []
  homepage: https://github.com/BartBroeckx/Mendelian
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T20:58:29.067273Z'
  license: GPL-2.0
  link: []
  maturity: Mature
  name: Mendelian
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Katrijn.Vannerum@ugent.be
  publication:
  - doi: 10.1186/s12859-015-0822-7
    metadata:
      abstract: 'Background: Identification of one or several disease causing variant(s)
        from the large collection of variants present in an individual is often achieved
        by the sequential use of heuristic filters. The recent development of whole
        exome sequencing enrichment designs for several non-model species created
        the need for a species-independent, fast and versatile analysis tool, capable
        of tackling a wide variety of standard and more complex inheritance models.
        With this aim, we developed "Mendelian", an R-package that can be used for
        heuristic variant filtering. Results: The R-package Mendelian offers fast
        and convenient filters to analyze putative variants for both recessive and
        dominant models of inheritance, with variable degrees of penetrance and detectance.
        Analysis of trios is supported. Filtering against variant databases and annotation
        of variants is also included. This package is not species specific and supports
        parallel computation. We validated this package by reanalyzing data from a
        whole exome sequencing experiment on intellectual disability in humans. In
        a second example, we identified the mutations responsible for coat color in
        the dog. This is the first example of whole exome sequencing without prior
        mapping in the dog. Conclusion: We developed an R-package that enables the
        identification of disease-causing variants from the long list of variants
        called in sequencing experiments. The software and a detailed manual are available
        at https://github.com/BartBroeckx/Mendelian.'
      authors:
      - name: Broeckx B.J.G.
      - name: Coopman F.
      - name: Verhoeven G.
      - name: Bosmans T.
      - name: Gielen I.
      - name: Dingemanse W.
      - name: Saunders J.H.
      - name: Deforce D.
      - name: Van Nieuwerburgh F.
      citationCount: 5
      date: '2015-11-19T00:00:00Z'
      journal: BMC Bioinformatics
      title: An heuristic filtering tool to identify phenotype-associated genetic
        variants applied to human intellectual disability and canine coat colors
    note: null
    pmcid: PMC4656174
    pmid: '26597515'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2015-10-07T18:11:10Z'
  biotoolsCURIE: biotools:orphanet
  biotoolsID: orphanet
  collectionID:
  - DRCAT
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Inserm US14
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.orpha.net/consor/cgi-bin/Directory_Contact.php?lng=EN
  description: Information on rare diseases and orphan drugs.
  documentation:
  - note: null
    type:
    - General
    url: http://www.orpha.net/consor/cgi-bin/Education_EducationTools.php?stapage=HM_BLOC_HEADER_HELP_SITEMAP&lng=EN
  download: []
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  function:
  - cmd: null
    input:
    - data:
        term: Orpha number
        uri: http://edamontology.org/data_2800
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://www.orpha.net/consor/cgi-bin/index.php
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T20:58:27.798370Z'
  license: null
  link: []
  maturity: Mature
  name: Orphanet
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: CRodwell
  publication:
  - doi: 10.1002/humu.22078
    metadata:
      abstract: 'Rare disorders are scarcely represented in international classifications
        and therefore invisible in information systems. One of the major needs in
        health information systems and for research is to share and/or to integrate
        data coming from heterogeneous sources with diverse reference terminologies.
        ORPHANET (www.orpha.net) is a multilingual information portal on rare diseases
        and orphan drugs. Orphanet information system is supported by a relational
        database built around the concept of rare disorders. Representation of rare
        diseases in Orphanet encompasses levels of increasing complexity: lexical
        (multilingual terminology), nosological (multihierarchical classifications),
        relational (annotations-epidemiological data-and classes of objects-genes,
        manifestations, and orphan drugs-integrated in a relational database), and
        interoperational (semantic interoperability). Rare disorders are mapped to
        International Classification of Diseases (10th version), SNOMED CT, MeSH,
        MedDRA, and UMLS. Genes are cross-referenced with HGNC, UniProt, OMIM, and
        Genatlas. A suite of tools allow for extraction of massive datasets giving
        different views that can be used in bioinformatics to answer complex questions,
        intended to serve the needs of researchers and the pharmaceutical industry
        in developing medicinal products for rare diseases. An ontology is under development.
        The Orphanet nomenclature is at the crossroads of scientific data repositories
        and of clinical terminology standards, and is suitable to be used as a standard
        terminology. © 2012 Wiley Periodicals, Inc.'
      authors:
      - name: Rath A.
      - name: Olry A.
      - name: Dhombres F.
      - name: Brandt M.M.
      - name: Urbero B.
      - name: Ayme S.
      citationCount: 268
      date: '2012-05-01T00:00:00Z'
      journal: Human Mutation
      title: 'Representation of rare diseases in health information systems: The orphanet
        approach to serve a wide range of end users'
    note: null
    pmcid: null
    pmid: '22422702'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-09-26T07:49:23Z'
  biotoolsCURIE: biotools:esp
  biotoolsID: esp
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: evsserver@uw.edu
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: 'NHLBI Exome Sequencing Project (ESP): Exome Variant Server (EVS) for
    browsing single nucleotide variation data from exome sequencing experiments mainly
    focused on heart, lung and blood disorders.'
  documentation:
  - note: null
    type:
    - User manual
    url: http://evs.gs.washington.edu/EVS/
  download: []
  editPermission:
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    type: private
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Variant filtering
      uri: http://edamontology.org/operation_3675
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://evs.gs.washington.edu/EVS/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T20:32:25.053431Z'
  license: null
  link: []
  maturity: Mature
  name: ESP
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_012761
    version: null
  owner: ELIXIR-EE
  publication:
  - doi: 10.1093/hmg/ddt384
    metadata:
      abstract: Massively parallel sequencing (MPS) has become an important tool for
        identifyingmedically significant variants in both research and the clinic.
        Accurate variation and genotype-phenotype databases are critical in our ability
        tomake sense of the vast amount of information thatMPSgenerates. The purpose
        of this review is to summarize the state of the art of variation and genotype-phenotype
        databases, how they can be used, and opportunities to improve these resources.
        Our working assumption is that the objective of the clinical genomicist is
        to identify highly penetrant variants that could explain existing disease
        or predict disease risk for individual patients or research participants.
        We have detailed how current databases contribute to this goal providing frequency
        data, literature reviews and predictions of causation for individual variants.
        For variant annotation, databases vary greatly in their ease of use, the use
        of standard mutation nomenclature, the comprehensiveness of the variant cataloging
        and the degree of expert opinion. Ultimately, we need a dynamic and comprehensive
        reference database of medically important variants that is easily cross referenced
        to exome and genome sequence data and allows for an accumulation of expert
        opinion.
      authors:
      - name: Johnston J.J.
      - name: Biesecker L.G.
      citationCount: 49
      date: '2013-10-01T00:00:00Z'
      journal: Human Molecular Genetics
      title: 'Databases of genomic variation and phenotypes: Existing resources and
        future needs'
    note: null
    pmcid: PMC3782073
    pmid: '23962721'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  - term: Haematology
    uri: http://edamontology.org/topic_3408
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Respiratory medicine
    uri: http://edamontology.org/topic_3322
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2018-08-11T09:29:45Z'
  biotoolsCURIE: biotools:marrvel
  biotoolsID: marrvel
  collectionID:
  - Rare Disease
  confidence_flag: null
  cost: null
  credit:
  - email: zhandonl@bcm.edu
    fundrefid: null
    gridid: null
    name: Zhandong Liu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Integration of Human and Model Organism Genetic Resources to Facilitate
    Functional Annotation of the Human Genome.
  documentation:
  - note: null
    type:
    - General
    url: http://marrvel.org/about
  download: []
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    - ChrisEvelo
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  homepage: http://marrvel.org/
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  name: MARRVEL
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    value: RRID:SCR_016871
    version: null
  owner: d.gabrielaitis
  publication:
  - doi: 10.1016/j.ajhg.2017.04.010
    metadata:
      abstract: One major challenge encountered with interpreting human genetic variants
        is the limited understanding of the functional impact of genetic alterations
        on biological processes. Furthermore, there remains an unmet demand for an
        efficient survey of the wealth of information on human homologs in model organisms
        across numerous databases. To efficiently assess the large volume of publically
        available information, it is important to provide a concise summary of the
        most relevant information in a rapid user-friendly format. To this end, we
        created MARRVEL (model organism aggregated resources for rare variant exploration).
        MARRVEL is a publicly available website that integrates information from six
        human genetic databases and seven model organism databases. For any given
        variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP,
        DGV, and DECIPHER. Importantly, it curates model organism-specific databases
        to concurrently display a concise summary regarding the human gene homologs
        in budding and fission yeast, worm, fly, fish, mouse, and rat on a single
        webpage. Experiment-based information on tissue expression, protein subcellular
        localization, biological process, and molecular function for the human gene
        and homologs in the seven model organisms are arranged into a concise output.
        Hence, rather than visiting multiple separate databases for variant and gene
        analysis, users can obtain important information by searching once through
        MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility
        to data collection and facilitates analysis of human genes and variants by
        cross-disciplinary integration of 18 million records available in public databases
        to facilitate clinical diagnosis and basic research.
      authors:
      - name: Wang J.
      - name: Al-Ouran R.
      - name: Hu Y.
      - name: Kim S.-Y.
      - name: Wan Y.-W.
      - name: Wangler M.F.
      - name: Yamamoto S.
      - name: Chao H.-T.
      - name: Comjean A.
      - name: Mohr S.E.
      - name: Adams C.J.
      - name: Adams D.R.
      - name: Alejandro M.E.
      - name: Allard P.
      - name: Ashley E.A.
      - name: Azamian M.S.
      - name: Bacino C.A.
      - name: Balasubramanyam A.
      - name: Barseghyan H.
      - name: Beggs A.H.
      - name: Bellen H.J.
      - name: Bernstein J.A.
      - name: Bican A.
      - name: Bick D.P.
      - name: Birch C.L.
      - name: Boone B.E.
      - name: Briere L.C.
      - name: Brown D.M.
      - name: Brush M.
      - name: Burke E.A.
      - name: Burrage L.C.
      - name: Chao K.R.
      - name: Clark G.D.
      - name: Cogan J.D.
      - name: Cooper C.M.
      - name: Craigen W.J.
      - name: Davids M.
      - name: Dayal J.G.
      - name: Dell'Angelica E.C.
      - name: Dhar S.U.
      - name: Dipple K.M.
      - name: Donnell-Fink L.A.
      - name: Dorrani N.
      - name: Dorset D.C.
      - name: Draper D.D.
      - name: Dries A.M.
      - name: Eckstein D.J.
      - name: Emrick L.T.
      - name: Eng C.M.
      - name: Esteves C.
      - name: Estwick T.
      - name: Fisher P.G.
      - name: Frisby T.S.
      - name: Frost K.
      - name: Gahl W.A.
      - name: Gartner V.
      - name: Godfrey R.A.
      - name: Goheen M.
      - name: Golas G.A.
      - name: Goldstein D.B.
      - name: Gordon M.G.
      - name: Gould S.E.
      - name: Gourdine J.-P.F.
      - name: Graham B.H.
      - name: Groden C.A.
      - name: Gropman A.L.
      - name: Hackbarth M.E.
      - name: Haendel M.
      - name: Hamid R.
      - name: Hanchard N.A.
      - name: Handley L.H.
      - name: Hardee I.
      - name: Herzog M.R.
      - name: Holm I.A.
      - name: Howerton E.M.
      - name: Jacob H.J.
      - name: Jain M.
      - name: Jiang Y.-H.
      - name: Johnston J.M.
      - name: Jones A.L.
      - name: Koehler A.E.
      - name: Koeller D.M.
      - name: Kohane I.S.
      - name: Kohler J.N.
      - name: Krasnewich D.M.
      - name: Krieg E.L.
      - name: Krier J.B.
      - name: Kyle J.E.
      - name: Lalani S.R.
      - name: Latham L.
      - name: Latour Y.L.
      - name: Lau C.C.
      - name: Lazar J.
      - name: Lee B.H.
      - name: Lee H.
      - name: Lee P.R.
      - name: Levy S.E.
      - name: Levy D.J.
      - name: Lewis R.A.
      - name: Liebendorfer A.P.
      - name: Lincoln S.A.
      - name: Loomis C.R.
      - name: Loscalzo J.
      - name: Maas R.L.
      - name: Macnamara E.F.
      - name: MacRae C.A.
      - name: Maduro V.V.
      - name: Malicdan M.C.V.
      - name: Mamounas L.A.
      - name: Manolio T.A.
      - name: Markello T.C.
      - name: Mazur P.
      - name: McCarty A.J.
      - name: McConkie-Rosell A.
      - name: McCray A.T.
      - name: Metz T.O.
      - name: Might M.
      - name: Moretti P.M.
      - name: Mulvihill J.J.
      - name: Murphy J.L.
      - name: Muzny D.M.
      - name: Nehrebecky M.E.
      - name: Nelson S.F.
      - name: Newberry J.S.
      - name: Newman J.H.
      - name: Nicholas S.K.
      - name: Novacic D.
      - name: Orange J.S.
      - name: Pallais J.C.
      - name: Palmer C.G.S.
      - name: Papp J.C.
      - name: Pena L.D.M.
      - name: Phillips J.A.
      - name: Posey J.E.
      - name: Postlethwait J.H.
      - name: Potocki L.
      - name: Pusey B.N.
      - name: Ramoni R.B.
      - name: Robertson A.K.
      - name: Rodan L.H.
      - name: Rosenfeld J.A.
      - name: Sadozai S.
      - name: Schaffer K.E.
      - name: Schoch K.
      - name: Schroeder M.C.
      - name: Scott D.A.
      - name: Sharma P.
      - name: Shashi V.
      - name: Silverman E.K.
      - name: Sinsheimer J.S.
      - name: Soldatos A.G.
      - name: Spillmann R.C.
      - name: Splinter K.
      - name: Stoler J.M.
      - name: Stong N.
      - name: Strong K.A.
      - name: Sullivan J.A.
      - name: Sweetser D.A.
      - name: Thomas S.P.
      - name: Tifft C.J.
      - name: Tolman N.J.
      - name: Toro C.
      - name: Tran A.A.
      - name: Valivullah Z.M.
      - name: Vilain E.
      - name: Waggott D.M.
      - name: Wahl C.E.
      - name: Walley N.M.
      - name: Walsh C.A.
      - name: Wangler M.F.
      - name: Warburton M.
      - name: Ward P.A.
      - name: Waters K.M.
      - name: Webb-Robertson B.-J.M.
      - name: Weech A.A.
      - name: Westerfield M.
      - name: Wheeler M.T.
      - name: Wise A.L.
      - name: Wolfe L.A.
      - name: Worthey E.A.
      - name: Yamamoto S.
      - name: Yang Y.
      - name: Yu G.
      - name: Zornio P.A.
      - name: Perrimon N.
      - name: Liu Z.
      citationCount: 150
      date: '2017-06-01T00:00:00Z'
      journal: American Journal of Human Genetics
      title: 'MARRVEL: Integration of Human and Model Organism Genetic Resources to
        Facilitate Functional Annotation of the Human Genome'
    note: null
    pmcid: PMC5670038
    pmid: '28502612'
    type: []
    version: null
  relation: []
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0199
  - term: Data acquisition
    uri: http://edamontology.org/topic_3077
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2016-02-04T15:55:00Z'
  biotoolsCURIE: biotools:intermine
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    note: null
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    typeEntity: Institute
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  description: Open source data warehouse built specifically for the integration and
    analysis of complex biological data. It enables the creation of biological databases
    accessed by sophisticated web query tools. Parsers are provided for integrating
    data from many common biological data sources and formats, and there is a framework
    for adding your own data.
  documentation:
  - note: null
    type:
    - General
    url: http://intermine.org/im-docs
  - note: null
    type:
    - User manual
    url: http://intermine.org/intermine-user-docs
  download:
  - note: null
    type: Source code
    url: http://github.com/intermine/
    version: null
  - note: null
    type: Binaries
    url: https://repo1.maven.org/maven2/org/intermine/
    version: null
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  elixirPlatform:
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  elixir_badge: 0
  function:
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    input:
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        term: Sequence features
        uri: http://edamontology.org/data_1255
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://www.intermine.org
  homepage_status: 0
  language:
  - R
  - Ruby
  - Java
  - Python
  - JavaScript
  - Perl
  lastUpdate: '2024-11-24T20:28:49.677323Z'
  license: LGPL-2.1
  link:
  - note: null
    type:
    - Software catalogue
    url: https://registry.intermine.org
  - note: null
    type:
    - Other
    url: http://intermine.org
  maturity: Mature
  name: InterMine
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: InterMine
  publication:
  - doi: 10.1093/bioinformatics/bts577
    metadata:
      abstract: InterMine is an open-source data warehouse system that facilitates
        the building of databases with complex data integration requirements and a
        need for a fast customizable query facility. Using InterMine, large biological
        databases can be created from a range of heterogeneous data sources, and the
        extensible data model allows for easy integration of new data types. The analysis
        tools include a flexible query builder, genomic region search and a library
        of 'widgets' performing various statistical analyses. The results can be exported
        in many commonly used formats. InterMine is a fully extensible framework where
        developers can add new tools and functionality. Additionally, there is a comprehensive
        set of web services, for which client libraries are provided in five commonly
        used programming languages. © The Author 2012. Published by Oxford University
        Press. All rights reserved.
      authors:
      - name: Smith R.N.
      - name: Aleksic J.
      - name: Butano D.
      - name: Carr A.
      - name: Contrino S.
      - name: Hu F.
      - name: Lyne M.
      - name: Lyne R.
      - name: Kalderimis A.
      - name: Rutherford K.
      - name: Stepan R.
      - name: Sullivan J.
      - name: Wakeling M.
      - name: Watkins X.
      - name: Micklem G.
      citationCount: 202
      date: '2012-12-01T00:00:00Z'
      journal: Bioinformatics
      title: 'InterMine: A flexible data warehouse system for the integration and
        analysis of heterogeneous biological data'
    note: null
    pmcid: PMC3516146
    pmid: '23023984'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gku301
    metadata:
      abstract: 'InterMine (www.intermine.org) is a biological data warehousing system
        providing extensive automatically generated and configurable RESTful web services
        that underpin the web interface and can be re-used in many other applications:
        to find and filter data; export it in a flexible and structured way; to upload,
        use, manipulate and analyze lists; to provide services for flexible retrieval
        of sequence segments, and for other statistical and analysis tools. Here we
        describe these features and discuss how they can be used separately or in
        combinations to support integrative and comparative analysis. © 2014 The Author(s).'
      authors:
      - name: Kalderimis A.
      - name: Lyne R.
      - name: Butano D.
      - name: Contrino S.
      - name: Lyne M.
      - name: Heimbach J.
      - name: Hu F.
      - name: Smith R.
      - name: Stepan R.
      - name: Sullivan J.
      - name: Micklem G.
      citationCount: 94
      date: '2014-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'InterMine: Extensive web services for modern biology'
    note: null
    pmcid: PMC4086141
    pmid: '24753429'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  - Web API
  - Database portal
  topic:
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - 5.0.0
- accessibility: Open access
  additionDate: '2017-01-13T13:16:53Z'
  biotoolsCURIE: biotools:tophat
  biotoolsID: tophat
  collectionID:
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  - Rare Disease
  - TopHat
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Daehwan Kim
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Program that aligns RNA-Seq reads to a genome in order to identify
    exon-exon splice junctions. It is built on the ultrafast short read mapping program
    Bowtie. A stable SAMtools version is now packaged with the program.
  documentation:
  - note: null
    type:
    - User manual
    url: http://ccb.jhu.edu/software/tophat/manual.shtml
  download: []
  editPermission:
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  function:
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    input:
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        term: Genome identifier
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      format: []
    - data:
        term: Sequence set (nucleic acid)
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      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: TopHat can find splice junctions without a reference annotation by mapping
      RNA-Seq reads to a reference genome. The basename of the genome index to be
      searched. A list of files containing reads. A list of read alignments Track
      of junctions reported by TopHat. Tracks of insertions and deletions reported
      by TopHat.
    operation:
    - term: Splice site prediction
      uri: http://edamontology.org/operation_0433
    - term: Read mapping
      uri: http://edamontology.org/operation_3198
    output:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: SAM
        uri: http://edamontology.org/format_2573
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: BED
        uri: http://edamontology.org/format_3003
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: BED
        uri: http://edamontology.org/format_3003
  homepage: http://ccb.jhu.edu/software/tophat/index.shtml
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-11-24T20:26:57.822012Z'
  license: null
  link:
  - note: null
    type:
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    url: https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/fmareuil/urgi_tools/tophat_parallel/1.0.0
  - note: null
    type:
    - Galaxy service
    url: https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/fmareuil/tophat/tophat/1.4.1.1
  maturity: Mature
  name: TopHat
  operatingSystem:
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  - Mac
  otherID:
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    value: RRID:SCR_013035
    version: null
  owner: seqwiki_import
  publication:
  - doi: 10.1093/bioinformatics/btp120
    metadata:
      abstract: 'Motivation: A new protocol for sequencing the messenger RNA in a
        cell, known as RNA-Seq, generates millions of short sequence fragments in
        a single run. These fragments, or ''reads'', can be used to measure levels
        of gene expression and to identify novel splice variants of genes. However,
        current software for aligning RNA-Seq data to a genome relies on known splice
        junctions and cannot identify novel ones. TopHat is an efficient read-mapping
        algorithm designed to align reads from an RNA-Seq experiment to a reference
        genome without relying on known splice sites. Results: We mapped the RNA-Seq
        reads from a recent mammalian RNA-Seq experiment and recovered more than 72%
        of the splice junctions reported by the annotation-based software from that
        study, along with nearly 20 000 previously unreported junctions. The TopHat
        pipeline is much faster than previous systems, mapping nearly 2.2 million
        reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment
        in less than a day on a standard desktop computer. We describe several challenges
        unique to ab initio splice site discovery from RNA-Seq reads that will require
        further algorithm development. © 2009 The Author(s).'
      authors:
      - name: Trapnell C.
      - name: Pachter L.
      - name: Salzberg S.L.
      citationCount: 9574
      date: '2009-05-07T00:00:00Z'
      journal: Bioinformatics
      title: 'TopHat: Discovering splice junctions with RNA-Seq'
    note: null
    pmcid: PMC2672628
    pmid: '19289445'
    type:
    - Primary
    version: null
  - doi: 10.7490/f1000research.1114334.1
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - 2.1.1
- accessibility: null
  additionDate: '2021-01-18T11:45:16Z'
  biotoolsCURIE: biotools:oncovar
  biotoolsID: oncovar
  collectionID:
  - Rare Disease
  confidence_flag: tool
  cost: null
  credit:
  - email: xiakun@sklmg.edu.cn
    fundrefid: null
    gridid: null
    name: Kun Xia
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: sunzs@biols.ac.cn
    fundrefid: null
    gridid: null
    name: Zhongsheng Sun
    note: null
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    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: maofengbiao08@163.com
    fundrefid: null
    gridid: null
    name: Fengbiao Mao
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'An integrated database and analysis platform for oncogenic driver
    variants in cancers.


    OncoVar is a platform to systematically prioritize the oncogenic ability of somatic
    mutations and cancer genes.'
  documentation: []
  download: []
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
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    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Variant effect prediction
      uri: http://edamontology.org/operation_0331
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://oncovar.org/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T20:24:06.547174Z'
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    type:
    - Other
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  maturity: null
  name: OncoVar
  operatingSystem: []
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  owner: Niclaskn
  publication:
  - doi: 10.1093/nar/gkaa1033
    metadata:
      abstract: The prevalence of neutral mutations in cancer cell population impedes
        the distinguishing of cancer-causing driver mutations from passenger mutations.
        To systematically prioritize the oncogenic ability of somatic mutations and
        cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/),
        which employed published bioinformatics algorithms and incorporated known
        driver events to identify driver mutations and driver genes. We identified
        20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways
        with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in
        The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International
        Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation',
        'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships
        between cancers and driver variants. Importantly, identification of actionable
        driver alterations provides promising druggable targets and repurposing opportunities
        of combinational therapies. OncoVar provides a user-friendly interface for
        browsing, searching and downloading somatic driver mutations, driver genes
        and pathogenic pathways in various cancer types. This platform will facilitate
        the identification of cancer drivers across individual cancer cohorts and
        helps to rank mutations or genes for better decision-making among clinical
        oncologists, cancer researchers and the broad scientific community interested
        in cancer precision medicine.
      authors:
      - name: Wang T.
      - name: Ruan S.
      - name: Zhao X.
      - name: Shi X.
      - name: Teng H.
      - name: Zhong J.
      - name: You M.
      - name: Xia K.
      - name: Sun Z.
      - name: Mao F.
      citationCount: 56
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'OncoVar: An integrated database and analysis platform for oncogenic
        driver variants in cancers'
    note: null
    pmcid: PMC7778899
    pmid: '33179738'
    type: []
    version: null
  relation: []
  toolType:
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  topic:
  - term: Oncology
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  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2016-06-30T09:32:44Z'
  biotoolsCURIE: biotools:predictsnp2
  biotoolsID: predictsnp2
  collectionID:
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  - Rare Disease
  - ELIXIR-CZ
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: Jaroslav Bendl
    note: null
    orcidid: https://orcid.org/0000-0001-9989-2720
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: stourac.jan@gmail.com
    fundrefid: null
    gridid: null
    name: Jan Stourac
    note: null
    orcidid: https://orcid.org/0000-0003-3139-3700
    rorid: null
    typeEntity: Person
    typeRole:
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    - Support
    url: null
  - email: xmusil46@stud.fit.vutbr.cz
    fundrefid: null
    gridid: null
    name: Milos Musil
    note: null
    orcidid: https://orcid.org/0000-0001-9373-7930
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: brezovsky@mail.muni.cz
    fundrefid: null
    gridid: null
    name: Jan Brezovsky
    note: null
    orcidid: https://orcid.org/0000-0001-9677-5078
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: 1441@mail.muni.cz
    fundrefid: null
    gridid: null
    name: Jiri Damborsky
    note: null
    orcidid: https://orcid.org/0000-0002-7848-8216
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Eric D. Wieben
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: zendulka@fit.vut.cz
    fundrefid: null
    gridid: null
    name: Jaroslav Zendulka
    note: null
    orcidid: https://orcid.org/0000-0001-8718-7493
    rorid: null
    typeEntity: Person
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    url: null
  - email: 99207@mail.muni.cz
    fundrefid: null
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    name: Antonin Pavelka
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: null
    fundrefid: null
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    name: Loschmidt Laboratories
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
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  - email: null
    fundrefid: null
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    name: Masaryk University, Brno, Czech Republic
    note: null
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    rorid: null
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    typeRole:
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  - email: null
    fundrefid: null
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  description: A consensus classifier that combines five of the top performing tools
    (CADD, DANN, FATHMM, FunSeq2 and GWAVA) for the evaluation of pathogenic effect
    of SNPs within the human genome. The obtained results are provided together with
    annotations extracted from dbSNP, GenBank, Clinvar, OMIM, RegulomeDB, HaploReg,
    UCSC and Ensembl databases.
  documentation:
  - note: null
    type:
    - User manual
    url: https://loschmidt.chemi.muni.cz/predictsnp2/docs/userguide.pdf
  download:
  - note: null
    type: Downloads page
    url: https://loschmidt.chemi.muni.cz/peg/software/predictsnp2-standalone/
    version: '1.0'
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
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        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
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        uri: http://edamontology.org/format_3019
      - term: VCF
        uri: http://edamontology.org/format_3016
    note: VCF file containing scores and PDF file containing scores and database links
    operation:
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: PDF
        uri: http://edamontology.org/format_3508
  homepage: https://loschmidt.chemi.muni.cz/predictsnp2
  homepage_status: 0
  language:
  - JavaScript
  - Java
  lastUpdate: '2024-11-24T20:23:08.475881Z'
  license: Proprietary
  link: []
  maturity: Mature
  name: PredictSNP2
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Loschmidt Laboratories
  publication:
  - doi: 10.1371/journal.pcbi.1004962
    metadata:
      abstract: 'An important message taken from human genome sequencing projects
        is that the human population exhibits approximately 99.9% genetic similarity.
        Variations in the remaining parts of the genome determine our identity, trace
        our history and reveal our heritage. The precise delineation of phenotypically
        causal variants plays a key role in providing accurate personalized diagnosis,
        prognosis, and treatment of inherited diseases. Several computational methods
        for achieving such delineation have been reported recently. However, their
        ability to pinpoint potentially deleterious variants is limited by the fact
        that their mechanisms of prediction do not account for the existence of different
        categories of variants. Consequently, their output is biased towards the variant
        categories that are most strongly represented in the variant databases. Moreover,
        most such methods provide numeric scores but not binary predictions of the
        deleteriousness of variants or confidence scores that would be more easily
        understood by users. We have constructed three datasets covering different
        types of disease-related variants, which were divided across five categories:
        (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense
        variants. These datasets were used to develop category-optimal decision thresholds
        and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM,
        FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages
        of the category-based approach. The results obtained with the five best-performing
        tools were then combined into a consensus score. Additional comparative analyses
        showed that in the case of missense variations, protein-based predictors perform
        better than DNA sequence-based predictors. A user-friendly web interface was
        developed that provides easy access to the five tools’ predictions, and their
        consensus scores, in a user-understandable format tailored to the specific
        features of different categories of variations. To enable comprehensive evaluation
        of variants, the predictions are complemented with annotations from eight
        databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.'
      authors:
      - name: Bendl J.
      - name: Musil M.
      - name: Stourac J.
      - name: Zendulka J.
      - name: Damborsky J.
      - name: Brezovsky J.
      citationCount: 143
      date: '2016-05-01T00:00:00Z'
      journal: PLoS Computational Biology
      title: 'PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects
        by Exploiting the Different Characteristics of Variants in Distinct Genomic
        Regions'
    note: null
    pmcid: PMC4880439
    pmid: '27224906'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  topic:
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: DNA mutation
    uri: http://edamontology.org/topic_2533
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2015-11-07T20:40:08Z'
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  cost: Free of charge (with restrictions)
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  - email: jaroslav.bendl@gmail.com
    fundrefid: null
    gridid: null
    name: Jaroslav Bendl
    note: null
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    rorid: null
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    url: null
  - email: stourac.jan@gmail.com
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    fundrefid: null
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    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: null
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    gridid: null
    name: European Regional Development Fund
    note: null
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    rorid: null
    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: European Social Fund
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole:
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    url: null
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    note: null
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    note: null
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    rorid: null
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    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
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    name: Brno University of Technology
    note: null
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  description: A consensus classifier that combines six of the top performing tools
    for the prediction of the effects of mutation on protein function. The obtained
    results are provided together with annotations extracted from the Protein Mutant
    Database and the UniProt database.
  documentation:
  - note: null
    type:
    - User manual
    url: https://loschmidt.chemi.muni.cz/predictsnp1/docs/USER_GUIDE.pdf
  download:
  - note: null
    type: Binaries
    url: https://loschmidt.chemi.muni.cz/predictsnp1/docs/predictsnp-1.0.tar.gz
    version: null
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  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction of the effect of amino acid substitution on protein function
    operation:
    - term: Variant effect prediction
      uri: http://edamontology.org/operation_0331
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: UniProt ID
        uri: http://edamontology.org/data_2291
      format: []
  homepage: https://loschmidt.chemi.muni.cz/predictsnp1
  homepage_status: 0
  language:
  - JavaScript
  - Java
  - Python
  lastUpdate: '2024-11-24T20:22:54.522074Z'
  license: Proprietary
  link: []
  maturity: Mature
  name: PredictSNP
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Loschmidt Laboratories
  publication:
  - doi: 10.1371/journal.pcbi.1003440
    metadata:
      abstract: 'Single nucleotide variants represent a prevalent form of genetic
        variation. Mutations in the coding regions are frequently associated with
        the development of various genetic diseases. Computational tools for the prediction
        of the effects of mutations on protein function are very important for analysis
        of single nucleotide variants and their prioritization for experimental characterization.
        Many computational tools are already widely employed for this purpose. Unfortunately,
        their comparison and further improvement is hindered by large overlaps between
        the training datasets and benchmark datasets, which lead to biased and overly
        optimistic reported performances. In this study, we have constructed three
        independent datasets by removing all duplicities, inconsistencies and mutations
        previously used in the training of evaluated tools. The benchmark dataset
        containing over 43,000 mutations was employed for the unbiased evaluation
        of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP,
        PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were
        combined into a consensus classifier PredictSNP, resulting into significantly
        improved prediction performance, and at the same time returned results for
        all mutations, confirming that consensus prediction represents an accurate
        and robust alternative to the predictions delivered by individual tools. A
        user-friendly web interface enables easy access to all eight prediction tools,
        the consensus classifier PredictSNP and annotations from the Protein Mutant
        Database and the UniProt database. The web server and the datasets are freely
        available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.
        © 2014 Bendl et al.'
      authors:
      - name: Bendl J.
      - name: Stourac J.
      - name: Salanda O.
      - name: Pavelka A.
      - name: Wieben E.D.
      - name: Zendulka J.
      - name: Brezovsky J.
      - name: Damborsky J.
      citationCount: 595
      date: '2014-01-01T00:00:00Z'
      journal: PLoS Computational Biology
      title: 'PredictSNP: Robust and Accurate Consensus Classifier for Prediction
        of Disease-Related Mutations'
    note: null
    pmcid: PMC3894168
    pmid: '24453961'
    type:
    - Primary
    version: '1.0'
  relation:
  - biotoolsID: mapp
    type: uses
  - biotoolsID: nssnpanalyzer
    type: uses
  - biotoolsID: panther
    type: uses
  - biotoolsID: phd-snp
    type: uses
  - biotoolsID: polyphen
    type: uses
  - biotoolsID: polyphen-2
    type: uses
  - biotoolsID: sift
    type: uses
  - biotoolsID: snap
    type: uses
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2022-01-17T09:10:55.154485Z'
  biotoolsCURIE: biotools:pageant
  biotoolsID: pageant
  collectionID:
  - Rare Disease
  confidence_flag: tool
  cost: null
  credit:
  - email: valerio.napolioni@unicam.it
    fundrefid: null
    gridid: null
    name: valerio.napolioni@unicam.it
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: jiehuang001@pku.edu.cn
    fundrefid: null
    gridid: null
    name: Jie Huang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: PAGEANT (Personal Access to Genome & Analysis of Natural Traits) is
    a self-completion genetic reporting tool for DTC genetic testers.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://github.com/jielab/pageant
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-24T20:12:59.248495Z'
  license: null
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/jielab/pageant/issues
  maturity: null
  name: PAGEANT
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1093/nar/gkab1245
    metadata:
      abstract: 'GWASs have identified numerous genetic variants associated with a
        wide variety of diseases, yet despite the wide availability of genetic testing
        the insights that would enhance the interpretability of these results are
        not widely available to members of the public. As a proof of concept and demonstration
        of technological feasibility, we developed PAGEANT (Personal Access to Genome
        & Analysis of Natural Traits), usable through Graphical User Interface or
        command line-based version, aiming to serve as a protocol and prototype that
        guides the overarching design of genetic reporting tools. PAGEANT is structured
        across five core modules, summarized by five Qs: (i) quality assurance of
        the genetic data; (ii) qualitative assessment of genetic characteristics;
        (iii) quantitative assessment of health risk susceptibility based on polygenic
        risk scores and population reference; (iv) query of third-party variant databases
        (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants
        of interest. Literature review was conducted to compare PAGEANT with academic
        and industry tools. For 2504 genomes made publicly available through the 1000
        Genomes Project, we derived their genomic characteristics for a suite of qualitative
        and quantitative traits. One exemplary trait is susceptibility to COVID-19,
        based on the most up-to-date scientific findings reported.'
      authors:
      - name: Huang J.
      - name: Liang Z.-S.
      - name: Pallotti S.
      - name: Ranson J.M.
      - name: Llewellyn D.J.
      - name: Zheng Z.-J.
      - name: King D.A.
      - name: Zhou Q.
      - name: Zheng H.
      - name: Napolioni V.
      citationCount: 1
      date: '2022-04-22T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'PAGEANT: Personal access to genome and analysis of natural traits'
    note: null
    pmcid: PMC9023285
    pmid: '34928375'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Literature and language
    uri: http://edamontology.org/topic_3068
  - term: Phenomics
    uri: http://edamontology.org/topic_3298
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Public health and epidemiology
    uri: http://edamontology.org/topic_3305
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  validated: 0
  version: []
- accessibility: null
  additionDate: '2017-08-20T15:59:18Z'
  biotoolsCURIE: biotools:waveqtl
  biotoolsID: waveqtl
  collectionID:
  - Animal and Crop Genomics
  - Rare Disease
  confidence_flag: null
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  credit:
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    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://groups.google.com/forum/?hl=en#!forum/waveqtlusers
  description: Wavelet-based genetic association analysis of functional phenotypes
    arising from high-throughput sequencing assays
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/heejungshim/WaveQTL
  download: []
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    - animalandcropgenomics
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  function:
  - cmd: null
    input: []
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      uri: http://edamontology.org/operation_0282
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  homepage: https://github.com/heejungshim/WaveQTL
  homepage_status: 0
  language:
  - R
  - C++
  lastUpdate: '2024-11-24T20:12:56.535070Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: WaveQTL
  operatingSystem:
  - Linux
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  owner: admin
  publication:
  - doi: 10.1214/14-aoas776
    metadata:
      abstract: Understanding how genetic variants influence cellular-level processes
        is an important step toward understanding how they influence important organismal-level
        traits, or “phenotypes,” including human disease susceptibility. To this end,
        scientists are undertaking large-scale genetic association studies that aim
        to identify genetic variants associated with molecular and cellular phenotypes,
        such as gene expression, transcription factor binding, or chromatin accessibility.
        These studies use high-throughput sequencing assays (e.g., RNA-seq, ChIP-seq,
        DNase-seq) to obtain high-resolution data on how the traits vary along the
        genome in each sample. However, typical association analyses fail to exploit
        these high-resolution measurements, instead aggregating the data at coarser
        resolutions, such as genes, or windows of fixed length. Here we develop and
        apply statistical methods that better exploit the high-resolution data. The
        key idea is to treat the sequence data as measuring an underlying “function”
        that varies along the genome, and then, building on wavelet-based methods
        for functional data analysis, test for association between genetic variants
        and the underlying function. Applying these methods to identify genetic variants
        associated with chromatin accessibility (dsQTLs), we find that they identify
        substantially more associations than a simpler window-based analysis, and
        in total we identify 772 novel dsQTLs not identified by the original analysis.
      authors:
      - name: Shim H.
      - name: Stephens M.
      citationCount: 18
      date: '2015-06-01T00:00:00Z'
      journal: Annals of Applied Statistics
      title: Wavelet-based genetic association analysis of functional phenotypes arising
        from high-throughput sequencing assays
    note: null
    pmcid: PMC5795621
    pmid: '29399242'
    type: []
    version: null
  - doi: 10.1186/s12864-021-07582-6
    metadata:
      abstract: 'Background: Birth weight (BW) is one of the most widely studied anthropometric
        traits in humans because of its role in various adult-onset diseases. The
        number of loci associated with BW has increased dramatically since the advent
        of whole-genome screening approaches such as genome-wide association studies
        (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating
        the genetic architecture of BW, we analyzed a genotyped Norwegian dataset
        with information on child’s BW (N=9,063) using a slightly modified version
        of a wavelet-based method by Shim and Stephens (2015) called WaveQTL. Results:
        WaveQTL uses wavelet regression for regional testing and offers a more flexible
        functional modeling framework compared to conventional GWAS methods. To further
        improve WaveQTL, we added a novel feature termed “zooming strategy” to enhance
        the detection of associations in typically small regions. The modified WaveQTL
        replicated five out of the 133 loci previously identified by the largest GWAMA
        of BW to date by Warrington et al. (2019), even though our sample size was
        26 times smaller than that study and 18 times smaller than the second largest
        GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL
        performed better in regions of high LD between SNPs. Conclusions: This study
        is the first adaptation of the original WaveQTL method to the analysis of
        genome-wide genotypic data. Our results highlight the utility of the modified
        WaveQTL as a complementary tool for identifying loci that might escape detection
        by conventional genome-wide screening methods due to power issues. An attractive
        application of the modified WaveQTL would be to select traits from various
        public GWAS repositories to investigate whether they might benefit from a
        second analysis.'
      authors:
      - name: Denault W.R.P.
      - name: Romanowska J.
      - name: Helgeland O.
      - name: Jacobsson B.
      - name: Gjessing H.K.
      - name: Jugessur A.
      citationCount: 0
      date: '2021-12-01T00:00:00Z'
      journal: BMC Genomics
      title: A fast wavelet-based functional association analysis replicates several
        susceptibility loci for birth weight in a Norwegian population
    note: null
    pmcid: PMC8088671
    pmid: '33932983'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: ChIP-seq
    uri: http://edamontology.org/topic_3169
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-01-17T14:55:09Z'
  biotoolsCURIE: biotools:bridgedbr
  biotoolsID: bridgedbr
  collectionID:
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  - BridgeDb
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: egon.willighagen@gmail.com
    fundrefid: null
    gridid: null
    name: Egon Willighagen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Use BridgeDb functions and load identifier mapping databases in R.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/html/BridgeDbR.html
  download:
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    type: Source code
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    input:
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    - data:
        term: Reaction ID
        uri: http://edamontology.org/data_2108
      format:
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        uri: http://edamontology.org/format_2330
    - data:
        term: Protein identifier
        uri: http://edamontology.org/data_0989
      format:
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        uri: http://edamontology.org/format_2330
    - data:
        term: Compound identifier
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    note: null
    operation:
    - term: Loading
      uri: http://edamontology.org/operation_1812
    - term: ID mapping
      uri: http://edamontology.org/operation_3282
    output:
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Reaction ID
        uri: http://edamontology.org/data_2108
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Protein identifier
        uri: http://edamontology.org/data_0989
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Compound identifier
        uri: http://edamontology.org/data_1086
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://bioconductor.org/packages/release/bioc/html/BridgeDbR.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T20:10:32.370453Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/bridgedb/BridgeDb
  - note: null
    type:
    - Mirror
    url: https://github.com/BiGCAT-UM/bridgedb-r
  maturity: Mature
  name: BridgeDbR
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: egonw
  publication:
  - doi: 10.1186/s12859-015-0708-8
    metadata:
      abstract: 'Background: Biological pathways are descriptive diagrams of biological
        processes widely used for functional analysis of differentially expressed
        genes or proteins. Primary data analysis, such as quality control, normalisation,
        and statistical analysis, is often performed in scripting languages like R,
        Perl, and Python. Subsequent pathway analysis is usually performed using dedicated
        external applications. Workflows involving manual use of multiple environments
        are time consuming and error prone. Therefore, tools are needed that enable
        pathway analysis directly within the same scripting languages used for primary
        data analyses. Existing tools have limited capability in terms of available
        pathway content, pathway editing and visualisation options, and export file
        formats. Consequently, making the full-fledged pathway analysis tool PathVisio
        available from various scripting languages will benefit researchers. Results:
        We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software
        PathVisio. PathVisioRPC enables creating and editing biological pathways,
        visualising data on pathways, performing pathway statistics, and exporting
        results in several image formats in multiple programming environments. We
        demonstrate PathVisioRPC functionalities using examples in Python. Subsequently,
        we analyse a publicly available NCBI GEO gene expression dataset studying
        tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate
        how calls to existing R packages for data processing and calls to PathVisioRPC
        can directly work together. To further support R users, we have created RPathVisio
        simplifying the use of PathVisioRPC in this environment. We have also created
        a pathway module for the microarray data analysis portal ArrayAnalysis.org
        that calls the PathVisioRPC interface to perform pathway analysis. This module
        allows users to use PathVisio functionality online without having to download
        and install the software and exemplifies how the PathVisioRPC interface can
        be used by data analysis pipelines for functional analysis of processed genomics
        data. Conclusions: PathVisioRPC enables data visualisation and pathway analysis
        directly from within various analytical environments used for preliminary
        analyses. It supports the use of existing pathways from WikiPathways or pathways
        created using the RPC itself. It also enables automation of tasks performed
        using PathVisio, making it useful to PathVisio users performing repeated visualisation
        and analysis tasks. PathVisioRPC is freely available for academic and commercial
        use at http://projects.bigcat.unimaas.nl/pathvisiorpc.'
      authors:
      - name: Bohler A.
      - name: Eijssen L.M.T.
      - name: van Iersel M.P.
      - name: Leemans C.
      - name: Willighagen E.L.
      - name: Kutmon M.
      - name: Jaillard M.
      - name: Evelo C.T.
      citationCount: 13
      date: '2015-08-23T00:00:00Z'
      journal: BMC Bioinformatics
      title: Automatically visualise and analyse data on pathways using PathVisioRPC
        from any programming environment
    note: null
    pmcid: PMC4546821
    pmid: '26298294'
    type:
    - Review
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Data identity and mapping
    uri: http://edamontology.org/topic_3345
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - 1.8.0
  - 2.4.0
- accessibility: Open access
  additionDate: '2017-07-20T18:33:51Z'
  biotoolsCURIE: biotools:wikipathways
  biotoolsID: wikipathways
  collectionID:
  - RD-connect
  - Rare Disease
  - WikiPathways
  - BIGCAT-UM
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: martina.kutmon@maastrichtuniversity.nl
    fundrefid: null
    gridid: null
    name: Martina Kutmon
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.wikipathways.org/index.php/WikiPathways:About
  - email: apico@gladstone.ucsf.edu
    fundrefid: null
    gridid: null
    name: Alexander R. Pico
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: WikiPathways is a database of biological pathways maintained by and
    for the scientific community.
  documentation:
  - note: null
    type:
    - General
    url: https://www.wikipathways.org/index.php/WikiPathways
  - note: null
    type:
    - API documentation
    url: http://www.wikipathways.org/index.php/Help:WikiPathways_Webservice
  download: []
  editPermission:
    authors:
    - ChrisEvelo
    - egonw
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: getPathway
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Pathway or network analysis
      uri: http://edamontology.org/operation_2497
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format:
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://www.wikipathways.org
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-11-24T20:10:27.500076Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/wikipathways/pvjs
  maturity: Mature
  name: WikiPathways
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ChrisEvelo
  publication:
  - doi: 10.1093/nar/gkv1024
    metadata:
      abstract: WikiPathways (http://www.wikipathways.org) is an open, collaborative
        platform for capturing and disseminating models of biological pathways for
        data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways
        has experienced massive growth in content, which continues to be contributed
        by hundreds of individuals each year. New aspects of the diversity and depth
        of the collected pathways are described from the perspective of researchers
        interested in using pathway information in their studies. We provide updates
        on extensions and services to support pathway analysis and visualization via
        popular standalone tools, i.e. PathVisio and Cytoscape, web applications and
        common programming environments. We introduce the Quick Edit feature for pathway
        authors and curators, in addition to new means of publishing pathways and
        maintaining custom pathway collections to serve specific research topics and
        communities. In addition to the latest milestones in our pathway collection
        and curation effort, we also highlight the latest means to access the content
        as publishable figures, as standard data files, and as linked data, including
        bulk and programmatic access.
      authors:
      - name: Kutmon M.
      - name: Riutta A.
      - name: Nunes N.
      - name: Hanspers K.
      - name: Willighagen E.L.
      - name: Bohler A.
      - name: Melius J.
      - name: Waagmeester A.
      - name: Sinha S.R.
      - name: Miller R.
      - name: Coort S.L.
      - name: Cirillo E.
      - name: Smeets B.
      - name: Evelo C.T.
      - name: Pico A.R.
      citationCount: 325
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'WikiPathways: Capturing the full diversity of pathway knowledge'
    note: null
    pmcid: PMC4702772
    pmid: '26481357'
    type:
    - Primary
    version: null
  - doi: 10.1371/journal.pone.0006447
    metadata:
      abstract: WikiPathways is a platform for creating, updating, and sharing biological
        pathways [1]. Pathways can be edited and downloaded using the wiki-style website.
        Here we present a SOAP web service that provides programmatic access to WikiPathways
        that is complementary to the website. We describe the functionality that this
        web service offers and discuss several use cases in detail. Exposing WikiPathways
        through a web service opens up new ways of utilizing pathway information and
        assisting the community curation process. © 2009 Kelder et al.
      authors:
      - name: Kelder T.
      - name: Pico A.R.
      - name: Hanspers K.
      - name: Van Iersel M.P.
      - name: Evelo C.
      - name: Conklin B.R.
      citationCount: 99
      date: '2009-07-30T00:00:00Z'
      journal: PLoS ONE
      title: Mining biological pathways using WikiPathways web services
    note: null
    pmcid: PMC2714472
    pmid: '19649250'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkaa1024
    metadata:
      abstract: WikiPathways (https://www.wikipathways.org) is a biological pathway
        database known for its collaborative nature and open science approaches. With
        the core idea of the scientific community developing and curating biological
        knowledge in pathway models, WikiPathways lowers all barriers for accessing
        and using its content. Increasingly more content creators, initiatives, projects
        and tools have started using WikiPathways. Central in this growth and increased
        use of WikiPathways are the various communities that focus on particular subsets
        of molecular pathways such as for rare diseases and lipid metabolism. Knowledge
        from published pathway figures helps prioritize pathway development, using
        optical character and named entity recognition. We show the growth of WikiPathways
        over the last three years, highlight the new communities and collaborations
        of pathway authors and curators, and describe various technologies to connect
        to external resources and initiatives. The road toward a sustainable, community-driven
        pathway database goes through integration with other resources such as Wikidata
        and allowing more use, curation and redistribution of WikiPathways content.
      authors:
      - name: Martens M.
      - name: Ammar A.
      - name: Riutta A.
      - name: Waagmeester A.
      - name: Slenter D.N.
      - name: Hanspers K.
      - name: Miller R.A.
      - name: Digles D.
      - name: Lopes E.N.
      - name: Ehrhart F.
      - name: Dupuis L.J.
      - name: Winckers L.A.
      - name: Coort S.L.
      - name: Willighagen E.L.
      - name: Evelo C.T.
      - name: Pico A.R.
      - name: Kutmon M.
      citationCount: 492
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'WikiPathways: Connecting communities'
    note: null
    pmcid: PMC7779061
    pmid: '33211851'
    type: []
    version: null
  - doi: 10.1093/nar/gkx1064
    metadata:
      abstract: WikiPathways (wikipathways.org) captures the collective knowledge
        represented in biological pathways. By providing a database in a curated,
        machine readable way, omics data analysis and visualization is enabled. WikiPathways
        and other pathway databases are used to analyze experimental data by research
        groups in many fields. Due to the open and collaborative nature of the WikiPathways
        platform, our content keeps growing and is getting more accurate, making WikiPathways
        a reliable and rich pathway database. Previously, however, the focus was primarily
        on genes and proteins, leaving many metabolites with only limited annotation.
        Recent curation efforts focused on improving the annotation of metabolism
        and metabolic pathways by associating unmapped metabolites with database identifiers
        and providing more detailed interaction knowledge. Here, we report the outcomes
        of the continued growth and curation efforts, such as a doubling of the number
        of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an
        OpenAPI documentation of our web services and the FAIR (Findable, Accessible,
        Interoperable and Reusable) annotation of resources to increase the interoperability
        of the knowledge encoded in these pathways and experimental omics data. New
        search options, monthly downloads, more links to metabolite databases, and
        new portals make pathway knowledge more effortlessly accessible to individual
        researchers and research communities.
      authors:
      - name: Slenter D.N.
      - name: Kutmon M.
      - name: Hanspers K.
      - name: Riutta A.
      - name: Windsor J.
      - name: Nunes N.
      - name: Melius J.
      - name: Cirillo E.
      - name: Coort S.L.
      - name: DIgles D.
      - name: Ehrhart F.
      - name: Giesbertz P.
      - name: Kalafati M.
      - name: Martens M.
      - name: Miller R.
      - name: Nishida K.
      - name: Rieswijk L.
      - name: Waagmeester A.
      - name: Eijssen L.M.T.
      - name: Evelo C.T.
      - name: Pico A.R.
      - name: Willighagen E.L.
      citationCount: 620
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'WikiPathways: A multifaceted pathway database bridging metabolomics
        to other omics research'
    note: null
    pmcid: PMC5753270
    pmid: '29136241'
    type: []
    version: null
  relation: []
  toolType:
  - Web API
  - Web application
  - Database portal
  - Web service
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-04-12T23:41:24.255364Z'
  biotoolsCURIE: biotools:strvctvre
  biotoolsID: strvctvre
  collectionID:
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sharo@compbio.berkeley.edu
    fundrefid: null
    gridid: null
    name: Andrew Sharo
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://andrewsharo.com/
  description: Method that predicts the pathogenicity of deletions and duplications
    that affect one or more exons.
  documentation:
  - note: null
    type:
    - General
    url: https://strvctvre.berkeley.edu/
  download:
  - note: null
    type: Source code
    url: https://github.com/andrewSharo/StrVCTVRE
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BED
        uri: http://edamontology.org/format_3003
    note: null
    operation:
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    output: []
  homepage: https://strvctvre.berkeley.edu/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-24T20:10:20.152356Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/andrewSharo/StrVCTVRE
  maturity: Mature
  name: StrVCTVRE
  operatingSystem:
  - Linux
  otherID: []
  owner: andrewSharo
  publication:
  - doi: 10.1016/j.ajhg.2021.12.007
    metadata:
      abstract: Whole-genome sequencing resolves many clinical cases where standard
        diagnostic methods have failed. However, at least half of these cases remain
        unresolved after whole-genome sequencing. Structural variants (SVs; genomic
        variants larger than 50 base pairs) of uncertain significance are the genetic
        cause of a portion of these unresolved cases. As sequencing methods using
        long or linked reads become more accessible and SV detection algorithms improve,
        clinicians and researchers are gaining access to thousands of reliable SVs
        of unknown disease relevance. Methods to predict the pathogenicity of these
        SVs are required to realize the full diagnostic potential of long-read sequencing.
        To address this emerging need, we developed StrVCTVRE to distinguish pathogenic
        SVs from benign SVs that overlap exons. In a random forest classifier, we
        integrated features that capture gene importance, coding region, conservation,
        expression, and exon structure. We found that features such as expression
        and conservation are important but are absent from SV classification guidelines.
        We leveraged multiple resources to construct a size-matched training set of
        rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately
        across a wide SV size range on independent test sets, which will allow clinicians
        and researchers to eliminate about half of SVs from consideration while retaining
        a 90% sensitivity. We anticipate clinicians and researchers will use StrVCTVRE
        to prioritize SVs in probands where no SV is immediately compelling, empowering
        deeper investigation into novel SVs to resolve cases and understand new mechanisms
        of disease. StrVCTVRE runs rapidly and is publicly available.
      authors:
      - name: Sharo A.G.
      - name: Hu Z.
      - name: Sunyaev S.R.
      - name: Brenner S.E.
      citationCount: 23
      date: '2022-02-03T00:00:00Z'
      journal: American Journal of Human Genetics
      title: 'StrVCTVRE: A supervised learning method to predict the pathogenicity
        of human genome structural variants'
    note: null
    pmcid: PMC8874149
    pmid: '35032432'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Copy number variation
    uri: http://edamontology.org/topic_3958
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2017-07-04T12:32:38Z'
  biotoolsCURIE: biotools:cadd_phred
  biotoolsID: cadd_phred
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: cadd-support@uw.edu
    fundrefid: null
    gridid: null
    name: CADD support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Combined Annotation Dependent Depletion (CADD) - tool for scoring the
    deleteriousness of single nucleotide variants as well as insertion/deletions variants
    in the human genome.
  documentation:
  - note: null
    type:
    - General
    url: http://cadd.gs.washington.edu/info
  download:
  - note: null
    type: Downloads page
    url: https://cadd.gs.washington.edu/download
    version: null
  editPermission:
    authors:
    - sergitobara
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    output: []
  homepage: http://cadd.gs.washington.edu/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:59:00.549338Z'
  license: Unlicense
  link: []
  maturity: Mature
  name: CADD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-EE
  publication:
  - doi: 10.1038/ng.2892
    metadata:
      abstract: Current methods for annotating and interpreting human genetic variation
        tend to exploit a single information type (for example, conservation) and/or
        are restricted in scope (for example, to missense changes). Here we describe
        Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating
        many diverse annotations into a single measure (C score) for each variant.
        We implement CADD as a support vector machine trained to differentiate 14.7
        million high-frequency human-derived alleles from 14.7 million simulated variants.
        We precompute C scores for all 8.6 billion possible human single-nucleotide
        variants and enable scoring of short insertions-deletions. C scores correlate
        with allelic diversity, annotations of functionality, pathogenicity, disease
        severity, experimentally measured regulatory effects and complex trait associations,
        and they highly rank known pathogenic variants within individual genomes.
        The ability of CADD to prioritize functional, deleterious and pathogenic variants
        across many functional categories, effect sizes and genetic architectures
        is unmatched by any current single-annotation method. © 2014 Nature America,
        Inc. © 2014 Nature America, Inc.
      authors:
      - name: Kircher M.
      - name: Witten D.M.
      - name: Jain P.
      - name: O'roak B.J.
      - name: Cooper G.M.
      - name: Shendure J.
      citationCount: 4423
      date: '2014-01-01T00:00:00Z'
      journal: Nature Genetics
      title: A general framework for estimating the relative pathogenicity of human
        genetic variants
    note: null
    pmcid: PMC3992975
    pmid: '24487276'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gky1016
    metadata:
      abstract: Combined Annotation-Dependent Depletion (CADD) is a widely used measure
        of variant deleteriousness that can effectively prioritize causal variants
        in genetic analyses, particularly highly penetrant contributors to severe
        Mendelian disorders. CADD is an integrative annotation built from more than
        60 genomic features, and can score human single nucleotide variants and short
        insertion and deletions anywhere in the reference assembly. CADD uses a machine
        learning model trained on a binary distinction between simulated de novo variants
        and variants that have arisen and become fixed in human populations since
        the split between humans and chimpanzees; the former are free of selective
        pressure and may thus include both neutral and deleterious alleles, while
        the latter are overwhelmingly neutral (or, at most, weakly deleterious) by
        virtue of having survived millions of years of purifying selection. Here we
        review the latest updates to CADD, including the most recent version, 1.4,
        which supports the human genome build GRCh38. We also present updates to our
        website that include simplified variant lookup, extended documentation, an
        Application Program Interface and improved mechanisms for integrating CADD
        scores into other tools or applications. CADD scores, software and documentation
        are available at https://cadd.gs.washington.edu.
      authors:
      - name: Rentzsch P.
      - name: Witten D.
      - name: Cooper G.M.
      - name: Shendure J.
      - name: Kircher M.
      citationCount: 2122
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'CADD: Predicting the deleteriousness of variants throughout the human
        genome'
    note: null
    pmcid: PMC6323892
    pmid: '30371827'
    type: []
    version: null
  - doi: 10.1186/s13073-021-00835-9
    metadata:
      abstract: 'Background: Splicing of genomic exons into mRNAs is a critical prerequisite
        for the accurate synthesis of human proteins. Genetic variants impacting splicing
        underlie a substantial proportion of genetic disease, but are challenging
        to identify beyond those occurring at donor and acceptor dinucleotides. To
        address this, various methods aim to predict variant effects on splicing.
        Recently, deep neural networks (DNNs) have been shown to achieve better results
        in predicting splice variants than other strategies. Methods: It has been
        unclear how best to integrate such process-specific scores into genome-wide
        variant effect predictors. Here, we use a recently published experimental
        data set to compare several machine learning methods that score variant effects
        on splicing. We integrate the best of those approaches into general variant
        effect prediction models and observe the effect on classification of known
        pathogenic variants. Results: We integrate two specialized splicing scores
        into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu),
        a widely used tool for genome-wide variant effect prediction that we previously
        developed to weight and integrate diverse collections of genomic annotations.
        With this new model, CADD-Splice, we show that inclusion of splicing DNN effect
        scores substantially improves predictions across multiple variant categories,
        without compromising overall performance. Conclusions: While splice effect
        scores show superior performance on splice variants, specialized predictors
        cannot compete with other variant scores in general variant interpretation,
        as the latter account for nonsense and missense effects that do not alter
        splicing. Although only shown here for splice scores, we believe that the
        applied approach will generalize to other specific molecular processes, providing
        a path for the further improvement of genome-wide variant effect prediction.'
      authors:
      - name: Rentzsch P.
      - name: Schubach M.
      - name: Shendure J.
      - name: Kircher M.
      citationCount: 376
      date: '2021-12-01T00:00:00Z'
      journal: Genome Medicine
      title: CADD-Splice—improving genome-wide variant effect prediction using deep
        learning-derived splice scores
    note: null
    pmcid: PMC7901104
    pmid: '33618777'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-03-27T07:56:24Z'
  biotoolsCURIE: biotools:ucsc_genome_browser
  biotoolsID: ucsc_genome_browser
  collectionID:
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: rhead@soe.ucsc.edu
    fundrefid: null
    gridid: null
    name: Brooke Rhead
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://genome.ucsc.edu/contacts.html
  description: Large database of publicly available sequence and annotation data along
    with an integrated tool set for examining and comparing the genomes of organisms,
    aligning sequence to genomes, and displaying and sharing users own annotation
    data.
  documentation:
  - note: null
    type:
    - General
    url: https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html
  download: []
  editPermission:
    authors:
    - sergitobara
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    - term: Genome visualisation
      uri: http://edamontology.org/operation_3208
    - term: Sequence alignment
      uri: http://edamontology.org/operation_0292
    - term: PCR primer design
      uri: http://edamontology.org/operation_0308
    output: []
  homepage: https://genome.ucsc.edu/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:58:33.301729Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: UCSC Genome Browser
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-EE
  publication:
  - doi: 10.1093/nar/gkp939
    metadata:
      abstract: The University of California, Santa Cruz (UCSC) Genome Browser website
        (http://genome.ucsc.edu/) provides a large database of publicly available
        sequence and annotation data along with an integrated tool set for examining
        and comparing the genomes of organisms, aligning sequence to genomes, and
        displaying and sharing users' own annotation data. As of September 2009, genomic
        sequence and a basic set of annotation 'tracks' are provided for 47 organisms,
        including 14 mammals, 10 non-mammal vertebrates, 3 invertebrate deuterostomes,
        13 insects, 6 worms and a yeast. New data highlights this year include an
        updated human genome browser, a 44-species multiple sequence alignment track,
        improved variation and phenotype tracks and 16 new genome-wide ENCODE tracks.
        New features include drag-and-zoom navigation, a Wiki track for user-added
        annotations, new custom track formats for large datasets (bigBed and bigWig),
        a new multiple alignment output tool, links to variation and protein structure
        tools, in silico PCR utility enhancements, and improved track configuration
        tools. © The Author(s) 2009. Published by Oxford University Press.
      authors:
      - name: Rhead B.
      - name: Karolchik D.
      - name: Kuhn R.M.
      - name: Hinrichs A.S.
      - name: Zweig A.S.
      - name: Fujita P.A.
      - name: Diekhans M.
      - name: Smith K.E.
      - name: Rosenbloom K.R.
      - name: Raney B.J.
      - name: Pohl A.
      - name: Pheasant M.
      - name: Meyer L.R.
      - name: Learned K.
      - name: Hsu F.
      - name: Hillman-Jackson J.
      - name: Harte R.A.
      - name: Giardine B.
      - name: Dreszer T.R.
      - name: Clawson H.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 522
      date: '2009-11-10T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC genome browser database: Update 2010'
    note: null
    pmcid: PMC2808870
    pmid: '19906737'
    type: []
    version: null
  - doi: 10.1093/nar/gkr1012
    metadata:
      abstract: The Encyclopedia of DNA Elements (ENCODE) Consortium is entering its
        5th year of productionlevel effort generating high-quality whole-genome functional
        annotations of the human genome. The past year has brought the ENCODE compendium
        of functional elements to critical mass, with a diverse set of 27 biochemical
        assays now covering 200 distinct human cell types. Within the mouse genome,
        which has been under study by ENCODE groups for the past 2 years, 37 cell
        types have been assayed. Over 2000 individual experiments have been completed
        and submitted to the Data Coordination Center for public use. UCSC makes this
        data available on the quality-reviewed public Genome Browser (http://genome.ucsc.edu)
        and on an early-access Preview Browser (http://genomepreview. ucsc.edu). Visual
        browsing, data mining and download of raw and processed data files are all
        supported. An ENCODE portal (http:// encodeproject.org) provides specialized
        tools and information about the ENCODE data sets. © The Author(s) 2011.
      authors:
      - name: Rosenbloom K.R.
      - name: Dreszer T.R.
      - name: Long J.C.
      - name: Malladi V.S.
      - name: Sloan C.A.
      - name: Raney B.J.
      - name: Cline M.S.
      - name: Karolchik D.
      - name: Barber G.P.
      - name: Clawson H.
      - name: Diekhans M.
      - name: Fujita P.A.
      - name: Goldman M.
      - name: Gravell R.C.
      - name: Harte R.A.
      - name: Hinrichs A.S.
      - name: Kirkup V.M.
      - name: Kuhn R.M.
      - name: Learned K.
      - name: Maddren M.
      - name: Meyer L.R.
      - name: Pohl A.
      - name: Rhead B.
      - name: Wong M.C.
      - name: Zweig A.S.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 211
      date: '2012-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ENCODE whole-genome data in the UCSC Genome Browser: Update 2012'
    note: null
    pmcid: PMC3245183
    pmid: '22075998'
    type: []
    version: null
  - doi: 10.1093/nar/gkr1055
    metadata:
      abstract: The University of California Santa Cruz Genome Browser (http://genome.ucsc.
        edu) offers online public access to a growing database of genomic sequence
        and annotations for a wide variety of organisms. The Browser is an integrated
        tool set for visualizing, comparing, analyzing and sharing both publicly available
        and user-generated genomic data sets. In the past year, the local database
        has been updated with four new species assemblies, and we anticipate another
        four will be released by the end of 2011. Further, a large number of annotation
        tracks have been either added, updated by contributors, or remapped to the
        latest human reference genome. Among these are new phenotype and disease annotations,
        UCSC genes, and a major dbSNP update, which required new visualization methods.
        Growing beyond the local database, this year we have introduced 'track data
        hubs', which allow the Genome Browser to provide access to remotely located
        sets of annotations. This feature is designed to significantly extend the
        number and variety of annotation tracks that are publicly available for visualization
        and analysis from within our site. We have also introduced several usability
        features including track search and a context-sensitive menu of options available
        with a right-click anywhere on the Browser's image. © The Author(s) 2011.
        Published by Oxford University Press.
      authors:
      - name: Dreszer T.R.
      - name: Karolchik D.
      - name: Zweig A.S.
      - name: Hinrichs A.S.
      - name: Raney B.J.
      - name: Kuhn R.M.
      - name: Meyer L.R.
      - name: Wong M.
      - name: Sloan C.A.
      - name: Rosenbloom K.R.
      - name: Roe G.
      - name: Rhead B.
      - name: Pohl A.
      - name: Malladi V.S.
      - name: Li C.H.
      - name: Learned K.
      - name: Kirkup V.
      - name: Hsu F.
      - name: Harte R.A.
      - name: Guruvadoo L.
      - name: Goldman M.
      - name: Giardine B.M.
      - name: Fujita P.A.
      - name: Diekhans M.
      - name: Cline M.S.
      - name: Clawson H.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 268
      date: '2012-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: Extensions and updates 2011'
    note: null
    pmcid: PMC3245018
    pmid: '22086951'
    type: []
    version: null
  - doi: 10.1101/gr.229102
    metadata:
      abstract: As vertebrate genome sequences near completion and research refocuses
        to their analysis, the issue of effective genome annotation display becomes
        critical. A mature web tool for rapid and reliable display of any requested
        portion of the genome at any scale, together with several dozen aligned annotation
        tracks, is provided at http://genome.ucsc.edu. This browser displays assembly
        contigs and gaps, mRNA and expressed sequence tag alignments, multiple gene
        predictions, cross-species homologies, single nucleotide polymorphisms, sequence-tagged
        sites, radiation hybrid data, transposon repeats, and more as a stack of coregistered
        tracks. Text and sequence-based searches provide quick and precise access
        to any region of specific interest. Secondary links from individual features
        lead to sequence details and supplementary off-site databases. One-half of
        the annotation tracks are computed at the University of California, Santa
        Cruz from publicly available sequence data; collaborators worldwide provide
        the rest. Users can stably add their own custom tracks to the browser for
        educational or research purposes. The conceptual and technical framework of
        the browser, its underlying MYSQL database, and overall use are described.
        The web site currently serves over 50,000 pages per day to over 3000 different
        users.
      authors:
      - name: James Kent W.
      - name: Sugnet C.W.
      - name: Furey T.S.
      - name: Roskin K.M.
      - name: Pringle T.H.
      - name: Zahler A.M.
      - name: Haussler D.
      citationCount: 7662
      date: '2002-01-01T00:00:00Z'
      journal: Genome Research
      title: The human genome browser at UCSC
    note: null
    pmcid: PMC186604
    pmid: '12045153'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkg129
    metadata:
      abstract: The University of California Santa Cruz (UCSC) Genome Browser Database
        is an up to date source for genome sequence data integrated with a large collection
        of related annotations. The database is optimized to support fast interactive
        performance with the web-based UCSC Genome Browser, a tool built on top of
        the database for rapid visualization and querying of the data at many levels.
        The annotations for a given genome are displayed in the browser as a series
        of tracks aligned with the genomic sequence. Sequence data and annotations
        may also be viewed in a text-based tabular format or downloaded as tab-delimited
        flat files. The Genome Browser Database, browsing tools and downloadable data
        files can all be found on the UCSC Genome Bioinformatics website (http://genome.ucsc.edu),
        which also contains links to documentation and related technical information.
      authors:
      - name: Karolchik D.
      - name: Baertsch R.
      - name: Diekhans M.
      - name: Furey T.S.
      - name: Hinrichs A.
      - name: Lu Y.T.
      - name: Roskin K.M.
      - name: Schwartz M.
      - name: Sugnet C.W.
      - name: Thomas D.J.
      - name: Weber R.J.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 1273
      date: '2003-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The UCSC Genome Browser Database
    note: null
    pmcid: PMC165576
    pmid: '12519945'
    type: []
    version: null
  - doi: 10.1093/nar/gkm966
    metadata:
      abstract: The University of California, Santa Cruz, Genome Browser Database
        (GBD) provides integrated sequence and annotation data for a large collection
        of vertebrate and model organism genomes. Seventeen new assemblies have been
        added to the database in the past year, for a total coverage of 19 vertebrate
        and 21 invertebrate species as of September 2007. For each assembly, the GBD
        contains a collection of annotation data aligned to the genomic sequence.
        Highlights of this year's additions include a 28-species human-based vertebrate
        conservation annotation, an enhanced UCSC Genes set, and more human variation,
        MGC, and ENCODE data. The database is optimized for fast interactive performance
        with a set of web-based tools that may be used to view, manipulate, filter
        and download the annotation data. New toolset features include the Genome
        Graphs tool for displaying genome-wide data sets, session saving and sharing,
        better custom track management, expanded Genome Browser configuration options
        and a Genome Browser wiki site. © 2007 The Author(s).
      authors:
      - name: Karolchik D.
      - name: Kuhn R.M.
      - name: Baertsch R.
      - name: Barber G.P.
      - name: Clawson H.
      - name: Diekhans M.
      - name: Giardine B.
      - name: Harte R.A.
      - name: Hinrichs A.S.
      - name: Hsu F.
      - name: Kober K.M.
      - name: Miller W.
      - name: Pedersen J.S.
      - name: Pohl A.
      - name: Raney B.J.
      - name: Rhead B.
      - name: Rosenbloom K.R.
      - name: Smith K.E.
      - name: Stanke M.
      - name: Thakkapallayil A.
      - name: Trumbower H.
      - name: Wang T.
      - name: Zweig A.S.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 431
      date: '2008-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC genome browser database: 2008 update'
    note: null
    pmcid: PMC2238835
    pmid: '18086701'
    type: []
    version: null
  - doi: 10.1093/nar/gkj144
    metadata:
      abstract: The University of California Santa Cruz Genome Browser Database (GBD)
        contains sequence and annotation data for the genomes of about a dozen vertebrate
        species and several major model organisms. Genome annotations typically include
        assembly data, sequence composition, genes and gene predictions, mRNA and
        expressed sequence tag evidence, comparative genomics, regulation, expression
        and variation data. The database is optimized to support fast interactive
        performance with web tools that provide powerful visualization and querying
        capabilities for mining the data. The Genome Browser displays a wide variety
        of annotations at all scales from single nucleotide level up to a full chromosome.
        The Table Browser provides direct access to the database tables and sequence
        data, enabling complex queries on genome-wide datasets. The Proteome Browser
        graphically displays protein properties. The Gene Sorter allows filtering
        and comparison of genes by several metrics including expression data and several
        gene properties. BLAT and In Silico PCR search for sequences in entire genomes
        in seconds. These tools are highly integrated and provide many hyperlinks
        to other databases and websites. The GBD, browsing tools, downloadable data
        files and links to documentation and other information can be found at http://genome.ucsc.edu/.
      authors:
      - name: Hinrichs A.S.
      - name: Karolchik D.
      - name: Baertsch R.
      - name: Barber G.P.
      - name: Bejerano G.
      - name: Clawson H.
      - name: Diekhans M.
      - name: Furey T.S.
      - name: Harte R.A.
      - name: Hsu F.
      - name: Hillman-Jackson J.
      - name: Kuhn R.M.
      - name: Pedersen J.S.
      - name: Pohl A.
      - name: Raney B.J.
      - name: Rosenbloom K.R.
      - name: Siepel A.
      - name: Smith K.E.
      - name: Sugnet C.W.
      - name: Sultan-Qurraie A.
      - name: Thomas D.J.
      - name: Trumbower H.
      - name: Weber R.J.
      - name: Weirauch M.
      - name: Zweig A.S.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 893
      date: '2006-01-01T00:00:00Z'
      journal: Nucleic acids research
      title: 'The UCSC Genome Browser Database: update 2006.'
    note: null
    pmcid: PMC1347506
    pmid: '16381938'
    type: []
    version: null
  - doi: 10.1093/nar/gkn875
    metadata:
      abstract: The UCSC Genome Browser Database (GBD, http://genome.ucsc.edu) is
        a publicly available collection of genome assembly sequence data and integrated
        annotations for a large number of organisms, including extensive comparative-genomic
        resources. In the past year, 13 new genome assemblies have been added, including
        two important primate species, orangutan and marmoset, bringing the total
        to 46 assemblies for 24 different vertebrates and 39 assemblies for 22 different
        invertebrate animals. The GBD datasets may be viewed graphically with the
        UCSC Genome Browser, which uses a coordinate-based display system allowing
        users to juxtapose a wide variety of data. These data include all mRNAs from
        GenBank mapped to all organisms, RefSeq alignments, gene predictions, regulatory
        elements, gene expression data, repeats, SNPs and other variation data, as
        well as pairwise and multiple-genome alignments. A variety of other bioinformatics
        tools are also provided, including BLAT, the Table Browser, the Gene Sorter,
        the Proteome Browser, VisiGene and Genome Graphs. © 2008 The Author(s).
      authors:
      - name: Kuhn R.M.
      - name: Karolchik D.
      - name: Zweig A.S.
      - name: Wang T.
      - name: Smith K.E.
      - name: Rosenbloom K.R.
      - name: Rhead B.
      - name: Raney B.J.
      - name: Pohl A.
      - name: Pheasant M.
      - name: Meyer L.
      - name: Hsu F.
      - name: Hinrichs A.S.
      - name: Harte R.A.
      - name: Giardine B.
      - name: Fujita P.
      - name: Diekhans M.
      - name: Dreszer T.
      - name: Clawson H.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 305
      date: '2009-01-09T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser Database: Update 2009'
    note: null
    pmcid: PMC2686463
    pmid: '18996895'
    type: []
    version: null
  - doi: 10.1093/nar/gkt1168
    metadata:
      abstract: The University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu)
        offers online public access to a growing database of genomic sequence and
        annotations for a large collection of organisms, primarily vertebrates, with
        an emphasis on the human and mouse genomes. The Browser's web-based tools
        provide an integrated environment for visualizing, comparing, analysing and
        sharing both publicly available and user-generated genomic data sets. As of
        September 2013, the database contained genomic sequence and a basic set of
        annotation 'tracks' for ∼90 organisms. Significant new annotations include
        a 60-species multiple alignment conservation track on the mouse, updated UCSC
        Genes tracks for human and mouse, and several new sets of variation and ENCODE
        data. New software tools include a Variant Annotation Integrator that returns
        predicted functional effects of a set of variants uploaded as a custom track,
        an extension to UCSC Genes that displays haplotype alleles for protein-coding
        genes and an expansion of data hubs that includes the capability to display
        remotely hosted user-provided assembly sequence in addition to annotation
        data. To improve European access, we have added a Genome Browser mirror (http://genome-euro.ucsc.edu)
        hosted at Bielefeld University in Germany. © 2013 The Author(s). Published
        by Oxford University Press.
      authors:
      - name: Karolchik D.
      - name: Barber G.P.
      - name: Casper J.
      - name: Clawson H.
      - name: Cline M.S.
      - name: Diekhans M.
      - name: Dreszer T.R.
      - name: Fujita P.A.
      - name: Guruvadoo L.
      - name: Haeussler M.
      - name: Harte R.A.
      - name: Heitner S.
      - name: Hinrichs A.S.
      - name: Learned K.
      - name: Lee B.T.
      - name: Li C.H.
      - name: Raney B.J.
      - name: Rhead B.
      - name: Rosenbloom K.R.
      - name: Sloan C.A.
      - name: Speir M.L.
      - name: Zweig A.S.
      - name: Haussler D.
      - name: Kuhn R.M.
      - name: Kent W.J.
      citationCount: 528
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: 2014 update'
    note: null
    pmcid: PMC3964947
    pmid: '24270787'
    type: []
    version: null
  - doi: 10.1093/nar/gku1177
    metadata:
      abstract: 'Launched in 2001 to showcase the draft human genome assembly, the
        UCSC Genome Browser database (http://genome.ucsc.edu) and associated tools
        continue to grow, providing a comprehensive resource of genome assemblies
        and annotations to scientists and students worldwide. Highlights of the past
        year include the release of a browser for the first new human genome reference
        assembly in 4 years in December 2013 (GRCh38, UCSC hg38), a watershed comparative
        genomics annotation (100-species multiple alignment and conservation) and
        a novel distribution mechanism for the browser (GBiB: Genome Browser in a
        Box). We created browsers for new species (Chinese hamster, elephant shark,
        minke whale), ''mined the web'' for DNA sequences and expanded the browser
        display with stacked color graphs and region highlighting. As our user community
        increasingly adopts the UCSC track hub and assembly hub representations for
        sharing large-scale genomic annotation data sets and genome sequencing projects,
        our menu of public data hubs has tripled.'
      authors:
      - name: Rosenbloom K.R.
      - name: Armstrong J.
      - name: Barber G.P.
      - name: Casper J.
      - name: Clawson H.
      - name: Diekhans M.
      - name: Dreszer T.R.
      - name: Fujita P.A.
      - name: Guruvadoo L.
      - name: Haeussler M.
      - name: Harte R.A.
      - name: Heitner S.
      - name: Hickey G.
      - name: Hinrichs A.S.
      - name: Hubley R.
      - name: Karolchik D.
      - name: Learned K.
      - name: Lee B.T.
      - name: Li C.H.
      - name: Miga K.H.
      - name: Nguyen N.
      - name: Paten B.
      - name: Raney B.J.
      - name: Smit A.F.A.
      - name: Speir M.L.
      - name: Zweig A.S.
      - name: Haussler D.
      - name: Kuhn R.M.
      - name: Kent W.J.
      citationCount: 709
      date: '2015-01-28T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: 2015 update'
    note: null
    pmcid: PMC4383971
    pmid: '25428374'
    type: []
    version: null
  - doi: 10.1093/nar/gkv1275
    metadata:
      abstract: For the past 15 years, the UCSC Genome Browser (http://genome.ucsc.edu/)
        has served the international research community by offering an integrated
        platform for viewing and analyzing information from a large database of genome
        assemblies and their associated annotations. The UCSC Genome Browser has been
        under continuous development since its inception with new data sets and software
        features added frequently. Some release highlights of this year include new
        and updated genome browsers for various assemblies, including bonobo and zebrafish;
        new gene annotation sets; improvements to track and assembly hub support;
        and a new interactive tool, the "Data Integrator", for intersecting data from
        multiple tracks. We have greatly expanded the data sets available on the most
        recent human assembly, hg38/GRCh38, to include updated gene prediction sets
        from GENCODE, more phenotype - and disease associated variants from ClinVar
        and ClinGen, more genomic regulatory data, and a new multiple genome alignment.
      authors:
      - name: Speir M.L.
      - name: Zweig A.S.
      - name: Rosenbloom K.R.
      - name: Raney B.J.
      - name: Paten B.
      - name: Nejad P.
      - name: Lee B.T.
      - name: Learned K.
      - name: Karolchik D.
      - name: Hinrichs A.S.
      - name: Heitner S.
      - name: Harte R.A.
      - name: Haeussler M.
      - name: Guruvadoo L.
      - name: Fujita P.A.
      - name: Eisenhart C.
      - name: Diekhans M.
      - name: Clawson H.
      - name: Casper J.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kuhn R.M.
      - name: Kent W.J.
      citationCount: 319
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: 2016 update'
    note: null
    pmcid: PMC4702902
    pmid: '26590259'
    type: []
    version: null
  - doi: 10.1093/nar/gkw1134
    metadata: null
    note: null
    pmcid: PMC5210591
    pmid: '27899642'
    type: []
    version: null
  - doi: 10.1093/nar/gky1095
    metadata:
      abstract: The UCSC Genome Browser (https://genome.ucsc.edu) is a graphical viewer
        for exploring genome annotations. For almost two decades, the Browser has
        provided visualization tools for genetics and molecular biology and continues
        to add new data and features. This year, we added a new tool that lets users
        interactively arrange existing graphing tracks into new groups. Other software
        additions include new formats for chromosome interactions, a ChIP-Seq peak
        display for track hubs and improved support for HGVS. On the annotation side,
        we have added gnomAD, TCGA expression, RefSeq Functional elements, GTEx eQTLs,
        CRISPR Guides, SNPpedia and created a 30-way primate alignment on the human
        genome. Nine assemblies now have RefSeq-mapped gene models.
      authors:
      - name: Haeussler M.
      - name: Zweig A.S.
      - name: Tyner C.
      - name: Speir M.L.
      - name: Rosenbloom K.R.
      - name: Raney B.J.
      - name: Lee C.M.
      - name: Lee B.T.
      - name: Hinrichs A.S.
      - name: Gonzalez J.N.
      - name: Gibson D.
      - name: Diekhans M.
      - name: Clawson H.
      - name: Casper J.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kuhn R.M.
      - name: Kent W.J.
      citationCount: 534
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: 2019 update'
    note: null
    pmcid: PMC6323953
    pmid: '30407534'
    type: []
    version: null
  - doi: 10.1093/nar/gks1048
    metadata:
      abstract: The University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu)
        offers online public access to a growing database of genomic sequence and
        annotations for a wide variety of organisms. The Browser is an integrated
        tool set for visualizing, comparing, analysing and sharing both publicly available
        and user-generated genomic datasets. As of September 2012, genomic sequence
        and a basic set of annotation 'tracks' are provided for 63 organisms, including
        26 mammals, 13 non-mammal vertebrates, 3 invertebrate deuterostomes, 13 insects,
        6 worms, yeast and sea hare. In the past year 19 new genome assemblies have
        been added, and we anticipate releasing another 28 in early 2013. Further,
        a large number of annotation tracks have been either added, updated by contributors
        or remapped to the latest human reference genome. Among these are an updated
        UCSC Genes track for human and mouse assemblies. We have also introduced several
        features to improve usability, including new navigation menus. This article
        provides an update to the UCSC Genome Browser database, which has been previously
        featured in the Database issue of this journal. © The Author(s) 2012.
      authors:
      - name: Meyer L.R.
      - name: Zweig A.S.
      - name: Hinrichs A.S.
      - name: Karolchik D.
      - name: Kuhn R.M.
      - name: Wong M.
      - name: Sloan C.A.
      - name: Rosenbloom K.R.
      - name: Roe G.
      - name: Rhead B.
      - name: Raney B.J.
      - name: Pohl A.
      - name: Malladi V.S.
      - name: Li C.H.
      - name: Lee B.T.
      - name: Learned K.
      - name: Kirkup V.
      - name: Hsu F.
      - name: Heitner S.
      - name: Harte R.A.
      - name: Haeussler M.
      - name: Guruvadoo L.
      - name: Goldman M.
      - name: Giardine B.M.
      - name: Fujita P.A.
      - name: Dreszer T.R.
      - name: Diekhans M.
      - name: Cline M.S.
      - name: Clawson H.
      - name: Barber G.P.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 623
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC Genome Browser database: Extensions and updates 2013'
    note: null
    pmcid: PMC3531082
    pmid: '23155063'
    type: []
    version: null
  - doi: 10.1093/nar/gkq963
    metadata:
      abstract: The University of California, Santa Cruz Genome Browser (http://genome.ucsc.edu)
        offers online access to a database of genomic sequence and annotation data
        for a wide variety of organisms. The Browser also has many tools for visualizing,
        comparing and analyzing both publicly available and user-generated genomic
        data sets, aligning sequences and uploading user data. Among the features
        released this year are a gene search tool and annotation track drag-reorder
        functionality as well as support for BAM and BigWig/BigBed file formats. New
        display enhancements include overlay of multiple wiggle tracks through use
        of transparent coloring, options for displaying transformed wiggle data, a
        'mean+whiskers' windowing function for display of wiggle data at high zoom
        levels, and more color schemes for microarray data. New data highlights include
        seven new genome assemblies, a Neandertal genome data portal, phenotype and
        disease association data, a human RNA editing track, and a zebrafish Conservation
        track. We also describe updates to existing tracks. © The Author(s) 2010.
      authors:
      - name: Fujita P.A.
      - name: Rhead B.
      - name: Zweig A.S.
      - name: Hinrichs A.S.
      - name: Karolchik D.
      - name: Cline M.S.
      - name: Goldman M.
      - name: Barber G.P.
      - name: Clawson H.
      - name: Coelho A.
      - name: Diekhans M.
      - name: Dreszer T.R.
      - name: Giardine B.M.
      - name: Harte R.A.
      - name: Hillman-Jackson J.
      - name: Hsu F.
      - name: Kirkup V.
      - name: Kuhn R.M.
      - name: Learned K.
      - name: Li C.H.
      - name: Meyer L.R.
      - name: Pohl A.
      - name: Raney B.J.
      - name: Rosenbloom K.R.
      - name: Smith K.E.
      - name: Haussler D.
      - name: Kent W.J.
      citationCount: 853
      date: '2011-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The UCSC genome browser database: Update 2011'
    note: null
    pmcid: PMC3242726
    pmid: '20959295'
    type: []
    version: null
  relation: []
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  - term: Genotype and phenotype
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  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
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  additionDate: '2017-03-30T22:04:37Z'
  biotoolsCURIE: biotools:omim
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    note: null
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  description: Online Mendelian inheritance in Man (OMIM) - catalog of human genes
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    metadata:
      abstract: Online Mendelian Inheritance in Man (OMIM™) is a comprehensive, authoritative
        and timely knowledgebase of human genes and genetic disorders compiled to
        support human genetics research and education and the practice of clinical
        genetics. Started by Dr Victor A. McKusick as the definitive reference Mendelian
        Inheritance in Man, OMIM (http://www.ncbi.nlm.nih.gov/omim/) is now distributed
        electronically by the National Center for Biotechnology Information, where
        it is integrated with the Entrez suite of databases. Derived from the biomedical
        literature, OMIM is written and edited at Johns Hopkins University with input
        from scientists and physicians around the world. Each OMIM entry has a full-text
        summary of a genetically determined phenotype and/or gene and has numerous
        links to other genetic databases such as DNA and protein sequence, PubMed
        references, general and locus-specific mutation databases, HUGO nomenclature,
        MapViewer, GeneTests, patient support groups and many others. OMIM is an easy
        and straightforward portal to the burgeoning information in human genetics.
        © Oxford University Press 2005; all rights reserved.
      authors:
      - name: Hamosh A.
      - name: Scott A.F.
      - name: Amberger J.S.
      - name: Bocchini C.A.
      - name: McKusick V.A.
      citationCount: 2237
      date: '2005-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human
        genes and genetic disorders
    note: null
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    type:
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    metadata:
      abstract: Online Mendelian Inheritance in Man, OMIM®, is a comprehensive, authoritative
        and timely research resource of curated descriptions of human genes and phenotypes
        and the relationships between them. The new official website for OMIM, OMIM.org
        (http://omim.org), was launched in January 2011. OMIM is based on the published
        peer-reviewed biomedical literature and is used by overlapping and diverse
        communities of clinicians, molecular biologists and genome scientists, as
        well as by students and teachers of these disciplines. Genes and phenotypes
        are described in separate entries and are given unique, stable six-digit identifiers
        (MIM numbers). OMIM entries have a structured free-text format that provides
        the flexibility necessary to describe the complex and nuanced relationships
        between genes and genetic phenotypes in an efficient manner. OMIM also has
        a derivative table of genes and genetic phenotypes, the Morbid Map. OMIM.org
        has enhanced search capabilities such as genome coordinate searching and thesaurus-enhanced
        search term options. Phenotypic series have been created to facilitate viewing
        genetic heterogeneity of phenotypes. Clinical synopsis features are enhanced
        with UMLS, Human Phenotype Ontology and Elements of Morphology terms and image
        links. All OMIM data are available for FTP download and through an API. MIMmatch
        is a novel outreach feature to disseminate updates and encourage collaboration.
      authors:
      - name: Amberger J.S.
      - name: Bocchini C.A.
      - name: Schiettecatte F.
      - name: Scott A.F.
      - name: Hamosh A.
      citationCount: 1642
      date: '2015-01-28T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an Online catalog
        of human genes and genetic disorders'
    note: null
    pmcid: PMC4383985
    pmid: '25428349'
    type: []
    version: null
  - doi: 10.1093/nar/gky1151
    metadata:
      abstract: For over 50 years Mendelian Inheritance in Man has chronicled the
        collective knowledge of the field of medical genetics. It initially cataloged
        the known X-linked, autosomal recessive and autosomal dominant inherited disorders,
        but grew to be the primary repository of curated information on both genes
        and genetic phenotypes and the relationships between them. Each phenotype
        and gene is given a separate entry assigned a stable, unique identifier. The
        entries contain structured summaries of new and important information based
        on expert review of the biomedical literature. OMIM.org provides interactive
        access to the knowledge repository, including genomic coordinate searches
        of the gene map, views of genetic heterogeneity of phenotypes in Phenotypic
        Series, and side-by-side comparisons of clinical synopses. OMIM.org also supports
        computational queries via a robust API. All entries have extensive targeted
        links to other genomic resources and additional references. Updates to OMIM
        can be found on the update list or followed through the MIMmatch service.
        Updated user guides and tutorials are available on the website. As of September
        2018, OMIM had over 24,600 entries, and the OMIM Morbid Map Scorecard had
        6,259 molecularized phenotypes connected to 3,961 genes.
      authors:
      - name: Amberger J.S.
      - name: Bocchini C.A.
      - name: Scott A.F.
      - name: Hamosh A.
      citationCount: 550
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'OMIM.org: Leveraging knowledge across phenotype-gene relationships'
    note: null
    pmcid: PMC6323937
    pmid: '30445645'
    type: []
    version: null
  - doi: 10.1093/nar/gkn665
    metadata:
      abstract: McKusick's Online Mendelian Inheritance in Man (OMIM® http://www.ncbi.nlm.nih.gov/omim),
        a knowledgebase of human genes and phenotypes, was originally published as
        a book, Mendelian Inheritance in Man, in 1966. The content of OMIM is derived
        exclusively from the published biomedical literature and is updated daily.
        It currently contains 18 961 full-text entries describing phenotypes and genes.
        To date, 2239 genes have mutations causing disease, and 3770 diseases have
        a molecular basis. Approximately 70 new entries are added and 700 entries
        are updated per month. OMIM® is expanding content and organization in response
        to shifting biological paradigms and advancing biotechnology. © 2008 The Author(s).
      authors:
      - name: Amberger J.
      - name: Bocchini C.A.
      - name: Scott A.F.
      - name: Hamosh A.
      citationCount: 565
      date: '2009-01-09T00:00:00Z'
      journal: Nucleic Acids Research
      title: McKusick's Online Mendelian Inheritance in Man (OMIM®)
    note: null
    pmcid: PMC2686440
    pmid: '18842627'
    type: []
    version: null
  relation: []
  toolType:
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  - Database portal
  topic:
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    uri: http://edamontology.org/topic_0625
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-07-04T12:28:58Z'
  biotoolsCURIE: biotools:gwas_catalog
  biotoolsID: gwas_catalog
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: gwas-info@ebi.ac.uk
    fundrefid: null
    gridid: null
    name: GWAS Catalog Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: A catalogue that provides a publicly available manually curated collection
    of published GWAS assaying at least 100 000 single-nucleotide polymorphisms (SNPs)
    and all SNP-trait associations with P <1 × 10−5.
  documentation:
  - note: null
    type:
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    url: https://www.ebi.ac.uk/gwas/docs
  - note: null
    type:
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  license: Apache-2.0
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  publication:
  - doi: 10.1093/nar/gkt1229
    metadata:
      abstract: The National Human Genome Research Institute (NHGRI) Catalog of Published
        Genome-Wide Association Studies (GWAS) Catalog provides a publicly available
        manually curated collection of published GWAS assaying at least 100 000 single-nucleotide
        polymorphisms (SNPs) and all SNP-trait associations with P <1 × 10 -5. The
        Catalog includes 1751 curated publications of 11 912 SNPs. In addition to
        the SNP-trait association data, the Catalog also publishes a quarterly diagram
        of all SNP-trait associations mapped to the SNPs' chromosomal locations. The
        Catalog can be accessed via a tabular web interface, via a dynamic visualization
        on the human karyotype, as a downloadable tab-delimited file and as an OWL
        knowledge base. This article presents a number of recent improvements to the
        Catalog, including novel ways for users to interact with the Catalog and changes
        to the curation infrastructure. © 2013 The Author(s). Published by Oxford
        University Press.
      authors:
      - name: Welter D.
      - name: MacArthur J.
      - name: Morales J.
      - name: Burdett T.
      - name: Hall P.
      - name: Junkins H.
      - name: Klemm A.
      - name: Flicek P.
      - name: Manolio T.
      - name: Hindorff L.
      - name: Parkinson H.
      citationCount: 2139
      date: '2014-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
    note: null
    pmcid: PMC3965119
    pmid: '24316577'
    type: []
    version: null
  - doi: 10.1093/nar/gkw1133
    metadata:
      abstract: The NHGRI-EBI GWAS Catalog has provided data from published genome-wide
        association studies since 2008. In 2015, the database was redesigned and relocated
        to EMBL-EBI. The new infrastructure includes a new graphical user interface
        (www.ebi. ac.uk/gwas/), ontology supported search functionality and an improved
        curation interface. These developments have improved the data release frequency
        by increasing automation of curation and providing scaling improvements. The
        range of available Catalog data has also been extended with structured ancestry
        and recruitment information added for all studies. The infrastructure improvements
        also support scaling for larger arrays, exome and sequencing studies, allowing
        the Catalog to adapt to the needs of evolving study design, genotyping technologies
        and user needs in the future.
      authors:
      - name: MacArthur J.
      - name: Bowler E.
      - name: Cerezo M.
      - name: Gil L.
      - name: Hall P.
      - name: Hastings E.
      - name: Junkins H.
      - name: McMahon A.
      - name: Milano A.
      - name: Morales J.
      - name: MayPendlington Z.
      - name: Welter D.
      - name: Burdett T.
      - name: Hindorff L.
      - name: Flicek P.
      - name: Cunningham F.
      - name: Parkinson H.
      citationCount: 1503
      date: '2017-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The new NHGRI-EBI Catalog of published genome-wide association studies
        (GWAS Catalog)
    note: null
    pmcid: PMC5210590
    pmid: '27899670'
    type: []
    version: null
  - doi: 10.1093/nar/gky1120
    metadata:
      abstract: 'The GWAS Catalog delivers a high-quality curated collection of all
        published genome-wide association studies enabling investigations to identify
        causal variants, understand disease mechanisms, and establish targets for
        novel therapies. The scope of the Catalog has also expanded to targeted and
        exome arrays with 1000 new associations added for these technologies. As of
        September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait
        associations from 3567 publications. New content includes 284 full P-value
        summary statistics datasets for genome-wide and new targeted array studies,
        representing 6 × 10 9 individual variant-trait statistics. In the last 12
        months, the Catalog''s user interface was accessed by 1/490000 unique users
        who viewed >1 million pages. We have improved data access with the release
        of a new RESTful API to support high-throughput programmatic access, an improved
        web interface and a new summary statistics database. Summary statistics provision
        is supported by a new format proposed as a community standard for summary
        statistics data representation. This format was derived from our experience
        in standardizing heterogeneous submissions, mapping formats and in harmonizing
        content. Availability: https://www.ebi.ac.uk/gwas/.'
      authors:
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      - name: Macarthur J.A.L.
      - name: Cerezo M.
      - name: Harris L.W.
      - name: Hayhurst J.
      - name: Malangone C.
      - name: McMahon A.
      - name: Morales J.
      - name: Mountjoy E.
      - name: Sollis E.
      - name: Suveges D.
      - name: Vrousgou O.
      - name: Whetzel P.L.
      - name: Amode R.
      - name: Guillen J.A.
      - name: Riat H.S.
      - name: Trevanion S.J.
      - name: Hall P.
      - name: Junkins H.
      - name: Flicek P.
      - name: Burdett T.
      - name: Hindorff L.A.
      - name: Cunningham F.
      - name: Parkinson H.
      citationCount: 2549
      date: '2019-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: The NHGRI-EBI GWAS Catalog of published genome-wide association studies,
        targeted arrays and summary statistics 2019
    note: null
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    pmid: '30445434'
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  topic:
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  - term: DNA polymorphism
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  - term: Mapping
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  - term: Data integration and warehousing
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  - term: Rare diseases
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  additionDate: '2017-01-13T13:13:33Z'
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    fundrefid: null
    gridid: null
    name: Kai Wang
    note: null
    orcidid: null
    rorid: null
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    url: null
  description: A tool for finding relevant genetic variants from high-throughput sequencing
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  - note: null
    type:
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  download: []
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    metadata:
      abstract: High-throughput sequencing platforms are generating massive amounts
        of genetic variation data for diverse genomes, but it remains a challenge
        to pinpoint a small subset of functionally important variants. To fill these
        unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants
        (SNVs) and insertions/deletions, such as examining their functional consequence
        on genes, inferring cytogenetic bands, reporting functional importance scores,
        finding variants in conserved regions, or identifying variants reported in
        the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases
        from the UCSC Genome Browser or any annotation data set conforming to Generic
        Feature Format version 3 (GFF3). We also illustrate a 'variants reduction'
        protocol on 4.7 million SNVs and indels from a human genome, including two
        causal mutations for Miller syndrome, a rare recessive disease. Through a
        stepwise procedure, we excluded variants that are unlikely to be causal, and
        identified 20 candidate genes including the causal gene. Using a desktop computer,
        ANNOVAR requires ~4 min to perform gene-based annotation and ~15 min to perform
        variants reduction on 4.7 million variants, making it practical to handle
        hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/.
        © The Author(s) 2010. Published by Oxford University Press.
      authors:
      - name: Wang K.
      - name: Li M.
      - name: Hakonarson H.
      citationCount: 9994
      date: '2010-07-03T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ANNOVAR: Functional annotation of genetic variants from high-throughput
        sequencing data'
    note: null
    pmcid: PMC2938201
    pmid: '20601685'
    type:
    - Primary
    version: null
  - doi: 10.1038/nprot.2015.105
    metadata:
      abstract: Recent developments in sequencing techniques have enabled rapid and
        high-throughput generation of sequence data, democratizing the ability to
        compile information on large amounts of genetic variations in individual laboratories.
        However, there is a growing gap between the generation of raw sequencing data
        and the extraction of meaningful biological information. Here, we describe
        a protocol to use the ANNOVAR (ANNOtate VARiation) software to facilitate
        fast and easy variant annotations, including gene-based, region-based and
        filter-based annotations on a variant call format (VCF) file generated from
        human genomes. We further describe a protocol for gene-based annotation of
        a newly sequenced nonhuman species. Finally, we describe how to use a user-friendly
        and easily accessible web server called wANNOVAR to prioritize candidate genes
        for a Mendelian disease. The variant annotation protocols take 5-30 min of
        computer time, depending on the size of the variant file, and 5-10 min of
        hands-on time. In summary, through the command-line tool and the web server,
        these protocols provide a convenient means to analyze genetic variants generated
        in humans and other species.
      authors:
      - name: Yang H.
      - name: Wang K.
      citationCount: 658
      date: '2015-10-29T00:00:00Z'
      journal: Nature Protocols
      title: Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR
    note: null
    pmcid: PMC4718734
    pmid: '26379229'
    type: []
    version: null
  - doi: 10.1136/jmedgenet-2012-100918
    metadata:
      abstract: Background High-throughput DNA sequencing platforms have become widely
        available. As a result, personal genomes are increasingly being sequenced
        in research and clinical settings. However, the resulting massive amounts
        of variants data pose significant challenges to the average biologists and
        clinicians without bioinformatics skills. Methods and results We developed
        a web server called wANNOVAR to address the critical needs for functional
        annotation of genetic variants from personal genomes. The server provides
        simple and intuitive interface to help users determine the functional significance
        of variants. These include annotating single nucleotide variants and insertions/deletions
        for their effects on genes, reporting their conservation levels (such as PhyloP
        and GERP++ scores), calculating their predicted functional importance scores
        (such as SIFT and PolyPhen scores), retrieving allele frequencies in public
        databases (such as the 1000 Genomes Project and NHLBI-ESP 5400 exomes), and
        implementing a 'variants reduction' protocol to identify a subset of potentially
        deleterious variants/genes. We illustrated how wANNOVAR can help draw biological
        insights from sequencing data, by analysing genetic variants generated on
        two Mendelian diseases. Conclusions We conclude that wANNOVAR will help biologists
        and clinicians take advantage of the personal genome information to expedite
        scientific discoveries. The wANNOVAR server is available at http://wannovar.
        usc.edu, and will be continuously updated to reflect the latest annotation
        information.
      authors:
      - name: Chang X.
      - name: Wang K.
      citationCount: 335
      date: '2012-07-01T00:00:00Z'
      journal: Journal of Medical Genetics
      title: 'Wannovar: Annotating genetic variants for personal genomes via the web'
    note: null
    pmcid: PMC3556337
    pmid: '22717648'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web service
  topic:
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2023-01-03T14:28:36.818779Z'
  biotoolsCURIE: biotools:remm_score
  biotoolsID: remm_score
  collectionID:
  - Rare Disease
  confidence_flag: null
  cost: null
  credit:
  - email: max.schubach@bih-charite.de
    fundrefid: null
    gridid: null
    name: Max Schubach
    note: null
    orcidid: https://orcid.org/0000-0002-2032-6679
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: martin.kircher@bih-charite.de
    fundrefid: null
    gridid: null
    name: Martin Kircher
    note: null
    orcidid: https://orcid.org/0000-0001-9278-5471
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: lusine.nazaretyan@bih-charite.de
    fundrefid: null
    gridid: null
    name: Lusiné Nazaretyan
    note: null
    orcidid: https://orcid.org/0000-0001-5820-4143
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: 'ReMM score is a tool that scores the positions in the human genome
    in terms of their regulatory probability.


    We use curated regulatory variants involved in Mendelian disease and contrast
    them with proxy-neutral variants that survived natural selection in a machine
    learning framework. We use an algorithm for highly imbalanced data, called hyperSMURF,
    to differentiate deleterious from neutral variants.'
  documentation:
  - note: null
    type:
    - General
    url: https://remm.bihealth.org/
  download:
  - note: null
    type: Downloads page
    url: https://doi.org/10.5281/zenodo.6576087
    version: v0.4
  editPermission:
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  homepage: https://remm.bihealth.org/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:14:10.168821Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/kircherlab/ReMM
  maturity: null
  name: ReMM score
  operatingSystem:
  - Linux
  otherID: []
  owner: visze
  publication:
  - doi: 10.1016/j.ajhg.2016.07.005
    metadata:
      abstract: The interpretation of non-coding variants still constitutes a major
        challenge in the application of whole-genome sequencing in Mendelian disease,
        especially for single-nucleotide and other small non-coding variants. Here
        we present Genomiser, an analysis framework that is able not only to score
        the relevance of variation in the non-coding genome, but also to associate
        regulatory variants to specific Mendelian diseases. Genomiser scores variants
        through either existing methods such as CADD or a bespoke machine learning
        method and combines these with allele frequency, regulatory sequences, chromosomal
        topological domains, and phenotypic relevance to discover variants associated
        to specific Mendelian disorders. Overall, Genomiser is able to identify causal
        regulatory variants as the top candidate in 77% of simulated whole genomes,
        allowing effective detection and discovery of regulatory variants in Mendelian
        disease.
      authors:
      - name: Smedley D.
      - name: Schubach M.
      - name: Jacobsen J.O.B.
      - name: Kohler S.
      - name: Zemojtel T.
      - name: Spielmann M.
      - name: Jager M.
      - name: Hochheiser H.
      - name: Washington N.L.
      - name: McMurry J.A.
      - name: Haendel M.A.
      - name: Mungall C.J.
      - name: Lewis S.E.
      - name: Groza T.
      - name: Valentini G.
      - name: Robinson P.N.
      citationCount: 181
      date: '2016-09-01T00:00:00Z'
      journal: American Journal of Human Genetics
      title: A Whole-Genome Analysis Framework for Effective Identification of Pathogenic
        Regulatory Variants in Mendelian Disease
    note: null
    pmcid: PMC5011059
    pmid: '27569544'
    type:
    - Primary
    version: null
  - doi: 10.1101/2022.03.14.484240
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Workflow
  - Web API
  - Bioinformatics portal
  topic:
  - term: Epigenomics
    uri: http://edamontology.org/topic_3173
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version:
  - v0.3.1.post1
  - v0.4
- accessibility: Restricted access
  additionDate: '2018-02-23T15:06:03Z'
  biotoolsCURIE: biotools:rd-connect_platform
  biotoolsID: rd-connect_platform
  collectionID:
  - RD-connect
  - Rare Disease
  - ELIXIR-ES
  - INB
  - RIS3CAT VEIS
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: Steven Laurie
    note: null
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    rorid: null
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    gridid: null
    name: Davide Piscia
    note: null
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    rorid: null
    typeEntity: Person
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  - email: leslie.matalonga@cnag.crg.eu
    fundrefid: null
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    name: Leslie Matalonga
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    rorid: null
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    typeRole:
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    url: null
  - email: alberto.corvo@cnag.crg.eu
    fundrefid: null
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    name: Alberto Corvó
    note: null
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    rorid: null
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    url: null
  - email: marcos.fernandez@cnag.crg.eu
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    name: Marcos Fernández-Callejo
    note: null
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    rorid: null
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  - email: carles.garcia@cnag.crg.eu
    fundrefid: null
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    name: Carles Garcia-Linares
    note: null
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    url: null
  - email: carles.hernandez@cnag.crg.eu
    fundrefid: null
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    name: Carles Hernandez-Ferrer
    note: null
    orcidid: https://orcid.org/0000-0002-8029-7160
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: http://www.carleshf.com
  - email: null
    fundrefid: null
    gridid: null
    name: Cristina Luengo
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
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    name: Inés Martínez
    note: null
    orcidid: null
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    typeEntity: Person
    typeRole:
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    url: null
  - email: anastasios.papakonstantinou@cnag.crg.eu
    fundrefid: null
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    name: Anastasios Papakonstantinou
    note: null
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    typeEntity: Person
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    url: null
  - email: daniel.pico@cnag.crg.eu
    fundrefid: null
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    name: Daniel Picó-Amador
    note: null
    orcidid: null
    rorid: null
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    url: null
  - email: null
    fundrefid: null
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    name: Joan Protasio
    note: null
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    typeEntity: Person
    typeRole:
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    url: null
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    fundrefid: null
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    name: Rachel Thompson
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: raul.tonda@cnag.crg.eu
    fundrefid: null
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    name: Raul Tonda
    note: null
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    fundrefid: null
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    name: Mònica Bayés
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  - email: gemma.bullich@cnag.crg.eu
    fundrefid: null
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    name: Gemma Bullich
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    note: null
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    url: null
  - email: ida.paramonov@cnag.crg.eu
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    note: null
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    name: Angel Alonso
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    fundrefid: null
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    name: Marcella Attimonelli
    note: null
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    fundrefid: null
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    name: Christophe Béroud
    note: null
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    url: null
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    fundrefid: null
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    name: Virginie Bros-Facer
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    fundrefid: null
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    fundrefid: null
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    name: Andrés Cañada-Pallarés
    note: null
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    typeRole:
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: José M. Fernández
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    url: null
  - email: null
    fundrefid: null
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    name: Mats G. Hansson
    note: null
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    url: null
  - email: null
    fundrefid: null
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    name: Rita Horvath
    note: null
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    typeRole:
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  - email: null
    fundrefid: null
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    name: Julius O.B. Jacobsen
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    fundrefid: null
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    name: Rajaram Kaliyaperumal
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    fundrefid: null
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    fundrefid: null
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    name: Deborah Mascalzoni
    note: null
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    fundrefid: null
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    fundrefid: null
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    fundrefid: null
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  description: An online tool for diagnosis and gene discovery in rare disease research.
    The platform features allow identifying disease-causing mutations in rare disease
    patients and linking them with detailed clinical information.
  documentation: []
  download: []
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  function:
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    input: []
    note: null
    operation:
    - term: Sequence analysis
      uri: http://edamontology.org/operation_2403
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    - term: Sequence annotation
      uri: http://edamontology.org/operation_0361
    output: []
  homepage: https://platform.rd-connect.eu/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:05:10.199711Z'
  license: null
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  maturity: Emerging
  name: RD-Connect Genome-Phenome Analysis Platform (GPAP)
  operatingSystem:
  - Linux
  - Windows
  - Mac
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  owner: lmatalonga
  publication:
  - doi: 10.1002/humu.24353
    metadata:
      abstract: Rare disease patients are more likely to receive a rapid molecular
        diagnosis nowadays thanks to the wide adoption of next-generation sequencing.
        However, many cases remain undiagnosed even after exome or genome analysis,
        because the methods used missed the molecular cause in a known gene, or a
        novel causative gene could not be identified and/or confirmed. To address
        these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates
        the collation, discovery, sharing, and analysis of standardized genome-phenome
        data within a collaborative environment. Authorized clinicians and researchers
        submit pseudonymised phenotypic profiles encoded using the Human Phenotype
        Ontology, and raw genomic data which is processed through a standardized pipeline.
        After an optional embargo period, the data are shared with other platform
        users, with the objective that similar cases in the system and queries from
        peers may help diagnose the case. Additionally, the platform enables bidirectional
        discovery of similar cases in other databases from the Matchmaker Exchange
        network. To facilitate genome-phenome analysis and interpretation by clinical
        researchers, the RD-Connect GPAP provides a powerful user-friendly interface
        and leverages tens of information sources. As a result, the resource has already
        helped diagnose hundreds of rare disease patients and discover new disease
        causing genes.
      authors:
      - name: Laurie S.
      - name: Piscia D.
      - name: Matalonga L.
      - name: Corvo A.
      - name: Fernandez-Callejo M.
      - name: Garcia-Linares C.
      - name: Hernandez-Ferrer C.
      - name: Luengo C.
      - name: Martinez I.
      - name: Papakonstantinou A.
      - name: Pico-Amador D.
      - name: Protasio J.
      - name: Thompson R.
      - name: Tonda R.
      - name: Bayes M.
      - name: Bullich G.
      - name: Camps-Puchadas J.
      - name: Paramonov I.
      - name: Trotta J.-R.
      - name: Alonso A.
      - name: Attimonelli M.
      - name: Beroud C.
      - name: Bros-Facer V.
      - name: Buske O.J.
      - name: Canada-Pallares A.
      - name: Fernandez J.M.
      - name: Hansson M.G.
      - name: Horvath R.
      - name: Jacobsen J.O.B.
      - name: Kaliyaperumal R.
      - name: Lair-Preterre S.
      - name: Licata L.
      - name: Lopes P.
      - name: Lopez-Martin E.
      - name: Mascalzoni D.
      - name: Monaco L.
      - name: Perez-Jurado L.A.
      - name: Posada de la Paz M.
      - name: Rambla J.
      - name: Rath A.
      - name: Riess O.
      - name: Robinson P.N.
      - name: Salgado D.
      - name: Smedley D.
      - name: Spalding D.
      - name: '''t Hoen P.A.C.'
      - name: Topf A.
      - name: Zaharieva I.
      - name: Graessner H.
      - name: Gut I.G.
      - name: Lochmuller H.
      - name: Beltran S.
      citationCount: 29
      date: '2022-06-01T00:00:00Z'
      journal: Human Mutation
      title: 'The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis,
        research, and gene discovery for rare diseases'
    note: null
    pmcid: PMC9324157
    pmid: '35178824'
    type:
    - Primary
    version: null
  - doi: 10.1007/s11606-014-2908-8
    metadata:
      abstract: Research into rare diseases is typically fragmented by data type and
        disease. Individual efforts often have poor interoperability and do not systematically
        connect data across clinical phenotype, genomic data, biomaterial availability,
        and research/trial data sets. Such data must be linked at both an individual-patient
        and whole-cohort level to enable researchers to gain a complete view of their
        disease and patient population of interest. Data access and authorization
        procedures are required to allow researchers in multiple institutions to securely
        compare results and gain new insights. Funded by the European Union's Seventh
        Framework Programme under the International Rare Diseases Research Consortium
        (IRDiRC), RD-Connect is a global infrastructure project initiated in November
        2012 that links genomic data with registries, biobanks, and clinical bioinformatics
        tools to produce a central research resource for rare diseases. © 2014 Society
        of General Internal Medicine.
      authors:
      - name: Thompson R.
      - name: Johnston L.
      - name: Taruscio D.
      - name: Monaco L.
      - name: Beroud C.
      - name: Gut I.G.
      - name: Hansson M.G.
      - name: T Hoen P.-B.A.
      - name: Patrinos G.P.
      - name: Dawkins H.
      - name: Ensini M.
      - name: Zatloukal K.
      - name: Koubi D.
      - name: Heslop E.
      - name: Paschall J.E.
      - name: Posada M.
      - name: Robinson P.N.
      - name: Bushby K.
      - name: Lochmuller H.
      citationCount: 146
      date: '2014-01-01T00:00:00Z'
      journal: Journal of General Internal Medicine
      title: 'RD-connect: An integrated platform connecting databases, registries,
        biobanks and clinical bioinformatics for rare disease research'
    note: null
    pmcid: PMC4124112
    pmid: '25029978'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  validated: 1
  version: []
- accessibility: null
  additionDate: '2023-02-13T20:20:42.025598Z'
  biotoolsCURIE: biotools:rvs_software
  biotoolsID: rvs_software
  collectionID:
  - Rare Disease
  confidence_flag: tool
  cost: null
  credit:
  - email: ingo@jhu.edu
    fundrefid: null
    gridid: null
    name: Ingo Ruczinski
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Detection of rare disease variants in extended pedigrees using RVS.


    Computes estimates of the probability of related individuals sharing a rare variant.


    Rare Variant Sharing (RVS) implements tests of association and linkage between
    rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status,
    in family samples. The tests are based on probabilities of rare variant sharing
    by relatives under the null hypothesis of absence of linkage and association between
    the rare variants and the phenotype and apply to single variants or multiple variants
    in a region (e.g. gene-based test).


    ExomeSeq, Genetics, GenomeWideAssociation, ImmunoOncology, Software, VariantDetection,
    WholeGenome.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Collapsing methods
      uri: http://edamontology.org/operation_3791
    output: []
  homepage: https://bioconductor.org/packages/release/bioc/html/RVS.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T15:01:20.275081Z'
  license: GPL-2.0
  link: []
  maturity: null
  name: RVS
  operatingSystem: []
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1093/bioinformatics/bty976
    metadata:
      abstract: 'Summary: Family-based sequencing studies enable researchers to identify
        highly penetrant genetic variants too rare to be tested in conventional case-control
        studies, by studying co-segregation of variant and disease phenotypes. When
        multiple affected subjects in a family are sequenced, the probability that
        a variant or a set of variants is shared identical-by-descent by some or all
        affected relatives provides evidence against the null hypothesis of complete
        absence of linkage and association. The Rare Variant Sharing software package
        RVS implements a suite of tools to assess association and linkage between
        rare genetic variants and a dichotomous disease indicator in family pedigrees.
        Availability and Implementation: RVS is available as open source software
        from the Bioconductor webpage at https://bioconductor.org/packages/release/bioc/html/RVS.html.
        Supplementary information: Supplementary data are available at Bioinformatics
        online.'
      authors:
      - name: Sherman T.
      - name: Fu J.
      - name: Scharpf R.B.
      - name: Bureau A.
      - name: Ruczinski I.
      citationCount: 3
      date: '2019-07-15T00:00:00Z'
      journal: Bioinformatics
      title: Detection of rare disease variants in extended pedigrees using RVS
    note: null
    pmcid: PMC6612888
    pmid: '30500888'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: null
  additionDate: '2015-12-01T16:27:10Z'
  biotoolsCURIE: biotools:bridgedb
  biotoolsID: bridgedb
  collectionID:
  - Proteomics
  - BIGCAT-UM
  - Rare Disease
  - BridgeDb
  - ELIXIR-NL
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Gladstone Institutes
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Maastricht University
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Manchester University
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Heroit Watt University
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: maastrichtuniversity.nl
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://groups.google.com/forum/#!forum/bridgedb-discuss
  description: BridgeDb is a framework to map identifiers between various databases.  It
    includes a Java library that provides an API to work with identifier-identifier
    mapping databases and resources.
  documentation:
  - note: null
    type:
    - General
    url: https://bridgedb.github.io/pages/docs.html
  download: []
  editPermission:
    authors:
    - proteomics.bio.tools
    - ChrisEvelo
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform:
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format: []
    note: API for converting gene identifiers (Ensembl, Entrez) to identifiers from
      another database. Depends on an identifier-identifier mapping database or service
      supported by the API.
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format: []
  - cmd: null
    input:
    - data:
        term: Protein identifier
        uri: http://edamontology.org/data_0989
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Protein identifier
        uri: http://edamontology.org/data_0989
      format: []
  - cmd: null
    input:
    - data:
        term: Compound identifier
        uri: http://edamontology.org/data_1086
      format: []
    note: API for converting compound identifiers (HMDB, ChEBI, ChemSpider, PubChem,
      etc) to identifiers from another database. Depends on an identifier-identifier
      mapping database or service supported by the API.
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: Compound identifier
        uri: http://edamontology.org/data_1086
      format: []
  homepage: http://bridgedb.github.io/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2024-11-24T14:49:46.902984Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: BridgeDb
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: egonw
  publication:
  - doi: 10.1007/978-3-319-11964-9_7
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1186/1471-2105-11-5
    metadata:
      abstract: 'Background: Endocrine disruptors (EDs) and their broad range of potential
        adverse effects in humans and other animals have been a concern for nearly
        two decades. Many putative EDs are widely used in commercial products regulated
        by the Food and Drug Administration (FDA) such as food packaging materials,
        ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine
        Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990''s by
        the FDA as a resource for the study of EDs. The EDKB database, a component
        of the project, contains data across multiple assay types for chemicals across
        a broad structural diversity. This paper demonstrates the utility of EDKB
        database, an integral part of the EDKB project, for understanding and prioritizing
        EDs for testing.Results: The EDKB database currently contains 3,257 records
        of over 1,800 EDs from different assays including estrogen receptor binding,
        androgen receptor binding, uterotropic activity, cell proliferation, and reporter
        gene assays. Information for each compound such as chemical structure, assay
        type, potency, etc. is organized to enable efficient searching. A user-friendly
        interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet
        and graphical displays for viewing results. The search engine implemented
        in the EDKB database enables searching by one or more of the following fields:
        chemical structure (including exact search and similarity search), name, molecular
        formula, CAS registration number, experiment source, molecular weight, etc.
        The data can be cross-linked to other publicly available and related databases
        including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. .
        Conclusion: The EDKB database enables scientists and regulatory reviewers
        to quickly access ED data from multiple assays for specific or similar compounds.
        The data have been used to categorize chemicals according to potential risks
        for endocrine activity, thus providing a basis for prioritizing chemicals
        for more definitive but expensive testing. The EDKB database is publicly available
        and can be found online at http://edkb.fda.gov/webstart/edkb/index.html.Disclaimer:The
        views presented in this article do not necessarily reflect those of the US
        Food and Drug Administration. © 2010 Tong et al; licensee BioMed Central Ltd.'
      authors:
      - name: Ding D.
      - name: Xu L.
      - name: Fang H.
      - name: Hong H.
      - name: Perkins R.
      - name: Harris S.
      - name: Bearden E.D.
      - name: Shi L.
      - name: Tong W.
      citationCount: 80
      date: '2010-10-07T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'The EDKB: An established knowledge base for endocrine disrupting chemicals'
    note: null
    pmcid: PMC2824678
    pmid: '20047655'
    type:
    - Primary
    version: null
  - doi: 10.3897/rio.8.e83031
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Toxicology
    uri: http://edamontology.org/topic_2840
  validated: 1
  version:
  - 3.0.18
- accessibility: Open access
  additionDate: '2019-07-03T15:13:23Z'
  biotoolsCURIE: biotools:Variant_Combinaton_Pathogenicity_Predictor
  biotoolsID: Variant_Combinaton_Pathogenicity_Predictor
  collectionID:
  - ELIXIR-BE
  - RD-Connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: tlenaert@ulb.ac.be
    fundrefid: null
    gridid: null
    name: Tom Lenaerts
    note: null
    orcidid: https://orcid.org/0000-0003-3645-1455
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.ibsquare.be
  - email: sofiapapad.bio@gmail.com
    fundrefid: null
    gridid: null
    name: Sofia Papadomitriou
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Interuniversity Institute of Bioinformatics in Brussels
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://ibsquare.be
  description: "VarCoPP is a machine learning method that predicts the potential pathogenicity\
    \ of variant combinations in gene pairs. It is based on digenic data present in\
    \ OLIDA and it was trained against variants from the 1000 Genomes Project. VarCoPP2.0\
    \ is a Balanced Random Forest predictor consisting of 400 decision trees. \nA\
    \ variant combination can be either predicted as disease-causing (i.e. candidate\
    \ or probably pathogenic) or neutral (i.e. probably neutral). \n\nVarCoPP can\
    \ be applied for Single Nucleotide Variants (SNVs) and small insertions/deletions\
    \ from a single individual. It is highly recommended to perform beforehand an\
    \ initial variant filtering procedure and generally restrict the analysis to variants\
    \ from up to 150 genes. \n\nVarCoPP was developed in the Interuniversity Institute\
    \ of Bioinformatics in Brussels, under the collaboration of Université libre de\
    \ Bruxelles and Vrije Universiteit Brussel.\n\nYou can use it through the online\
    \ tool ORVAL: https://orval.ibsquare.be."
  documentation: []
  download: []
  editPermission:
    authors:
    - emmaver
    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Belgium
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://varcopp.ibsquare.be
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:45:34.041241Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/oligogenic/VarCoPP2.0
  - note: null
    type:
    - Repository
    url: https://github.com/sofiapapad90/VarCoPP/
  - note: Web app integrating VarCoPP2.0
    type:
    - Service
    url: https://orval.ibsquare.be
  maturity: Mature
  name: Variant Combination Pathogenicity Predictor (VarCoPP) 2.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: tlenaert@ulb.ac.be
  publication:
  - doi: 10.1073/pnas.1815601116
    metadata:
      abstract: Notwithstanding important advances in the context of single-variant
        pathogenicity identification, novel breakthroughs in discerning the origins
        of many rare diseases require methods able to identify more complex genetic
        models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP),
        a machine-learning approach that identifies pathogenic variant combinations
        in gene pairs (called digenic or bilocus variant combinations). We show that
        the results produced by this method are highly accurate and precise, an efficacy
        that is endorsed when validating the method on recently published independent
        disease-causing data. Confidence labels of 95% and 99% are identified, representing
        the probability of a bilocus combination being a true pathogenic result, providing
        geneticists with rational markers to evaluate the most relevant pathogenic
        combinations and limit the search space and time. Finally, the VarCoPP has
        been designed to act as an interpretable method that can provide explanations
        on why a bilocus combination is predicted as pathogenic and which biological
        information is important for that prediction. This work provides an important
        step toward the genetic understanding of rare diseases, paving the way to
        clinical knowledge and improved patient care.
      authors:
      - name: Papadimitriou S.
      - name: Gazzo A.
      - name: Versbraegen N.
      - name: Nachtegael C.
      - name: Aerts J.
      - name: Moreau Y.
      - name: Van Dooren S.
      - name: Nowe A.
      - name: Smits G.
      - name: Lenaerts T.
      citationCount: 67
      date: '2019-06-11T00:00:00Z'
      journal: Proceedings of the National Academy of Sciences of the United States
        of America
      title: Predicting disease-causing variant combinations
    note: null
    pmcid: PMC6575632
    pmid: '31127050'
    type: []
    version: '1.0'
  - doi: 10.1186/s12859-023-05291-3
    metadata:
      abstract: 'Background: The prediction of potentially pathogenic variant combinations
        in patients remains a key task in the field of medical genetics for the understanding
        and detection of oligogenic/multilocus diseases. Models tailored towards such
        cases can help shorten the gap of missing diagnoses and can aid researchers
        in dealing with the high complexity of the derived data. The predictor VarCoPP
        (Variant Combinations Pathogenicity Predictor) that was published in 2019
        and identified potentially pathogenic variant combinations in gene pairs (bilocus
        variant combinations), was the first important step in this direction. Despite
        its usefulness and applicability, several issues still remained that hindered
        a better performance, such as its False Positive (FP) rate, the quality of
        its training set and its complex architecture. Results: We present VarCoPP2.0:
        the successor of VarCoPP that is a simplified, faster and more accurate predictive
        model identifying potentially pathogenic bilocus variant combinations. Results
        from cross-validation and on independent data sets reveal that VarCoPP2.0
        has improved in terms of both sensitivity (95% in cross-validation and 98%
        during testing) and specificity (5% FP rate). At the same time, its running
        time shows a significant 150-fold decrease due to the selection of a simpler
        Balanced Random Forest model. Its positive training set now consists of variant
        combinations that are more confidently linked with evidence of pathogenicity,
        based on the confidence scores present in OLIDA, the Oligogenic Diseases Database
        (https://olida.ibsquare.be). The improvement of its performance is also attributed
        to a more careful selection of up-to-date features identified via an original
        wrapper method. We show that the combination of different variant and gene
        pair features together is important for predictions, highlighting the usefulness
        of integrating biological information at different levels. Conclusions: Through
        its improved performance and faster execution time, VarCoPP2.0 enables a more
        accurate analysis of larger data sets linked to oligogenic diseases. Users
        can access the ORVAL platform (https://orval.ibsquare.be) to apply VarCoPP2.0
        on their data.'
      authors:
      - name: Versbraegen N.
      - name: Gravel B.
      - name: Nachtegael C.
      - name: Renaux A.
      - name: Verkinderen E.
      - name: Nowe A.
      - name: Lenaerts T.
      - name: Papadimitriou S.
      citationCount: 7
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: Faster and more accurate pathogenic combination predictions with VarCoPP2.0
    note: null
    pmcid: PMC10152795
    pmid: '37127601'
    type: []
    version: '2.0'
  relation:
  - biotoolsID: oligogenic_resource_for_variant_analysis
    type: includedIn
  - biotoolsID: olida
    type: uses
  toolType:
  - Plug-in
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version:
  - '1.0'
  - '2.0'
- accessibility: Open access
  additionDate: '2016-06-24T14:08:44Z'
  biotoolsCURIE: biotools:dida
  biotoolsID: dida
  collectionID:
  - ELIXIR-BE
  - RD-Connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Interuniversity Institute of Bioinformatics in Brussels
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.ibsquare.be
  - email: tlenaert@ulb.ac.be
    fundrefid: null
    gridid: null
    name: Tom Lenaerts
    note: null
    orcidid: https://orcid.org/0000-0003-3645-1455
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.ibsquare.be
  - email: Charlotte.Nachtegael@ulb.ac.be
    fundrefid: null
    gridid: null
    name: Charlotte Nachtegael
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  description: Digenic Diseases Database (DiDA) provides detailed information on genes
    and associated genetic variants involved in digenic diseases, the simplest form
    of oligogenic inheritance.
  documentation:
  - note: null
    type:
    - General
    url: http://dida.ibsquare.be/help/
  - note: More detailed documentation.
    type:
    - General
    url: http://dida.ibsquare.be/documentation/
  download: []
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    input: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_2421
    - term: SNP annotation
      uri: http://edamontology.org/operation_3661
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://dida.ibsquare.be
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:45:32.345721Z'
  license: CC-BY-NC-4.0
  link: []
  maturity: Legacy
  name: Digenic Diseases Database (DIDA)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: tlenaert@ulb.ac.be
  publication:
  - doi: 10.1093/nar/gkv1068
    metadata:
      abstract: DIDA (DIgenic diseases DAtabase) is a novel database that provides
        for the first time detailed information on genes and associated genetic variants
        involved in digenic diseases, the simplest form of oligogenic inheritance.
        The database is accessible via http://dida.ibsquare.be and currently includes
        213 digenic combinations involved in 44 different digenic diseases. These
        combinations are composed of 364 distinct variants, which are distributed
        over 136 distinct genes. The web interface provides browsing and search functionalities,
        as well as documentation and help pages, general database statistics and references
        to the original publications from which the data have been collected. The
        possibility to submit novel digenic data to DIDA is also provided. Creating
        this new repository was essential as current databases do not allow one to
        retrieve detailed records regarding digenic combinations. Genes, variants,
        diseases and digenic combinations in DIDA are annotated with manually curated
        information and information mined from other online resources. Next to providing
        a unique resource for the development of new analysis methods, DIDA gives
        clinical and molecular geneticists a tool to find the most comprehensive information
        on the digenic nature of their diseases of interest.
      authors:
      - name: Gazzo A.M.
      - name: Daneels D.
      - name: Cilia E.
      - name: Bonduelle M.
      - name: Abramowicz M.
      - name: Van Dooren S.
      - name: Smits G.
      - name: Lenaerts T.
      citationCount: 78
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'DIDA: A curated and annotated digenic diseases database'
    note: null
    pmcid: PMC4702791
    pmid: '26481352'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: olida
    type: hasNewVersion
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-07-03T15:25:53Z'
  biotoolsCURIE: biotools:Oligogenic_resource_for_variant_analysis
  biotoolsID: Oligogenic_resource_for_variant_analysis
  collectionID:
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  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: tlenaert@ulb.ac.be
    fundrefid: null
    gridid: null
    name: Tom Lenaerts
    note: null
    orcidid: https://orcid.org/0000-0003-3645-1455
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://www.ibsquare.be
  - email: alexandre.renaux@ulb.ac.be
    fundrefid: null
    gridid: null
    name: Alexandre Renaux
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://www.ibsquare.be
  - email: null
    fundrefid: null
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    name: Interuniversity Institute of Bioinformatics in Brussels
    note: null
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    typeRole:
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    url: https://www.ibsquare.be
  description: ORVAL is the first web bioinformatics platform for the exploration
    of predicted candidate disease-causing variant combinations, aiming to aid in
    uncovering the causes of oligogenic diseases (i.e. diseases caused by variants
    in a small number of genes). This tool integrates innovative machine learning
    methods for combinatorial variant pathogenicity prediction, further external annotations
    and interactive and exploratory visualisation techniques.
  documentation:
  - note: null
    type:
    - User manual
    - FAQ
    url: https://orval.ibsquare.be/documentation
  - note: null
    type:
    - Release notes
    url: https://orval-dev.ibsquare.be/updates
  download: []
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  function:
  - cmd: null
    input:
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        term: Sequence variations
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      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
    note: null
    operation:
    - term: Variant effect prediction
      uri: http://edamontology.org/operation_0331
    - term: Pathway or network prediction
      uri: http://edamontology.org/operation_3439
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    output: []
  homepage: https://orval.ibsquare.be
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:45:30.540434Z'
  license: CC-BY-4.0
  link: []
  maturity: Mature
  name: Oligogenic resource for variant analysis (ORVAL)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: tlenaert@ulb.ac.be
  publication:
  - doi: 10.1093/nar/gkz437
    metadata:
      abstract: A tremendous amount of DNA sequencing data is being produced around
        the world with the ambition to capture in more detail the mechanisms underlying
        human diseases. While numerous bioinformatics tools exist that allow the discovery
        of causal variants in Mendelian diseases, little to no support is provided
        to do the same for variant combinations, an essential task for the discovery
        of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant
        AnaLysis), which is presented here, provides an answer to this problem by
        focusing on generating networks of candidate pathogenic variant combinations
        in gene pairs, as opposed to isolated variants in unique genes. This online
        platform integrates innovative machine learning methods for combinatorial
        variant pathogenicity prediction with visualization techniques, offering several
        interactive and exploratory tools, such as pathogenic gene and protein interaction
        networks, a ranking of pathogenic gene pairs, as well as visual mappings of
        the cellular location and pathway information. ORVAL is the first web-based
        exploration platform dedicated to identifying networks of candidate pathogenic
        variant combinations with the sole ambition to help in uncovering oligogenic
        causes for patients that cannot rely on the classical disease analysis tools.
        ORVAL is available at https://orval.ibsquare.be.
      authors:
      - name: Renaux A.
      - name: Papadimitriou S.
      - name: Versbraegen N.
      - name: Nachtegael C.
      - name: Boutry S.
      - name: Nowe A.
      - name: Smits G.
      - name: Lenaerts T.
      citationCount: 46
      date: '2019-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ORVAL: a novel platform for the prediction and exploration of disease-causing
        oligogenic variant combinations'
    note: null
    pmcid: PMC6602484
    pmid: '31147699'
    type: []
    version: null
  relation:
  - biotoolsID: variant_combinaton_pathogenicity_predictor
    type: uses
  toolType:
  - Web service
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Function analysis
    uri: http://edamontology.org/topic_1775
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version:
  - 3.0.0
- accessibility: Restricted access
  additionDate: '2017-03-04T22:58:49Z'
  biotoolsCURIE: biotools:hgmd
  biotoolsID: hgmd
  collectionID:
  - RD-connect
  - Rare Disease
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Contact Form
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.hgmd.cf.ac.uk/docs/comment_form.html
  description: The Human Gene Mutation Database (HGMD) - comprehensive collection
    of germline mutations in nuclear genes that underlie, or are associated with,
    human inherited disease. It is a flexible resource that can be adapted to a broad
    range of applications including searching for an overview of known mutations associated
    with a particular disease, interpreting clinical test results or looking for the
    likely causal mutation in a list of variants.
  documentation:
  - note: null
    type:
    - General
    url: http://www.hgmd.cf.ac.uk/docs/new_help.html
  download: []
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene set testing
      uri: http://edamontology.org/operation_3224
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: http://www.hgmd.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:15:54.565147Z'
  license: null
  link: []
  maturity: Mature
  name: HGMD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_001621
    version: null
  owner: ELIXIR-EE
  publication:
  - doi: 10.1007/s00439-013-1358-4
    metadata:
      abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive collection
        of germline mutations in nuclear genes that underlie, or are associated with,
        human inherited disease. By June 2013, the database contained over 141,000
        different lesions detected in over 5,700 different genes, with new mutation
        entries currently accumulating at a rate exceeding 10,000 per annum. HGMD
        was originally established in 1996 for the scientific study of mutational
        mechanisms in human genes. However, it has since acquired a much broader utility
        as a central unified disease-oriented mutation repository utilized by human
        molecular geneticists, genome scientists, molecular biologists, clinicians
        and genetic counsellors as well as by those specializing in biopharmaceuticals,
        bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org)
        is freely available to registered users from academic institutions/non-profit
        organizations whilst the subscription version (HGMD Professional) is available
        to academic, clinical and commercial users under license via BIOBASE GmbH.
        © 2013 The Author(s).
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Shaw K.
      - name: Phillips A.D.
      - name: Cooper D.N.
      citationCount: 1054
      date: '2014-01-01T00:00:00Z'
      journal: Human Genetics
      title: 'The Human Gene Mutation Database: Building a comprehensive mutation
        repository for clinical and molecular genetics, diagnostic testing and personalized
        genomic medicine'
    note: null
    pmcid: PMC3898141
    pmid: '24077912'
    type:
    - Primary
    version: null
  - doi: 10.1136/jmg.2007.055210
    metadata:
      abstract: ''
      authors:
      - name: Stenson P.D.
      - name: Ball E.
      - name: Howells K.
      - name: Phillips A.
      - name: Mort M.
      - name: Cooper D.N.
      citationCount: 91
      date: '2008-02-01T00:00:00Z'
      journal: Journal of Medical Genetics
      title: 'Human Gene Mutation Database: Towards a comprehensive central mutation
        database'
    note: null
    pmcid: null
    pmid: '18245393'
    type: []
    version: null
  - doi: 10.1002/humu.10212
    metadata:
      abstract: 'The Human Gene Mutation Database (HGMD) constitutes a comprehensive
        core collection of data on germ-line mutations in nuclear genes underlying
        or associated with human inherited disease (www.hgmd.org). Data catalogued
        includes: single base-pair substitutions in coding, regulatory and splicing-relevant
        regions; micro-deletions and micro-insertions; indels; triplet repeat expansions
        as well as gross deletions; insertions; duplications; and complex rearrangements.
        Each mutation is entered into HGMD only once in order to avoid confusion between
        recurrent and identical-by-descent lesions. By March 2003, the database contained
        in excess of 39,415 different lesions detected in 1,516 different nuclear
        genes, with new entries currently accumulating at a rate exceeding 5,000 per
        annum. Since its inception, HGMD has been expanded to include cDNA reference
        sequences for more than 87% of listed genes, splice junction sequences, disease-associated
        and functional polymorphisms, as well as links to data present in publicly
        available online locus-specific mutation databases. Although HGMD has recently
        entered into a licensing agreement with Celera Genomics (Rockville, MD), mutation
        data will continue to be made freely available via the Internet. © 2003 Wiley-Liss,
        Inc.'
      authors:
      - name: Stenson P.D.
      - name: Ball E.V.
      - name: Mort M.
      - name: Phillips A.D.
      - name: Shiel J.A.
      - name: Thomas N.S.T.
      - name: Abeysinghe S.
      - name: Krawczak M.
      - name: Cooper D.N.
      citationCount: 1522
      date: '2003-06-16T00:00:00Z'
      journal: Human Mutation
      title: 'Human Gene Mutation Database (HGMD®): 2003 Update'
    note: null
    pmcid: null
    pmid: '12754702'
    type: []
    version: null
  - doi: 10.1186/gm13
    metadata:
      abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive core collection
        of germlinemutations in nuclear genes that underlie or are associated with
        human inherited disease. Here we summarize the history of the database and
        its current resources. By December 2008, thedatabase contained over 85,000
        different lesions detected in 3,253 ifferent genes, with new entries currently
        accumulating at a rate exceeding 9,000 per annum. Although originally established
        for the scientific study of mutational mechanisms in human genes, HGMD has
        since acquired a much broader utility for researchers, physicians, clinicians
        and genetic counselors as well as for companies specializing in biopharmaceuticals,
        bioinformatics and personalized genomics. HGMD was first made publicly available
        in April 1996, and a collaboration was initiated in 2006 between HGMD and
        BIOBASE GmbH. This cooperative agreement covers the exclusive worldwide marketing
        of the most up-to-date (subscription) version of HGMD, HGMD Professional,
        to academic, clinical and commercial users. © 2009 BioMed Central Ltd.
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Howells K.
      - name: Phillips P.D.
      - name: Cooper D.N.
      - name: Thomas N.S.T.
      citationCount: 735
      date: '2009-01-22T00:00:00Z'
      journal: Genome Medicine
      title: 'The human gene mutation database: 2008 update'
    note: null
    pmcid: PMC2651586
    pmid: '19348700'
    type: []
    version: null
  - doi: 10.1007/s00439-017-1779-6
    metadata:
      abstract: The Human Gene Mutation Database (HGMD®) constitutes a comprehensive
        collection of published germline mutations in nuclear genes that underlie,
        or are closely associated with human inherited disease. At the time of writing
        (March 2017), the database contained in excess of 203,000 different gene lesions
        identified in over 8000 genes manually curated from over 2600 journals. With
        new mutation entries currently accumulating at a rate exceeding 17,000 per
        annum, HGMD represents de facto the central unified gene/disease-oriented
        repository of heritable mutations causing human genetic disease used worldwide
        by researchers, clinicians, diagnostic laboratories and genetic counsellors,
        and is an essential tool for the annotation of next-generation sequencing
        data. The public version of HGMD (http://www.hgmd.org) is freely available
        to registered users from academic institutions and non-profit organisations
        whilst the subscription version (HGMD Professional) is available to academic,
        clinical and commercial users under license via QIAGEN Inc.
      authors:
      - name: Stenson P.D.
      - name: Mort M.
      - name: Ball E.V.
      - name: Evans K.
      - name: Hayden M.
      - name: Heywood S.
      - name: Hussain M.
      - name: Phillips A.D.
      - name: Cooper D.N.
      citationCount: 1003
      date: '2017-06-01T00:00:00Z'
      journal: Human Genetics
      title: 'The Human Gene Mutation Database: towards a comprehensive repository
        of inherited mutation data for medical research, genetic diagnosis and next-generation
        sequencing studies'
    note: null
    pmcid: PMC5429360
    pmid: '28349240'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-01-17T15:07:48Z'
  biotoolsCURIE: biotools:cytoscape
  biotoolsID: cytoscape
  collectionID:
  - Rare Disease
  - EBI Training Tools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://groups.google.com/forum/?fromgroups#!forum/cytoscape-helpdesk
  description: Open source software platform for visualizing molecular interaction
    networks and biological pathways and integrating these networks with annotations,
    gene expression profiles and other state data.
  documentation:
  - note: Tutorial material
    type:
    - Training material
    url: https://github.com/cytoscape/cytoscape-tutorials/wiki
  - note: null
    type:
    - General
    url: https://github.com/cytoscape/cytoscape
  download:
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    type: Downloads page
    url: https://cytoscape.org/download.html
    version: null
  editPermission:
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  function:
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    input: []
    note: null
    operation:
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    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    output: []
  homepage: http://www.cytoscape.org/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2024-11-24T14:15:02.391096Z'
  license: null
  link: []
  maturity: Mature
  name: Cytoscape
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ebi_training_import
  publication:
  - doi: 10.1101/gr.1239303
    metadata:
      abstract: Cytoscape is an open source software project for integrating biomolecular
        interaction networks with high-throughput expression data and other molecular
        states into a unified conceptual framework. Although applicable to any system
        of molecular components and interactions, Cytoscape is most powerful when
        used in conjunction with large databases of protein-protein, protein-DNA,
        and genetic interactions that are increasingly available for humans and model
        organisms. Cytoscape's software Core provides basic functionality to layout
        and query the network; to visually integrate the network with expression profiles,
        phenotypes, and other molecular states; and to link the network to databases
        of functional annotations. The Core is extensible through a straightforward
        plug-in architecture, allowing rapid development of additional computational
        analyses and features. Several case studies of Cytoscape plug-ins are surveyed,
        including a search for interaction pathways correlating with changes in gene
        expression, a study of protein complexes involved in cellular recovery to
        DNA damage, inference of a combined physical/functional interaction network
        for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory
        models.
      authors:
      - name: Shannon P.
      - name: Markiel A.
      - name: Ozier O.
      - name: Baliga N.S.
      - name: Wang J.T.
      - name: Ramage D.
      - name: Amin N.
      - name: Schwikowski B.
      - name: Ideker T.
      citationCount: 33622
      date: '2003-11-01T00:00:00Z'
      journal: Genome Research
      title: 'Cytoscape: A software Environment for integrated models of biomolecular
        interaction networks'
    note: null
    pmcid: PMC403769
    pmid: '14597658'
    type: []
    version: null
  relation:
  - biotoolsID: scnetviz
    type: uses
  toolType:
  - Suite
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - 3.7.1
- accessibility: Open access
  additionDate: '2017-03-01T16:16:45Z'
  biotoolsCURIE: biotools:ws_snps_and_go
  biotoolsID: ws_snps_and_go
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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    url: http://www.biocomp.unibo.it
  - email: emidio.capriotti@gmail.com
    fundrefid: null
    gridid: null
    name: Emidio Capriotti
    note: null
    orcidid: http://orcid.org/0000-0002-2323-0963
    rorid: null
    typeEntity: Person
    typeRole:
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    - Primary contact
    url: null
  - email: casadio@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A web server for predicting disease associated variations from protein
    sequence and structure.
  documentation:
  - note: null
    type:
    - General
    url: http://snps.biofold.org/snps-and-go/
  download:
  - note: null
    type: Container file
    url: https://hub.docker.com/r/biofold/snps-and-go
    version: '2.0'
  editPermission:
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    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: PDB ID
        uri: http://edamontology.org/data_1127
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Protein chain
        uri: http://edamontology.org/data_1467
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: GO concept ID
        uri: http://edamontology.org/data_1176
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Mutation ID
        uri: http://edamontology.org/data_2209
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: SNPs&GO 3D
    operation:
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://snps.biofold.org/snps-and-go
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:11:29.726150Z'
  license: null
  link: []
  maturity: Mature
  name: WS-SNPs-and-GO
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/1471-2105-12-s4-s3
    metadata:
      abstract: 'Background: Single Nucleotide Polymorphisms (SNPs) are an important
        source of human genome variability. Non-synonymous SNPs occurring in coding
        regions result in single amino acid polymorphisms (SAPs) that may affect protein
        function and lead to pathology. Several methods attempt to estimate the impact
        of SAPs using different sources of information. Although sequence-based predictors
        have shown good performance, the quality of these predictions can be further
        improved by introducing new features derived from three-dimensional protein
        structures.Results: In this paper, we present a structure-based machine learning
        approach for predicting disease-related SAPs. We have trained a Support Vector
        Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral
        polymorphisms from 784 protein chains. We use SVM input features derived from
        the protein''s sequence, structure, and function. After dataset balancing,
        the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a
        correlation coefficient of 0.70, and an area under the receiving operating
        characteristic curve (AUC) of 0.92. When compared with a similar sequence-based
        predictor, SVM-3D results in an increase of the overall accuracy and AUC by
        3%, and correlation coefficient by 0.06. The robustness of this improvement
        has been tested on different datasets and in all the cases SVM-3D performs
        better than previously developed methods even when compared with PolyPhen2,
        which explicitly considers in input protein structure information.Conclusion:
        This work demonstrates that structural information can increase the accuracy
        of disease-related SAPs identification. Our results also quantify the magnitude
        of improvement on a large dataset. This improvement is in agreement with previously
        observed results, where structure information enhanced the prediction of protein
        stability changes upon mutation. Although the structural information contained
        in the Protein Data Bank is limiting the application and the performance of
        our structure-based method, we expect that SVM-3D will result in higher accuracy
        when more structural date become available. © 2011 Capriotti; licensee BioMed
        Central Ltd.'
      authors:
      - name: Capriotti E.
      - name: Altman R.B.
      citationCount: 104
      date: '2011-07-05T00:00:00Z'
      journal: BMC Bioinformatics
      title: Improving the prediction of disease-related variants using protein three-dimensional
        structure
    note: null
    pmcid: PMC3194195
    pmid: '21992054'
    type:
    - Method
    version: null
  - doi: 10.1002/humu.21047
    metadata:
      abstract: Single nucleotide polymorphisms (SNPs) are the simplest and most frequent
        form of human DNA variation, also valuable as genetic markers of disease susceptibility.
        The most investigated SNPs are missense mutations resulting in residue substitutions
        in the protein. Here we propose SNPs&GO, an accurate method that, starting
        from a protein sequence, can predict whether a mutation is disease related
        or not by exploiting the protein functional annotation. The scoring efficiency
        of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal
        to 0.63 over a wide set of annotated nonsynonymous mutations in proteins,
        including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO
        collects in unique framework information derived from protein sequence, evolutionary
        information, and function as encoded in the Gene Ontology terms, and outperforms
        other available predictive methods. © 2009 Wiley-Liss, Inc.
      authors:
      - name: Calabrese R.
      - name: Capriotti E.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 515
      date: '2009-08-01T00:00:00Z'
      journal: Human Mutation
      title: Functional annotations improve the predictive score of human disease-related
        mutations in proteins
    note: null
    pmcid: null
    pmid: '19514061'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Web service
  topic:
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2021-01-20T10:58:38Z'
  biotoolsCURIE: biotools:phenpath
  biotoolsID: phenpath
  collectionID:
  - Rare Disease
  - Bologna Biocomputing Group
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: giulia.babbi@unibo.it
    fundrefid: null
    gridid: null
    name: Giulia Babbi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: 'A tool for characterizing biological functions underlying different
    phenotypes.


    a web server for associating phenotypes with molecular functional annotations.


    PhenPath includes a database and a tool:.

    '''
  documentation:
  - note: null
    type:
    - User manual
    url: http://phenpath.biocomp.unibo.it/phenpath/help_page.html
  download: []
  editPermission:
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    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Disease ID
        uri: http://edamontology.org/data_1150
      format: []
    - data:
        term: Disease name
        uri: http://edamontology.org/data_3668
      format: []
    - data:
        term: Phenotype name
        uri: http://edamontology.org/data_3275
      format: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format: []
  homepage: http://phenpath.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:10:12.678273Z'
  license: CC-BY-NC-4.0
  link: []
  maturity: Mature
  name: PhenPath
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/s12864-019-5868-x
    metadata:
      abstract: 'Background: Many diseases are associated with complex patterns of
        symptoms and phenotypic manifestations. Parsimonious explanations aim at reconciling
        the multiplicity of phenotypic traits with the perturbation of one or few
        biological functions. For this, it is necessary to characterize human phenotypes
        at the molecular and functional levels, by exploiting gene annotations and
        known relations among genes, diseases and phenotypes. This characterization
        makes it possible to implement tools for retrieving functions shared among
        phenotypes, co-occurring in the same patient and facilitating the formulation
        of hypotheses about the molecular causes of the disease. Results: We introduce
        PhenPath, a new resource consisting of two parts: PhenPathDB and PhenPathTOOL.
        The former is a database collecting the human genes associated with the phenotypes
        described in Human Phenotype Ontology (HPO) and OMIM Clinical Synopses. Phenotypes
        are then associated with biological functions and pathways by means of NET-GE,
        a network-based method for functional enrichment of sets of genes. The present
        version considers only phenotypes related to diseases. PhenPathDB collects
        information for 18 OMIM Clinical synopses and 7137 HPO phenotypes, related
        to 4292 diseases and 3446 genes. Enrichment of Gene Ontology annotations endows
        some 87.7, 86.9 and 73.6% of HPO phenotypes with Biological Process, Molecular
        Function and Cellular Component terms, respectively. Furthermore, 58.8 and
        77.8% of HPO phenotypes are also enriched for KEGG and Reactome pathways,
        respectively. Based on PhenPathDB, PhenPathTOOL analyzes user-defined sets
        of phenotypes retrieving diseases, genes and functional terms which they share.
        This information can provide clues for interpreting the co-occurrence of phenotypes
        in a patient. Conclusions: The resource allows finding molecular features
        useful to investigate diseases characterized by multiple phenotypes, and by
        this, it can help researchers and physicians in identifying molecular mechanisms
        and biological functions underlying the concomitant manifestation of phenotypes.
        The resource is freely available at http://phenpath.biocomp.unibo.it.'
      authors:
      - name: Babbi G.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 8
      date: '2019-07-16T00:00:00Z'
      journal: BMC Genomics
      title: 'PhenPath: A tool for characterizing biological functions underlying
        different phenotypes'
    note: null
    pmcid: PMC6631446
    pmid: '31307376'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Public health and epidemiology
    uri: http://edamontology.org/topic_3305
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2015-01-22T11:31:41Z'
  biotoolsCURIE: biotools:snps_and_go
  biotoolsID: snps_and_go
  collectionID:
  - RD-connect
  - Rare Disease
  - 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: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - 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: null
  description: A server for the prediction of single point protein mutations likely
    to be involved in the insurgence of diseases in humans.s.
  documentation:
  - note: null
    type:
    - General
    url: http://snps-and-go.biocomp.unibo.it/snps-and-go/help2.htm
  download: []
  editPermission:
    authors:
    - ELIXIR-EE
    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: UniProt accession
        uri: http://edamontology.org/data_3021
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: Prediction
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Protein function analysis
      uri: http://edamontology.org/operation_2414
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://snps-and-go.biocomp.unibo.it/snps-and-go/index.html
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:09:44.753852Z'
  license: null
  link: []
  maturity: Mature
  name: SNPs and GO
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1002/humu.21047
    metadata:
      abstract: Single nucleotide polymorphisms (SNPs) are the simplest and most frequent
        form of human DNA variation, also valuable as genetic markers of disease susceptibility.
        The most investigated SNPs are missense mutations resulting in residue substitutions
        in the protein. Here we propose SNPs&GO, an accurate method that, starting
        from a protein sequence, can predict whether a mutation is disease related
        or not by exploiting the protein functional annotation. The scoring efficiency
        of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal
        to 0.63 over a wide set of annotated nonsynonymous mutations in proteins,
        including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO
        collects in unique framework information derived from protein sequence, evolutionary
        information, and function as encoded in the Gene Ontology terms, and outperforms
        other available predictive methods. © 2009 Wiley-Liss, Inc.
      authors:
      - name: Calabrese R.
      - name: Capriotti E.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 515
      date: '2009-08-01T00:00:00Z'
      journal: Human Mutation
      title: Functional annotations improve the predictive score of human disease-related
        mutations in proteins
    note: null
    pmcid: null
    pmid: '19514061'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2017-03-13T17:04:55Z'
  biotoolsCURIE: biotools:edgar
  biotoolsID: edgar
  collectionID:
  - RD-connect
  - Rare Disease
  - Bologna Biocomputing Group
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: giulia.babbi3@unibo.it
    fundrefid: null
    gridid: null
    name: Giulia Babbi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it/
  description: A database of Disease-Gene Associations with annotated Relationships
    among genes.
  documentation:
  - note: null
    type:
    - User manual
    url: http://edgar.biocomp.unibo.it/gene_disease_db/tutorial.html
  download: []
  editPermission:
    authors:
    - ELIXIR-EE
    - lmatalonga
    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Disease name
        uri: http://edamontology.org/data_3668
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: For each heterogeneous or polygenic disease, eDGAR provides information
      on the relationship among the proteins encoded by the involved genes.
    operation:
    - term: Database comparison
      uri: http://edamontology.org/operation_3561
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    - term: Pathway or network analysis
      uri: http://edamontology.org/operation_2497
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: OMIM ID
        uri: http://edamontology.org/data_1153
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: For each heterogeneous or polygenic disease, eDGAR provides information
      on the relationship among the proteins encoded by the involved genes.
    operation:
    - term: Database comparison
      uri: http://edamontology.org/operation_3561
    - term: Pathway or network analysis
      uri: http://edamontology.org/operation_2497
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://edgar.biocomp.unibo.it
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-11-24T14:09:23.960492Z'
  license: CC-BY-4.0
  link: []
  maturity: Mature
  name: eDGAR
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: doi
    value: doi:10.25504/FAIRsharing.29EHM2
    version: null
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/s12864-017-3911-3
    metadata:
      abstract: 'Background: Genetic investigations, boosted by modern sequencing
        techniques, allow dissecting the genetic component of different phenotypic
        traits. These efforts result in the compilation of lists of genes related
        to diseases and show that an increasing number of diseases is associated with
        multiple genes. Investigating functional relations among genes associated
        with the same disease contributes to highlighting molecular mechanisms of
        the pathogenesis. Results: We present eDGAR, a database collecting and organizing
        the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar.
        For each disease-associated gene, eDGAR collects information on its annotation.
        Specifically, for lists of genes, eDGAR provides information on: i) interactions
        retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional
        structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG
        and REACTOME pathways; v) enriched functional annotations computed with NET-GE;
        vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes
        and/or co-localisation in neighboring loci. The present release of eDGAR includes
        2672 diseases, related to 3658 different genes, for a total number of 5729
        gene-disease associations. 71% of the genes are linked to 621 multigenic diseases
        and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical
        and regulatory interactions. eDGAR includes a network based enrichment method
        for detecting statistically significant functional terms associated to groups
        of genes. Conclusions: eDGAR offers a resource to analyze disease-gene associations.
        In multigenic diseases genes can share physical interactions and/or co-occurrence
        in the same functional processes. eDGAR is freely available at: edgar. biocomp.unibo.it.'
      authors:
      - name: Babbi G.
      - name: Martelli P.L.
      - name: Profiti G.
      - name: Bovo S.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 48
      date: '2017-01-01T00:00:00Z'
      journal: BMC Genomics
      title: 'eDGAR: A database of disease-gene associations with annotated relationships
        among genes'
    note: null
    pmcid: PMC5558190
    pmid: '28812536'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-07-20T18:20:28Z'
  biotoolsCURIE: biotools:bridgedb_api
  biotoolsID: bridgedb_api
  collectionID:
  - Rare Disease
  - BridgeDb
  confidence_flag: null
  cost: null
  credit:
  - email: bridgedb-discuss@googlegroups.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: 'BridgeDb is a framework for finding and mapping equivalent database
    identifiers. It has many facets: it is both a framework, live services, and are
    identifier mapping files for genes, proteins, and metabolites.'
  documentation:
  - note: null
    type:
    - General
    url: https://www.bridgedb.org/
  download: []
  editPermission:
    authors:
    - ChrisEvelo
    - LanfearJ
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform:
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: Returns a list of xrefs that map to a given identifier, and data source.
      Optionally restrict results to a given data source.
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
  - cmd: null
    input:
    - data:
        term: Species name
        uri: http://edamontology.org/data_1045
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: Returns `true` or `false` based on whether or not an xref exists in the
      database given an identifier, data source, and organism.
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
  homepage: http://www.bridgedb.org/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2024-11-24T14:02:46.528009Z'
  license: null
  link: []
  maturity: null
  name: BridgeDb API
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: egonw
  publication:
  - doi: 10.1186/1471-2105-11-5
    metadata:
      abstract: 'Background: Endocrine disruptors (EDs) and their broad range of potential
        adverse effects in humans and other animals have been a concern for nearly
        two decades. Many putative EDs are widely used in commercial products regulated
        by the Food and Drug Administration (FDA) such as food packaging materials,
        ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine
        Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990''s by
        the FDA as a resource for the study of EDs. The EDKB database, a component
        of the project, contains data across multiple assay types for chemicals across
        a broad structural diversity. This paper demonstrates the utility of EDKB
        database, an integral part of the EDKB project, for understanding and prioritizing
        EDs for testing.Results: The EDKB database currently contains 3,257 records
        of over 1,800 EDs from different assays including estrogen receptor binding,
        androgen receptor binding, uterotropic activity, cell proliferation, and reporter
        gene assays. Information for each compound such as chemical structure, assay
        type, potency, etc. is organized to enable efficient searching. A user-friendly
        interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet
        and graphical displays for viewing results. The search engine implemented
        in the EDKB database enables searching by one or more of the following fields:
        chemical structure (including exact search and similarity search), name, molecular
        formula, CAS registration number, experiment source, molecular weight, etc.
        The data can be cross-linked to other publicly available and related databases
        including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. .
        Conclusion: The EDKB database enables scientists and regulatory reviewers
        to quickly access ED data from multiple assays for specific or similar compounds.
        The data have been used to categorize chemicals according to potential risks
        for endocrine activity, thus providing a basis for prioritizing chemicals
        for more definitive but expensive testing. The EDKB database is publicly available
        and can be found online at http://edkb.fda.gov/webstart/edkb/index.html.Disclaimer:The
        views presented in this article do not necessarily reflect those of the US
        Food and Drug Administration. © 2010 Tong et al; licensee BioMed Central Ltd.'
      authors:
      - name: Ding D.
      - name: Xu L.
      - name: Fang H.
      - name: Hong H.
      - name: Perkins R.
      - name: Harris S.
      - name: Bearden E.D.
      - name: Shi L.
      - name: Tong W.
      citationCount: 80
      date: '2010-10-07T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'The EDKB: An established knowledge base for endocrine disrupting chemicals'
    note: null
    pmcid: PMC2824678
    pmid: '20047655'
    type: []
    version: null
  relation: []
  toolType:
  - Web API
  topic:
  - term: Data identity and mapping
    uri: http://edamontology.org/topic_3345
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-11-04T11:11:36.831993Z'
  biotoolsCURIE: biotools:jdispatcher
  biotoolsID: jdispatcher
  collectionID:
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  - Job Dispatcher Tools
  - BioExcel
  - Rare Disease
  - COVID-19
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: EMBL - EBI
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Job Dispatcher
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Primary contact
    url: http://www.ebi.ac.uk/jdispatcher/
  description: EBI Tools is a project that aims to provide programmatic access to
    the various databases and retrieval and analysis services that the European Bioinformatics
    Institute (EBI) provides through Simple Object Access Protocol (SOAP) and other
    related web service technologies. Example tools include those to compute sequence
    similarity searches, pairwise/multiple sequence alignment and protein functional
    analysis.
  documentation:
  - note: null
    type:
    - Terms of use
    url: http://www.ebi.ac.uk/about/terms-of-use
  - note: null
    type:
    - General
    url: https://www.ebi.ac.uk/jdispatcher/help
  download: []
  editPermission:
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    - nandana
    type: group
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence similarity search
      uri: http://edamontology.org/operation_0346
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Protein function analysis
      uri: http://edamontology.org/operation_2414
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    output: []
  homepage: http://www.ebi.ac.uk/jdispatcher/
  homepage_status: 0
  language:
  - Java
  - Perl
  - Python
  lastUpdate: '2024-11-24T14:00:40.196248Z'
  license: null
  link:
  - note: null
    type:
    - Helpdesk
    url: https://www.ebi.ac.uk/about/contact/support/job-dispatcher-services
  maturity: Mature
  name: Job Dispatcher
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: jdispatcher
  publication:
  - doi: 10.1093/nar/gkae241
    metadata:
      abstract: The EMBL-EBI Job Dispatcher sequence analysis tools framework (https://www.ebi.ac.uk/jdispatcher)
        enables the scientific community to perform a diverse range of sequence analyses
        using popular bioinformatics applications. Free access to the tools and required
        sequence datasets is provided through user-friendly web applications, as well
        as via RESTful and SOAP-based APIs. These are integrated into popular EMBL-EBI
        resources such as UniProt, InterPro, ENA and Ensembl Genomes. This paper overviews
        recent improvements to Job Dispatcher, including its brand new website and
        documentation, enhanced visualisations, improved job management, and a rising
        trend of user reliance on the service from low- and middle-income regions.
      authors:
      - name: Madeira F.
      - name: Madhusoodanan N.
      - name: Lee J.
      - name: Eusebi A.
      - name: Niewielska A.
      - name: Tivey A.R.N.
      - name: Lopez R.
      - name: Butcher S.
      citationCount: 60
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024
    note: null
    pmcid: PMC11223882
    pmid: '38597606'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkac240
    metadata:
      abstract: The EMBL-EBI search and sequence analysis tools frameworks provide
        integrated access to EMBL-EBI's data resources and core bioinformatics analytical
        tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search
        engine across nearly 5 billion entries, while the Job Dispatcher tools framework
        (https://www.ebi.ac.uk/services) enables the scientific community to perform
        a diverse range of sequence analysis using popular bioinformatics applications.
        Both allow users to interact through user-friendly web applications, as well
        as via RESTful and SOAP-based APIs. Here, we describe recent improvements
        to these services and updates made to accommodate the increasing data requirements
        during the COVID-19 pandemic.
      authors:
      - name: Madeira F.
      - name: Pearce M.
      - name: Tivey A.R.N.
      - name: Basutkar P.
      - name: Lee J.
      - name: Edbali O.
      - name: Madhusoodanan N.
      - name: Kolesnikov A.
      - name: Lopez R.
      citationCount: 1248
      date: '2022-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: Search and sequence analysis tools services from EMBL-EBI in 2022
    note: null
    pmcid: PMC9252731
    pmid: '35412617'
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkz268
    metadata:
      abstract: The EMBL-EBI provides free access to popular bioinformatics sequence
        analysis applications as well as to a full-featured text search engine with
        powerful cross-referencing and data retrieval capabilities. Access to these
        services is provided via user-friendly web interfaces and via established
        RESTful and SOAP Web Services APIs (https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/EMBL-EBI+Web+Services+APIs+-+Data+Retrieval).
        Both systems have been developed with the same core principles that allow
        them to integrate an ever-increasing volume of biological data, making them
        an integral part of many popular data resources provided at the EMBL-EBI.
        Here, we describe the latest improvements made to the frameworks which enhance
        the interconnectivity between public EMBL-EBI resources and ultimately enhance
        biological data discoverability, accessibility, interoperability and reusability.
      authors:
      - name: Madeira F.
      - name: Park Y.M.
      - name: Lee J.
      - name: Buso N.
      - name: Gur T.
      - name: Madhusoodanan N.
      - name: Basutkar P.
      - name: Tivey A.R.N.
      - name: Potter S.C.
      - name: Finn R.D.
      - name: Lopez R.
      citationCount: 3124
      date: '2019-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The EMBL-EBI search and sequence analysis tools APIs in 2019
    note: null
    pmcid: PMC6602479
    pmid: '30976793'
    type: []
    version: null
  relation: []
  toolType:
  - Web API
  - Web application
  - Web service
  topic:
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Function analysis
    uri: http://edamontology.org/topic_1775
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-05-17T20:35:45.357863Z'
  biotoolsCURIE: biotools:pykvfinder
  biotoolsID: pykvfinder
  collectionID:
  - KVFinder suite
  - Rare Disease
  - COVID-19
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: joao.guerra@lnbio.cnpem.br
    fundrefid: null
    gridid: null
    name: João V. S. Guerra
    note: null
    orcidid: https://orcid.org/0000-0002-6800-4425
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: https://github.com/jvsguerra
  - email: null
    fundrefid: null
    gridid: null
    name: Helder V. Ribeiro-Filho
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Gabriel E. Jara
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Leandro O. Bortot
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: José G. C. Pereira
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: paulo.oliveira@lnbio.cnpem.br
    fundrefid: null
    gridid: null
    name: Paulo S. Lopes-de-Oliveira
    note: null
    orcidid: https://orcid.org/0000-0002-1287-8019
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Provider
    url: null
  description: Python-C Parallel KVFinder (pyKVFinder) is an efficient and integrable
    Python package for biomolecular cavity detection and characterization in data
    science. Besides conventional cavity properties such as volume and area, which
    are stored as Python dictionaries, pyKVFinder computes cavity depth and hydropathy.
    Similar to cavity points, these spatial and physicochemical properties are stored
    as Python ndarrays and can be visualized using Python molecular visualization
    widgets. In general, pyKVFinder is designed for efficient scripting routines built
    on easy-to-handle data structures, such as Python dictionaries and NumPy N-dimensional
    arrays (ndarrays), and can be building blocks for data science and drug design
    applications.
  documentation:
  - note: null
    type:
    - User manual
    url: https://lbc-lnbio.github.io/pyKVFinder/
  download: []
  editPermission:
    authors:
    - jvsguerra
    type: group
  elixirCommunity: []
  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
      - term: XYZ
        uri: http://edamontology.org/format_3877
    note: null
    operation:
    - term: Molecular surface analysis
      uri: http://edamontology.org/operation_3351
    - term: Structure comparison
      uri: http://edamontology.org/operation_2483
    - term: Binding site prediction
      uri: http://edamontology.org/operation_2575
    - term: Protein property calculation
      uri: http://edamontology.org/operation_0250
    output:
    - data:
        term: Protein structural motif
        uri: http://edamontology.org/data_2878
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/LBC-LNBio/pyKVFinder
  homepage_status: 0
  language:
  - Python
  - C
  lastUpdate: '2024-11-24T13:49:46.803306Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/pyKVFinder
  maturity: Mature
  name: pyKVFinder
  operatingSystem:
  - Mac
  - Windows
  - Linux
  otherID: []
  owner: jvsguerra
  publication:
  - doi: 10.1186/s12859-021-04519-4
    metadata:
      abstract: 'Background: Biomolecular interactions that modulate biological processes
        occur mainly in cavities throughout the surface of biomolecular structures.
        In the data science era, structural biology has benefited from the increasing
        availability of biostructural data due to advances in structural determination
        and computational methods. In this scenario, data-intensive cavity analysis
        demands efficient scripting routines built on easily manipulated data structures.
        To fulfill this need, we developed pyKVFinder, a Python package to detect
        and characterize cavities in biomolecular structures for data science and
        automated pipelines. Results: pyKVFinder efficiently detects cavities in biomolecular
        structures and computes their volume, area, depth and hydropathy, storing
        these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability
        and data structures, pyKVFinder can be integrated with third-party scientific
        packages and libraries for mathematical calculations, machine learning and
        3D visualization in automated workflows. As proof of pyKVFinder’s capabilities,
        we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2
        and a set of homologous proteins with pyKVFinder, showing its integrability
        with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter
        notebook. Conclusions: We introduce an efficient, highly versatile and easily
        integrable software for detecting and characterizing biomolecular cavities
        in data science applications and automated protocols. pyKVFinder facilitates
        biostructural data analysis with scripting routines in the Python ecosystem
        and can be building blocks for data science and drug design applications.'
      authors:
      - name: Guerra J.V.S.
      - name: Ribeiro-Filho H.V.
      - name: Jara G.E.
      - name: Bortot L.O.
      - name: Pereira J.G.C.
      - name: Lopes-de-Oliveira P.S.
      citationCount: 12
      date: '2021-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'pyKVFinder: an efficient and integrable Python package for biomolecular
        cavity detection and characterization in data science'
    note: null
    pmcid: PMC8685811
    pmid: '34930115'
    type:
    - Primary
    version: null
  - doi: 10.1021/acs.jcim.3c00328
    metadata:
      abstract: Confining molecular guests within artificial hosts has provided a
        major driving force in the rational design of supramolecular cages with tailored
        properties. Over the last 30 years, a set of design strategies have been developed
        that enabled the controlled synthesis of a myriad of cages. Recently, there
        has been a growing interest in involving in silico methods in this toolbox.
        Cavity shape and size are important parameters that can be easily accessed
        by inexpensive geometric algorithms. Although these algorithms are well developed
        for the detection of nonartificial cavities (e.g., enzymes), they are not
        routinely used for the rational design of supramolecular cages. In order to
        test the capabilities of this tool, we have evaluated the performance and
        characteristics of seven different cavity characterization software in the
        context of 22 analogues of well-known supramolecular cages. Among the tested
        software, KVFinder project and Fpocket proved to be the most software to characterize
        supramolecular cavities. With the results of this work, we aim to popularize
        this underused technique within the supramolecular community.
      authors:
      - name: Guerra J.V.S.
      - name: Alves L.F.G.
      - name: Bourissou D.
      - name: Lopes-De-Oliveira P.S.
      - name: Szaloki G.
      citationCount: 2
      date: '2023-06-26T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: Cavity Characterization in Supramolecular Cages
    note: null
    pmcid: null
    pmid: '37129917'
    type:
    - Benchmarking study
    version: null
  relation:
  - biotoolsID: KVFinder
    type: isNewVersionOf
  toolType:
  - Library
  - Script
  - Command-line tool
  topic:
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Mathematics
    uri: http://edamontology.org/topic_3315
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Computational chemistry
    uri: http://edamontology.org/topic_3332
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-09-20T15:25:01.787084Z'
  biotoolsCURIE: biotools:parkvfinder
  biotoolsID: parkvfinder
  collectionID:
  - KVFinder suite
  - COVID-19
  - Rare Disease
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: joao.guerra@lnbio.cnpem.br
    fundrefid: null
    gridid: null
    name: João Victor da Silva Guerra
    note: null
    orcidid: https://orcid.org/0000-0002-6800-4425
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: https://github.com/jvsguerra
  - email: null
    fundrefid: null
    gridid: null
    name: Helder Veras Ribeiro-Filho
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Leandro Oliveira Bortot
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Rodrigo Vargas Honorato
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: José Geraldo de Carvalho Pereira
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: paulo.oliveira@lnbio.cnpem.br
    fundrefid: null
    gridid: null
    name: Paulo Sergio Lopes-de-Oliveira
    note: null
    orcidid: https://orcid.org/0000-0002-1287-8019
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Parallel KVFinder (parKVFinder) is an open-source software designed
    for the detection and spatial characterization (shape, volume, area and surrounding
    residues) of any type of biomolecular cavity. parKVFinder is available alongside
    an easy-to-use PyMOL plugin (PyMOL2 parKVFinder Tools) with an intuitive graphical
    user interface that allows users to explore customizable parameters for cavity
    detection and characterization.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/LBC-LNBio/parKVFinder/wiki
  download: []
  editPermission:
    authors:
    - jvsguerra
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Binding site prediction
      uri: http://edamontology.org/operation_2575
    output:
    - data:
        term: Protein structural motif
        uri: http://edamontology.org/data_2878
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/LBC-LNBio/parKVFinder
  homepage_status: 0
  language:
  - C
  - PyMOL
  lastUpdate: '2024-11-24T13:49:40.862143Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/parKVFinder
  - note: null
    type:
    - Repository
    url: https://github.com/LBC-LNBio/parKVFinder-win
  maturity: Mature
  name: parKVFinder
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: jvsguerra
  publication:
  - doi: 10.1016/j.softx.2020.100606
    metadata:
      abstract: Biological processes are regulated mainly by the binding of small
        molecules into cavities distributed throughout the biomolecular structure.
        Computational tools to detect these cavities have an essential role in rational
        drug design. With the exponential availability of high-order 3D atomic structures
        and large sets of atomic models, the tools must balance accuracy and speed.
        In this sense, we developed parKVFinder, a parallelized software for geometry-based
        cavity detection. Here, we described its functionalities and presented its
        easy-to-use PyMOL plugin and command-line interface. Finally, we demonstrated
        parKVFinder by identifying an important HIV-1 protease cavity and compared
        it with other geometry-based programs.
      authors:
      - name: Guerra J.V.D.S.
      - name: Ribeiro Filho H.V.
      - name: Bortot L.O.
      - name: Honorato R.V.
      - name: Pereira J.G.D.C.
      - name: Lopes-de-Oliveira P.S.
      citationCount: 16
      date: '2020-07-01T00:00:00Z'
      journal: SoftwareX
      title: 'ParKVFinder: A thread-level parallel approach in biomolecular cavity
        detection'
    note: null
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    version: null
  - doi: 10.1021/acs.jcim.3c00328
    metadata:
      abstract: Confining molecular guests within artificial hosts has provided a
        major driving force in the rational design of supramolecular cages with tailored
        properties. Over the last 30 years, a set of design strategies have been developed
        that enabled the controlled synthesis of a myriad of cages. Recently, there
        has been a growing interest in involving in silico methods in this toolbox.
        Cavity shape and size are important parameters that can be easily accessed
        by inexpensive geometric algorithms. Although these algorithms are well developed
        for the detection of nonartificial cavities (e.g., enzymes), they are not
        routinely used for the rational design of supramolecular cages. In order to
        test the capabilities of this tool, we have evaluated the performance and
        characteristics of seven different cavity characterization software in the
        context of 22 analogues of well-known supramolecular cages. Among the tested
        software, KVFinder project and Fpocket proved to be the most software to characterize
        supramolecular cavities. With the results of this work, we aim to popularize
        this underused technique within the supramolecular community.
      authors:
      - name: Guerra J.V.S.
      - name: Alves L.F.G.
      - name: Bourissou D.
      - name: Lopes-De-Oliveira P.S.
      - name: Szaloki G.
      citationCount: 2
      date: '2023-06-26T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: Cavity Characterization in Supramolecular Cages
    note: null
    pmcid: null
    pmid: '37129917'
    type:
    - Benchmarking study
    version: null
  relation:
  - biotoolsID: KVFinder
    type: isNewVersionOf
  - biotoolsID: KVFinder-web
    type: usedBy
  toolType:
  - Plug-in
  - Command-line tool
  topic:
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Computational chemistry
    uri: http://edamontology.org/topic_3332
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  validated: 0
  version: []
next: ?page=2
previous: null
