count: 57
list:
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  additionDate: '2025-05-15T11:35:23.428773Z'
  biotoolsCURIE: biotools:plantismash
  biotoolsID: plantismash
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: PlantiSMASH is a specialized extension of antiSMASH for the identification
    and analysis of biosynthetic gene clusters (BGCs) in plant genomes. It supports
    advanced plant-specific detection rules and features for comparative genomics,
    visualization, and more.
  documentation:
  - note: null
    type:
    - General
    url: https://plantismash.github.io/documentation/
  download:
  - note: null
    type: Software package
    url: https://github.com/plantismash/plantismash/releases
    version: 2.0-beta4
  editPermission:
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    type: private
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  homepage: https://plantismash.bioinformatics.nl/
  homepage_status: 0
  language: []
  lastUpdate: '2026-05-15T12:25:33.148463Z'
  license: AGPL-3.0
  link: []
  maturity: null
  name: plantiSMASH
  operatingSystem:
  - Mac
  - Windows
  - Linux
  otherID: []
  owner: elena0
  publication:
  - doi: 10.1093/nar/gkx305
    metadata: null
    note: null
    pmcid: PMC5570173
    pmid: null
    type: []
    version: '1.0'
  - doi: 10.1016/j.jmb.2026.169798
    metadata: null
    note: 'plantiSMASH 2.0: Improvements to Detection, Annotation, and Prioritization
      of Plant Biosynthetic Gene Clusters'
    pmcid: null
    pmid: '41962628'
    type:
    - Primary
    version: '2.0'
  relation: []
  toolType: []
  topic:
  - term: Transcription factors and regulatory sites
    uri: http://edamontology.org/topic_0749
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version:
  - 2.0-beta4
- accessibility: Open access
  additionDate: '2016-03-17T13:51:10Z'
  biotoolsCURIE: biotools:webnma
  biotoolsID: webnma
  collectionID:
  - BiB tools
  - CBU tools
  - UiB tools
  - ELIXIR-NO
  - ELIXIR-Norway
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: Nathalie.Reuter@uib.no
    fundrefid: null
    gridid: null
    name: Nathalie Reuter
    note: null
    orcidid: https://orcid.org/0000-0002-3649-7675
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: http://www.cbu.uib.no/reuter/
  - email: null
    fundrefid: null
    gridid: null
    name: Sandhya P Tiwari
    note: null
    orcidid: https://orcid.org/0000-0002-0747-3826
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Kidane M Tekle
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Tristan Cragnolini
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Svenn H Grindhaug
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Lars Skjærven
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Gisle Salensminde
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Edvin Fuglebakk
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Siv M Hollup
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Department of Molecular Biology, University of Bergen, Norway
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Computational Biology Unit, Department of Informatics, University of Bergen,
      Norway
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: UiB
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: WEBnm@ provides quick, automated computation and analysis of low-frequency
    normal modes for protein structures.
  documentation:
  - note: null
    type:
    - Quick start guide
    url: http://apps.cbu.uib.no/webnma3/howto/single
  - note: null
    type:
    - FAQ
    url: http://apps.cbu.uib.no/webnma3/qanda
  - note: null
    type:
    - General
    - Citation instructions
    url: http://apps.cbu.uib.no/webnma3/about
  download: []
  editPermission:
    authors:
    - eca008
    - korbinib
    type: group
  elixirCommunity:
  - 3D-BioInfo
  elixirNode:
  - Norway
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: Constructs elastic network model from alpha carbon coordinates of the protein,
      and computes properties to describe large scale conformations. Computes normal
      modes, fluctuation profiles, inter-residue correlations, conformational overlap
      analysis and vector field representations. Structural amino acid profiles, and
      normal mode characteristics describing protein motion, visualized in plots and
      decorated structure visualizations. White space delimited tabular data for normal
      modes and the provided plots
    operation:
    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    - term: Protein flexibility and motion analysis
      uri: http://edamontology.org/operation_0244
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Sequence profile
        uri: http://edamontology.org/data_1354
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  - cmd: null
    input:
    - data:
        term: Structure alignment
        uri: http://edamontology.org/data_0886
      format:
      - term: FASTA-like (text)
        uri: http://edamontology.org/format_2200
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: Performs comparative analysis of the normal modes of protein structures.
      Computes the Bhattacharyya Coefficient (BC) and the Root Mean Squared Inner
      Product (RMSIP) of aligned parts of the proteins. Alignment of sets of proteins
      to be compared. Multiple protein structures Heatmaps, dendrograms and structural
      amino acid profiles for visual comparison of structural similarity. White space
      delimited tabular data for the provided plots
    operation:
    - term: Protein structure comparison
      uri: http://edamontology.org/operation_2487
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Structural profile
        uri: http://edamontology.org/data_0889
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://apps.cbu.uib.no/webnma
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2026-03-11T08:56:55.566213Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    - Issue tracker
    url: https://github.com/reuter-group/webnma3
  - note: Helpdesk and support for ELIXIR Norway services.
    type:
    - Helpdesk
    url: https://elixir.no/helpdesk
  maturity: Mature
  name: WEBnma
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: UiB
  publication:
  - doi: 10.1186/s12859-014-0427-6
    metadata:
      abstract: 'Background: Normal mode analysis (NMA) using elastic network models
        is a reliable and cost-effective computational method to characterise protein
        flexibility and by extension, their dynamics. Further insight into the dynamics-function
        relationship can be gained by comparing protein motions between protein homologs
        and functional classifications. This can be achieved by comparing normal modes
        obtained from sets of evolutionary related proteins. Results: We have developed
        an automated tool for comparative NMA of a set of pre-aligned protein structures.
        The user can submit a sequence alignment in the FASTA format and the corresponding
        coordinate files in the Protein Data Bank (PDB) format. The computed normalised
        squared atomic fluctuations and atomic deformation energies of the submitted
        structures can be easily compared on graphs provided by the web user interface.
        The web server provides pairwise comparison of the dynamics of all proteins
        included in the submitted set using two measures: the Root Mean Squared Inner
        Product and the Bhattacharyya Coefficient. The Comparative Analysis has been
        implemented on our web server for NMA, WEBnmat, which also provides recently
        upgraded functionality for NMA of single protein structures. This includes
        new visualisations of protein motion, visualisation of inter-residue correlations
        and the analysis of conformational change using the. In addition, programmatic
        access to WEBnmat is now available through a SOAP-based web service. WEBnmat
        is available at. Conclusion: WEBnmat v2.0 is an online tool offering unique
        capability for comparative NMA on multiple protein structures. Along with
        a convenient web interface, powerful computing resources, and several methods
        for mode analyses, WEBnmat facilitates the assessment of protein flexibility
        within protein families and superfamilies. These analyses can give a good
        view of how the structures move and how the flexibility is conserved over
        the different structures.'
      authors:
      - name: Tiwari S.P.
      - name: Fuglebakk E.
      - name: Hollup S.M.
      - name: Skjaerven L.
      - name: Cragnolini T.
      - name: Grindhaug S.H.
      - name: Tekle K.M.
      - name: Reuter N.
      citationCount: 87
      date: '2014-12-30T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'WEBnmat v2.0: Web server and services for comparing protein flexibility'
    note: null
    pmcid: PMC4339738
    pmid: '25547242'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1186/1471-2105-6-52
    metadata:
      abstract: 'Background: Normal mode analysis (NMA) has become the method of choice
        to investigate the slowest motions in macromolecular systems. NMA is especially
        useful for large biomolecular assemblies, such as transmembrane channels or
        virus capsids. NMA relies on the hypothesis that the vibrational normal modes
        having the lowest frequencies (also named soft modes) describe the largest
        movements in a protein and are the ones that are functionally relevant. Results:
        We developed a web-based server to perform normal modes calculations and different
        types of analyses. Starting from a structure file provided by the user in
        the PDB format, the server calculates the normal modes and subsequently offers
        the user a series of automated calculations; normalized squared atomic displacements,
        vector field representation and animation of the first six vibrational modes.
        Each analysis is performed independently from the others and results can be
        visualized using only a web browser. No additional plug-in or software is
        required. For users who would like to analyze the results with their favorite
        software, raw results can also be downloaded. The application is available
        on http://www.bioinfo.no/tools/normalmodes. We present here the underlying
        theory, the application architecture and an illustration of its features using
        a large transmembrane protein as an example. Conclusion: We built an efficient
        and modular web application for normal mode analysis of proteins. Non specialists
        can easily and rapidly evaluate the degree of flexibility of multi-domain
        protein assemblies and characterize the large amplitude movements of their
        domains. © 2005 Hollup et al; licensee BioMed Central Ltd.'
      authors:
      - name: Hollup S.M.
      - name: Salensminde G.
      - name: Reuter N.
      citationCount: 105
      date: '2005-03-11T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'WEBnm@: A web application for normal mode analyses of proteins'
    note: null
    pmcid: PMC1274249
    pmid: '15762993'
    type:
    - Primary
    version: '1.0'
  relation:
  - biotoolsID: numpy
    type: uses
  - biotoolsID: biopython
    type: uses
  - biotoolsID: matplotlib
    type: uses
  - biotoolsID: mustang
    type: uses
  - biotoolsID: dssp
    type: uses
  toolType:
  - Web API
  - Suite
  topic:
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  - term: Protein folds and structural domains
    uri: http://edamontology.org/topic_0736
  validated: 1
  version:
  - '3.5'
- accessibility: Open access
  additionDate: '2025-12-16T08:20:17.123908Z'
  biotoolsCURIE: biotools:profiler
  biotoolsID: profiler
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: michel.salzet@univ-lille.fr
    fundrefid: null
    gridid: null
    name: Michel Salzet
    note: Professor Michel Salzet known for his work in immunology, oncology and proteomic
    orcidid: https://orcid.org/0000-0003-4318-0817
    rorid: null
    typeEntity: Person
    typeRole: []
    url: https://www.laboratoire-prism.fr/member/michel-salzet/
  description: 'Profiler is a peer-reviewed, open-source web platform and desktop
    version for end-to-end multi-omics data analysis, integrating statistical methods,
    machine learning, and artificial intelligence into a unified workflow.


    The platform supports proteomics, metabolomics, lipidomics, transcriptomics, genomics,
    and other omics datasets from raw or tabular inputs. Profiler provides automated
    preprocessing, data exploration, biomarker discovery, AI-driven modeling, survival
    analysis, and biological interpretation using enrichment analysis.


    Designed for both bioinformaticians and experimental scientists, Profiler enables
    reproducible, explainable, and high-performance omics analysis through an intuitive
    web interface.


    Profiler is developed by the PRISM U1192 laboratory and protected by INSERM Transfer.'
  documentation:
  - note: null
    type:
    - Citation instructions
    url: https://prism-profiler.univ-lille.fr/citation/
  download:
  - note: null
    type: Source code
    url: https://doi.org/10.5281/zenodo.17478158
    version: null
  - note: null
    type: Test data
    url: https://github.com/yanisZirem/Profiler_v1_requests_datatests
    version: '1.0'
  - note: null
    type: Software package
    url: https://prism-profiler.univ-lille.fr/app/
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Metabolomics
  - Proteomics
  - Rare Diseases
  elixirNode:
  - France
  elixirPlatform:
  - Tools
  - Training
  - Interoperability
  elixir_badge: 0
  function: []
  homepage: https://prism-profiler.univ-lille.fr/
  homepage_status: 0
  language: []
  lastUpdate: '2025-12-16T08:20:49.442259Z'
  license: Other
  link:
  - note: softeware link
    type:
    - Other
    url: https://prism-profiler.univ-lille.fr/
  - note: dataset examples
    type:
    - Repository
    url: https://github.com/yanisZirem/Profiler_v1_requests_datatests
  - note: Desktop version
    type:
    - Repository
    url: https://github.com/yanisZirem/prism-profiler
  maturity: Mature
  name: Profiler
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID:
  - type: doi
    value: doi:10.1093/bioinformatics/btaf644
    version: '1.0'
  owner: YZirem
  publication:
  - doi: 10.1093/bioinformatics/btaf644
    metadata: null
    note: null
    pmcid: null
    pmid: '41324558'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Desktop application
  - Web application
  - Workflow
  topic:
  - term: Computer science
    uri: http://edamontology.org/topic_3316
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-12-15T14:17:14.627791Z'
  biotoolsCURIE: biotools:asafind
  biotoolsID: asafind
  collectionID:
  - ELIXIR-CZ
  - Czech Republic
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: mvohnoutova@jcu.cz
    fundrefid: null
    gridid: null
    name: Marta Vohnoutová
    note: null
    orcidid: https://orcid.org/0000-0002-8915-8626
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    - Maintainer
    url: https://www.jcu.cz/cz/univerzita/lide/clovek?identita=Vohnoutova_Marta_61699
  - email: agruber@prf.jcu.cz
    fundrefid: null
    gridid: null
    name: Ansgar Gruber
    note: null
    orcidid: https://orcid.org/0000-0002-5876-4391
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Documentor
    url: https://www.jcu.cz/cz/univerzita/lide/clovek?identita=Gruber_Ansgar_118401
  description: "ASAFind is a software that predicts the intracellular location of\
    \ proteins in cells with four membrane-bound complex plastids of red algal origin.\
    \ \nThese plastids evolved via eukaryote-eukaryote endosymbiosis and for example\
    \ can be found in diatoms and cryptophytes. ASAFind uses protein sequence data\
    \ and external predictions of signal peptides (by the tools SignalP and TargetP),\
    \ and predicts plastid proteins, and proteins that are targeted to the periplastidic\
    \ compartment from these data; optionally, a graphical output can generated."
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://asafind.jcu.cz/download-page/
  download:
  - note: Download page on web-service, links to repository
    type: Downloads page
    url: https://asafind.jcu.cz/download-page/
    version: '2.0'
  editPermission:
    authors:
    - agruber
    type: group
  elixirCommunity:
  - Plant Sciences
  - Marine Metagenomics
  - Microbial Biotechnology
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Feature table
        uri: http://edamontology.org/data_1270
      format: []
    note: null
    operation:
    - term: Subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Sequence motif recognition
      uri: http://edamontology.org/operation_0239
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format: []
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format: []
  homepage: https://asafind.jcu.cz/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-12-15T17:36:43.189214Z'
  license: CC-BY-SA-4.0
  link:
  - note: Web Service
    type:
    - Service
    url: https://asafind.jcu.cz
  - note: Information and source code, for local installation or development
    type:
    - Repository
    url: https://github.com/ASAFind/ASAFind-2
  maturity: Mature
  name: ASAFind
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: martavohnoutova
  publication:
  - doi: 10.1111/tpj.70138
    metadata:
      abstract: ''
      authors: []
      citationCount: 0
      date: '2025-06-01T00:00:00Z'
      journal: Plant Journal
      title: 'ASAFind 2.0: multi-class protein targeting prediction for diatoms and
        algae with complex plastids'
    note: Publication of the current version of ASAFind (2.0)
    pmcid: PMC12136025
    pmid: '40464854'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1111/tpj.12734
    metadata:
      abstract: ''
      authors: []
      citationCount: 138
      date: '2015-02-01T00:00:00Z'
      journal: Plant Journal
      title: Plastid proteome prediction for diatoms and other algae with secondary
        plastids of the red lineage
    note: Publication of the first version of ASAFind
    pmcid: PMC4329603
    pmid: '25438865'
    type:
    - Other
    version: '1.0'
  - doi: 10.48550/arXiv.2303.02509
    metadata: null
    note: Benchmarking of the performance of the first version of ASAFind
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    version: '1.0'
  relation:
  - biotoolsID: signalp
    type: uses
  - biotoolsID: targetp
    type: uses
  toolType:
  - Script
  - Command-line tool
  - Web service
  topic:
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - '2.0'
- accessibility: null
  additionDate: '2020-03-17T13:50:50Z'
  biotoolsCURIE: biotools:seqfu
  biotoolsID: seqfu
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: A collection of utilities to manipulate FASTA and FASTQ files, supporting
    Gzipped input.
  documentation:
  - note: null
    type:
    - User manual
    url: https://telatin.github.io/seqfu2/
  download:
  - note: null
    type: Software package
    url: https://github.com/telatin/seqfu2
    version: null
  - note: null
    type: Downloads page
    url: https://github.com/telatin/seqfu2/releases
    version: null
  - note: null
    type: Software package
    url: https://bioconda.github.io/recipes/seqfu/README.html
    version: 1.0.0-1.22.3
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: seqfu [subcommand] FASTX > FASTX
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format: []
    note: null
    operation:
    - term: Nucleic acid sequence analysis
      uri: http://edamontology.org/operation_2478
    output:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format: []
  homepage: https://telatin.github.io/seqfu2/
  homepage_status: 0
  language: []
  lastUpdate: '2025-12-12T21:43:34.328705Z'
  license: null
  link:
  - note: Documentation
    type:
    - Other
    url: https://telatin.github.io/seqfu2
  - note: null
    type:
    - Repository
    url: https://github.com/telatin/seqfu2/
  maturity: null
  name: seqfu
  operatingSystem: []
  otherID: []
  owner: telatin
  publication:
  - doi: 10.3390/bioengineering8050059
    metadata:
      abstract: ''
      authors: []
      citationCount: 36
      date: '2021-01-01T00:00:00Z'
      journal: Bioengineering
      title: 'Seqfu: A suite of utilities for the robust and reproducible manipulation
        of sequence files'
    note: null
    pmcid: PMC8148589
    pmid: '34066939'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Nucleic acids
    uri: http://edamontology.org/topic_0077
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - 1.10.0
  - 1.11.0
  - 1.12.0
  - 1.13.0
  - 1.14.0
  - 1.15.0
  - 1.16.0
  - 1.17.0
  - 1.19.0
  - 1.20.0
  - 1.21.0
  - 1.22.0
  - 1.22.3
- accessibility: Open access
  additionDate: '2023-07-26T14:40:01.768324Z'
  biotoolsCURIE: biotools:ms_annika
  biotoolsID: ms_annika
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: viktoria.dorfer@fh-hagenberg.at
    fundrefid: null
    gridid: null
    name: Viktoria Dorfer
    note: null
    orcidid: https://orcid.org/0000-0002-5332-5701
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://bioinformatics.fh-hagenberg.at/
  - email: micha.birklbauer@fh-hagenberg.at
    fundrefid: null
    gridid: null
    name: Micha Birklbauer
    note: null
    orcidid: https://orcid.org/0009-0005-1051-179X
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: http://bioinformatics.fh-hagenberg.at/
  description: MS Annika is a crosslink search engine based on MS Amanda, aimed at
    identifying crosslinks of cleavable and non-cleavable crosslinkers from MS2 and
    MS3 spectra. It can deal with a wide variety of cross-linkers and provides robust
    and transparent FDR control based on a target-decoy approach. MS Annika is available
    free of charge as a plug-in for Proteome Discoverer.
  documentation:
  - note: null
    type:
    - General
    url: https://hgb-bin-proteomics.github.io/MSAnnika/
  - note: null
    type:
    - User manual
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/MS_Annika_3.0_UserManual.pdf
  - note: Detailed tutorial how to use MS Annika
    type:
    - Training material
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/tutorial/MS_Annika_3.0_Tutorial.pdf
  - note: Detailed tutorial how to use MS Annika
    type:
    - Training material
    url: https://www.youtube.com/watch?v=L1lVt35PYv4
  download:
  - note: Latest MS Annika version for Proteome Disoverer 3.2
    type: Software package
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/releases/latest/PD3.2/latest.zip
    version: PD3.2:latest
  - note: Latest MS Annika version for Proteome Disoverer 3.1
    type: Software package
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/releases/latest/PD3.1/latest.zip
    version: PD3.1:latest
  - note: Latest MS Annika version for Proteome Disoverer 3.0
    type: Software package
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/releases/latest/PD3.0/latest.zip
    version: PD3.0:latest
  - note: Latest MS Annika version for Proteome Disoverer 2.5
    type: Software package
    url: https://github.com/hgb-bin-proteomics/MSAnnika/raw/master/releases/latest/PD2.5/latest.zip
    version: PD2.5:latest
  editPermission:
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrum
        uri: http://edamontology.org/data_0943
      format:
      - term: MGF
        uri: http://edamontology.org/format_3651
      - term: Thermo RAW
        uri: http://edamontology.org/format_3712
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Peptide identification
      uri: http://edamontology.org/operation_3631
    - term: Peptide database search
      uri: http://edamontology.org/operation_3646
    - term: PTM localisation
      uri: http://edamontology.org/operation_3755
    - term: PTM identification
      uri: http://edamontology.org/operation_3645
    - term: Target-Decoy
      uri: http://edamontology.org/operation_3649
    output:
    - data:
        term: Sequence search results
        uri: http://edamontology.org/data_0857
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/hgb-bin-proteomics/MSAnnika
  homepage_status: 0
  language:
  - C#
  lastUpdate: '2025-10-26T02:38:48.643054Z'
  license: Freeware
  link:
  - note: MS Annika version archive
    type:
    - Repository
    url: https://github.com/hgb-bin-proteomics/MSAnnika
  maturity: Mature
  name: MS Annika
  operatingSystem:
  - Windows
  otherID: []
  owner: michabirklbauer
  publication:
  - doi: 10.1021/acs.jproteome.0c01000
    metadata:
      abstract: ''
      authors: []
      citationCount: 22
      date: '2021-05-07T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'MS Annika: A New Cross-Linking Search Engine'
    note: MS Annika for cleavable crosslink identification from MS2 spectra
    pmcid: PMC8155564
    pmid: '33852321'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1021/acs.jproteome.3c00325
    metadata:
      abstract: ''
      authors: []
      citationCount: 5
      date: '2023-09-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows
        at High Sensitivity and Specificity
    note: MS Annika 2.0 for cleavable crosslink identification from MS3 spectra
    pmcid: PMC10476269
    pmid: '37566781'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1038/s42004-024-01386-x
    metadata:
      abstract: ''
      authors: []
      citationCount: 2
      date: '2024-12-01T00:00:00Z'
      journal: Communications Chemistry
      title: Proteome-wide non-cleavable crosslink identification with MS Annika 3.0
        reveals the structure of the C. elegans Box C/D complex
    note: MS Annika 3.0 for non-cleavable crosslink identification
    pmcid: PMC11659399
    pmid: '39702463'
    type:
    - Primary
    version: '3.0'
  relation:
  - biotoolsID: ms_amanda
    type: uses
  toolType:
  - Plug-in
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Protein folds and structural domains
    uri: http://edamontology.org/topic_0736
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 'Proteome Discoverer 3.2: MS Annika 3.0 v3.0.7'
  - 'Proteome Discoverer 3.1: MS Annika 3.0 v3.0.7'
  - 'Proteome Discoverer 3.0: MS Annika 2.0 v1.1.4fix'
  - 'Proteome Discoverer 2.5: MS Annika 2.0 v1.1.3'
  - 'Proteome Discoverer 2.4: MS Annika v1.0.0'
  - 'Proteome Discoverer 2.3: MS Annika v1.0.0'
- accessibility: Open access
  additionDate: '2025-10-23T09:01:44.958137Z'
  biotoolsCURIE: biotools:vivo_platform
  biotoolsID: vivo_platform
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: canudobarreras@unizar.es
    fundrefid: null
    gridid: null
    name: Guillermo Canudo-Barreras
    note: Researcher at Spanish National Research Council (CSIC). Developer.
    orcidid: https://orcid.org/0000-0002-1949-9185
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    - Maintainer
    - Support
    url: null
  - email: eromanos.iacs@aragon.es
    fundrefid: null
    gridid: null
    name: Eduardo Romanos
    note: Researcher at Biomedical Research Center of Aragon (CIBA).
    orcidid: https://orcid.org/0000-0002-9918-3374
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: raquelph@unizar.es
    fundrefid: null
    gridid: null
    name: Raquel P. Herrera
    note: Senior scientist at Spanish National Research Council (CSIC).
    orcidid: https://orcid.org/0000-0002-5244-9569
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: gimeno@unizar.es
    fundrefid: null
    gridid: null
    name: M. Concepción Gimeno
    note: Full professor at Spanish National Research Council (CSIC).
    orcidid: https://orcid.org/0000-0003-0553-0695
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: 'ViVo Platform is a free, open-access web application for the analysis
    of in vivo tumor growth data in mouse models.

    Designed to standardize in vivo efficacy analysis, improve research reproducibility,
    and reduce animal usage, it provides tools to upload tumor volume or bioluminescence
    imaging (BLI) datasets, configure analysis parameters, detect outliers, and automatically
    compute tumor growth rates (TGR), group comparisons, and reports.


    By standardizing data processing and incorporating advanced temporal modeling,
    ViVo Platform enhances statistical power and contributes to the 3Rs principle
    by reducing the number of animals needed in preclinical efficacy studies.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/gcanudo-barreras/ViVo-Platform/blob/main/README.md
  - note: null
    type:
    - User manual
    url: https://github.com/gcanudo-barreras/ViVo-Platform/blob/main/ViVo_USER_MANUAL.md
  - note: Full methodology
    type:
    - Other
    url: https://www.biorxiv.org/content/10.1101/2025.10.14.682266v1
  - note: null
    type:
    - Release notes
    url: https://github.com/gcanudo-barreras/ViVo-Platform/releases/tag/v1.0-beta
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data analysis
      uri: http://edamontology.org/operation_2945
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    output: []
  - cmd: null
    input:
    - data:
        term: Physiology parameter
        uri: http://edamontology.org/data_3722
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Morphology parameter
        uri: http://edamontology.org/data_3723
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: CSV
        uri: http://edamontology.org/format_3752
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://gcanudo-barreras.github.io/ViVo-Platform/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-10-23T09:09:29.560348Z'
  license: MIT
  link:
  - note: null
    type:
    - Service
    url: https://gcanudo-barreras.github.io/ViVo-Platform/
  - note: bioRxiv preprint
    type:
    - Other
    url: https://www.biorxiv.org/content/10.1101/2025.10.14.682266v1
  - note: null
    type:
    - Repository
    url: https://github.com/gcanudo-barreras/ViVo-Platform
  maturity: Emerging
  name: ViVo Platform
  operatingSystem: []
  otherID: []
  owner: gcanudo-barreras
  publication:
  - doi: 10.1101/2025.10.14.682266
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Method
    version: Research Beta 1.0
  relation: []
  toolType:
  - Web application
  topic:
  - term: Preclinical and clinical studies
    uri: http://edamontology.org/topic_3379
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Laboratory animal science
    uri: http://edamontology.org/topic_3386
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - Research Beta 1.0
- accessibility: Open access
  additionDate: '2023-07-25T12:35:27.605861Z'
  biotoolsCURIE: biotools:macpepdb_-_mass_centric_peptide_database
  biotoolsID: macpepdb_-_mass_centric_peptide_database
  collectionID:
  - CUBiMed.RUB
  - BioInfra.Prot
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Julian Uszkoreit
    note: null
    orcidid: http://orcid.org/0000-0001-7522-4007
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: dirk.winkelhardt@rub.de
    fundrefid: null
    gridid: null
    name: Dirk Winkelhardt
    note: null
    orcidid: https://orcid.org/0000-0001-8770-2221
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: PD Dr. Martin Eisenacher
    note: null
    orcidid: https://orcid.org/0000-0003-2687-7444
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A Database to Quickly Access All Tryptic Peptides of the UniProtKB
  documentation:
  - note: null
    type:
    - API documentation
    url: https://docs.rs/macpepdb/latest/macpepdb/web/index.html
  download: []
  editPermission:
    authors:
    - di_hardt
    - julianu
    - BioInfra.Prot
    type: group
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://macpepdb.cubimed.rub.de/
  homepage_status: 0
  language:
  - Rust
  - JavaScript
  - Other
  lastUpdate: '2025-07-31T09:03:29.192901Z'
  license: null
  link:
  - note: Backend implementation written in Rust / Axum
    type:
    - Repository
    url: https://github.com/medbioinf/macpepdb
  - note: Frontend implementation written in Rust / Dioxus
    type:
    - Repository
    url: https://github.com/medbioinf/macpepdb-frontend
  maturity: Mature
  name: MaCPepDB - Mass Centric Peptide Database
  operatingSystem: []
  otherID: []
  owner: di_hardt
  publication:
  - doi: 10.1021/acs.jproteome.0c00967
    metadata:
      abstract: Protein sequence databases play a crucial role in the majority of
        the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB
        serves as one of the major sources, as it combines the information of several
        smaller databases and enriches the entries with additional biological information.
        For the identification of peptides in a sample by tandem mass spectra, as
        generated by data-dependent acquisition, protein sequence databases provide
        the basis for most spectrum identification search engines. In addition, for
        targeted proteomics approaches like selected reaction monitoring (SRM) and
        parallel reaction monitoring (PRM), knowledge of the peptide sequences, their
        masses, and whether they are unique for a protein is essential. Because most
        bottom-up proteomics approaches use trypsin to cleave the proteins in a sample,
        the tryptic peptides contained in a protein database are of great interest.
        We present a database, called MaCPepDB (mass-centric peptide database), that
        consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts
        of UniProtKB. This database is especially designed to not only allow queries
        of peptide sequences and return the respective information about connected
        proteins and thus whether a peptide is unique but also allow queries of specific
        masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational
        modifications can be considered in a query as well as different mass deviations
        for posttranslational modifications. Hence the database can be used by a sequence
        query not only to, for example, check in which proteins of the UniProt database
        a tryptic peptide can be found but also to find possibly interfering peptides
        in PRM/SRM experiments using the mass query. The complete database contains
        currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version
        2020_03), for which a single query usually takes less than 1 s. For easy exploration
        of the data, a web interface was developed. A REST application programming
        interface (API) for programmatic and workflow access is also available at
        https://macpepdb.mpc.rub.de.
      authors:
      - name: Uszkoreit J.
      - name: Winkelhardt D.
      - name: Barkovits K.
      - name: Wulf M.
      - name: Roocke S.
      - name: Marcus K.
      - name: Eisenacher M.
      citationCount: 9
      date: '2021-04-02T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB'
    note: null
    pmcid: null
    pmid: '33724838'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version:
  - 3.0.0
- accessibility: Open access
  additionDate: '2016-02-09T13:19:44Z'
  biotoolsCURIE: biotools:mirgen
  biotoolsID: mirgen
  collectionID:
  - UiO tools
  - ELIXIR-NO
  - ELIXIR-Norway
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: BastianFromm@gmail.com
    fundrefid: null
    gridid: null
    name: Bastian Fromm
    note: null
    orcidid: https://orcid.org/0000-0003-0352-3037
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    - Support
    url: null
  - email: kevin.j.peterson@dartmouth.edu
    fundrefid: null
    gridid: null
    name: Kevin J. Peterson
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Maintainer
    url: null
  - email: support@elixir.no
    fundrefid: null
    gridid: null
    name: The Norwegian Bioinformatics Platform (ELIXIR-Norway) Helpdesk
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole:
    - Support
    url: https://elixir.no/helpdesk
  - email: null
    fundrefid: 10.13039/501100005366
    gridid: grid.5510.1
    name: University of Oslo
    note: null
    orcidid: null
    rorid: 01xtthb56
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.uio.no/english/index.html
  description: MirGeneDB is a database of manually curated microRNA genes that have
    been validated and annotated as initially described in Fromm et al. 2015 , Fromm
    et al. 2020 and Fromm et al 2022. MirGeneDB 3.0 (Clarke and Hoye et al. 2024 )
    includes more than 21,000 microRNA gene entries representing more than 1,700 microRNA
    families from 114 metazoan species. All microRNAs can be browsed, searched and
    downloaded.
  documentation:
  - note: null
    type:
    - General
    url: https://www.mirgenedb.org/information
  download:
  - note: Sequence downloads for 75 species
    type: Biological data
    url: https://www.mirgenedb.org/download
    version: '3.0'
  editPermission:
    authors:
    - eca008
    type: group
  elixirCommunity: []
  elixirNode:
  - Norway
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Organism identifier
        uri: http://edamontology.org/data_1869
      format: []
    note: 'Data retrieval: curated miRNA. Organism identifier: a specific miRNA identifier
      or a species  for all miRNAs for that species. Gene transcript report: with
      metadata and visualization. RNA secondary structure: the hairpin loop of the
      miRNA with bases.'
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    output:
    - data:
        term: Gene transcript report
        uri: http://edamontology.org/data_3134
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: RNA secondary structure
        uri: http://edamontology.org/data_0880
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Sequence accession (nucleic acid)
        uri: http://edamontology.org/data_1097
      format: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
      - term: BED
        uri: http://edamontology.org/format_3003
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://mirgenedb.org/
  homepage_status: 0
  language: []
  lastUpdate: '2025-07-05T22:33:30.082420Z'
  license: CC0-1.0
  link:
  - note: null
    type:
    - Helpdesk
    url: https://elixir.no/helpdesk
  maturity: Mature
  name: MirGeneDB
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: doi
    value: doi:10.25504/FAIRsharing.QXSgvF
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  owner: UiO
  publication:
  - doi: 10.1093/nar/gkab1101
    metadata:
      abstract: We describe an update of MirGeneDB, the manually curated microRNA
        gene database. Adhering to uniform and consistent criteria for microRNA annotation
        and nomenclature, we substantially expanded MirGeneDB with 30 additional species
        representing previously missing metazoan phyla such as sponges, jellyfish,
        rotifers and flatworms. MirGeneDB 2.1 now consists of 75 species spanning
        over ∼800 million years of animal evolution, and contains a total number of
        16 670 microRNAs from 1549 families. Over 6000 microRNAs were added in this
        update using ∼550 datasets with ∼7.5 billion sequencing reads. By adding new
        phylogenetically important species, especially those relevant for the study
        of whole genome duplication events, and through updating evolutionary nodes
        of origin for many families and genes, we were able to substantially refine
        our nomenclature system. All changes are traceable in the specifically developed
        MirGeneDB version tracker. The performance of read-pages is improved and microRNA
        expression matrices for all tissues and species are now also downloadable.
        Altogether, this update represents a significant step toward a complete sampling
        of all major metazoan phyla, and a widely needed foundation for comparative
        microRNA genomics and transcriptomics studies. MirGeneDB 2.1 is part of RNAcentral
        and Elixir Norway, publicly and freely available at http://www.mirgenedb.org/.
      authors:
      - name: Fromm B.
      - name: Hoye E.
      - name: Domanska D.
      - name: Zhong X.
      - name: Aparicio-Puerta E.
      - name: Ovchinnikov V.
      - name: Umu S.U.
      - name: Chabot P.J.
      - name: Kang W.
      - name: Aslanzadeh M.
      - name: Tarbier M.
      - name: Marmol-Sanchez E.
      - name: Urgese G.
      - name: Johansen M.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Friedlander M.R.
      - name: Peterson K.J.
      citationCount: 95
      date: '2022-01-07T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 2.1: Toward a complete sampling of all major animal phyla'
    note: null
    pmcid: PMC8728216
    pmid: '34850127'
    type:
    - Primary
    version: '2.1'
  - doi: 10.1093/nar/gkz885
    metadata:
      abstract: Small non-coding RNAs have gained substantial attention due to their
        roles in animal development and human disorders. Among them, microRNAs are
        special because individual gene sequences are conserved across the animal
        kingdom. In addition, unique and mechanistically well understood features
        can clearly distinguish bona fide miRNAs from the myriad other small RNAs
        generated by cells. However, making this distinction is not a common practice
        and, thus, not surprisingly, the heterogeneous quality of available miRNA
        complements has become a major concern in microRNA research. We addressed
        this by extensively expanding our curated microRNA gene database-MirGeneDB-to
        45 organisms, encompassing a wide phylogenetic swath of animal evolution.
        By consistently annotating and naming 10,899 microRNA genes in these organisms,
        we show that previous microRNA annotations contained not only many false positives,
        but surprisingly lacked >2000 bona fide microRNAs. Indeed, curated microRNA
        complements of closely related organisms are very similar and can be used
        to reconstruct ancestral miRNA repertoires. MirGeneDB represents a robust
        platform for microRNA-based research, providing deeper and more significant
        insights into the biology and evolution of miRNAs as well as biomedical and
        biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.
      authors:
      - name: Fromm B.
      - name: Domanska D.
      - name: Hoye E.
      - name: Ovchinnikov V.
      - name: Kang W.
      - name: Aparicio-Puerta E.
      - name: Johansen M.
      - name: Flatmark K.
      - name: Mathelier A.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Friedlander M.R.
      - name: Peterson K.J.
      citationCount: 178
      date: '2020-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 2.0: The metazoan microRNA complement'
    note: null
    pmcid: PMC6943042
    pmid: '31598695'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1146/annurev-genet-120213-092023
    metadata:
      abstract: Although microRNAs (miRNAs) are among the most intensively studied
        molecules of the past 20 years, determining what is and what is not a miRNA
        has not been straightforward. Here, we present a uniform system for the annotation
        and nomenclature of miRNA genes. We show that less than a third of the 1,881
        human miRBase entries, and only approximately 16% of the 7,095 metazoan miRBase
        entries, are robustly supported as miRNA genes. Furthermore, we show that
        the human repertoire of miRNAs has been shaped by periods of intense miRNA
        innovation and that mature gene products show a very different tempo and mode
        of sequence evolution than star products. We establish a new open access database-MirGeneDB
        (http://mirgenedb.org)-to catalog this set of miRNAs, which complements the
        efforts of miRBase but differs from it by annotating the mature versus star
        products and by imposing an evolutionary hierarchy upon this curated and consistently
        named repertoire.
      authors:
      - name: Fromm B.
      - name: Billipp T.
      - name: Peck L.E.
      - name: Johansen M.
      - name: Tarver J.E.
      - name: King B.L.
      - name: Newcomb J.M.
      - name: Sempere L.F.
      - name: Flatmark K.
      - name: Hovig E.
      - name: Peterson K.J.
      citationCount: 435
      date: '2015-11-23T00:00:00Z'
      journal: Annual Review of Genetics
      title: A Uniform System for the Annotation of Vertebrate microRNA Genes and
        the Evolution of the Human microRNAome
    note: null
    pmcid: PMC4743252
    pmid: '26473382'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1093/nar/gkae1094
    metadata:
      abstract: We present a major update of MirGeneDB (3.0), the manually curated
        animal microRNA gene database. Beyond moving to a new server and the creation
        of a computational mirror, we have expanded the database with the addition
        of 33 invertebrate species, including representatives of 5 previously unsampled
        phyla, and 6 mammal species. MirGeneDB now contains entries for 21 822 microRNA
        genes (5160 of these from the new species) belonging to 1743 microRNA families.
        The inclusion of these new species allowed us to refine both the evolutionary
        node of appearance of a number of microRNA genes/families, as well as MirGeneDB's
        phylogenetically informed nomenclature system. Updated covariance models of
        all microRNA families, along with all smallRNA read data are now downloadable.
        These enhanced annotations will allow researchers to analyze microRNA properties
        such as secondary structure and features of their biogenesis within a robust
        phylogenetic context and without the database plagued with numerous false
        positives and false negatives. In light of these improvements, MirGeneDB 3.0
        will assume the responsibility for naming conserved novel metazoan microRNAs.
        MirGeneDB is part of RNAcentral and Elixir Norway and is publicly and freely
        available at mirgenedb.org.
      authors:
      - name: Clarke A.W.
      - name: Hoye E.
      - name: Hembrom A.A.
      - name: Paynter V.M.
      - name: Vinther J.
      - name: Wyrozemski L.
      - name: Biryukova I.
      - name: Formaggioni A.
      - name: Ovchinnikov V.
      - name: Herlyn H.
      - name: Pierce A.
      - name: Wu C.
      - name: Aslanzadeh M.
      - name: Cheneby J.
      - name: Martinez P.
      - name: Friedlander M.R.
      - name: Hovig E.
      - name: Hackenberg M.
      - name: Umu S.U.
      - name: Johansen M.
      - name: Peterson K.J.
      - name: Fromm B.
      citationCount: 6
      date: '2025-01-06T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'MirGeneDB 3.0: Improved taxonomic sampling, uniform nomenclature of
        novel conserved microRNA families and updated covariance models'
    note: null
    pmcid: PMC11701709
    pmid: '39673268'
    type:
    - Primary
    version: '3.0'
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Gene regulation
    uri: http://edamontology.org/topic_0204
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  - term: Zoology
    uri: http://edamontology.org/topic_3500
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  validated: 1
  version:
  - '3.0'
- accessibility: Open access
  additionDate: '2018-05-28T14:50:09Z'
  biotoolsCURIE: biotools:deepsig
  biotoolsID: deepsig
  collectionID:
  - Bologna Biocomputing Group
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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    url: http://biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
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    - Primary contact
    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
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    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
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    url: http://biocomp.unibo.it
  description: Prediction of secretory signal peptides in protein sequences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/deepsig
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  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/deepsig
    version: 1.2.5
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/deepsig
    version: 1.2.5
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
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        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://busca.biocomp.unibo.it/deepsig/
  homepage_status: 0
  language:
  - Python
  - C++
  lastUpdate: '2025-06-19T11:55:09.017105Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: DeepSig
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btx818
    metadata:
      abstract: Motivation The identification of signal peptides in protein sequences
        is an important step toward protein localization and function characterization.
        Results Here, we present DeepSig, an improved approach for signal peptide
        detection and cleavage-site prediction based on deep learning methods. Comparative
        benchmarks performed on an updated independent dataset of proteins show that
        DeepSig is the current best performing method, scoring better than other available
        state-of-the-art approaches on both signal peptide detection and precise cleavage-site
        identification. Availability and implementation DeepSig is available as both
        standalone program and web server at https://deepsig.biocomp.unibo.it. All
        datasets used in this study can be obtained from the same website.
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 96
      date: '2018-05-15T00:00:00Z'
      journal: Bioinformatics
      title: 'DeepSig: Deep learning improves signal peptide detection in proteins'
    note: null
    pmcid: PMC5946842
    pmid: '29280997'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - 1.2.5
- accessibility: Open access
  additionDate: '2021-01-12T12:02:47Z'
  biotoolsCURIE: biotools:mugvre
  biotoolsID: mugvre
  collectionID:
  - RIS3CAT VEIS
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: laia.codo@bsc.es
    fundrefid: null
    gridid: null
    name: Laia Codó
    note: null
    orcidid: https://orcid.org/0000-0002-6797-8746
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Josep Lluís Gelpí
    note: null
    orcidid: http://orcid.org/0000-0002-0566-7723
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: vre@multiscalegenomics.eu
    fundrefid: null
    gridid: null
    name: Multiscale Complex Genomics Community
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole:
    - Developer
    url: https://www.multiscalegenomics.eu/
  description: The MuG Virtual Research Environment is an analysis platform for 3D/4D
    genomics analyses. It integrates genomics tools for chromatin dynamics data.
  documentation:
  - note: null
    type:
    - Terms of use
    url: https://www.multiscalegenomics.eu/MuGVRE/terms-of-use/
  - note: null
    type:
    - Quick start guide
    url: http://vre.multiscalegenomics.eu/help/starting.php
  - note: null
    type:
    - Training material
    url: https://www.multiscalegenomics.eu/MuGVRE/training/
  download: []
  editPermission:
    authors:
    - gelpi@ub.edu
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Genome index
        uri: http://edamontology.org/data_3210
      format: []
    note: null
    operation:
    - term: Sequence alignment
      uri: http://edamontology.org/operation_0292
    - term: Read mapping
      uri: http://edamontology.org/operation_3198
    output:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA-like (text)
        uri: http://edamontology.org/format_2200
    - data:
        term: Sequence features
        uri: http://edamontology.org/data_1255
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
    note: null
    operation:
    - term: Nucleic acid structure prediction
      uri: http://edamontology.org/operation_0475
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Nucleic acid folding analysis
      uri: http://edamontology.org/operation_0279
    output:
    - data:
        term: Trajectory data
        uri: http://edamontology.org/data_3870
      format: []
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Molecular docking
      uri: http://edamontology.org/operation_0478
    - term: DNA-binding protein prediction
      uri: http://edamontology.org/operation_3900
    output:
    - data:
        term: Protein complex
        uri: http://edamontology.org/data_2877
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  - cmd: null
    input:
    - data:
        term: Protein interaction raw data
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      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
    note: null
    operation:
    - term: Peak calling
      uri: http://edamontology.org/operation_3222
    - term: Gene regulatory network analysis
      uri: http://edamontology.org/operation_1781
    output:
    - data:
        term: Nucleic acid features
        uri: http://edamontology.org/data_1276
      format:
      - term: BED
        uri: http://edamontology.org/format_3003
  - cmd: null
    input:
    - data:
        term: Protein interaction raw data
        uri: http://edamontology.org/data_0905
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    - term: Nucleic acid folding analysis
      uri: http://edamontology.org/operation_0279
    output:
    - data:
        term: Topology data
        uri: http://edamontology.org/data_3872
      format:
      - term: pickle
        uri: http://edamontology.org/format_4002
    - data:
        term: Protein interaction data
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      format:
      - term: pickle
        uri: http://edamontology.org/format_4002
  - cmd: null
    input:
    - data:
        term: Protein-ligand complex
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      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Nucleic acid sequence analysis
      uri: http://edamontology.org/operation_2478
    output:
    - data:
        term: Nucleic acid report
        uri: http://edamontology.org/data_2084
      format: []
  - cmd: null
    input:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format:
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        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Molecular dynamics
      uri: http://edamontology.org/operation_2476
    - term: Nucleic acid structure analysis
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    output:
    - data:
        term: Trajectory data
        uri: http://edamontology.org/data_3870
      format: []
  - cmd: null
    input:
    - data:
        term: Protein interaction raw data
        uri: http://edamontology.org/data_0905
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
    note: null
    operation:
    - term: Nucleosome position prediction
      uri: http://edamontology.org/operation_0432
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    output:
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
  - cmd: null
    input:
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        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
      format: []
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format: []
    - data:
        term: Simulation
        uri: http://edamontology.org/data_3869
      format: []
    - data:
        term: Structure
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      format: []
    note: null
    operation:
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    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
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      uri: http://edamontology.org/operation_3925
    output: []
  homepage: https://www.multiscalegenomics.eu/
  homepage_status: 0
  language:
  - PHP
  - Python
  lastUpdate: '2025-05-29T11:46:19.236621Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Service
    url: http://vre.multiscalegenomics.eu
  - note: null
    type:
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  name: MuGVRE
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  owner: lcodo
  publication:
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    metadata: null
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    type:
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  relation:
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    type: includes
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    type: includes
  toolType:
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  - Workbench
  topic:
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    uri: http://edamontology.org/topic_3176
  - term: ChIP-seq
    uri: http://edamontology.org/topic_3169
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  validated: 1
  version:
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- accessibility: null
  additionDate: '2016-04-15T11:52:42Z'
  biotoolsCURIE: biotools:comparems2
  biotoolsID: comparems2
  collectionID:
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  confidence_flag: tool
  cost: Free of charge
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    fundrefid: null
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    name: lumc.nl
    note: null
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    fundrefid: null
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    name: Magnus Palmblad
    note: null
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    rorid: null
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    - Primary contact
    - Documentor
    url: https://github.com/magnuspalmblad
  - email: null
    fundrefid: null
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    name: Rob Marissen
    note: null
    orcidid: https://orcid.org/0000-0002-1220-9173
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://github.com/524D
  description: compareMS2 is a tool for comparing sets of (tandem) mass spectra for
    clustering samples, molecular phylogenetics, identification of biological species
    or tissues, and quality control. compareMS2 currently consumes Mascot Generic
    Format, or MGF, and produces output in a variety of common image and distance
    matrix formats.
  documentation:
  - note: null
    type:
    - General
    - Command-line options
    url: http://www.ms-utils.org/compareMS2.html
  - note: null
    type:
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    - Installation instructions
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  download:
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    url: http://www.ms-utils.org/compareMS2.c
    version: '1.0'
  - note: null
    type: Source code
    url: https://github.com/524D/compareMS2/tree/main/src
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  - note: null
    type: Binaries
    url: https://github.com/524D/compareMS2/tree/main
    version: '2.0'
  editPermission:
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  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: MGF
        uri: http://edamontology.org/format_3651
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    - term: Phylogenetic tree visualisation
      uri: http://edamontology.org/operation_0567
    output:
    - data:
        term: Species tree
        uri: http://edamontology.org/data_3272
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Distance matrix
        uri: http://edamontology.org/data_2855
      format:
      - term: mega
        uri: http://edamontology.org/format_1991
      - term: Nexus format
        uri: http://edamontology.org/format_1912
      - term: newick
        uri: http://edamontology.org/format_1910
  homepage: https://github.com/524D/compareMS2
  homepage_status: 0
  language:
  - C
  - JavaScript
  lastUpdate: '2025-04-16T14:22:04.957317Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/524D/compareMS2
  - note: null
    type:
    - Software catalogue
    url: https://www.ms-utils.org/compareMS2.html
  - note: null
    type:
    - Software catalogue
    url: https://research-software-directory.org/software/comparems2
  maturity: Mature
  name: compareMS2
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1002/rcm.6162
    metadata:
      abstract: 'Rationale: Molecular phylogenetics is the study of evolution and
        relatedness of organisms or genes. Mass spectrometry is used routinely for
        bacterial identification and has also been used for phylogenetic analysis,
        for instance from bone material. Unfortunately, only a small fraction of the
        acquired tandem mass spectra allow direct interpretation. Methods: We describe
        a new algorithm and software for molecular phylogenetics using pairwise comparisons
        of tandem mass spectra from enzymatically digested proteins. The spectra need
        not be annotated and all acquired data is used in the analysis. To demonstrate
        the method, we analyzed tryptic digests of sera from four great apes and two
        other primates. Results: The distribution of spectra dot products for thousands
        of tandem mass spectra collected from two samples provides a measure on the
        fraction of shared peptides between the two samples. When inverted, this becomes
        a distance metric. By pairwise comparison between species and averaging over
        four individuals per species, it was possible to reconstruct the unique correct
        phylogenetic tree for the great apes and other primates. Conclusions: The
        new method described here has several attractive features compared with existing
        methods, among them simplicity, the unbiased use of all acquired data rather
        than a small subset of spectra, and the potential use of heavily degraded
        proteins or proteins with a priori unknown modifications. © 2012 John Wiley
        & Sons, Ltd.'
      authors:
      - name: Palmblad M.
      - name: Deelder A.M.
      citationCount: 30
      date: '2012-04-15T00:00:00Z'
      journal: Rapid Communications in Mass Spectrometry
      title: Molecular phylogenetics by direct comparison of tandem mass spectra
    note: null
    pmcid: null
    pmid: '22368051'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1021/acs.jproteome.2c00457
    metadata:
      abstract: It has long been known that biological species can be identified from
        mass spectrometry data alone. Ten years ago, we described a method and software
        tool, compareMS2, for calculating a distance between sets of tandem mass spectra,
        as routinely collected in proteomics. This method has seen use in species
        identification and mixture characterization in food and feed products, as
        well as other applications. Here, we present the first major update of this
        software, including a new metric, a graphical user interface and additional
        functionality. The data have been deposited to ProteomeXchange with dataset
        identifier PXD034932.
      authors:
      - name: Marissen R.
      - name: Varunjikar M.S.
      - name: Laros J.F.J.
      - name: Rasinger J.D.
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 7
      date: '2023-02-03T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry
        Datasets'
    note: null
    pmcid: PMC9903320
    pmid: '36173614'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1021/acs.jproteome.1c00528
    metadata:
      abstract: Science is full of overlooked and undervalued research waiting to
        be rediscovered. Proteomics is no exception. In this perspective, we follow
        the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing
        tryptic peptides across animal species. This pioneering work directly led
        to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics.
        In the decades following, proteins continued to provide essential clues on
        evolutionary history. While technology has continued to improve, contemporary
        proteomics has strayed from this larger biological context, rarely comparing
        species or asking how protein structure, function, and interactions have evolved.
        Here we recombine proteomics with molecular phylogenetics, highlighting the
        value of framing proteomic results in a larger biological context and how
        almost forgotten research, though technologically surpassed, can still generate
        new ideas and illuminate our work from a different perspective. Though it
        is infeasible to read all research published on a large topic, looking up
        older papers can be surprisingly rewarding when rediscovering a "gem"at the
        end of a long citation chain, aided by digital collections and perpetually
        helpful librarians. Proper literature study reduces unnecessary repetition
        and allows research to be more insightful and impactful by truly standing
        on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/)
        as dataset MSV000087993.
      authors:
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 1
      date: '2021-10-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics
        Experiments in the Light of Proteomics'
    note: null
    pmcid: PMC8491155
    pmid: '34523928'
    type:
    - Review
    version: '2.0'
  relation: []
  toolType:
  - Command-line tool
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - '1.0'
  - '2.0'
- accessibility: Open access
  additionDate: '2024-09-09T07:49:35.385723Z'
  biotoolsCURIE: biotools:artemis3D
  biotoolsID: artemis3D
  collectionID:
  - 3D-BioInfo-Structure-Function
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Davyd Bohdan
    note: null
    orcidid: https://orcid.org/0000-0002-6456-6658
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Janusz Bujnicki
    note: null
    orcidid: https://orcid.org/0000-0002-6633-165X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Eugene Baulin
    note: null
    orcidid: https://orcid.org/0000-0003-4694-9783
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: a method for topology-independent superposition of RNA 3D structures
    and structure-based sequence alignment
  documentation:
  - note: README file
    type:
    - Quick start guide
    url: https://github.com/david-bogdan-r/ARTEMIS/blob/main/README.md
  - note: Tutorial in pdf
    type:
    - User manual
    url: https://github.com/david-bogdan-r/ARTEMIS/blob/main/ARTEMIS_TUTORIAL_v1.51.pdf
  download:
  - note: null
    type: Source code
    url: https://github.com/david-bogdan-r/ARTEMIS
    version: version 1.5
  editPermission:
    authors:
    - febos
    type: private
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: python3 artemis.py r=FILENAME q=FILENAME [OPTIONS]
    input:
    - data:
        term: Nucleic acid structure
        uri: http://edamontology.org/data_1459
      format:
      - term: mmCIF
        uri: http://edamontology.org/format_1477
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Nucleic acid structure
        uri: http://edamontology.org/data_1459
      format:
      - term: mmCIF
        uri: http://edamontology.org/format_1477
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Pairwise structure alignment
      uri: http://edamontology.org/operation_0503
    - term: Nucleic acid structure comparison
      uri: http://edamontology.org/operation_2518
    output:
    - data:
        term: Nucleic acid structure alignment
        uri: http://edamontology.org/data_1482
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Structure similarity score
        uri: http://edamontology.org/data_0888
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Nucleic acid structure
        uri: http://edamontology.org/data_1459
      format:
      - term: mmCIF
        uri: http://edamontology.org/format_1477
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: https://github.com/david-bogdan-r/ARTEMIS
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-01-30T09:07:50.841042Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/david-bogdan-r/ARTEMIS
  maturity: Mature
  name: ARTEMIS
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: febos
  publication:
  - doi: 10.1093/nar/gkae758
    metadata:
      abstract: Non-coding RNAs play a major role in diverse processes in living cells
        with their sequence and spatial structure serving as the principal determinants
        of their function. Superposition of RNA 3D structures is the most accurate
        method for comparative analysis of RNA molecules and for inferring structure-based
        sequence alignments. Topology-independent superposition is particularly relevant,
        as evidenced by structurally similar RNAs with sequence permutations such
        as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure
        superposition rely on intricate heuristics, and the potential for topology-independent
        superposition has not been exhausted. Recently, we introduced the ARTEM method
        for unrestrained pairwise superposition of RNA 3D modules and now we developed
        it further to solve the global RNA 3D structure alignment problem. Our new
        tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered
        and topology-independent RNA 3D structure superposition. Using ARTEMIS we
        discovered a helical packing motif to be preserved within different backbone
        topology contexts across various non-coding RNAs, including multiple ribozymes
        and riboswitches. We anticipate that ARTEMIS will be essential for elucidating
        the landscape of RNA 3D folds and motifs featuring sequence permutations that
        thus far remained unexplored due to limitations in previous computational
        approaches.
      authors:
      - name: Bohdan D.R.
      - name: Bujnicki J.M.
      - name: Baulin E.F.
      citationCount: 1
      date: '2024-10-14T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ARTEMIS: a method for topology-independent superposition of RNA 3D structures
        and structure-based sequence alignment'
    note: null
    pmcid: PMC11472068
    pmid: '39258540'
    type:
    - Primary
    version: version 1.5
  - doi: 10.1101/2024.04.06.588371
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: version 1.0
  relation:
  - biotoolsID: artem
    type: uses
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Nucleic acids
    uri: http://edamontology.org/topic_0077
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Nucleic acid sites, features and motifs
    uri: http://edamontology.org/topic_3511
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: RNA
    uri: http://edamontology.org/topic_0099
  validated: 0
  version:
  - version 1.51
- accessibility: Open access
  additionDate: '2017-08-03T12:46:16Z'
  biotoolsCURIE: biotools:channelsdb
  biotoolsID: channelsdb
  collectionID:
  - LCC NCBR
  - ELIXIR-CZ
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: xpravda@ncbr.muni.cz
    fundrefid: null
    gridid: null
    name: Lukáš Pravda
    note: null
    orcidid: https://orcid.org/0000-0002-6435-4719
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: david.sehnal@mail.muni.cz
    fundrefid: null
    gridid: null
    name: David Sehnal
    note: null
    orcidid: https://orcid.org/0000-0002-0682-3089
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: karel.berka@upol.cz
    fundrefid: null
    gridid: null
    name: Karel Berka
    note: null
    orcidid: https://orcid.org/0000-0001-9472-2589
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Contributor
    url: null
  - email: radka.svobodova@ceitec.muni.cz
    fundrefid: null
    gridid: null
    name: Radka Svobodová
    note: null
    orcidid: https://orcid.org/0000-0002-3840-8760
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Provider
    url: null
  - email: 222755@mail.muni.cz
    fundrefid: null
    gridid: null
    name: David Bednář
    note: null
    orcidid: https://orcid.org/0000-0002-6803-0340
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Palacky University Olomouc
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.upol.cz/en/
  - email: null
    fundrefid: null
    gridid: null
    name: Masaryk University Brno
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.muni.cz/en
  - email: vaclav.bazgier@upol.cz
    fundrefid: null
    gridid: null
    name: Václav Bazgier
    note: null
    orcidid: https://orcid.org/0000-0003-3393-3010
    rorid: null
    typeEntity: Person
    typeRole:
    - Documentor
    - Contributor
    url: null
  - email: anna.spackova@upol.cz
    fundrefid: null
    gridid: null
    name: Anna Špačková
    note: null
    orcidid: https://orcid.org/0000-0001-7142-3092
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Documentor
    url: null
  - email: vavra@enantis.com
    fundrefid: null
    gridid: null
    name: Ondřej Vávra
    note: null
    orcidid: https://orcid.org/0000-0003-1396-2543
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: 324965@mail.muni.cz
    fundrefid: null
    gridid: null
    name: Tomáš Raček
    note: null
    orcidid: https://orcid.org/0000-0002-0296-2452
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Maintainer
    url: null
  - email: jiri@chemi.muni.cz
    fundrefid: null
    gridid: null
    name: Jiří Damborský
    note: null
    orcidid: https://orcid.org/0000-0002-7848-8216
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: A comprehensive resource of channels, pores and tunnels found in biomacromolecular
    structures deposited in the Protein Data Bank.
  documentation:
  - note: 'Database content

      Channels nomenclature

      MOLE settings

      List of cofactors used for channel determination.

      Description how to read the resuls page.

      How to access content of the database programatically.'
    type:
    - User manual
    - Quick start guide
    url: https://channelsdb2.biodata.ceitec.cz/documentation.html
  - note: Info about methodology
    type:
    - General
    - FAQ
    url: https://channelsdb2.biodata.ceitec.cz/methods.html
  - note: null
    type:
    - Citation instructions
    - Contributions policy
    url: https://channelsdb2.biodata.ceitec.cz/about.html
  - note: null
    type:
    - API documentation
    url: https://channelsdb2.biodata.ceitec.cz/api
  download: []
  editPermission:
    authors: []
    type: private
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  - 3D-BioInfo
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Data
  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
    - data:
        term: PDB ID
        uri: http://edamontology.org/data_1127
      format:
      - term: mmCIF
        uri: http://edamontology.org/format_1477
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Data deposition
      uri: http://edamontology.org/operation_3431
    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    - term: Protein property calculation
      uri: http://edamontology.org/operation_0250
    - term: Protein structural motif recognition
      uri: http://edamontology.org/operation_0245
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Protein structural motif
        uri: http://edamontology.org/data_2878
      format:
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: JSON
        uri: http://edamontology.org/format_3464
  homepage: https://channelsdb2.biodata.ceitec.cz/
  homepage_status: 0
  language:
  - JavaScript
  - C#
  lastUpdate: '2024-11-25T13:45:26.806319Z'
  license: Freeware
  link:
  - note: null
    type:
    - Service
    url: https://channelsdb2.biodata.ceitec.cz
  maturity: Mature
  name: ChannelsDB 2.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-CZ
  publication:
  - doi: 10.1093/nar/gkx868
    metadata:
      abstract: ChannelsDB (http://ncbr.muni.cz/ChannelsDB) is a database providing
        information about the positions, geometry and physicochemical properties of
        channels (pores and tunnels) found within biomacromolecular structures deposited
        in the Protein Data Bank. Channels were deposited from two sources; from literature
        using manual deposition and from a software tool automatically detecting tunnels
        leading to the enzymatic active sites and selected cofactors, and transmembrane
        pores. The database stores information about geometrical features (e.g. length
        and radius profile along a channel) and physicochemical properties involving
        polarity, hydrophobicity, hydropathy, charge and mutability. The stored data
        are interlinked with available UniProt annotation data mapping known mutation
        effects to channel-lining residues. All structures with channels are displayed
        in a clear interactive manner, further facilitating data manipulation and
        interpretation. As such, ChannelsDB provides an invaluable resource for research
        related to deciphering the biological function of biomacromolecular channels.
      authors:
      - name: Pravda L.
      - name: Sehnal D.
      - name: Svobodova VaEkova R.
      - name: Navratilova V.
      - name: Tousek D.
      - name: Berka K.
      - name: Otyepka M.
      - name: Koca J.
      citationCount: 29
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ChannelsDB: Database of biomacromolecular tunnels and pores'
    note: null
    pmcid: null
    pmid: null
    type:
    - Method
    version: '1.0'
  - doi: 10.1093/nar/gkad1012
    metadata:
      abstract: 'ChannelsDB 2.0 isãn updated database providing str uct ural informationãbout
        the position, geometryãnd ph y sicochemical properties of protein channels-tunnelsãnd
        pores-within deposited biomacromolecular str uct ures from PDBãnd AlphaFoldDB
        databases. T he ne wly deposited information originated from several sources.
        Firstly, we included data calculated usingã popular CAVER tool to complement
        the datã obtãined using original MOLE tool for detectionãndãnalysis of protein
        tunnelsãnd pores. Secondly, weãdded tunnels starting from cofactors within
        the AlphaFill database to enlarge the scope of the database to protein models
        based on Uniprot. This has enlargedã vãilable channelãnnotations ∼4.6 timesãs
        of 1 September 2023. The database stores informationãbout geometrical features,
        e.g. lengthãnd radius,ãnd ph y sico-chemical properties based on channel-liningãminoãcids.
        The stored dataãre interlinked with theã vãilable UniP rot mutãtionãnnotãtion
        datã. ChannelsDB 2.0 providesãn excellent resource for deepãnalysis of the
        role of biomacromolecular tunnelsãnd pores. The database isãvailable free
        of charge: ht tps://c hannelsdb2.biodata.ceitec.cz .'
      authors:
      - name: Spackova A.
      - name: Vavra O.
      - name: Racek T.
      - name: Bazgier V.
      - name: Sehnal D.
      - name: Damborsky J.
      - name: Svobodova R.
      - name: Bednar D.
      - name: Berka K.
      citationCount: 2
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: ChannelsDB 2.0:ã comprehensive database of protein tunnelsãnd pores in
        Alpha Foldera
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '2.0'
  relation:
  - biotoolsID: mole
    type: uses
  - biotoolsID: caver
    type: uses
  - biotoolsID: moleonline_2.0
    type: uses
  - biotoolsID: caver_web
    type: uses
  toolType:
  - Database portal
  topic:
  - term: Structural biology
    uri: http://edamontology.org/topic_1317
  - term: Biophysics
    uri: http://edamontology.org/topic_3306
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Enzymes
    uri: http://edamontology.org/topic_0821
  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Literature and language
    uri: http://edamontology.org/topic_3068
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2016-05-17T10:17:55Z'
  biotoolsCURIE: biotools:proteoformer
  biotoolsID: proteoformer
  collectionID:
  - BIG N2N
  - Proteomics
  - BioBix
  - UGent
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: Steven.Verbruggen@UGent.be
    fundrefid: null
    gridid: null
    name: Steven Verbruggen
    note: Main developer of the 2.0 version
    orcidid: https://orcid.org/0000-0001-9441-9539
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Documentor
    - Maintainer
    url: null
  - email: Gerben.Menschaert@ugent.be
    fundrefid: null
    gridid: null
    name: Gerben Menschaert
    note: PI of this project
    orcidid: https://orcid.org/0000-0002-7575-2085
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jeroen Crappé
    note: Main developer of the 1.0 version
    orcidid: https://orcid.org/0000-0003-1677-9533
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Elvis Ndah
    note: Main developer of the 1.0 version
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sandra Steyaert
    note: Contributor to the 1.0 version
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Alexander Koch
    note: Contributor to the 1.0 version
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ghent University
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.ugent.be/en
  - email: null
    fundrefid: null
    gridid: null
    name: BioBix
    note: null
    orcidid: null
    rorid: null
    typeEntity: Division
    typeRole: []
    url: http://www.biobix.be/
  description: Proteogenomic pipeline integrating ribosome profiling (RIBO-seq) data
    in the search for new proteoforms in proteomic validation data. The pipeline does
    a genome-wide construction of candidate translation products based on ribosome
    occupancy.
  documentation:
  - note: Full elaborate documentation on usage and installation with lots of examples.
      Main documentation source.
    type:
    - User manual
    url: https://github.com/Biobix/proteoformer/blob/master/README.md
  - note: GPU 3.0 license
    type:
    - Terms of use
    url: https://github.com/Biobix/proteoformer/blob/master/LICENSE
  download:
  - note: GitHub repo
    type: Source code
    url: https://github.com/Biobix/proteoformer
    version: '2.0'
  - note: Galaxy wrapper files
    type: Tool wrapper (Galaxy)
    url: https://github.com/Biobix/proteoformer/tree/master/Galaxy%20files
    version: '1.0'
  editPermission:
    authors:
    - proteomics.bio.tools
    - StevenVerbruggen
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: perl mapping.pl --help
    input:
    - data:
        term: RNA sequence (raw)
        uri: http://edamontology.org/data_3496
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    - term: RNA-seq read count analysis
      uri: http://edamontology.org/operation_3563
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    - term: Nucleic acid density plotting
      uri: http://edamontology.org/operation_2284
    - term: Read mapping
      uri: http://edamontology.org/operation_3198
    - term: Genome alignment
      uri: http://edamontology.org/operation_3182
    - term: RNA-Seq quantification
      uri: http://edamontology.org/operation_3800
    - term: SNP annotation
      uri: http://edamontology.org/operation_3661
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: SQLite format
        uri: http://edamontology.org/format_3621
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/Biobix/proteoformer
  homepage_status: 0
  language:
  - R
  - Perl
  - SQL
  - Python
  lastUpdate: '2024-11-24T21:08:45.743010Z'
  license: GPL-3.0
  link:
  - note: GitHub tool repo (PROTEOFORMER version 2.0)
    type:
    - Repository
    url: https://github.com/Biobix/proteoformer
  - note: Galaxy service of PROTEOFORMER version 1.0 (the Galaxy service of PROTEOFORMER
      version 2.0 is under way)
    type:
    - Galaxy service
    url: http://galaxy.ugent.be/
  - note: Explanation of the tool on the lab's website
    type:
    - Other
    url: http://www.biobix.be/research/downloads/proteoformer/
  maturity: Mature
  name: PROTEOFORMER
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: Katrijn.Vannerum@ugent.be
  publication:
  - doi: 10.1093/nar/gku1283
    metadata:
      abstract: An increasing amount of studies integrate mRNA sequencing data into
        MS-based proteomics to complement the translation product search space. However,
        several factors, including extensive regulation of mRNA translation and the
        need for threeor six-frame-translation, impede the use of mRNAseq data for
        the construction of a protein sequence search database.With that inmind, we
        developed the PROTEOFORMER tool that automatically processes data of the recently
        developed ribosome profiling method (sequencing of ribosome-protected mRNA
        fragments), resulting in genome-wide visualization of ribosome occupancy.
        Our tool also includes a translation initiation site calling algorithm allowing
        the delineation of the open reading frames (ORFs) of all translation products.
        A complete protein synthesisbased sequence database can thus be compiled for
        mass spectrometry-based identification. This approach increases the overall
        protein identification rates with 3% and 11% (improved and new identifications)
        for human and mouse, respectively, and enables proteome-wide detection of
        5'-extended proteoforms, upstream ORF translation and near-cognate translation
        start sites. The PROTEOFORMER tool is available as a stand-alone pipeline
        and has been implemented in the galaxy framework for ease of use.
      authors:
      - name: Crappe J.
      - name: Ndah E.
      - name: Koch A.
      - name: Steyaert S.
      - name: Gawron D.
      - name: De Keulenaer S.
      - name: De Meester E.
      - name: De Meyer T.
      - name: Van Criekinge W.
      - name: Van Damme P.
      - name: Menschaert G.
      citationCount: 111
      date: '2015-03-11T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'PROTEOFORMER: Deep proteome coverage through ribosome profiling and
        MS integration'
    note: null
    pmcid: PMC4357689
    pmid: '25510491'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1074/mcp.ra118.001218
    metadata:
      abstract: PROTEOFORMER is a pipeline that enables the automated processing of
        data derived from ribosome profiling (RIBO-seq, i.e. The sequencing of ribosome-protected
        mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation
        of data-specific translation product candidates and these can improve the
        mass spectrometry-based identification. Since its first publication, different
        upgrades, new features and extensions have been added to the PROTEOFORMER
        pipeline. Some of the most important upgrades include P-site offset calculation
        during mapping, comprehensive data preexploration, the introduction of two
        alternative proteoform calling strategies and extended pipeline output features.
        These novelties are illustrated by analyzing ribosome profiling data of human
        HCT116 and Jurkat data. The different proteoform calling strategies are used
        alongside one another and in the end combined together with reference sequences
        from UniProt. Matching mass spectrometry data are searched against this extended
        search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline
        leads to the identification and validation of different categories of new
        proteoforms, including translation products of up- and downstream open reading
        frames, 5' and 3' extended and truncated proteoforms, single amino acid variants,
        splice variants and translation products of so-called noncoding regions. Further,
        proof-of-concept is reported for the improvement of spectrum matching by including
        Prosit, a deep neural network strategy that adds extra fragmentation spectrum
        intensity features to the analysis. In the light of ribosome profiling-driven
        proteogenomics, it is shown that this allows validating the spectrum matches
        of newly identified proteoforms with elevated stringency. These updates and
        novel conclusions provide new insights and lessons for the ribosome profiling-based
        proteogenomic research field.
      authors:
      - name: Verbruggen S.
      - name: Ndah E.
      - name: Van Criekinge W.
      - name: Gessulat S.
      - name: Kuster B.
      - name: Wilhelm M.
      - name: Van Damme P.
      - name: Menschaert G.
      citationCount: 36
      date: '2019-01-01T00:00:00Z'
      journal: Molecular and Cellular Proteomics
      title: 'PROTEOFORMER 2.0: Further developments in the ribosome profiling-assisted
        proteogenomic hunt for new proteoforms'
    note: null
    pmcid: PMC6692777
    pmid: '31040227'
    type:
    - Primary
    version: '2.0'
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Protein variants
    uri: http://edamontology.org/topic_3120
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - '1.0'
  - '2.0'
- accessibility: Open access
  additionDate: '2019-01-23T12:32:59Z'
  biotoolsCURIE: biotools:ebprot
  biotoolsID: ebprot
  collectionID:
  - Proteomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hiromi_koh@nuhs.edu.sg
    fundrefid: null
    gridid: null
    name: Hiromi W.L. Koh
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: yz2236@nyu.edu
    fundrefid: null
    gridid: null
    name: Yunbin Zhang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: cvogel@nyu.edu
    fundrefid: null
    gridid: null
    name: Christine Vogel
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: hwchoi@nus.edu.sg
    fundrefid: null
    gridid: null
    name: Hyungwon Choi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Perseus plugin for differential protein abundance analysis of labeling-based
    and label-free quantitative proteomics data
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/cssblab/EBprot/wiki
  download:
  - note: null
    type: Software package
    url: https://github.com/cssblab/EBprot/releases
    version: null
  editPermission:
    authors:
    - proteomics.bio.tools
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Peptide identification
        uri: http://edamontology.org/data_0945
      format: []
    note: null
    operation:
    - term: Label-free quantification
      uri: http://edamontology.org/operation_3634
    - term: Labeled quantification
      uri: http://edamontology.org/operation_3635
    output: []
  homepage: https://github.com/cssblab/EBprot
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-11-24T21:05:25.086214Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/cssblab/EBprot
  - note: null
    type:
    - Issue tracker
    url: https://github.com/cssblab/EBprot/issues
  maturity: Mature
  name: EBprot
  operatingSystem:
  - Windows
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1021/acs.jproteome.8b00483
    metadata:
      abstract: We present EBprotV2, a Perseus plugin for peptide-ratio-based differential
        protein abundance analysis in labeling-based proteomics experiments. The original
        version of EBprot models the distribution of log-transformed peptide-level
        ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially
        abundant proteins and computes the probability score of differential abundance
        for each protein based on the reproducible magnitude of peptide ratios. However,
        the fully parametric model can be inflexible, and its R implementation is
        time-consuming for data sets containing a large number of peptides (e.g.,
        >100000). The new tool built in the C++ language is not only faster in computation
        time but also equipped with a flexible semiparametric model that handles skewed
        ratio distributions better. We have also developed a Perseus plugin for EBprotV2
        for easy access to the tool. In addition, the tool now offers a new submodule
        (MakeGrpData) to transform label-free peptide intensity data into peptide
        ratio data for group comparisons and performs differential abundance analysis
        using mixture modeling. This approach is especially useful when the label-free
        data have many missing peptide intensity data points.
      authors:
      - name: Koh H.W.L.
      - name: Zhang Y.
      - name: Vogel C.
      - name: Choi H.
      citationCount: 3
      date: '2019-02-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis
        of Labeling-Based Quantitative Proteomics Data'
    note: null
    pmcid: PMC6433620
    pmid: '30411623'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1002/pmic.201400620
    metadata:
      abstract: Labeling-based proteomics is a powerful method for detection of differentially
        expressed proteins (DEPs). The current data analysis platform typically relies
        on protein-level ratios, which is obtained by summarizing peptide-level ratios
        for each protein. In shotgun proteomics, however, some proteins are quantified
        with more peptides than others, and this reproducibility information is not
        incorporated into the differential expression (DE) analysis. Here, we propose
        a novel probabilistic framework EBprot that directly models the peptide-protein
        hierarchy and rewards the proteins with reproducible evidence of DE over multiple
        peptides. To evaluate its performance with known DE states, we conducted a
        simulation study to show that the peptide-level analysis of EBprot provides
        better receiver-operating characteristic and more accurate estimation of the
        false discovery rates than the methods based on protein-level ratios. We also
        demonstrate superior classification performance of peptide-level EBprot analysis
        in a spike-in dataset. To illustrate the wide applicability of EBprot in different
        experimental designs, we applied EBprot to a dataset for lung cancer subtype
        analysis with biological replicates and another dataset for time course phosphoproteome
        analysis of EGF-stimulated HeLa cells with multiplexed labeling. Through these
        examples, we show that the peptide-level analysis of EBprot is a robust alternative
        to the existing statistical methods for the DE analysis of labeling-based
        quantitative datasets. The software suite is freely available on the Sourceforge
        website http://ebprot.sourceforge.net/. All MS data have been deposited in
        the ProteomeXchange with identifier PXD001426 (http://proteomecentral.proteomexchange.org/dataset/PXD001426/).
      authors:
      - name: Koh H.W.L.
      - name: Swa H.L.F.
      - name: Fermin D.
      - name: Ler S.G.
      - name: Gunaratne J.
      - name: Choi H.
      citationCount: 10
      date: '2015-08-01T00:00:00Z'
      journal: Proteomics
      title: 'EBprot: Statistical analysis of labeling-based quantitative proteomics
        data'
    note: null
    pmcid: null
    pmid: '25913743'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Plug-in
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2018-07-06T11:57:04Z'
  biotoolsCURIE: biotools:interevdock2
  biotoolsID: interevdock2
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: raphael.guerois@cea.fr
    fundrefid: null
    gridid: null
    name: Raphael Guerois
    note: null
    orcidid: https://orcid.org/0000-0001-5294-2858
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: jessica.andreani@cea.fr
    fundrefid: null
    gridid: null
    name: Jessica Andreani
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Ab initio protein docking based on rigid-body sampling followed by
    consensus scoring using physics-based and statistical potentials, including the
    InterEvScore function specifically developed to incorporate co-evolutionary information
    in docking.
  documentation:
  - note: null
    type:
    - General
    url: http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock2/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Protein-protein docking
      uri: http://edamontology.org/operation_3899
    output:
    - data:
        term: Protein complex
        uri: http://edamontology.org/data_2877
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein modelling
      uri: http://edamontology.org/operation_0477
    - term: Protein-protein docking
      uri: http://edamontology.org/operation_3899
    output:
    - data:
        term: Protein complex
        uri: http://edamontology.org/data_2877
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::InterEvDock2
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T21:00:12.284563Z'
  license: Freeware
  link: []
  maturity: Mature
  name: InterEvDock
  operatingSystem: []
  otherID: []
  owner: reyjul
  publication:
  - doi: 10.1093/nar/gky377
    metadata:
      abstract: Computational protein docking is a powerful strategy to predict structures
        of protein-protein interactions and provides crucial insights for the functional
        characterization of macromolecular cross-talks. We previously developed InterEvDock,
        a server for ab initio protein docking based on rigid-body sampling followed
        by consensus scoring using physics-based and statistical potentials, including
        the InterEvScore function specifically developed to incorporate co-evolutionary
        information in docking. InterEvDock2 is a major evolution of InterEvDock which
        allows users to submit input sequences - not only structures - and multimeric
        inputs and to specify constraints for the pairwise docking process based on
        previous knowledge about the interaction. For this purpose, we added modules
        in InterEvDock2 for automatic template search and comparative modeling of
        the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes
        for which unbound homology models of the two partners and co-evolutionary
        information are available in the PPI4DOCK database. InterEvDock2 identified
        a correct model among the top 10 consensus in 29% of these cases (compared
        to 15-24% for individual scoring functions) and at least one correct interface
        residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique
        protein docking server, designed to be useful for the experimental biology
        community. The InterEvDock2 web interface is available at http://bioserv.rpbs.univ-Paris-diderot.fr/services/InterEvDock2/.
      authors:
      - name: Quignot C.
      - name: Rey J.
      - name: Yu J.
      - name: Tuffery P.
      - name: Guerois R.
      - name: Andreani J.
      citationCount: 44
      date: '2018-07-02T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'InterEvDock2: An expanded server for protein docking using evolutionary
        and biological information from homology models and multimeric inputs'
    note: null
    pmcid: PMC6030979
    pmid: '29741647'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1093/nar/gkw340
    metadata:
      abstract: The structural modeling of protein-protein interactions is key in
        understanding how cell machineries cross-talk with each other. Molecular docking
        simulations provide efficient means to explore how two unbound protein structures
        interact. InterEvDock is a server for protein docking based on a free rigidbody
        docking strategy. A systematic rigid-body docking search is performed using
        the FRODOCK program and the resulting models are re-scored with InterEvScore
        and SOAP-PP statistical potentials. The InterEvScore potential was specifically
        designed to integrate co-evolutionary information in the docking process.
        InterEvDock server is thus particularly well suited in case homologous sequences
        are available for both binding partners. The server returns 10 structures
        of the most likely consensus models together with 10 predicted residues most
        likely involved in the interface. In 91% of all complexes tested in the benchmark,
        at least one residue out of the 10 predicted is involved in the interface,
        providing useful guidelines for mutagenesis. InterEvDock is able to identify
        a correct model among the top10 models for 49% of the rigid-body cases with
        evolutionary information, making it a unique and efficient tool to explore
        structural interactomes under an evolutionary perspective. The InterEvDock
        web interface is available at http://bioserv.rpbs.univ-Paris-diderot.fr/services/InterEvDock/.
      authors:
      - name: Yu J.
      - name: Vavrusa M.
      - name: Andreani J.
      - name: Rey J.
      - name: Tuffery P.
      - name: Guerois R.
      citationCount: 60
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'InterEvDock: A docking server to predict the structure of protein-protein
        interactions using evolutionary information'
    note: null
    pmcid: PMC4987904
    pmid: '27131368'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1093/bioinformatics/btt260
    metadata:
      abstract: 'Motivation: Structural prediction of protein interactions currently
        remains a challenging but fundamental goal. In particular, progress in scoring
        functions is critical for the efficient discrimination of near-native interfaces
        among large sets of decoys. Many functions have been developed using knowledge-based
        potentials, but few make use of multi-body interactions or evolutionary information,
        although multi-residue interactions are crucial for protein-protein binding
        and protein interfaces undergo significant selection pressure to maintain
        their interactions.Results: This article presents InterEvScore, a novel scoring
        function using a coarse-grained statistical potential including two- and three-body
        interactions, which provides each residue with the opportunity to contribute
        in its most favorable local structural environment. Combination of this potential
        with evolutionary information considerably improves scoring results on the
        54 test cases from the widely used protein docking benchmark for which evolutionary
        information can be collected. We analyze how our way to include evolutionary
        information gradually increases the discriminative power of InterEvScore.
        Comparison with several previously published scoring functions (ZDOCK, ZRANK
        and SPIDER) shows the significant progress brought by InterEvScore. © 2013
        The Author 2013. Published by Oxford University Press. All rights reserved.'
      authors:
      - name: Andreani J.
      - name: Faure G.
      - name: Guerois R.
      citationCount: 65
      date: '2013-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'InterEvScore: A novel coarse-grained interface scoring function using
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    note: null
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    pmid: '23652426'
    type:
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    version: null
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  topic:
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
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    name: Bidossessi Wilfried Hounkpe
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  description: 'Redefining human and mouse housekeeping genes and candidate reference
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    Web-based tools to search tissue specific housekeeping genes.'
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  - note: null
    type:
    - General
    url: http://housekeeping.unicamp.br/?about
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    - term: Multilocus sequence typing
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    output: []
  homepage: http://www.housekeeping.unicamp.br
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  language:
  - R
  lastUpdate: '2024-11-24T20:56:06.014004Z'
  license: null
  link:
  - note: Database home page
    type:
    - Service
    - Repository
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  - note: Hrt Atlas Github
    type:
    - Repository
    url: https://github.com/Bidossessih/HRT_Atlas
  maturity: null
  name: Housekeeping and Reference  Transcript Atlas
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  owner: Pub2Tools
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  - doi: 10.1093/nar/gkaa609
    metadata:
      abstract: Housekeeping (HK) genes are constitutively expressed genes that are
        required for the maintenance of basic cellular functions. Despite their importance
        in the calibration of gene expression, as well as the understanding of many
        genomic and evolutionary features, important discrepancies have been observed
        in studies that previously identified these genes. Here, we present Housekeeping
        and Reference Transcript Atlas (HRT Atlas v1.0, www.housekeeping.unicamp.br)
        a web-based database which addresses some of the previously observed limitations
        in the identification of these genes, and offers a more accurate database
        of human and mouse HK genes and transcripts. The database was generated by
        mining massive human and mouse RNA-seq data sets, including 11 281 and 507
        high-quality RNA-seq samples from 52 human non-disease tissues/cells and 14
        healthy tissues/cells of C57BL/6 wild type mouse, respectively. User can visualize
        the expression and download lists of 2158 human HK transcripts from 2176 HK
        genes and 3024 mouse HK transcripts from 3277 mouse HK genes. HRT Atlas also
        offers the most stable and suitable tissue selective candidate reference transcripts
        for normalization of qPCR experiments. Specific primers and predicted modifiers
        of gene expression for some of these HK transcripts are also proposed. HRT
        Atlas has also been integrated with a regulatory elements resource from Epiregio
        server.
      authors:
      - name: Hounkpe B.W.
      - name: Chenou F.
      - name: de Lima F.
      - name: de Paula E.V.
      citationCount: 112
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'HRT Atlas v1.0 database: Redefining human and mouse housekeeping genes
        and candidate reference transcripts by mining massive RNA-seq datasets'
    note: null
    pmcid: PMC7778946
    pmid: '32663312'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Probes and primers
    uri: http://edamontology.org/topic_0632
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Laboratory techniques
    uri: http://edamontology.org/topic_3361
  - term: Experimental design and studies
    uri: http://edamontology.org/topic_3678
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2021-01-18T11:25:29Z'
  biotoolsCURIE: biotools:fast-gbs
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  description: Fast-GBS is an analysis toolkit for genotyping-by-sequencing data.
    Genotyping-by-sequencing (GBS) is a rapid, flexible, low-cost, and robust genotyping
    method that simultaneously discovers variants and calls genotypes within a broad
    range of samples. These characteristics make GBS an excellent tool for many applications
    and research questions from conservation biology to functional genomics in both
    model and non-model species. Continued improvement of GBS relies on a more comprehensive
    understanding of data analysis, development of fast and efficient bioinformatics
    pipelines, accurate missing data imputation, and active post-release support.
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    operation:
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    - term: Variant calling
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    output: []
  homepage: https://bitbucket.org/jerlar73/fast-gbs_v2/src/master/
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  name: Fast-GBS
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  owner: Kigaard
  publication:
  - doi: 10.1139/gen-2020-0077
    metadata:
      abstract: Genotyping-by-sequencing (GBS) is a rapid, flexible, low-cost, and
        robust genotyping method that simultaneously discovers variants and calls
        genotypes within a broad range of samples. These characteristics make GBS
        an excellent tool for many applications and research questions from conservation
        biology to functional genomics in both model and non-model species. Continued
        improvement of GBS relies on a more comprehensive understanding of data analysis,
        development of fast and efficient bioinformatics pipelines, accurate missing
        data imputation, and active post-release support. Here, we present the second
        generation of Fast-GBS (v2.0) that offers several new options (e.g., processing
        paired-end reads and imputation of missing data) and features (e.g., summary
        statistics of genotypes) to improve the GBS data analysis process. The performance
        assessment analysis showed that Fast-GBS v2.0 outperformed other available
        analytical pipelines, such as GBS-SNP-CROP and Gb-eaSy. Fast-GBS v2.0 provides
        an analysis platform that can be run with different types of sequencing data,
        modest computational resources, and allows for missing-data imputation for
        various species in different contexts.
      authors:
      - name: Torkamaneh D.
      - name: Laroche J.
      - name: Belzile F.
      citationCount: 20
      date: '2020-01-01T00:00:00Z'
      journal: Genome
      title: 'Fast-gbs v2.0: An analysis toolkit for genotyping-by-sequencing data'
    note: null
    pmcid: null
    pmid: '33006480'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1186/s12859-016-1431-9
    metadata:
      abstract: 'Background: Next-generation sequencing (NGS) technologies have accelerated
        considerably the investigation into the composition of genomes and their functions.
        Genotyping-by-sequencing (GBS) is a genotyping approach that makes use of
        NGS to rapidly and economically scan a genome. It has been shown to allow
        the simultaneous discovery and genotyping of thousands to millions of SNPs
        across a wide range of species. For most users, the main challenge in GBS
        is the bioinformatics analysis of the large amount of sequence information
        derived from sequencing GBS libraries in view of calling alleles at SNP loci.
        Herein we describe a new GBS bioinformatics pipeline, Fast-GBS, designed to
        provide highly accurate genotyping, to require modest computing resources
        and to offer ease of use. Results: Fast-GBS is built upon standard bioinformatics
        language and file formats, is capable of handling data from different sequencing
        platforms, is capable of detecting different kinds of variants (SNPs, MNPs,
        and Indels). To illustrate its performance, we called variants in three collections
        of samples (soybean, barley, and potato) that cover a range of different genome
        sizes, levels of genome complexity, and ploidy. Within these small sets of
        samples, we called 35 k, 32 k and 38 k SNPs for soybean, barley and potato,
        respectively. To assess genotype accuracy, we compared these GBS-derived SNP
        genotypes with independent data sets obtained from whole-genome sequencing
        or SNP arrays. This analysis yielded estimated accuracies of 98.7, 95.2, and
        94% for soybean, barley, and potato, respectively. Conclusions: We conclude
        that Fast-GBS provides a highly efficient and reliable tool for calling SNPs
        from GBS data.'
      authors:
      - name: Torkamaneh D.
      - name: Laroche J.
      - name: Bastien M.
      - name: Abed A.
      - name: Belzile F.
      citationCount: 88
      date: '2017-01-03T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Fast-GBS: A new pipeline for the efficient and highly accurate calling
        of SNPs from genotyping-by-sequencing data'
    note: null
    pmcid: PMC5210301
    pmid: '28049422'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType: []
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2016-06-30T09:32:44Z'
  biotoolsCURIE: biotools:predictsnp2
  biotoolsID: predictsnp2
  collectionID:
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  - Rare Disease
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  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
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    name: Jaroslav Bendl
    note: null
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    typeEntity: Person
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    url: null
  - email: xmusil46@stud.fit.vutbr.cz
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: brezovsky@mail.muni.cz
    fundrefid: null
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    name: Jan Brezovsky
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: 1441@mail.muni.cz
    fundrefid: null
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    name: Jiri Damborsky
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    rorid: null
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  - email: null
    fundrefid: null
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    name: Eric D. Wieben
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  - email: 99207@mail.muni.cz
    fundrefid: null
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    name: Brno University of Technology, Brno, Czech Republic
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  - email: predictsnp@sci.muni.cz
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    typeEntity: Person
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    name: European Social Fund
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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/
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  editPermission:
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  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:
      - term: GVF
        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: null
  additionDate: '2018-04-06T08:55:47Z'
  biotoolsCURIE: biotools:caverdock
  biotoolsID: caverdock
  collectionID:
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  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: Jiri Filipovic
    note: null
    orcidid: https://orcid.org/0000-0002-5703-9673
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: o.vavra@mail.muni.cz
    fundrefid: null
    gridid: null
    name: Ondrej Vavra
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: 408420@mail.muni.cz
    fundrefid: null
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    name: Jan Plhak
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: 222755@mail.muni.cz
    fundrefid: null
    gridid: null
    name: David Bednar
    note: null
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    rorid: null
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    typeRole:
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    url: null
  - email: smar96@gmail.com
    fundrefid: null
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    name: Sergio Marques
    note: null
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    url: null
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    typeRole:
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    - Support
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    url: https://www.elixir-czech.cz/
  - email: caver@caver.cz
    fundrefid: null
    gridid: null
    name: CaverDock team
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
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    name: European Commission
    note: null
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    rorid: null
    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Czech Science Foundation
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Brno City Municipality
    note: null
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    typeEntity: Funding agency
    typeRole:
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    url: null
  description: Performs rapid analysis of transport processes in proteins. It models
    the transportation of a ligand from outside environment into the protein active
    or binding site and vice versa. It implements a novel algorithm to produce contiguous
    ligand trajectory and estimation of a binding energy along the pathway. The current
    version uses CAVER for pathway identification and heavily modified Autodock Vina
    as a docking engine.
  documentation:
  - note: version 1.0
    type:
    - User manual
    url: https://www.fi.muni.cz/~xfilipov/caverdock/manual.pdf
  - note: version 1.1
    type:
    - User manual
    url: https://www.fi.muni.cz/~xfilipov/caverdock/manual-1.1.pdf
  download:
  - note: v1.0, Ubuntu 14.04
    type: Binaries
    url: https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-ubuntu-14.04.tar.gz
    version: '1.0'
  - note: v1.0, Ubuntu 16.04
    type: Binaries
    url: https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-ubuntu-16.04.tar.gz
    version: '1.0'
  - note: v1.1, Ubuntu 16.04
    type: Binaries
    url: https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-1.1-ubuntu-16.04.tar.xz
    version: '1.1'
  - note: v1.1, Ubuntu 18.04
    type: Binaries
    url: https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-1.1-ubuntu-18.04.tar.xz
    version: '1.1'
  editPermission:
    authors:
    - ELIXIR-CZ
    type: group
  elixirCommunity:
  - 3D-BioInfo
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Small molecule structure
        uri: http://edamontology.org/data_1463
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: trajectory of ligand passing through protein tunnel and its energy
    operation:
    - term: Protein-ligand docking
      uri: http://edamontology.org/operation_0482
    output:
    - data:
        term: Protein structure report
        uri: http://edamontology.org/data_1537
      format:
      - term: raw
        uri: http://edamontology.org/format_1957
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: https://loschmidt.chemi.muni.cz/caverdock/
  homepage_status: 0
  language:
  - C++
  - Python
  lastUpdate: '2024-11-24T20:23:05.902530Z'
  license: Proprietary
  link: []
  maturity: Mature
  name: CaverDock
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Loschmidt Laboratories
  publication:
  - doi: 10.1093/bioinformatics/btz386
    metadata:
      abstract: 'Motivation: Protein tunnels and channels are key transport pathways
        that allow ligands to pass between proteins'' external and internal environments.
        These functionally important structural features warrant detailed attention.
        It is difficult to study the ligand binding and unbinding processes experimentally,
        while molecular dynamics simulations can be time-consuming and computationally
        demanding. Results: CaverDock is a new software tool for analysing the ligand
        passage through the biomolecules. The method uses the optimized docking algorithm
        of AutoDock Vina for ligand placement docking and implements a parallel heuristic
        algorithm to search the space of possible trajectories. The duration of the
        simulations takes from minutes to a few hours. Here we describe the implementation
        of the method and demonstrate CaverDock''s usability by: (i) comparison of
        the results with other available tools, (ii) determination of the robustness
        with large ensembles of ligands and (iii) the analysis and comparison of the
        ligand trajectories in engineered tunnels. Thorough testing confirms that
        CaverDock is applicable for the fast analysis of ligand binding and unbinding
        in fundamental enzymology and protein engineering. Availability and implementation:
        User guide and binaries for Ubuntu are freely available for non-commercial
        use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation
        is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code
        is available upon request. Supplementary information: Supplementary data are
        available at Bioinformatics online.'
      authors:
      - name: Vavra O.
      - name: Filipovic J.
      - name: Plhak J.
      - name: Bednar D.
      - name: Marques S.M.
      - name: Brezovsky J.
      - name: Stourac J.
      - name: Matyska L.
      - name: Damborsky J.
      citationCount: 55
      date: '2019-12-01T00:00:00Z'
      journal: Bioinformatics
      title: 'CaverDock: A molecular docking-based tool to analyse ligand transport
        through protein tunnels and channels'
    note: null
    pmcid: null
    pmid: '31077297'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1109/tcbb.2019.2907492
    metadata:
      abstract: Here we present a novel method for the analysis of transport processes
        in proteins and its implementation called CaverDock. Our method is based on
        a modified molecular docking algorithm. It iteratively places the ligand along
        the access tunnel in such a way that the ligand movement is contiguous and
        the energy is minimized. The result of CaverDock calculation is a ligand trajectory
        and an energy profile of transport process. CaverDock uses the modified docking
        program Autodock Vina for molecular docking and implements a parallel heuristic
        algorithm for searching the space of possible trajectories. Our method lies
        in between the geometrical approaches and molecular dynamics simulations.
        Contrary to the geometrical methods, it provides an evaluation of chemical
        forces. However, it is far less computationally demanding and easier to set
        up compared to molecular dynamics simulations. CaverDock will find a broad
        use in the fields of computational enzymology, drug design, and protein engineering.
        The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.
      authors:
      - name: Filipovic J.
      - name: Vavra O.
      - name: Plhak J.
      - name: Bednar D.
      - name: Marques S.M.
      - name: Brezovsky J.
      - name: Matyska L.
      - name: Damborsk J.
      citationCount: 26
      date: '2020-09-01T00:00:00Z'
      journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
      title: 'CaverDock: A Novel Method for the Fast Analysis of Ligand Transport'
    note: null
    pmcid: null
    pmid: '30932844'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  validated: 1
  version:
  - '1.0'
  - '1.1'
- accessibility: Open access
  additionDate: '2015-11-07T20:40:08Z'
  biotoolsCURIE: biotools:predictsnp
  biotoolsID: predictsnp
  collectionID:
  - Czech Republic
  - Rare Disease
  - ELIXIR-CZ
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: jaroslav.bendl@gmail.com
    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:
    - Developer
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ondrej Salanda
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: brezovsky@mail.muni.cz
    fundrefid: null
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    name: Jan Brezovsky
    note: null
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    rorid: null
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    typeRole:
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    url: null
  - email: 1441@mail.muni.cz
    fundrefid: null
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    name: Jiri Damborsky
    note: null
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    rorid: null
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    typeRole:
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    url: null
  - email: null
    fundrefid: null
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    name: Eric D. Wieben
    note: null
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  - email: zendulka@fit.vut.cz
    fundrefid: null
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    name: Jaroslav Zendulka
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: 99207@mail.muni.cz
    fundrefid: null
    gridid: null
    name: Antonin Pavelka
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
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    name: Loschmidt Laboratories
    note: null
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    url: https://loschmidt.chemi.muni.cz/peg/
  - email: null
    fundrefid: null
    gridid: null
    name: Masaryk University, Brno, Czech Republic
    note: null
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    url: https://www.muni.cz/
  - email: null
    fundrefid: null
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    name: International Centre for Clinical Research, Brno, Czech Republic
    note: null
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    typeRole:
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    url: https://www.fnusa-icrc.org/en/
  - email: null
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    name: Brno University of Technology, Brno, Czech Republic
    note: null
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    typeRole:
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    url: https://www.vutbr.cz/
  - email: null
    fundrefid: null
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    name: Mayo Clinic, Rochester, New York, United States of America
    note: null
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    rorid: null
    typeEntity: Institute
    typeRole:
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    url: https://www.mayoclinic.org/
  - email: null
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    note: null
    orcidid: null
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    typeEntity: Consortium
    typeRole:
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    url: https://www.elixir-czech.cz/
  - email: predictsnp@sci.muni.cz
    fundrefid: null
    gridid: null
    name: PredictSNP team
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: European Regional Development Fund
    note: null
    orcidid: null
    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
  - email: null
    fundrefid: null
    gridid: null
    name: Grant Agency of the Czech Republic
    note: null
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    typeRole:
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    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ministry of Education of the Czech Republic
    note: null
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    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Brno University of Technology
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole:
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    url: null
  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
  editPermission:
    authors: []
    type: private
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  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    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: '2021-03-23T16:13:44Z'
  biotoolsCURIE: biotools:araqtl
  biotoolsID: araqtl
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: harm.nijveen@wur.nl
    fundrefid: null
    gridid: null
    name: Harm Nijveen
    note: null
    orcidid: https://orcid.org/0000-0002-9167-4945
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Wilco Ligterink
    note: null
    orcidid: https://orcid.org/0000-0002-0228-169X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Joost J. B. Keurentjes
    note: null
    orcidid: https://orcid.org/0000-0001-8918-0711
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Olivier Loudet
    note: null
    orcidid: https://orcid.org/0000-0003-3717-0137
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jiao Long
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Mark G. Sterken
    note: null
    orcidid: https://orcid.org/0000-0001-7119-6213
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Pjotr Prins
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Henk W. Hilhorst
    note: null
    orcidid: https://orcid.org/0000-0002-6743-583X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Dick de Ridder
    note: null
    orcidid: https://orcid.org/0000-0002-4944-4310
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jan E. Kammenga
    note: null
    orcidid: null
    rorid: null
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    url: null
  - email: basten.snoek@wur.nl
    fundrefid: null
    gridid: null
    name: Basten L. Snoek
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: 'AraQTL is the A.thaliana workbench and database for eQTL analysis.

    It allows easy access to the data of all published Arabidopsis thaliana genetical
    genomics experiments.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene expression QTL analysis
      uri: http://edamontology.org/operation_3232
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genetic mapping
      uri: http://edamontology.org/operation_0282
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: https://www.bioinformatics.nl/AraQTL
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T20:21:13.730744Z'
  license: null
  link: []
  maturity: Mature
  name: AraQTL
  operatingSystem: []
  otherID: []
  owner: marieBvr
  publication:
  - doi: 10.1111/tpj.13457
    metadata:
      abstract: Genetical genomics studies uncover genome-wide genetic interactions
        between genes and their transcriptional regulators. High-throughput measurement
        of gene expression in recombinant inbred line populations has enabled investigation
        of the genetic architecture of variation in gene expression. This has the
        potential to enrich our understanding of the molecular mechanisms affected
        by and underlying natural variation. Moreover, it contributes to the systems
        biology of natural variation, as a substantial number of experiments have
        resulted in a valuable amount of interconnectable phenotypic, molecular and
        genotypic data. A number of genetical genomics studies have been published
        for Arabidopsis thaliana, uncovering many expression quantitative trait loci
        (eQTLs). However, these complex data are not easily accessible to the plant
        research community, leaving most of the valuable genetic interactions unexplored
        as cross-analysis of these studies is a major effort. We address this problem
        with AraQTL (http://www.bioinformatics.nl/Ara QTL/), an easily accessible
        workbench and database for comparative analysis and meta-analysis of all published
        Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using
        and extending upon the results of these experiments. For example, one can
        easily screen a physical region for specific local eQTLs that could harbour
        candidate genes for phenotypic QTLs, or detect gene-by-environment interactions
        by comparing eQTLs under different conditions.
      authors:
      - name: Nijveen H.
      - name: Ligterink W.
      - name: Keurentjes J.J.B.
      - name: Loudet O.
      - name: Long J.
      - name: Sterken M.G.
      - name: Prins P.
      - name: Hilhorst H.W.
      - name: de Ridder D.
      - name: Kammenga J.E.
      - name: Snoek B.L.
      citationCount: 15
      date: '2017-03-01T00:00:00Z'
      journal: Plant Journal
      title: AraQTL – workbench and archive for systems genetics in Arabidopsis thaliana
    note: null
    pmcid: null
    pmid: '27995664'
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Web application
  topic:
  - term: Biomarkers
    uri: http://edamontology.org/topic_3360
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2020-02-21T13:12:50Z'
  biotoolsCURIE: biotools:TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler
  biotoolsID: TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fpsom@certh.gr
    fundrefid: null
    gridid: null
    name: Fotis E. Psomopoulos
    note: null
    orcidid: https://orcid.org/0000-0002-0222-4273
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://biodataanalysisgroup.github.io/
  - email: null
    fundrefid: null
    gridid: null
    name: Maria Th. Kotouza
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Katerina Gemenetzi
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Chrysi Galigalidou
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: T-cell Receptor/Immunoglobulin Profiler (TRIP)
  documentation:
  - note: null
    type:
    - User manual
    url: https://www.bioconductor.org/packages/devel/bioc/manuals/tripr/man/tripr.pdf
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode:
  - Greece
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence analysis
      uri: http://edamontology.org/operation_2403
    output: []
  homepage: https://github.com/BiodataAnalysisGroup/TRIP
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T15:49:18.952349Z'
  license: MIT
  link:
  - note: null
    type:
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    url: https://www.bioconductor.org/packages/devel/bioc/html/tripr.html
  maturity: Emerging
  name: tripr
  operatingSystem:
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  - Linux
  - Windows
  otherID: []
  owner: fpsom
  publication:
  - doi: 10.1186/s12859-020-03669-1
    metadata:
      abstract: 'Background: Antigen receptors are characterized by an extreme diversity
        of specificities, which poses major computational and analytical challenges,
        particularly in the era of high-throughput immunoprofiling by next generation
        sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool
        offers the opportunity for an in-depth analysis based on the processing of
        the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling,
        through a number of interoperable modules. These provide detailed information
        about antigen receptor gene rearrangements, including variable (V), diversity
        (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition
        and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins
        (BcR IG), and characteristics of the somatic hypermutation within the BcR
        IG genes. TRIP is a web application implemented in R shiny. Results: Two sets
        of experiments have been performed in order to evaluate the efficiency and
        performance of the TRIP tool. The first used a number of synthetic datasets,
        ranging from 250k to 1M sequences, and established the linear response time
        of the tool (about 6 h for 1M sequences processed through the entire BcR IG
        data pipeline). The reproducibility of the tool was tested comparing the results
        produced by the main TRIP workflow with the results from a previous pipeline
        used on the Galaxy platform. As expected, no significant differences were
        noted between the two tools; although the preselection process seems to be
        stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered
        out, with no impact on the final results. Conclusions: TRIP is a software
        framework that provides analytical services on antigen receptor gene sequence
        data. It is accurate and contains functions for data wrangling, cleaning,
        analysis and visualization, enabling the user to build a pipeline tailored
        to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.'
      authors:
      - name: Kotouza M.T.
      - name: Gemenetzi K.
      - name: Galigalidou C.
      - name: Vlachonikola E.
      - name: Pechlivanis N.
      - name: Agathangelidis A.
      - name: Sandaltzopoulos R.
      - name: Mitkas P.A.
      - name: Stamatopoulos K.
      - name: Chatzidimitriou A.
      - name: Psomopoulos F.E.
      citationCount: 11
      date: '2020-09-29T00:00:00Z'
      journal: BMC Bioinformatics
      title: TRIP - T cell receptor/immunoglobulin profiler
    note: null
    pmcid: PMC7525938
    pmid: '32993478'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Library
  topic:
  - term: Immunoproteins and antigens
    uri: http://edamontology.org/topic_2830
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2022-08-16T14:46:38.276886Z'
  biotoolsCURIE: biotools:tripr
  biotoolsID: tripr
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fpsom@certh.gr
    fundrefid: null
    gridid: null
    name: Fotis E. Psomopoulos
    note: null
    orcidid: https://orcid.org/0000-0002-0222-4273
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://biodataanalysisgroup.github.io/
  - email: null
    fundrefid: null
    gridid: null
    name: Maria Th. Kotouza
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Katerina Gemenetzi
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Chrysi Galigalidou
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: T-cell Receptor/Immunoglobulin Profiler (TRIP)
  documentation:
  - note: null
    type:
    - User manual
    url: https://www.bioconductor.org/packages/devel/bioc/manuals/tripr/man/tripr.pdf
  download: []
  editPermission:
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  elixirNode:
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence analysis
      uri: http://edamontology.org/operation_2403
    output: []
  homepage: https://github.com/BiodataAnalysisGroup/TRIP
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T15:49:17.603843Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://www.bioconductor.org/packages/devel/bioc/html/tripr.html
  maturity: Emerging
  name: tripr
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1186/s12859-020-03669-1
    metadata:
      abstract: 'Background: Antigen receptors are characterized by an extreme diversity
        of specificities, which poses major computational and analytical challenges,
        particularly in the era of high-throughput immunoprofiling by next generation
        sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool
        offers the opportunity for an in-depth analysis based on the processing of
        the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling,
        through a number of interoperable modules. These provide detailed information
        about antigen receptor gene rearrangements, including variable (V), diversity
        (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition
        and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins
        (BcR IG), and characteristics of the somatic hypermutation within the BcR
        IG genes. TRIP is a web application implemented in R shiny. Results: Two sets
        of experiments have been performed in order to evaluate the efficiency and
        performance of the TRIP tool. The first used a number of synthetic datasets,
        ranging from 250k to 1M sequences, and established the linear response time
        of the tool (about 6 h for 1M sequences processed through the entire BcR IG
        data pipeline). The reproducibility of the tool was tested comparing the results
        produced by the main TRIP workflow with the results from a previous pipeline
        used on the Galaxy platform. As expected, no significant differences were
        noted between the two tools; although the preselection process seems to be
        stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered
        out, with no impact on the final results. Conclusions: TRIP is a software
        framework that provides analytical services on antigen receptor gene sequence
        data. It is accurate and contains functions for data wrangling, cleaning,
        analysis and visualization, enabling the user to build a pipeline tailored
        to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.'
      authors:
      - name: Kotouza M.T.
      - name: Gemenetzi K.
      - name: Galigalidou C.
      - name: Vlachonikola E.
      - name: Pechlivanis N.
      - name: Agathangelidis A.
      - name: Sandaltzopoulos R.
      - name: Mitkas P.A.
      - name: Stamatopoulos K.
      - name: Chatzidimitriou A.
      - name: Psomopoulos F.E.
      citationCount: 11
      date: '2020-09-29T00:00:00Z'
      journal: BMC Bioinformatics
      title: TRIP - T cell receptor/immunoglobulin profiler
    note: null
    pmcid: PMC7525938
    pmid: '32993478'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Library
  topic:
  - term: Immunoproteins and antigens
    uri: http://edamontology.org/topic_2830
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2022-01-25T09:42:42.478699Z'
  biotoolsCURIE: biotools:bamtocov
  biotoolsID: bamtocov
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Andrea Telatin
    note: null
    orcidid: https://orcid.org/0000-0001-7619-281X
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://github.com/telatin
  - email: null
    fundrefid: null
    gridid: null
    name: Giovanni Birolo
    note: null
    orcidid: https://orcid.org/0000-0003-0160-9312
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: Tools to extract coverage informations from BAM (and CRAM) files, based
    on the covtobed algorithm that supports stranded coverage and physical coverage,
    input from streams and uses a memory-efficient algorithm.
  documentation: []
  download: []
  editPermission:
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence alignment
        uri: http://edamontology.org/data_1383
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: CRAM
        uri: http://edamontology.org/format_3462
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: BED
        uri: http://edamontology.org/format_3003
    note: null
    operation:
    - term: Sequence alignment conversion
      uri: http://edamontology.org/operation_0260
    output:
    - data:
        term: Annotation track
        uri: http://edamontology.org/data_3002
      format:
      - term: bedgraph
        uri: http://edamontology.org/format_3583
      - term: WIG
        uri: http://edamontology.org/format_3005
  homepage: https://telatin.github.io/bamtocov/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2024-11-24T15:31:24.841052Z'
  license: MIT
  link:
  - note: null
    type:
    - Other
    url: https://telatin.github.io/bamtocov/
  - note: null
    type:
    - Repository
    url: https://github.com/telatin/bamtocov
  - note: null
    type:
    - Software catalogue
    url: https://anaconda.org/bioconda/bamtocov
  maturity: null
  name: bamtocov
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: telatin
  publication:
  - doi: 10.1101/2021.11.12.466787
    metadata: null
    note: Preprint hosted by biorXiv
    pmcid: null
    pmid: null
    type:
    - Method
    version: null
  - doi: 10.1093/bioinformatics/btac125
    metadata:
      abstract: 'Motivation: Many genomics applications require the computation of
        nucleotide coverage of a reference genome or the ability to determine how
        many reads map to a reference region. Results: BamToCov is a toolkit for rapid
        and flexible coverage computation that relies on the most memory efficient
        algorithm and is designed for integration in pipelines, given its ability
        to read alignment files from streams. The tools in the suite can process sorted
        BAM or CRAM files, allowing the user to extract coverage information via different
        filtering approaches and to save the output in different formats (BED, Wig
        or counts). The BamToCov algorithm can also handle strand-specific and/or
        physical coverage analyses.'
      authors:
      - name: Birolo G.
      - name: Telatin A.
      citationCount: 11
      date: '2022-05-01T00:00:00Z'
      journal: Bioinformatics
      title: 'BamToCov: An efficient toolkit for sequence coverage calculations'
    note: Bioinformatics Application note on BamToCov
    pmcid: PMC9048650
    pmid: '35199151'
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Command-line tool
  topic: []
  validated: 0
  version:
  - 2.5.0
  - 2.7.0
  - 2.8.0
- accessibility: null
  additionDate: '2020-12-10T17:29:48Z'
  biotoolsCURIE: biotools:cordax
  biotoolsID: cordax
  collectionID:
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  - KU Leuven
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Joost Schymkowitz
    note: null
    orcidid: https://orcid.org/0000-0003-2020-0168
    rorid: null
    typeEntity: null
    typeRole:
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    url: null
  description: The amyloid conformation can be adopted by a variety of sequences,
    but the precise boundaries of amyloid sequence space are still unclear. The currently
    charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet
    prone sequences that form the core of globular proteins and by Q/N/Y rich yeast
    prions. Here, we took advantage of the increasing amount of high-resolution structural
    information on amyloid cores currently available in the protein databank to implement
    a machine learning approach, named Cordax (https://cordax.switchlab.org), that
    explores amyloid sequence beyond its current boundaries.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://cordax.switchlab.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:19:34.365901Z'
  license: null
  link: []
  maturity: null
  name: Cordax
  operatingSystem: []
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  owner: bits@vib.be
  publication:
  - doi: 10.1038/s41467-020-17207-3
    metadata:
      abstract: The amyloid conformation can be adopted by a variety of sequences,
        but the precise boundaries of amyloid sequence space are still unclear. The
        currently charted amyloid sequence space is strongly biased towards hydrophobic,
        beta-sheet prone sequences that form the core of globular proteins and by
        Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount
        of high-resolution structural information on amyloid cores currently available
        in the protein databank to implement a machine learning approach, named Cordax
        (https://cordax.switchlab.org), that explores amyloid sequence beyond its
        current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding
        (t-SNE) shows how our approach resulted in an expansion away from hydrophobic
        amyloid sequences towards clusters of lower aliphatic content and higher charge,
        or regions of helical and disordered propensities. These clusters uncouple
        amyloid propensity from solubility representing sequence flavours compatible
        with surface-exposed patches in globular proteins, functional amyloids or
        sequences associated to liquid-liquid phase transitions.
      authors:
      - name: Louros N.
      - name: Orlando G.
      - name: De Vleeschouwer M.
      - name: Rousseau F.
      - name: Schymkowitz J.
      citationCount: 54
      date: '2020-12-01T00:00:00Z'
      journal: Nature Communications
      title: Structure-based machine-guided mapping of amyloid sequence space reveals
        uncharted sequence clusters with higher solubilities
    note: null
    pmcid: PMC7335209
    pmid: '32620861'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType: []
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2020-07-04T14:19:43Z'
  biotoolsCURIE: biotools:pdbinder
  biotoolsID: pdbinder
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: PDBinder is a bioinformatic method for the prediction of small ligand
    binding sites in protein structures
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: protein
        uri: http://edamontology.org/format_1208
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format: []
    note: null
    operation:
    - term: Ligand-binding site prediction
      uri: http://edamontology.org/operation_3897
    - term: Protein-protein binding site prediction
      uri: http://edamontology.org/operation_2464
    - term: Nucleic acids-binding site prediction
      uri: http://edamontology.org/operation_0420
    output: []
  homepage: http://cbm.bio.uniroma2.it/pdbinder/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:18:14.668324Z'
  license: null
  link: []
  maturity: null
  name: PDBinder
  operatingSystem: []
  otherID: []
  owner: HelmerCitterich
  publication:
  - doi: 10.1186/1471-2105-13-s4-s17
    metadata:
      abstract: 'Background: The identification of ligand binding sites is a key task
        in the annotation of proteins with known structure but uncharacterized function.
        Here we describe a knowledge-based method exploiting the observation that
        unrelated binding sites share small structural motifs that bind the same chemical
        fragments irrespective of the nature of the ligand as a whole.Results: PDBinder
        compares a query protein against a library of binding and non-binding protein
        surface regions derived from the PDB. The results of the comparison are used
        to derive a propensity value for each residue which is correlated with the
        likelihood that the residue is part of a ligand binding site. The method was
        applied to two different problems: i) the prediction of ligand binding residues
        and ii) the identification of which surface cleft harbours the binding site.
        In both cases PDBinder performed consistently better than existing methods.PDBinder
        has been trained on a non-redundant set of 1356 high-quality protein-ligand
        complexes and tested on a set of 239 holo and apo complex pairs. We obtained
        an MCC of 0.313 on the holo set with a PPV of 0.413 while on the apo set we
        achieved an MCC of 0.271 and a PPV of 0.372.Conclusions: We show that PDBinder
        performs better than existing methods. The good performance on the unbound
        proteins is extremely important for real-world applications where the location
        of the binding site is unknown. Moreover, since our approach is orthogonal
        to those used in other programs, the PDBinder propensity value can be integrated
        in other algorithms further increasing the final performance. © 2012 Bianchi
        et al.; licensee BioMed Central Ltd.'
      authors:
      - name: Bianchi V.
      - name: Gherardini P.F.
      - name: Helmer-Citterich M.
      - name: Ausiello G.
      citationCount: 16
      date: '2012-03-28T00:00:00Z'
      journal: BMC Bioinformatics
      title: Identification of binding pockets in protein structures using a knowledge-based
        potential derived from local structural similarities
    note: null
    pmcid: PMC3434446
    pmid: '22536963'
    type:
    - Method
    version: '1.0'
  relation: []
  toolType: []
  topic:
  - term: Protein binding sites
    uri: http://edamontology.org/topic_3534
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2020-08-11T18:06:30Z'
  biotoolsCURIE: biotools:plant_ptm_viewer
  biotoolsID: plant_ptm_viewer
  collectionID:
  - VIB
  - UGent
  - BIG N2N
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Patrick Willems
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Provider
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Thomas Van Parys
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Alison Horne
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: The Plant PTM Viewer is an integrative PTM resource that comprises
    approximately 370 000 PTM sites for 19 types of protein modifications in plant
    proteins from five different species.
  documentation:
  - note: null
    type:
    - User manual
    url: https://www.psb.ugent.be/webtools/ptm-viewer/tutorial.php
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Belgium
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: PTM identification
      uri: http://edamontology.org/operation_3645
    - term: PTM site prediction
      uri: http://edamontology.org/operation_0417
    output: []
  homepage: http://www.psb.ugent.be/PlantPTMViewer
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-11-24T15:18:08.021106Z'
  license: Not licensed
  link: []
  maturity: Mature
  name: Plant PTM Viewer
  operatingSystem: []
  otherID: []
  owner: bits@vib.be
  publication:
  - doi: 10.1111/tpj.14345
    metadata:
      abstract: Post-translational modifications (PTMs) of proteins are central in
        any kind of cellular signaling. Modern mass spectrometry technologies enable
        comprehensive identification and quantification of various PTMs. Given the
        increased numbers and types of mapped protein modifications, a database is
        necessary that simultaneously integrates and compares site-specific information
        for different PTMs, especially in plants for which the available PTM data
        are poorly catalogued. Here, we present the Plant PTM Viewer (http://www.psb.ugent.be/PlantPTMViewer),
        an integrative PTM resource that comprises approximately 370 000 PTM sites
        for 19 types of protein modifications in plant proteins from five different
        species. The Plant PTM Viewer provides the user with a protein sequence overview
        in which the experimentally evidenced PTMs are highlighted together with an
        estimate of the confidence by which the modified peptides and, if possible,
        the actual modification sites were identified and with functional protein
        domains or active site residues. The PTM sequence search tool can query PTM
        combinations in specific protein sequences, whereas the PTM BLAST tool searches
        for modified protein sequences to detect conserved PTMs in homologous sequences.
        Taken together, these tools help to assume the role and potential interplay
        of PTMs in specific proteins or within a broader systems biology context.
        The Plant PTM Viewer is an open repository that allows the submission of mass
        spectrometry-based PTM data to remain at pace with future PTM plant studies.
      authors:
      - name: Willems P.
      - name: Horne A.
      - name: Van Parys T.
      - name: Goormachtig S.
      - name: De Smet I.
      - name: Botzki A.
      - name: Van Breusegem F.
      - name: Gevaert K.
      citationCount: 89
      date: '2019-01-01T00:00:00Z'
      journal: Plant Journal
      title: The Plant PTM Viewer, a central resource for exploring plant protein
        modifications
    note: null
    pmcid: null
    pmid: '31004550'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  validated: 0
  version:
  - '1.0'
- accessibility: Open access (with restrictions)
  additionDate: '2021-06-14T12:59:22Z'
  biotoolsCURIE: biotools:beyondcell
  biotoolsID: beyondcell
  collectionID:
  - IMPaCT-Data
  confidence_flag: tool
  cost: Free of charge (with restrictions)
  credit:
  - email: falshahrour@cnio.es
    fundrefid: null
    gridid: null
    name: Dr. Fátima Al-Shahrour
    note: null
    orcidid: https://orcid.org/0000-0003-2373-769X
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: CNIO
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: BU_CNIO
    note: null
    orcidid: null
    rorid: null
    typeEntity: Division
    typeRole:
    - Provider
    url: null
  description: Beyondcell is a computational methodology for identifying tumour cell
    subpopulations with distinct drug responses in single-cell RNA-seq data and proposing
    cancer-specific treatments.
  documentation:
  - note: null
    type:
    - Quick start guide
    url: https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow
  - note: null
    type:
    - Installation instructions
    url: https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow
  - note: null
    type:
    - Citation instructions
    url: https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow
  - note: null
    type:
    - Release notes
    url: https://gitlab.com/bu_cnio/beyondcell/-/blob/master/CHANGELOG.md
  download:
  - note: Source code for the current software version
    type: Source code
    url: https://gitlab.com/bu_cnio/beyondcell/-/archive/master/beyondcell-master.zip
    version: 1.3.3.
  - note: Conda package for the current software version.
    type: Software package
    url: https://anaconda.org/bioconda/r-beyondcell
    version: 1.3.3
  editPermission:
    authors:
    - sagarcia
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Gene expression profile
        uri: http://edamontology.org/data_0928
      format:
      - term: Format
        uri: http://edamontology.org/format_1915
    note: 'Beyondcell can make use of custom gene expression profiles on top of its
      own catalogue of drug related signatures. Beyondcell allows the user to input
      a GMT file containing the functional pathways/signatures of interest as well
      as numeric matrices containing a ranking

      criteria such as the t-statistic or logFoldChange.'
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Small molecule design
      uri: http://edamontology.org/operation_4009
    - term: Expression profile clustering
      uri: http://edamontology.org/operation_0313
    output:
    - data:
        term: Statistical estimate score
        uri: http://edamontology.org/data_0951
      format: []
    - data:
        term: Clustered expression profiles
        uri: http://edamontology.org/data_3768
      format: []
    - data:
        term: Pharmacogenomic test report
        uri: http://edamontology.org/data_1621
      format: []
  homepage: https://github.com/cnio-bu/beyondcell
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T14:48:24.542882Z'
  license: Other
  link:
  - note: null
    type:
    - Issue tracker
    url: https://gitlab.com/bu_cnio/beyondcell/-/issues?sort=updated_desc&state=opened
  maturity: Mature
  name: Beyondcell
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: tdido
  publication:
  - doi: 10.1101/2021.04.08.438954
    metadata: null
    note: Publication about the methodology and its application in a variety of experimental
      models, from cancer cell lines to human data.
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Workflow
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Pharmacogenomics
    uri: http://edamontology.org/topic_0208
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 0
  version:
  - '2.1'
- accessibility: Open access
  additionDate: '2021-05-17T09:02:15Z'
  biotoolsCURIE: biotools:kmeranalyzer
  biotoolsID: kmeranalyzer
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fpsom@certh.gr
    fundrefid: null
    gridid: null
    name: Fotis E. Psomopoulos
    note: null
    orcidid: https://orcid.org/0000-0002-0222-4273
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Nikolaos Pechlivanis
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Anastasios Togkousidis
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ilias Kappas
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: An alignment-free method capable of processing and counting k-mers
    in a reasonable time, while evaluating multiple values of the k parameter concurrently.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Greece
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: E-value
        uri: http://edamontology.org/data_1667
      format: []
    note: null
    operation:
    - term: k-mer counting
      uri: http://edamontology.org/operation_3472
    - term: Phylogenetic reconstruction
      uri: http://edamontology.org/operation_3478
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://github.com/BiodataAnalysisGroup/kmerAnalyzer
  homepage_status: 0
  language:
  - Python
  - R
  lastUpdate: '2024-11-24T14:48:06.612968Z'
  license: MIT
  link: []
  maturity: Emerging
  name: kmerAnalyzer
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: fpsom
  publication:
  - doi: 10.3389/fgene.2021.618170
    metadata:
      abstract: The exponential growth of genome sequences available has spurred research
        on pattern detection with the aim of extracting evolutionary signal. Traditional
        approaches, such as multiple sequence alignment, rely on positional homology
        in order to reconstruct the phylogenetic history of taxa. Yet, mining information
        from the plethora of biological data and delineating species on a genetic
        basis, still proves to be an extremely difficult problem to consider. Multiple
        algorithms and techniques have been developed in order to approach the problem
        multidimensionally. Here, we propose a computational framework for identifying
        potentially meaningful features based on k-mers retrieved from unaligned sequence
        data. Specifically, we have developed a process which makes use of unsupervised
        learning techniques in order to identify characteristic k-mers of the input
        dataset across a range of different k-values and within a reasonable time
        frame. We use these k-mers as features for clustering the input sequences
        and identifying differences between the distributions of k-mers across the
        dataset. The developed algorithm is part of an innovative and much promising
        approach both to the problem of grouping sequence data based on their inherent
        characteristic features, as well as for the study of changes in the distributions
        of k-mers, as the k-value is fluctuating within a range of values. Our framework
        is fully developed in Python language as an open source software licensed
        under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.
      authors:
      - name: Pechlivanis N.
      - name: Togkousidis A.
      - name: Tsagiopoulou M.
      - name: Sgardelis S.
      - name: Kappas I.
      - name: Psomopoulos F.
      citationCount: 0
      date: '2021-05-28T00:00:00Z'
      journal: Frontiers in Genetics
      title: 'A Computational Framework for Pattern Detection on Unaligned Sequences:
        An Application on SARS-CoV-2 Data'
    note: null
    pmcid: PMC8194296
    pmid: '34122498'
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Script
  topic:
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2023-07-26T14:34:25.798022Z'
  biotoolsCURIE: biotools:kojak
  biotoolsID: kojak
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A database search algorithm for solving cross-linked peptide mass spectra.
  documentation:
  - note: null
    type:
    - Quick start guide
    - Installation instructions
    - User manual
    url: https://kojak-ms.systemsbiology.net/docs/index.html
  - note: null
    type:
    - Command-line options
    url: https://kojak-ms.systemsbiology.net/param/index.html
  download:
  - note: null
    type: Downloads page
    url: https://kojak-ms.systemsbiology.net/download.html
    version: 1.0 - 2.0.0
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Proteomics
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://kojak-ms.systemsbiology.net/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-11-24T14:45:40.796568Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: Kojak
  operatingSystem:
  - Linux
  - Windows
  otherID: []
  owner: mhoopmann
  publication:
  - doi: 10.1021/pr501321h
    metadata:
      abstract: Protein chemical cross-linking and mass spectrometry enable the analysis
        of protein-protein interactions and protein topologies; however, complicated
        cross-linked peptide spectra require specialized algorithms to identify interacting
        sites. The Kojak cross-linking software application is a new, efficient approach
        to identify cross-linked peptides, enabling large-scale analysis of protein-protein
        interactions by chemical cross-linking techniques. The algorithm integrates
        spectral processing and scoring schemes adopted from traditional database
        search algorithms and can identify cross-linked peptides using many different
        chemical cross-linkers with or without heavy isotope labels. Kojak was used
        to analyze both novel and existing data sets and was compared to existing
        cross-linking algorithms. The algorithm provided increased cross-link identifications
        over existing algorithms and, equally importantly, the results in a fraction
        of computational time. The Kojak algorithm is open-source, cross-platform,
        and freely available. This software provides both existing and new cross-linking
        researchers alike an effective way to derive additional cross-link identifications
        from new or existing data sets. For new users, it provides a simple analytical
        resource resulting in more cross-link identifications than other methods.
      authors:
      - name: Hoopmann M.R.
      - name: Zelter A.
      - name: Johnson R.S.
      - name: Riffle M.
      - name: Maccoss M.J.
      - name: Davis T.N.
      - name: Moritz R.L.
      citationCount: 142
      date: '2015-05-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Kojak: Efficient analysis of chemically cross-linked protein complexes'
    note: null
    pmcid: PMC4428575
    pmid: '25812159'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1021/acs.jproteome.2c00670
    metadata:
      abstract: Fragmentation ion spectral analysis of chemically cross-linked proteins
        is an established technology in the proteomics research repertoire for determining
        protein interactions, spatial orientation, and structure. Here we present
        Kojak version 2.0, a major update to the original Kojak algorithm, which was
        developed to identify cross-linked peptides from fragment ion spectra using
        a database search approach. A substantially improved algorithm with updated
        scoring metrics, support for cleavable cross-linkers, and identification of
        cross-links between 15N-labeled homomultimers are among the newest features
        of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic
        Pipeline, enabling access to dozens of additional tools within that suite.
        In particular, the PeptideProphet and iProphet tools for validation of cross-links
        improve the sensitivity and accuracy of correct cross-link identifications
        at user-defined thresholds. These new features improve the versatility of
        the algorithm, enabling its use in a wider range of experimental designs and
        analysis pipelines. Kojak 2.0 remains open-source and multiplatform.
      authors:
      - name: Hoopmann M.R.
      - name: Shteynberg D.D.
      - name: Zelter A.
      - name: Riffle M.
      - name: Lyon A.S.
      - name: Agard D.A.
      - name: Luan Q.
      - name: Nolen B.J.
      - name: MacCoss M.J.
      - name: Davis T.N.
      - name: Moritz R.L.
      citationCount: 4
      date: '2023-02-03T00:00:00Z'
      journal: Journal of Proteome Research
      title: Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak
        2.0, Advanced by Integration into the Trans-Proteomic Pipeline
    note: null
    pmcid: PMC10234491
    pmid: '36629399'
    type:
    - Primary
    version: 2.0.0
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  validated: 0
  version:
  - 2.0.0
- 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: '2023-09-26T23:26:58.411470Z'
  biotoolsCURIE: biotools:planet_microbe
  biotoolsID: planet_microbe
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: bhurwitz@arizona.edu
    fundrefid: null
    gridid: null
    name: Bonnie Hurwitz
    note: Professor at the University of Arizona known for her work in cyberinfrastructure
      development.
    orcidid: https://orcid.org/0000-0001-8699-957X
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.hurwitzlab.org/
  - email: aponsero@arizona.edu
    fundrefid: null
    gridid: null
    name: Alise Ponsero
    note: null
    orcidid: https://orcid.org/0000-0002-4125-7561
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: kblumberg@arizona.edu
    fundrefid: null
    gridid: null
    name: Kai Blumberg
    note: null
    orcidid: https://orcid.org/0000-0002-3410-4655
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: mattmiller899@arizona.edu
    fundrefid: null
    gridid: null
    name: Matthew Miller
    note: null
    orcidid: https://orcid.org/0000-0002-3491-8763
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: mbomhoff@arizona.edu
    fundrefid: null
    gridid: null
    name: Matthew Bomhoff
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: kyclark@arizona.edu
    fundrefid: null
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    name: Ken Youens-Clark
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: Enabling the discovery and integration of oceanographic ‘omics, environmental
    and physiochemical data layers.
  documentation: []
  download:
  - note: Source code for the website
    type: Source code
    url: https://github.com/hurwitzlab/planet-microbe-app
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    input: []
    note: null
    operation:
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    - term: Sequence clustering
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    - term: Aggregation
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    - term: Sequence annotation
      uri: http://edamontology.org/operation_0361
    output: []
  homepage: https://www.planetmicrobe.org/
  homepage_status: 0
  language:
  - Elm
  lastUpdate: '2024-11-24T14:39:10.273022Z'
  license: MIT
  link:
  - note: Planet Microbe Application
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-app
  - note: Planet Microbe frictionless data packages
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-datapackages
  - note: Planet Microbe ontology
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-ontology
  - note: Functional Annotation Pipeline
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-functional-annotation
  - note: Semantic web analysis
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis
  maturity: Mature
  name: Planet Microbe
  operatingSystem:
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  otherID: []
  owner: bhurwitz
  publication:
  - doi: 10.1093/nar/gkaa637
    metadata:
      abstract: In recent years, large-scale oceanic sequencing efforts have provided
        a deeper understanding of marine microbial communities and their dynamics.
        These research endeavors require the acquisition of complex and varied datasets
        through large, interdisciplinary and collaborative efforts. However, no unifying
        framework currently exists for the marine science community to integrate sequencing
        data with physical, geological, and geochemical datasets. Planet Microbe is
        a web-based platform that enables data discovery from curated historical and
        ongoing oceanographic sequencing efforts. In Planet Microbe, each 'omics sample
        is linked with other biological and physiochemical measurements collected
        for the same water samples or during the same sample collection event, to
        provide a broader environmental context. This work highlights the need for
        curated aggregation efforts that can enable new insights into high-quality
        metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.
      authors:
      - name: Ponsero A.J.
      - name: Bomhoff M.
      - name: Blumberg K.
      - name: Youens-Clark K.
      - name: Herz N.M.
      - name: Wood-Charlson E.M.
      - name: Delong E.F.
      - name: Hurwitz B.L.
      citationCount: 13
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'Planet Microbe: A platform for marine microbiology to discover and analyze
        interconnected ''omics and environmental data'''
    note: In recent years, large-scale oceanic sequencing efforts have provided a
      deeper understanding of marine microbial communities and their dynamics. These
      research endeavors require the acquisition of complex and varied datasets through
      large, interdisciplinary and collaborative efforts. However, no unifying framework
      currently exists for the marine science community to integrate sequencing data
      with physical, geological, and geochemical datasets. Planet Microbe is a web-based
      platform that enables data discovery from curated historical and on-going oceanographic
      sequencing efforts. In Planet Microbe, each 'omics sample is linked with other
      biological and physiochemical measurements collected for the same water samples
      or during the same sample collection event, to provide a broader environmental
      context. This work highlights the need for curated aggregation efforts that
      can enable new insights into high-quality metagenomic datasets.
    pmcid: PMC7778950
    pmid: '32735679'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
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  topic:
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  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Ontology and terminology
    uri: http://edamontology.org/topic_0089
  validated: 0
  version:
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- accessibility: Open access
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  cost: Free of charge
  credit:
  - email: bhurwitz@arizona.edu
    fundrefid: null
    gridid: null
    name: Bonnie Hurwitz
    note: Professor at the University of Arizona known for her work in cyberinfrastructure
      development.
    orcidid: https://orcid.org/0000-0001-8699-957X
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.hurwitzlab.org/
  - email: aponsero@arizona.edu
    fundrefid: null
    gridid: null
    name: Alise Ponsero
    note: null
    orcidid: https://orcid.org/0000-0002-4125-7561
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: kblumberg@arizona.edu
    fundrefid: null
    gridid: null
    name: Kai Blumberg
    note: null
    orcidid: https://orcid.org/0000-0002-3410-4655
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: mattmiller899@arizona.edu
    fundrefid: null
    gridid: null
    name: Matthew Miller
    note: null
    orcidid: https://orcid.org/0000-0002-3491-8763
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: mbomhoff@arizona.edu
    fundrefid: null
    gridid: null
    name: Matthew Bomhoff
    note: null
    orcidid: https://orcid.org/0000-0002-8014-9184
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: kyclark@arizona.edu
    fundrefid: null
    gridid: null
    name: Ken Youens-Clark
    note: null
    orcidid: https://orcid.org/0000-0001-9961-144X
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: Enabling the discovery and integration of oceanographic ‘omics, environmental
    and physiochemical data layers.
  documentation:
  - note: Documentation on how to use the website
    type:
    - General
    url: https://hurwitzlab.gitbook.io/planet-microbe-documentation/
  download:
  - note: Source code for the website
    type: Source code
    url: https://github.com/hurwitzlab/planet-microbe-app
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  function:
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    input: []
    note: null
    operation:
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    - term: Sequence clustering
      uri: http://edamontology.org/operation_0291
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Sequence annotation
      uri: http://edamontology.org/operation_0361
    output: []
  homepage: https://www.planetmicrobe.org/
  homepage_status: 0
  language:
  - Elm
  lastUpdate: '2024-11-24T14:39:08.746037Z'
  license: MIT
  link:
  - note: Planet Microbe Application
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-app
  - note: Planet Microbe frictionless data packages
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-datapackages
  - note: Planet Microbe ontology
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-ontology
  - note: Functional Annotation Pipeline
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-functional-annotation
  - note: Semantic web analysis
    type:
    - Repository
    url: https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis
  maturity: Mature
  name: Planet Microbe
  operatingSystem:
  - Linux
  otherID: []
  owner: bhurwitz
  publication:
  - doi: 10.1093/nar/gkaa637
    metadata:
      abstract: In recent years, large-scale oceanic sequencing efforts have provided
        a deeper understanding of marine microbial communities and their dynamics.
        These research endeavors require the acquisition of complex and varied datasets
        through large, interdisciplinary and collaborative efforts. However, no unifying
        framework currently exists for the marine science community to integrate sequencing
        data with physical, geological, and geochemical datasets. Planet Microbe is
        a web-based platform that enables data discovery from curated historical and
        ongoing oceanographic sequencing efforts. In Planet Microbe, each 'omics sample
        is linked with other biological and physiochemical measurements collected
        for the same water samples or during the same sample collection event, to
        provide a broader environmental context. This work highlights the need for
        curated aggregation efforts that can enable new insights into high-quality
        metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.
      authors:
      - name: Ponsero A.J.
      - name: Bomhoff M.
      - name: Blumberg K.
      - name: Youens-Clark K.
      - name: Herz N.M.
      - name: Wood-Charlson E.M.
      - name: Delong E.F.
      - name: Hurwitz B.L.
      citationCount: 13
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'Planet Microbe: A platform for marine microbiology to discover and analyze
        interconnected ''omics and environmental data'''
    note: In recent years, large-scale oceanic sequencing efforts have provided a
      deeper understanding of marine microbial communities and their dynamics. These
      research endeavors require the acquisition of complex and varied datasets through
      large, interdisciplinary and collaborative efforts. However, no unifying framework
      currently exists for the marine science community to integrate sequencing data
      with physical, geological, and geochemical datasets. Planet Microbe is a web-based
      platform that enables data discovery from curated historical and on-going oceanographic
      sequencing efforts. In Planet Microbe, each 'omics sample is linked with other
      biological and physiochemical measurements collected for the same water samples
      or during the same sample collection event, to provide a broader environmental
      context. This work highlights the need for curated aggregation efforts that
      can enable new insights into high-quality metagenomic datasets.
    pmcid: PMC7778950
    pmid: '32735679'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Bioinformatics portal
  topic:
  - term: Marine biology
    uri: http://edamontology.org/topic_3387
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Ontology and terminology
    uri: http://edamontology.org/topic_0089
  validated: 0
  version:
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- accessibility: Open access
  additionDate: '2021-05-17T08:32:18Z'
  biotoolsCURIE: biotools:lineagespot
  biotoolsID: lineagespot
  collectionID:
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  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fpsom@certh.gr
    fundrefid: null
    gridid: null
    name: Fotis E. Psomopoulos
    note: null
    orcidid: https://orcid.org/0000-0002-0222-4273
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Nikolaos Pechlivanis,
    note: null
    orcidid: https://orcid.org/0000-0003-2502-612X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Maria Tsagiopoulou
    note: null
    orcidid: https://orcid.org/0000-0002-1653-0327
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Lineagespot is a framework written in R, and aims to identify and assign
    different SARS-CoV-2 lineages based on a single variant file (i.e., variant calling
    format).
  documentation: []
  download: []
  editPermission:
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    - ELIXIR-CZ
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  function:
  - cmd: null
    input: []
    note: null
    operation:
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    output: []
  homepage: https://github.com/BiodataAnalysisGroup/lineagespot
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-24T14:34:44.476503Z'
  license: MIT
  link: []
  maturity: Emerging
  name: lineagespot
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: fpsom
  publication:
  - doi: 10.1038/s41598-022-06625-6
    metadata:
      abstract: The COVID-19 pandemic represents an unprecedented global crisis necessitating
        novel approaches for, amongst others, early detection of emerging variants
        relating to the evolution and spread of the virus. Recently, the detection
        of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the
        prevalence of the virus in the community. Here, we propose a novel methodology,
        called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2
        lineages in wastewater samples using next-generation sequencing (NGS). Our
        proposed method was tested and evaluated using NGS data produced by the sequencing
        of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering
        a 6-month period. The results showed the presence of SARS-CoV-2 variants in
        wastewater data. lineagespot was able to record the evolution and rapid domination
        of the Alpha variant (B.1.1.7) in the community, and allowed the correlation
        between the mutations evident through our approach and the mutations observed
        in patients from the same area and time periods. lineagespot is an open-source
        tool, implemented in R, and is freely available on GitHub and registered on
        bio.tools.
      authors:
      - name: Pechlivanis N.
      - name: Tsagiopoulou M.
      - name: Maniou M.C.
      - name: Togkousidis A.
      - name: Mouchtaropoulou E.
      - name: Chassalevris T.
      - name: Chaintoutis S.C.
      - name: Petala M.
      - name: Kostoglou M.
      - name: Karapantsios T.
      - name: Laidou S.
      - name: Vlachonikola E.
      - name: Chatzidimitriou A.
      - name: Papadopoulos A.
      - name: Papaioannou N.
      - name: Dovas C.I.
      - name: Argiriou A.
      - name: Psomopoulos F.
      citationCount: 19
      date: '2022-12-01T00:00:00Z'
      journal: Scientific Reports
      title: Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater
        and a use-case in the metropolitan area of Thessaloniki, Greece
    note: null
    pmcid: PMC8854625
    pmid: '35177697'
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Library
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  validated: 0
  version:
  - '1.0'
- accessibility: null
  additionDate: '2017-03-01T14:15:32Z'
  biotoolsCURIE: biotools:meta-snp
  biotoolsID: meta-snp
  collectionID: []
  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: Division
    typeRole:
    - Support
    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:
    - Developer
    - Primary contact
    url: null
  description: Meta-predictor of disease causing variants.
  documentation:
  - note: null
    type:
    - General
    url: http://snps.biofold.org/meta-snp/pages/help.html
  download: []
  editPermission:
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    - ELIXIR-ITA-BOLOGNA
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    type: group
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  elixirNode:
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Mutation ID
        uri: http://edamontology.org/data_2209
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    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/meta-snp
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:11:28.058280Z'
  license: null
  link: []
  maturity: Mature
  name: Meta-SNP
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/1471-2164-14-s3-s2
    metadata:
      abstract: 'Single nucleotide polymorphisms (SNPs) are the most common form of
        genetic variation in human DNA. The sequence of SNPs in each of the two copies
        of a given chromosome in a diploid organism is referred to as a haplotype.
        Haplotype information has many applications such as gene disease diagnoses,
        drug design, etc. The haplotype assembly problem is defined as follows: Given
        a set of fragments sequenced from the two copies of a chromosome of a single
        individual, and their locations in the chromosome, which can be pre-determined
        by aligning the fragments to a reference DNA sequence, the goal here is to
        reconstruct two haplotypes (h1, h2) from the input fragments. Existing algorithms
        do not work well when the error rate of fragments is high. Here we design
        an algorithm that can give accurate solutions, even if the error rate of fragments
        is high. We first give a dynamic programming algorithm that can give exact
        solutions to the haplotype assembly problem. The time complexity of the algorithm
        is O(n × 2t × t), where n is the number of SNPs, and t is the maximum coverage
        of a SNP site. The algorithm is slow when t is large. To solve the problem
        when t is large, we further propose a heuristic algorithm on the basis of
        the dynamic programming algorithm. Experiments show that our heuristic algorithm
        can give very accurate solutions. We have tested our algorithm on a set of
        benchmark datasets. Experiments show that our algorithm can give very accurate
        solutions. It outperforms most of the existing programs when the error rate
        of the input fragments is high.'
      authors:
      - name: Deng F.
      - name: Cui W.
      - name: Wang L.
      citationCount: 27
      date: '2013-01-01T00:00:00Z'
      journal: BMC genomics
      title: A highly accurate heuristic algorithm for the haplotype assembly problem.
    note: null
    pmcid: PMC3839641
    pmid: '23819846'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web service
  topic:
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2024-06-13T14:41:01.342264Z'
  biotoolsCURIE: biotools:plantrna
  biotoolsID: plantrna
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: valerie.cognat@cnrs.fr
    fundrefid: null
    gridid: null
    name: Valerie Cognat
    note: null
    orcidid: https://orcid.org/0000-0001-9337-2767
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: The PlantRNA database compiles tRNA gene sequences retrieved from fully
    annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  function: []
  homepage: http://plantrna.ibmp.cnrs.fr/
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  lastUpdate: '2024-11-24T13:55:20.404756Z'
  license: CC-BY-SA-4.0
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  maturity: Mature
  name: plantRNA
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  otherID: []
  owner: vcognat
  publication:
  - doi: 10.1111/tpj.15997
    metadata:
      abstract: PlantRNA (http://plantrna.ibmp.cnrs.fr/) is a comprehensive database
        of transfer RNA (tRNA) gene sequences retrieved from fully annotated nuclear,
        plastidial and mitochondrial genomes of photosynthetic organisms. In the first
        release (PlantRNA 1.0), tRNA genes from 11 organisms were annotated. In this
        second version, the annotation was implemented to 51 photosynthetic species
        covering the whole phylogenetic tree of photosynthetic organisms, from the
        most basal group of Archeplastida, the glaucophyte Cyanophora paradoxa, to
        various land plants. tRNA genes from lower photosynthetic organisms such as
        streptophyte algae or lycophytes as well as extremophile photosynthetic species
        such as Eutrema parvulum were incorporated in the database. As a whole, about
        37 000 tRNA genes were accurately annotated. In the frame of the tRNA genes
        annotation from the genome of the Rhodophyte Chondrus crispus, non-canonical
        splicing sites in the D- or T-regions of tRNA molecules were identified and
        experimentally validated. As for PlantRNA 1.0, comprehensive biological information
        including 5′- and 3′-flanking sequences, A and B box sequences, region of
        transcription initiation and poly(T) transcription termination stretches,
        tRNA intron sequences and tRNA mitochondrial import are included.
      authors:
      - name: Cognat V.
      - name: Pawlak G.
      - name: Pflieger D.
      - name: Drouard L.
      citationCount: 9
      date: '2022-11-01T00:00:00Z'
      journal: Plant Journal
      title: 'PlantRNA 2.0: an updated database dedicated to tRNAs of photosynthetic
        eukaryotes'
    note: null
    pmcid: null
    pmid: '36196656'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1093/nar/gks935
    metadata:
      abstract: 'PlantRNA database (http://plantrna.ibmp.cnrs.fr/) compiles transfer
        RNA (tRNA) gene sequences retrieved from fully annotated plant nuclear, plastidial
        and mitochondrial genomes. The set of annotated tRNA gene sequences has been
        manually curated for maximum quality and confidence. The novelty of this database
        resides in the inclusion of biological information relevant to the function
        of all the tRNAs entered in the library. This includes 5′- and 3′-flanking
        sequences, A and B box sequences, region of transcription initiation and poly(T)
        transcription termination stretches, tRNA intron sequences, aminoacyl-tRNA
        synthetases and enzymes responsible for tRNA maturation and modification.
        Finally, data on mitochondrial import of nuclear-encoded tRNAs as well as
        the bibliome for the respective tRNAs and tRNA-binding proteins are also included.
        The current annotation concerns complete genomes from 11 organisms: five flowering
        plants (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Medicago
        truncatula and Brachypodium distachyon), a moss (Physcomitrella patens), two
        green algae (Chlamydomonas rein-hardtii and Ostreococcus tauri), one glaucophyte
        (Cyanophora paradoxa), one brown alga (Ectocarpus siliculosus) and a pennate
        diatom (Phaeodactylum tricornutum). The database will be regularly updated
        and implemented with new plant genome annotations so as to provide extensive
        information on tRNA biology to the research community. © The Author(s) 2012.'
      authors:
      - name: Cognat V.
      - name: Pawlak G.
      - name: Duchene A.-M.
      - name: Daujat M.
      - name: Gigant A.
      - name: Salinas T.
      - name: Michaud M.
      - name: Gutmann B.
      - name: Giege P.
      - name: Gobert A.
      - name: Marechal-Drouard L.
      citationCount: 55
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: PlantRNA, a database for tRNAs of photosynthetic eukaryotes
    note: null
    pmcid: PMC3531208
    pmid: '23066098'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  validated: 0
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2017-08-03T18:51:07Z'
  biotoolsCURIE: biotools:metapathways
  biotoolsID: metapathways
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: shallam@mail.ubc.ca
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://hallam.microbiology.ubc.ca/
  description: 'MetaPathways is a meta’omic analysis pipeline for the annotation and
    analysis for environmental sequence information. MetaPathways include metagenomic
    or metatranscriptomic sequence data. The pipeline consists of four operational
    stages including: Quality Control, Feature Prediction, Functional Annotation,
    Pathway Inference.'
  documentation:
  - note: null
    type:
    - General
    url: https://metapathways.readthedocs.io/en/dev/index.html
  download:
  - note: null
    type: Tool wrapper (Other)
    url: https://anaconda.org/Hallamlab/metapathways
    version: '3.5'
  - note: null
    type: Container file
    url: https://quay.io/repository/hallamlab/metapathways
    version: '3.5'
  - note: null
    type: Source code
    url: https://bitbucket.org/BCB2/metapathways/src/dev/
    version: '3.5'
  editPermission:
    authors:
    - mclaughlinr2
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: "metapathways run \\\n   -i $[input_metagenome.fa] \\\n   -d ${path/to/save/reference_databases}\
      \ \\\n   -o ${path/to/output} \\\n   -t ${threads}"
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Use MetaPathways to annotate a metagenome.
    operation:
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    output:
    - data:
        term: Annotated text
        uri: http://edamontology.org/data_3779
      format:
      - term: DSV
        uri: http://edamontology.org/format_3751
  homepage: https://metapathways.readthedocs.io/en/dev/index.html
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-24T13:47:09.737653Z'
  license: MPL-2.0
  link:
  - note: null
    type:
    - Other
    url: https://metapathways.readthedocs.io/en/dev/index.html
  maturity: null
  name: MetaPathways
  operatingSystem:
  - Linux
  otherID: []
  owner: mbs_import
  publication:
  - doi: 10.1101/2024.06.04.597460
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: '1.0'
  - doi: 10.1093/bioinformatics/btv361
    metadata:
      abstract: Next-generation sequencing is producing vast amounts of sequence information
        from natural and engineered ecosystems. Although this data deluge has an enormous
        potential to transform our lives, knowledge creation and translation need
        software applications that scale with increasing data processing and analysis
        requirements. Here, we present improvements to MetaPathways, an annotation
        and analysis pipeline for environmental sequence information that expedites
        this transformation. We specifically address pathway prediction hazards through
        integration of a weighted taxonomic distance and enable quantitative comparison
        of assembled annotations through a normalized read-mapping measure. Additionally,
        we improve LAST homology searches through BLAST-equivalent E-values and output
        formats that are natively compatible with prevailing software applications.
        Finally, an updated graphical user interface allows for keyword annotation
        query and projection onto user-defined functional gene hierarchies, including
        the Carbohydrate-Active Enzyme database.
      authors:
      - name: Konwar K.M.
      - name: Hanson N.W.
      - name: Bhatia M.P.
      - name: Kim D.
      - name: Wu S.-J.
      - name: Hahn A.S.
      - name: Morgan-Lang C.
      - name: Cheung H.K.
      - name: Hallam S.J.
      citationCount: 40
      date: '2015-03-25T00:00:00Z'
      journal: Bioinformatics
      title: 'MetaPathways v2.5: Quantitative functional, taxonomic and usability
        improvements'
    note: null
    pmcid: PMC4595896
    pmid: '26076725'
    type: []
    version: '2.5'
  - doi: 10.1186/1471-2105-14-202
    metadata:
      abstract: 'Background: A central challenge to understanding the ecological and
        biogeochemical roles of microorganisms in natural and human engineered ecosystems
        is the reconstruction of metabolic interaction networks from environmental
        sequence information. The dominant paradigm in metabolic reconstruction is
        to assign functional annotations using BLAST. Functional annotations are then
        projected onto symbolic representations of metabolism in the form of KEGG
        pathways or SEED subsystems.Results: Here we present MetaPathways, an open
        source pipeline for pathway inference that uses the PathoLogic algorithm to
        map functional annotations onto the MetaCyc collection of reactions and pathways,
        and construct environmental Pathway/Genome Databases (ePGDBs) compatible with
        the editing and navigation features of Pathway Tools. The pipeline accepts
        assembled or unassembled nucleotide sequences, performs quality assessment
        and control, predicts and annotates noncoding genes and open reading frames,
        and produces inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways
        uses MLTreeMap to build phylogenetic trees for selected taxonomic anchor and
        functional gene markers, converts General Feature Format (GFF) files into
        concatenated GenBank files for ePGDB construction based on third-party annotations,
        and generates useful file formats including Sequin files for direct GenBank
        submission and gene feature tables summarizing annotations, MLTreeMap trees,
        and ePGDB pathway coverage summaries for statistical comparisons.Conclusions:
        MetaPathways provides users with a modular annotation and analysis pipeline
        for predicting metabolic interaction networks from environmental sequence
        information using an alternative to KEGG pathways and SEED subsystems mapping.
        It is extensible to genomic and transcriptomic datasets from a wide range
        of sequencing platforms, and generates useful data products for microbial
        community structure and function analysis. The MetaPathways software package,
        installation instructions, and example data can be obtained from http://hallam.microbiology.ubc.ca/MetaPathways.
        © 2013 Konwar et al.; licensee BioMed Central Ltd.'
      authors:
      - name: Konwar K.M.
      - name: Hanson N.W.
      - name: Page A.P.
      - name: Hallam S.J.
      citationCount: 82
      date: '2013-06-21T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'MetaPathways: A modular pipeline for constructing pathway/genome databases
        from environmental sequence information'
    note: null
    pmcid: PMC3695837
    pmid: '23800136'
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Population genomics
    uri: http://edamontology.org/topic_3796
  validated: 1
  version:
  - '3.5'
- accessibility: null
  additionDate: '2015-02-05T13:24:16Z'
  biotoolsCURIE: biotools:reditools
  biotoolsID: reditools
  collectionID:
  - ELIXIR-ITA-CNR
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-CNR
    note: null
    orcidid: https://orcid.org/0000-0002-6549-0114
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: ernesto.picardi@uniba.it
    fundrefid: null
    gridid: null
    name: Ernesto Picardi
    note: null
    orcidid: https://orcid.org/0000-0002-6549-0114
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.uniba.it/docenti/picardi-ernesto/en
  description: Python scripts to detect RNA editing in deep transcriptome sequencing
    data (RNAseq)
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BioinfoUNIBA/REDItools
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BARI
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: REDItoolDnaRna.py -i rnaseq.bam -f myreference.fa -o myoutputfolder
    input:
    - data:
        term: Alignment
        uri: http://edamontology.org/data_1916
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
    note: Run REDItools on RNA sequences
    operation:
    - term: Analysis
      uri: http://edamontology.org/operation_2945
    output:
    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/BioinfoUNIBA/REDItools
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-02-09T14:59:52.330758Z'
  license: MIT
  link: []
  maturity: Mature
  name: REDItools
  operatingSystem:
  - Linux
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_012133
    version: null
  owner: ELIXIR-ITA-CNR
  publication:
  - doi: 10.1093/bioinformatics/btt287
    metadata:
      abstract: 'The reliable detection of RNA editing sites from massive sequencing
        data remains challenging and, although several methodologies have been proposed,
        no computational tools have been released to date. Here, we introduce REDItools
        a suite of python scripts to perform high-throughput investigation of RNA
        editing using next-generation sequencing data.Availability and implementation:
        REDItools are in python programming language and freely available at http://code.google.
        com/p/reditools/.Contact: or graziano.pesole@uniba.itSupplementary information:
        Supplementary data are available at Bioinformatics online. © 2013 The Author
        2013. Published by Oxford University Press. All rights reserved. For Permissions,
        please e-mail: journals.permissions@oup.com.'
      authors:
      - name: Picardi E.
      - name: Pesole G.
      citationCount: 201
      date: '2013-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'REDItools: High-throughput RNA editing detection made easy'
    note: null
    pmcid: null
    pmid: '23742983'
    type:
    - Primary
    version: 1.0-1.3
  - doi: 10.1186/s12859-020-03562-x
    metadata:
      abstract: 'Background: RNA editing is a widespread co-/post-transcriptional
        mechanism that alters primary RNA sequences through the modification of specific
        nucleotides and it can increase both the transcriptome and proteome diversity.
        The automatic detection of RNA-editing from RNA-seq data is computational
        intensive and limited to small data sets, thus preventing a reliable genome-wide
        characterisation of such process. Results: In this work we introduce HPC-REDItools,
        an upgraded tool for accurate RNA-editing events discovery from large dataset
        repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2. Conclusions:
        HPC-REDItools is dramatically faster than the previous version, REDItools,
        enabling big-data analysis by means of a MPI-based implementation and scaling
        almost linearly with the number of available cores.'
      authors:
      - name: Flati T.
      - name: Gioiosa S.
      - name: Spallanzani N.
      - name: Tagliaferri I.
      - name: Diroma M.A.
      - name: Pesole G.
      - name: Chillemi G.
      - name: Picardi E.
      - name: Castrignano T.
      citationCount: 21
      date: '2020-08-21T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'HPC-REDItools: A novel HPC-aware tool for improved large scale RNA-editing
        analysis'
    note: null
    pmcid: PMC7446188
    pmid: '32838738'
    type: []
    version: '2.0'
  - doi: 10.1038/s41596-019-0279-7
    metadata:
      abstract: RNA editing is a widespread post-transcriptional mechanism able to
        modify transcripts through insertions/deletions or base substitutions. It
        is prominent in mammals, in which millions of adenosines are deaminated to
        inosines by members of the ADAR family of enzymes. A-to-I RNA editing has
        a plethora of biological functions, but its detection in large-scale transcriptome
        datasets is still an unsolved computational task. To this aim, we developed
        REDItools, the first software package devoted to the RNA editing profiling
        in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes,
        proving the tissue and cell type specificity of RNA editing as well as its
        pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq
        data have been collected in our specialized REDIportal database, containing
        more than 4.5 million events. Here we describe in detail two bioinformatic
        procedures based on our computational resources, REDItools and REDIportal.
        In the first procedure, we outline a workflow to detect RNA editing in the
        human cell line NA12878, for which transcriptome and whole genome data are
        available. In the second procedure, we show how to identify dysregulated editing
        at specific recoding sites in post-mortem brain samples of Huntington disease
        donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory
        (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols
        have been designed to investigate RNA editing in different organisms with
        available transcriptomic and/or genomic reads. Scripts to complete both procedures
        and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.
      authors:
      - name: Lo Giudice C.
      - name: Tangaro M.A.
      - name: Pesole G.
      - name: Picardi E.
      citationCount: 69
      date: '2020-03-01T00:00:00Z'
      journal: Nature Protocols
      title: Investigating RNA editing in deep transcriptome datasets with REDItools
        and REDIportal
    note: null
    pmcid: null
    pmid: '31996844'
    type: []
    version: null
  - doi: 10.1007/978-1-0716-1307-8_14
    metadata:
      abstract: RNA editing by A-to-I deamination is a relevant co/posttranscriptional
        modification carried out by ADAR enzymes. In humans, it has pivotal cellular
        effects and its deregulation has been linked to a variety of human disorders
        including neurological and neurodegenerative diseases and cancer. Despite
        its biological relevance, the detection of RNA editing variants in large transcriptome
        sequencing experiments (RNAseq) is yet a challenging computational task. To
        drastically reduce computing times we have developed a novel REDItools version
        able to identify A-to-I events in huge amount of RNAseq data employing High
        Performance Computing (HPC) infrastructures. Here we show how to use REDItools
        v2 in HPC systems.
      authors:
      - name: Giudice C.L.
      - name: Mansi L.
      - name: Flati T.
      - name: Gioiosa S.
      - name: Chillemi G.
      - name: Libro P.
      - name: Castrignano T.
      - name: Pesole G.
      - name: Picardi E.
      citationCount: 1
      date: '2021-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: RNA Editing Detection in HPC Infrastructures
    note: null
    pmcid: null
    pmid: '33835447'
    type:
    - Usage
    version: null
  - doi: 10.1002/0471250953.bi1212s49
    metadata:
      abstract: RNAediting is a post-transcriptional/co-transcriptional molecular
        phenomenon whereby a genetic message is modified from the corresponding DNA
        template by means of substitutions, insertions, and/or deletions. It occurs
        in a variety of organisms and different cellular locations through evolutionally
        and biochemically unrelated proteins. RNA editing has a plethora of biological
        effects including the modulation of alternative splicing and fine-tuning of
        gene expression. RNA editing events by base substitutions can be detected
        on a genomic scale by NGS technologies through the REDItools package, an ad
        hoc suite of Python scripts to study RNA editing using RNA-Seq and DNA-Seq
        data or RNA-Seq data alone. REDItools implement effective filters to minimize
        biases due to sequencing errors, mapping errors, and SNPs. The package is
        freely available at Google Code repository (http://code.google.com/p/reditools/)
        and released under the MIT license. In the present unit we show three basic
        protocols corresponding to three main REDItools scripts.
      authors:
      - name: Picardi E.
      - name: D'Erchia A.M.
      - name: A. Antonio
      - name: G. Graziano
      citationCount: 25
      date: '2015-01-01T00:00:00Z'
      journal: Current Protocols in Bioinformatics
      title: Using REDItools to detect RNA editing events in NGS datasets
    note: null
    pmcid: null
    pmid: '25754992'
    type:
    - Usage
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - '1.3'
  - '2.0'
- accessibility: null
  additionDate: '2024-01-18T10:05:20.317333Z'
  biotoolsCURIE: biotools:mass_dynamics
  biotoolsID: mass_dynamics
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: A software platform for analysis and visualization of label-free and
    tandem mass tag (TMT) data-dependent acquisition (DDA) bottom-up proteomics data,
    including pathway and gene set enrichment analyses.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format: []
    note: null
    operation:
    - term: Analysis
      uri: http://edamontology.org/operation_2945
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format: []
  homepage: https://massdynamics.com/
  homepage_status: 0
  language: []
  lastUpdate: '2024-01-18T12:57:09.002286Z'
  license: null
  link: []
  maturity: null
  name: Mass Dynamics
  operatingSystem: []
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1021/acs.jproteome.1c00683
    metadata:
      abstract: 'Label-free quantification (LFQ) of shotgun proteomics data is a popular
        and robust method for the characterization of relative protein abundance between
        samples. Many analytical pipelines exist for the automation of this analysis,
        and some tools exist for the subsequent representation and inspection of the
        results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis
        environment that can analyze and visualize LFQ data produced by software such
        as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture
        to enable researchers to store their data, enabling researchers to not only
        automatically process and visualize their LFQ data but also annotate and share
        their findings with collaborators and, if chosen, to easily publish results
        to the community. With a view toward increased reproducibility and standardization
        in proteomics data analysis and streamlining collaboration between researchers,
        MD 1.0 requires minimal parameter choices and automatically generates quality
        control reports to verify experiment integrity. Here, we demonstrate that
        MD 1.0 provides reliable results for protein expression quantification, emulating
        Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform
        is available globally via: https://app.massdynamics.com/.'
      authors:
      - name: Bloom J.
      - name: Triantafyllidis A.
      - name: Quaglieri A.
      - name: Burton Ngov P.
      - name: Infusini G.
      - name: Webb A.
      citationCount: 1
      date: '2021-11-05T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing,
        Sharing, and Integrating Label-Free Data'
    note: null
    pmcid: null
    pmid: '34647461'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1101/2022.12.12.517480
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '2.0'
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - '1.0'
  - '2.0'
- accessibility: Open access
  additionDate: '2022-03-24T09:57:50.414985Z'
  biotoolsCURIE: biotools:mistic_predictor
  biotoolsID: mistic_predictor
  collectionID:
  - Rare Disease
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: kchennen@unistra.fr
    fundrefid: null
    gridid: null
    name: Kirsley Chennen
    note: null
    orcidid: https://orcid.org/0000-0001-9268-6748
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Thomas Weber
    note: null
    orcidid: https://orcid.org/0000-0002-0807-6363
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: olivier.poch@unistra.fr
    fundrefid: null
    gridid: null
    name: Olivier Poch
    note: null
    orcidid: https://orcid.org/0000-0002-7134-3217
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Contributor
    url: null
  description: MISTIC (MISsense deleTeriousness predICtor), a prediction tool to reveal
    disease-relevant deleterious missense variants based on an original combination
    of two complementary machine learning algorithms using a soft voting system that
    integrates 113 missense features, ranging from multi-ethnic minor allele frequencies
    and evolutionary conservation, to physiochemical and biochemical properties of
    amino acids.
  documentation: []
  download:
  - note: null
    type: Downloads page
    url: http://lbgi.fr/mistic/download
    version: '1.0'
  - note: null
    type: API specification
    url: https://lbgi.fr/api/index.rvt?api=mistic
    version: '1.0'
  editPermission:
    authors:
    - kchennen
    type: private
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - France
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Variant effect prediction
      uri: http://edamontology.org/operation_0331
    output: []
  homepage: http://lbgi.fr/mistic
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2022-03-24T13:12:19.605062Z'
  license: MIT
  link: []
  maturity: Mature
  name: MISTIC
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: kchennen
  publication:
  - doi: 10.1371/journal.pone.0236962
    metadata:
      abstract: 'Copyright: © 2020 Chennen et al. This is an open access article distributed
        under the terms of the Creative Commons Attribution License, which permits
        unrestricted use, distribution, and reproduction in any medium, provided the
        original author and source are credited.The diffusion of next-generation sequencing
        technologies has revolutionized research and diagnosis in the field of rare
        Mendelian disorders, notably via whole-exome sequencing (WES). However, one
        of the main issues hampering achievement of a diagnosis via WES analyses is
        the extended list of variants of unknown significance (VUS), mostly composed
        of missense variants. Hence, improved solutions are needed to address the
        challenges of identifying potentially deleterious variants and ranking them
        in a prioritized short list. We present MISTIC (MISsense deleTeriousness predICtor),
        a new prediction tool based on an original combination of two complementary
        machine learning algorithms using a soft voting system that integrates 113
        missense features, ranging from multi-ethnic minor allele frequencies and
        evolutionary conservation, to physiochemical and biochemical properties of
        amino acids. Our approach also uses training sets with a wide spectrum of
        variant profiles, including both high-confidence positive (deleterious) and
        negative (benign) variants. Compared to recent state-of-the-art prediction
        tools in various benchmark tests and independent evaluation scenarios, MISTIC
        exhibits the best and most consistent performance, notably with the highest
        AUC value (> 0.95). Importantly, MISTIC maintains its high performance in
        the specific case of discriminating deleterious variants from benign variants
        that are rare or population-specific. In a clinical context, MISTIC drastically
        reduces the list of VUS (<30%) and significantly improves the ranking of “causative”
        deleterious variants. Pre-computed MISTIC scores for all possible human missense
        variants are available at http://lbgi.fr/mistic.'
      authors:
      - name: Chennen K.
      - name: Weber T.
      - name: Lornage X.
      - name: Kress A.
      - name: Bohm J.
      - name: Thompson J.
      - name: Laporte J.
      - name: Poch O.
      citationCount: 6
      date: '2020-07-01T00:00:00Z'
      journal: PLoS ONE
      title: 'MISTIC: A prediction tool to reveal disease-relevant deleterious missense
        variants'
    note: null
    pmcid: PMC7394404
    pmid: '32735577'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web application
  - Web API
  - Script
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2019-01-22T09:41:33Z'
  biotoolsCURIE: biotools:edam-browser
  biotoolsID: edam-browser
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Bryan Brancotte
    note: null
    orcidid: https://orcid.org/0000-0001-8669-5525
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Hervé Ménager
    note: null
    orcidid: https://orcid.org/0000-0002-7552-1009
    rorid: null
    typeEntity: null
    typeRole:
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Christophe Blanchet
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Hager Eldakroury
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: https://github.com/HagerDakroury
  description: "The EDAM Browser is a client-side web-based visualization javascript\
    \ widget of the EDAM ontology. \nThe EDAM Browser provides users a simple and\
    \ performant interface to explore EDAM when annotating or searching for bioinformatics\
    \ resources.\nIts goals are to help describing bio-related resources and service\
    \ with EDAM, and to facilitate and foster community contributions to EDAM."
  documentation:
  - note: A demo on how to use it; Tutorial
    type:
    - Training material
    url: https://ifb-elixirfr.github.io/edam-browser/demo.html
  - note: null
    type:
    - Other
    url: https://github.com/IFB-ElixirFr/edam-browser/blob/master/paper_resources/long_paper_version.md
  download: []
  editPermission:
    authors:
    - hmenager
    - hmenager@asu.edu
    type: group
  elixirCommunity: []
  elixirNode:
  - France
  elixirPlatform:
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Ontology
        uri: http://edamontology.org/data_0582
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
    note: null
    operation:
    - term: Ontology visualisation
      uri: http://edamontology.org/operation_3559
    output: []
  homepage: https://github.com/IFB-ElixirFr/edam-browser
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2022-01-03T15:45:09.883379Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/IFB-ElixirFr/edam-browser
  - note: null
    type:
    - Service
    url: https://ifb-elixirfr.github.io/edam-browser/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/IFB-ElixirFr/edam-browser/issues
  maturity: Mature
  name: EDAM-Browser
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: BryanBrancotte
  publication:
  - doi: 10.21105/joss.00698
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Plug-in
  - Web application
  topic:
  - term: Informatics
    uri: http://edamontology.org/topic_0605
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2019-02-14T08:17:30Z'
  biotoolsCURIE: biotools:VarGenius
  biotoolsID: VarGenius
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: f.musacchia@tigem.it
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-TELETHON
    note: null
    orcidid: https://orcid.org/0000-0001-9440-1080
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: VarGenius is a platform for analysis of variants from DNA sequencing
    data. Currently it can be used for WES and Panels. Starting from fastq files it
    can execute the GATK Best Practices pipeline doing both single calling and joint
    calling. Then it executes Annovar for variant annotation and generates a readable
    output in tabular and XLS format. All the data extracted from the samples (variants,
    genotypes, etc..) are uploaded into a Postgres database which can be used for
    further downstream analyses.
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://github.com/frankMusacchia/VarGenius/tree/master/GUIDE
  download:
  - note: null
    type: Source code
    url: https://github.com/frankMusacchia/VarGenius
    version: '1.0'
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function: []
  homepage: https://github.com/frankMusacchia/VarGenius
  homepage_status: 0
  language:
  - R
  - Perl
  lastUpdate: '2021-10-08T08:49:21.521364Z'
  license: null
  link:
  - note: null
    type:
    - Helpdesk
    url: https://groups.google.com/forum/#!forum/VarGenius
  maturity: Mature
  name: VarGenius
  operatingSystem:
  - Linux
  otherID: []
  owner: FrankMusacchia
  publication:
  - doi: 10.1186/s12859-018-2532-4
    metadata:
      abstract: '© 2018 The Author(s).Background: Targeted resequencing has become
        the most used and cost-effective approach for identifying causative mutations
        of Mendelian diseases both for diagnostics and research purposes. Due to very
        rapid technological progress, NGS laboratories are expanding their capabilities
        to address the increasing number of analyses. Several open source tools are
        available to build a generic variant calling pipeline, but a tool able to
        simultaneously execute multiple analyses, organize, and categorize the samples
        is still missing. Results: Here we describe VarGenius, a Linux based command
        line software able to execute customizable pipelines for the analysis of multiple
        targeted resequencing data using parallel computing. VarGenius provides a
        database to store the output of the analysis (calling quality statistics,
        variant annotations, internal allelic variant frequencies) and sample information
        (personal data, genotypes, phenotypes). VarGenius can also perform the "joint
        analysis" of hundreds of samples with a single command, drastically reducing
        the time for the configuration and execution of the analysis. VarGenius executes
        the standard pipeline of the Genome Analysis Tool-Kit (GATK) best practices
        (GBP) for germinal variant calling, annotates the variants using Annovar,
        and generates a user-friendly output displaying the results through a web
        page. VarGenius has been tested on a parallel computing cluster with 52 machines
        with 120GB of RAM each. Under this configuration, a 50 M whole exome sequencing
        (WES) analysis for a family was executed in about 7h (trio or quartet); a
        joint analysis of 30 WES in about 24 h and the parallel analysis of 34 single
        samples from a 1 M panel in about 2 h. Conclusions: We developed VarGenius,
        a "master" tool that faces the increasing demand of heterogeneous NGS analyses
        and allows maximum flexibility for downstream analyses. It paves the way to
        a different kind of analysis, centered on cohorts rather than on singleton.
        Patient and variant information are stored into the database and any output
        file can be accessed programmatically. VarGenius can be used for routine analyses
        by biomedical researchers with basic Linux skills providing additional flexibility
        for computational biologists to develop their own algorithms for the comparison
        and analysis of data. The software is freely available at: https://github.com/frankMusacchia/VarGenius'
      authors:
      - name: Musacchia F.
      - name: Ciolfi A.
      - name: Mutarelli M.
      - name: Bruselles A.
      - name: Castello R.
      - name: Pinelli M.
      - name: Basu S.
      - name: Banfi S.
      - name: Casari G.
      - name: Tartaglia M.
      - name: Nigro V.
      - name: Torella A.
      - name: Esposito G.
      - name: Cappuccio G.
      - name: Mancano G.
      - name: Maitz S.
      - name: Brunetti-Pierri N.
      - name: Parenti G.
      - name: Selicorni A.
      citationCount: 8
      date: '2018-12-12T00:00:00Z'
      journal: BMC Bioinformatics
      title: VarGenius executes cohort-level DNA-seq variant calling and annotation
        and allows to manage the resulting data through a PostgreSQL database
    note: null
    pmcid: PMC6291943
    pmid: '30541431'
    type:
    - Primary
    - Benchmarking study
    version: '1.0'
  relation:
  - biotoolsID: vargenius-hzd
    type: includes
  toolType:
  - Command-line tool
  topic:
  - term: DNA mutation
    uri: http://edamontology.org/topic_2533
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2020-03-10T11:06:45Z'
  biotoolsCURIE: biotools:metaQuantome
  biotoolsID: metaQuantome
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: metaQuantome software suite analyzes the state of a microbiome by leveraging
    complex taxonomic and functional hierarchies to summarize peptide-level quantitative
    information. metaQuantome offers differential abundance analysis, principal components
    analysis, and clustered heat map visualizations, as well as exploratory analysis
    for a single sample or experimental condition.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Peptide identification
        uri: http://edamontology.org/data_0945
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Taxonomic classification
        uri: http://edamontology.org/data_1872
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: GO concept ID (molecular function)
        uri: http://edamontology.org/data_2583
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: EC number
        uri: http://edamontology.org/data_1011
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Principal component visualisation
      uri: http://edamontology.org/operation_2939
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Functional clustering
      uri: http://edamontology.org/operation_3459
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Differential protein expression analysis
      uri: http://edamontology.org/operation_3741
    - term: Heat map generation
      uri: http://edamontology.org/operation_0531
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Statistical inference
      uri: http://edamontology.org/operation_3658
    output:
    - data:
        term: GO concept ID (molecular function)
        uri: http://edamontology.org/data_2583
      format:
      - term: Image format
        uri: http://edamontology.org/format_3547
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: Image format
        uri: http://edamontology.org/format_3547
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Image format
        uri: http://edamontology.org/format_3547
  homepage: https://github.com/galaxyproteomics/metaquantome
  homepage_status: 0
  language: []
  lastUpdate: '2021-05-27T07:59:11Z'
  license: null
  link: []
  maturity: null
  name: metaQuantome
  operatingSystem: []
  otherID: []
  owner: pjagtap
  publication:
  - doi: 10.1074/mcp.RA118.001240
    metadata:
      abstract: '© 2019 Easterly et al.Microbiome research offers promising insights
        into the impact of microorganisms on biological systems. Metaproteomics, the
        study of microbial proteins at the community level, integrates genomic, transcriptomic,
        and proteomic data to determine the taxonomic and functional state of a microbiome.
        However, standard metaproteomics software is subject to several limitations,
        commonly supporting only spectral counts, emphasizing exploratory analysis
        rather than hypothesis testing and rarely offering the ability to analyze
        the interaction of function and taxonomy - that is, which taxa are responsible
        for different processes. Here we present metaQuantome, a novel, multifaceted
        software suite that analyzes the state of a microbiome by leveraging complex
        taxonomic and functional hierarchies to summarize peptide-level quantitative
        information, emphasizing label-free intensity-based methods. For experiments
        with multiple experimental conditions, metaQuantome offers differential abundance
        analysis, principal components analysis, and clustered heat map visualizations,
        as well as exploratory analysis for a single sample or experimental condition.
        We benchmark meta- Quantome analysis against standard methods, using two previously
        published datasets: (1) an artificially assembled microbial community dataset
        (taxonomy benchmarking) and (2) a dataset with a range of recombinant human
        proteins spiked into an Escherichia coli background (functional benchmarking).
        Furthermore, we demonstrate the use of metaQuantome on a previously published
        human oral microbiome dataset. In both the taxonomic and functional benchmarking
        analyses, metaQuantome quantified taxonomic and functional terms more accurately
        than standard summarization- based methods. We use the oral microbiome dataset
        to demonstrate metaQuantome''s ability to produce publication- quality figures
        and elucidate biological processes of the oral microbiome. metaQuantome enables
        advanced investigation of metaproteomic datasets, which should be broadly
        applicable to microbiome-related research. In the interest of accessible,
        flexible, and reproducible analysis, metaQuantome is open source and available
        on the command line and in Galaxy.'
      authors:
      - name: Easterly C.W.
      - name: Sajulga R.
      - name: Mehta S.
      - name: Johnson J.
      - name: Kumar P.
      - name: Hubler S.
      - name: Mesuere B.
      - name: Rudney J.
      - name: Griffin T.J.
      - name: Jagtap P.D.
      citationCount: 11
      date: '2019-01-01T00:00:00Z'
      journal: Molecular and Cellular Proteomics
      title: 'MetaQuantome: An integrated, quantitative metaproteomics approach reveals
        connections between taxonomy and protein function in complex microbiomes'
    note: https://www.mcponline.org/content/18/8_suppl_1/S82
    pmcid: PMC6692774
    pmid: '31235611'
    type:
    - Method
    version: '1.0'
  - doi: 10.1021/ACS.JPROTEOME.0C00960
    metadata:
      abstract: © 2021 American Chemical Society.metaQuantome is a software suite
        that enables the quantitative analysis, statistical evaluation. and visualization
        of mass-spectrometry-based metaproteomics data. In the latest update of this
        software, we have provided several extensions, including a step-by-step training
        guide, the ability to perform statistical analysis on samples from multiple
        conditions, and a comparative analysis of metatranscriptomics data. The training
        module, accessed via the Galaxy Training Network, will help users to use the
        suite effectively both for functional as well as for taxonomic analysis. We
        extend the ability of metaQuantome to now perform multi-data-point quantitative
        and statistical analyses so that studies with measurements across multiple
        conditions, such as time-course studies, can be analyzed. With an eye on the
        multiomics analysis of microbial communities, we have also initiated the use
        of metaQuantome statistical and visualization tools on outputs from metatranscriptomics
        data, which complements the metagenomic and metaproteomic analyses already
        available. For this, we have developed a tool named MT2MQ ("metatranscriptomics
        to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics
        workflow and transforms them so that the data can be used as an input for
        comparative statistical analysis and visualization via metaQuantome. We believe
        that these improvements to metaQuantome will facilitate the use of the software
        for quantitative metaproteomics and metatranscriptomics and will enable multipoint
        data analysis. These improvements will take us a step toward integrative multiomic
        microbiome analysis so as to understand dynamic taxonomic and functional responses
        of these complex systems in a variety of biological contexts. The updated
        metaQuantome and MT2MQ are open-source software and are available via the
        Galaxy Toolshed and GitHub.
      authors:
      - name: Mehta S.
      - name: Kumar P.
      - name: Crane M.
      - name: Johnson J.E.
      - name: Sajulga R.
      - name: Nguyen D.D.A.
      - name: McGowan T.
      - name: Arntzen M.
      - name: Griffin T.J.
      - name: Jagtap P.D.
      citationCount: 0
      date: '2021-04-02T00:00:00Z'
      journal: Journal of Proteome Research
      title: Updates on metaQuantome Software for Quantitative Metaproteomics
    note: null
    pmcid: null
    pmid: '33683127'
    type: []
    version: null
  relation: []
  toolType:
  - Suite
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2021-01-18T08:40:04Z'
  biotoolsCURIE: biotools:aradeepopsis
  biotoolsID: aradeepopsis
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: patrick.huether@gmail.com
    fundrefid: null
    gridid: null
    name: Patrick Hüther
    note: null
    orcidid: https://orcid.org/0000-0003-3315-2484
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    url: null
  - email: claude.becker@gmi.oeaw.ac.at
    fundrefid: null
    gridid: null
    name: Claude Becker
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: ARADEEPOPSIS is a software tool that enables plant researchers to non-invasively
    score plant growth, biomass accumulation and senescence from image data in a highly
    parallelized, high throughput, yet easy to use manner.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/Gregor-Mendel-Institute/aradeepopsis#readme
  download:
  - note: null
    type: Container file
    url: https://quay.io/beckerlab/aradeepopsis-base
    version: '1.3'
  - note: null
    type: Container file
    url: https://quay.io/beckerlab/aradeepopsis-shiny
    version: '1.3'
  - note: Trained models
    type: Downloads page
    url: https://zenodo.org/record/3946618
    version: null
  - note: Training data
    type: Downloads page
    url: https://zenodo.org/record/3946393
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: JPG
        uri: http://edamontology.org/format_3579
    note: null
    operation:
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Image analysis
      uri: http://edamontology.org/operation_3443
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format: []
  homepage: https://github.com/Gregor-Mendel-Institute/aradeepopsis
  homepage_status: 0
  language:
  - Python
  - R
  - Groovy
  lastUpdate: '2021-05-18T08:11:06Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Gregor-Mendel-Institute/aradeepopsis
  maturity: null
  name: ARADEEPOPSIS
  operatingSystem: []
  otherID:
  - type: doi
    value: doi:10.5281/zenodo.3946320
    version: null
  owner: phue
  publication:
  - doi: 10.1105/tpc.20.00318
    metadata:
      abstract: '© 2020 The authors.Linking plant phenotype to genotype is a common
        goal to both plant breeders and geneticists. However, collecting phenotypic
        data for large numbers of plants remain a bottleneck. Plant phenotyping is
        mostly image based and therefore requires rapid and robust extraction of phenotypic
        measurements from image data. However, because segmentation tools usually
        rely on color information, they are sensitive to background or plant color
        deviations. We have developed a versatile, fully open-source pipeline to extract
        phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS
        (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation
        of top-view images to classify leaf tissue into three categories: healthy,
        anthocyanin rich, and senescent. This makes it particularly powerful at quantitative
        phenotyping of different developmental stages, mutants with aberrant leaf
        color and/or phenotype, and plants growing in stressful conditions. On a panel
        of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able
        to not only accurately segment images of phenotypically diverse genotypes
        but also to identify known loci related to anthocyanin production and early
        necrosis in genome-wide association analyses. Our pipeline accurately processed
        images of diverse origin, quality, and background composition, and of a distantly
        related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems
        and high-performance computing environments and can be used independently
        of bioinformatics expertise and resources.'
      authors:
      - name: Huther P.
      - name: Schandry N.
      - name: Jandrasits K.
      - name: Bezrukov I.
      - name: Becker C.
      citationCount: 2
      date: '2020-12-01T00:00:00Z'
      journal: Plant Cell
      title: ARADEEPOPSIS, an automated workflow for top-view plant phenomics using
        semantic segmentation of leaf States
    note: null
    pmcid: PMC7721323
    pmid: '33037149'
    type: []
    version: 1.2.1
  - doi: 10.1101/2020.04.01.018192
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: '1.0'
  relation: []
  toolType:
  - Command-line tool
  - Workflow
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Phenomics
    uri: http://edamontology.org/topic_3298
  validated: 0
  version:
  - '1.3'
  - 1.2.1
  - '1.2'
  - '1.1'
  - '1.0'
  - 1.3.1
- accessibility: Open access
  additionDate: '2017-09-11T10:43:38Z'
  biotoolsCURIE: biotools:pickle
  biotoolsID: pickle
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: n_moschonas@med.upatras.gr
    fundrefid: null
    gridid: null
    name: Nicholas Moschonas
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.pickle.gr
  - email: mklapa@iceht.forth.gr
    fundrefid: null
    gridid: null
    name: Maria Klapa
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.iceht.forth.gr/staff/klapa.html
  description: PICKLE (Protein InteraCtion KnowLedgebasE) is a meta-database for the
    direct protein-protein interactome of the human and the mouse proteomes, integrating
    publicly available source protein-protein interaction (PPI) databases via genetic
    information ontology. PICKLE integrates the primary PPI datasets by superimposing
    them on the UniProtKB/Swiss-Prot reviewed complete proteome of the human (RHCP)
    and the mouse (RMCP) ontology network without any a priori transformations.
  documentation:
  - note: The web portal of the meta-database
    type:
    - General
    url: http://www.pickle.gr
  download:
  - note: The current and archived PICKLE PPI datasets at the UniProt and gene levels
      can be freely downloaded from the PICKLE webpage.
    type: Biological data
    url: http://www.pickle.gr/Downloads
    version: active and archived versions
  - note: The current and archived PICKLE genetic information ontology networks are
      available in owl format.
    type: Biological data
    url: http://www.pickle.gr/Downloads
    version: active and archived versions
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Greece
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein name
        uri: http://edamontology.org/data_1009
      format: []
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Ontology identifier
        uri: http://edamontology.org/data_2338
      format: []
    note: null
    operation:
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    - term: Sorting
      uri: http://edamontology.org/operation_3802
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    output: []
  homepage: http://www.pickle.gr
  homepage_status: 0
  language:
  - C#
  lastUpdate: '2021-05-17T08:54:15Z'
  license: null
  link:
  - note: 'Content available to any user with no restrictions

      Copyright: University of Patras & FORTH/ICE-HT, Greece'
    type:
    - Other
    url: http://www.pickle.gr
  maturity: Emerging
  name: PICKLE
  operatingSystem:
  - Windows
  otherID: []
  owner: mklapa
  publication:
  - doi: 10.1186/1752-0509-7-96
    metadata:
      abstract: 'Background: Understanding the topology and dynamics of the human
        protein-protein interaction (PPI) network will significantly contribute to
        biomedical research, therefore its systematic reconstruction is required.
        Several meta-databases integrate source PPI datasets, but the protein node
        sets of their networks vary depending on the PPI data combined. Due to this
        inherent heterogeneity, the way in which the human PPI network expands via
        multiple dataset integration has not been comprehensively analyzed. We aim
        at assembling the human interactome in a global structured way and exploring
        it to gain insights of biological relevance. Results: First, we defined the
        UniProtKB manually reviewed human " complete" proteome as the reference protein-node
        set and then we mined five major source PPI datasets for direct PPIs exclusively
        between the reference proteins. We updated the protein and publication identifiers
        and normalized all PPIs to the UniProt identifier level. The reconstructed
        interactome covers approximately 60% of the human proteome and has a scale-free
        structure. No apparent differentiating gene functional classification characteristics
        were identified for the unrepresented proteins. The source dataset integration
        augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree
        node, but the inclusion of most of its identified PPIs may be reconsidered.
        The high number (>300) of connections of the subsequent fifteen proteins correlates
        well with their essential biological role. According to the power-law network
        structure, the unrepresented proteins should mainly have up to four connections
        with equally poorly-connected interactors. Conclusions: Reconstructing the
        human interactome based on the a priori definition of the protein nodes enabled
        us to identify the currently included part of the human " complete" proteome,
        and discuss the role of the proteins within the network topology with respect
        to their function. As the network expansion has to comply with the scale-free
        theory, we suggest that the core of the human interactome has essentially
        emerged. Thus, it could be employed in systems biology and biomedical research,
        despite the considerable number of currently unrepresented proteins. The latter
        are probably involved in specialized physiological conditions, justifying
        the scarcity of related PPI information, and their identification can assist
        in designing relevant functional experiments and targeted text mining algorithms.
        © 2013 Klapa et al.; licensee BioMed Central Ltd.'
      authors:
      - name: Klapa M.I.
      - name: Tsafou K.
      - name: Theodoridis E.
      - name: Tsakalidis A.
      - name: Moschonas N.K.
      citationCount: 15
      date: '2013-10-02T00:00:00Z'
      journal: BMC Systems Biology
      title: 'Reconstruction of the experimentally supported human protein interactome:
        What can we learn?'
    note: null
    pmcid: PMC5638325
    pmid: '24088582'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1371/journal.pone.0186039
    metadata:
      abstract: © 2017 Gioutlakis et al. This is an open access article distributed
        under the terms of the Creative Commons Attribution License, which permits
        unrestricted use, distribution, and reproduction in any medium, provided the
        original author and source are credited.It has been acknowledged that source
        databases recording experimentally supported human protein-protein interactions
        (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive
        PPI network requires appropriate integration of multiple heterogeneous primary
        datasets, presenting the PPIs at various genetic reference levels. Existing
        PPI meta-databases perform integration via normalization; namely, PPIs are
        merged after converted to a certain target level. Hence, the node set of the
        integrated network depends each time on the number and type of the combined
        datasets. Moreover, the irreversible a priori normalization process hinders
        the identification of normalization artifacts in the integrated network, which
        originate from the nonlinearity characterizing the genetic information flow.
        PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture
        for this recently introduced human PPI meta-database. Its main novel feature
        over the existing meta-databases is its approach to primary PPI dataset integration
        via genetic information ontology. Building upon the PICKLE principles of using
        the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the
        reference protein interactor set, and filtering out protein interactions with
        low probability of being direct based on the available evidence, PICKLE 2.0
        first assembles the RHCP genetic information ontology network by connecting
        the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt
        entries) and then integrates PPI datasets by superimposing them on the ontology
        network without any a priori transformations. Importantly, this process allows
        the resulting heterogeneous integrated network to be reversibly normalized
        to any level of genetic reference without loss of the original information,
        the latter being used for identification of normalization biases, and enables
        the appraisal of potential false positive interactions through PPI source
        database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows
        for the simultaneous query of multiple entities and provides integrated human
        PPI networks at either the protein (UniProt) or the gene level, at three PPI
        filtering modes.
      authors:
      - name: Gioutlakis A.
      - name: Klapa M.I.
      - name: Moschonas N.K.
      citationCount: 20
      date: '2017-10-01T00:00:00Z'
      journal: PLoS ONE
      title: 'PICKLE 2.0: A human protein-protein interaction meta-database employing
        data integration via genetic information ontology'
    note: null
    pmcid: null
    pmid: '29023571'
    type:
    - Primary
    version: '2.0'
  - doi: 10.13140/RG.2.2.16985.21608
    metadata: null
    note: Poster Presentation in the ELIXIR All Hands Meeting
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/btaa1070
    metadata: null
    note: The PICKLE 3.0 upgrade refers to the enrichment of this human protein–protein
      interaction (PPI) meta-database with the mouse protein interactome.
    pmcid: PMC8034533
    pmid: '33367505'
    type:
    - Method
    version: '3.0'
  relation: []
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  - term: Human biology
    uri: http://edamontology.org/topic_2815
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Systems medicine
    uri: http://edamontology.org/topic_3396
  validated: 1
  version:
  - '3.0'
- accessibility: Open access
  additionDate: '2015-12-02T14:55:21Z'
  biotoolsCURIE: biotools:rpredictor
  biotoolsID: rpredictor
  collectionID:
  - Czech Republic
  - ELIXIR-CZ
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-CZ
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.elixir-czech.cz/
  - email: david.hoksza@gmail.com
    fundrefid: null
    gridid: null
    name: David Hoksza
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: panek@biomed.cas.cz
    fundrefid: null
    gridid: null
    name: Josef Pánek
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: null
  description: A web database for prediction, analysis and storage of secondary structures
    of RNAs.
  documentation:
  - note: null
    type:
    - General
    - Citation instructions
    - Release notes
    - Quick start guide
    url: http://rpredictor.ms.mff.cuni.cz/documentation/
  - note: null
    type:
    - Code of conduct
    - Terms of use
    url: http://rpredictordb.elixir-czech.cz/documentation/terms.html
  - note: null
    type:
    - FAQ
    url: http://rpredictordb.elixir-czech.cz/documentation/rDoc-User/FAQ.html
  - note: null
    type:
    - Installation instructions
    url: http://rpredictordb.elixir-czech.cz/documentation/rDoc-Technical/setup.html
  - note: null
    type:
    - User manual
    url: http://rpredictordb.elixir-czech.cz/documentation/rDoc-User/index.html
  download:
  - note: null
    type: Source code
    url: https://github.com/handrbaal/cppredict/
    version: null
  - note: null
    type: Downloads page
    url: http://rpredictordb.elixir-czech.cz/download
    version: null
  editPermission:
    authors:
    - jelinek
    type: group
  elixirCommunity: []
  elixirNode:
  - Czech Republic
  elixirPlatform:
  - Data
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format: []
    note: Search in the database
    operation:
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: Sequence annotation
      uri: http://edamontology.org/operation_0361
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    output:
    - data:
        term: RNA secondary structure
        uri: http://edamontology.org/data_0880
      format:
      - term: Dot-bracket format
        uri: http://edamontology.org/format_1457
      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Predict secondary structure for own sequences using templates in the database
    operation:
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    output:
    - data:
        term: RNA secondary structure
        uri: http://edamontology.org/data_0880
      format:
      - term: Dot-bracket format
        uri: http://edamontology.org/format_1457
      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://rpredictordb.elixir-czech.cz
  homepage_status: 0
  language:
  - MATLAB
  - C++
  - C#
  - JavaScript
  - PHP
  - Bash
  - Python
  - Java
  - SQL
  lastUpdate: '2021-05-15T10:41:27Z'
  license: Freeware
  link:
  - note: Prediction algorithm
    type:
    - Repository
    url: https://github.com/handrbaal/cppredict/
  - note: null
    type:
    - Mailing list
    url: http://rpredictordb.elixir-czech.cz/contact
  - note: null
    type:
    - Service
    url: http://rpredictordb.elixir-czech.cz/search
  - note: null
    type:
    - Service
    url: http://rpredictordb.elixir-czech.cz/predict#cppredict2
  maturity: Mature
  name: rPredictorDB
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-CZ
  publication:
  - doi: 10.1093/database/baz047
    metadata:
      abstract: © 2019 The Author(s) 2019. Published by Oxford University Press.Secondary
        data structure of RNA molecules provides insights into the identity and function
        of RNAs. With RNAs readily sequenced, the question of their structural characterization
        is increasingly important. However, RNA structure is difficult to acquire.
        Its experimental identification is extremely technically demanding, while
        computational prediction is not accurate enough, especially for large structures
        of long sequences. We address this difficult situation with rPredictorDB,
        a predictive database of RNA secondary structures that aims to form a middle
        ground between experimentally identified structures in PDB and predicted consensus
        secondary structures in Rfam. The database contains individual secondary structures
        predicted using a tool for template-based prediction of RNA secondary structure
        for the homologs of the RNA families with at least one homolog with experimentally
        solved structure. Experimentally identified structures are used as the structural
        templates and thus the prediction has higher reliability than de novo predictions
        in Rfam. The sequences are downloaded from public resources. So far rPredictorDB
        covers 7365 RNAs with their secondary structures. Plots of the secondary structures
        use the Traveler package for readable display of RNAs with long sequences
        and complex structures, such as ribosomal RNAs. The RNAs in the output of
        rPredictorDB are extensively annotated and can be viewed, browsed, searched
        and downloaded according to taxonomic, sequence and structure data. Additionally,
        structure of user-provided sequences can be predicted using the templates
        stored in rPredictorDB.
      authors:
      - name: Jelinek J.
      - name: Hoksza D.
      - name: Hajic J.
      - name: Pesek J.
      - name: Drozen J.
      - name: Hladik T.
      - name: Klimpera M.
      - name: Vohradsky J.
      - name: Panek J.
      citationCount: 2
      date: '2019-01-01T00:00:00Z'
      journal: Database
      title: 'RPredictorDB: A predictive database of individual secondary structures
        of RNAs and their formatted plots'
    note: null
    pmcid: PMC6482342
    pmid: '31032840'
    type:
    - Primary
    version: null
  - doi: 10.1109/BIBM.2014.6999394
    metadata:
      abstract: © 2014 IEEE.Determining the structure of ribosomal RNAs (rRNAs) is
        one of the crucial steps in understanding the process of protein synthesis,
        for which rRNAs are one of the basic components. Nevertheless, due to extreme
        technical difficulties, spatial (3D) structures have been resolved experimentally
        for only 14 organisms. Also, computational prediction of 3D rRNA structure
        is almost impossible, and prediction of secondary structure (the list of base
        pairs in the folded RNA), an important intermediate step between sequence
        and 3D structure that is used broadly in modeling of RNA structures, is in
        the case of rRNAs hindered by both extreme sequence length and high structure
        complexity. Here we present a proof-of-concept for an rRNA secondary structure
        prediction method that utilizes known structures as structural templates.
        Our template-based prediction algorithm determines those regions of the sequence
        for which structure is being predicted that are conserved well enough so that
        their secondary structure can be copied over from the template. The structure
        of the remaining, unconserved regions is predicted using a thermodynamic folding
        model. Applying a baseline implementation of our algorithm to the E. coli
        16S rRNA, we have achieved state-of-the-art recall and precision using the
        structure of T. thermophilus 16S rRNA as a template.
      authors:
      - name: Panek J.
      - name: Hajic J.
      - name: Hoksza D.
      citationCount: 0
      date: '2014-01-01T00:00:00Z'
      journal: Proceedings - 2014 IEEE International Conference on Bioinformatics
        and Biomedicine, IEEE BIBM 2014
      title: Template-based prediction of ribosomal RNA secondary structure
    note: Early version of the prediction algorithm
    pmcid: null
    pmid: null
    type:
    - Method
    version: '0.1'
  - doi: 10.3389/fgene.2017.00147
    metadata:
      abstract: '© 2017 Pánek, Modrák and Schwarz.While understanding the structure
        of RNA molecules is vital for deciphering their functions, determining RNA
        structures experimentally is exceptionally hard. At the same time, extant
        approaches to computational RNA structure prediction have limited applicability
        and reliability. In this paper we provide a method to solve a simpler yet
        still biologically relevant problem: prediction of secondary RNA structure
        using structure of different molecules as a template. Our method identifies
        conserved and unconserved subsequences within an RNA molecule. For conserved
        subsequences, the template structure is directly transferred into the generated
        structure and combined with de-novo predicted structure for the unconserved
        subsequences with low evolutionary conservation. The method also determines,
        when the generated structure is unreliable. The method is validated using
        experimentally identified structures. The accuracy of the method exceeds that
        of classical prediction algorithms and constrained prediction methods. This
        is demonstrated by comparison using large number of heterogeneous RNAs. The
        presented method is fast and robust, and useful for various applications requiring
        knowledge of secondary structures of individual RNA sequences.'
      authors:
      - name: Panek J.
      - name: Modrak M.
      - name: Schwarz M.
      citationCount: 2
      date: '2017-10-10T00:00:00Z'
      journal: Frontiers in Genetics
      title: An algorithm for template-based prediction of secondary structures of
        individual RNA sequences
    note: Final version of the prediction algorithm
    pmcid: PMC5641303
    pmid: '29067038'
    type:
    - Method
    version: '1.0'
  - doi: 10.1186/s12859-017-1885-4
    metadata:
      abstract: '© 2017 The Author(s).Background: Visualization of RNA secondary structures
        is a complex task, and, especially in the case of large RNA structures where
        the expected layout is largely habitual, the existing visualization tools
        often fail to produce suitable visualizations. This led us to the idea to
        use existing layouts as templates for the visualization of new RNAs similarly
        to how templates are used in homology-based structure prediction. Results:
        This article introduces Traveler, a software tool enabling visualization of
        a target RNA secondary structure using an existing layout of a sufficiently
        similar RNA structure as a template. Traveler is based on an algorithm which
        converts the target and template structures into corresponding tree representations
        and utilizes tree edit distance coupled with layout modification operations
        to transform the template layout into the target one. Traveler thus accepts
        a pair of secondary structures and a template layout and outputs a layout
        for the target structure. Conclusions: Traveler is a command-line open source
        tool able to quickly generate layouts for even the largest RNA structures
        in the presence of a sufficiently similar layout. It is available at http://github.com/davidhoksza/traveler.'
      authors:
      - name: Elias R.
      - name: Hoksza D.
      citationCount: 10
      date: '2017-11-15T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'TRAVeLer: A tool for template-based RNA secondary structure visualization'
    note: Visualisation tool
    pmcid: PMC5688744
    pmid: '29141608'
    type:
    - Method
    version: null
  relation:
  - biotoolsID: traveler
    type: uses
  - biotoolsID: ena
    type: uses
  - biotoolsID: ncbi_taxonomy_database
    type: uses
  - biotoolsID: rfam
    type: uses
  - biotoolsID: silva
    type: uses
  - biotoolsID: blast
    type: uses
  - biotoolsID: fasta
    type: uses
  - biotoolsID: vienna_rna_package
    type: uses
  - biotoolsID: blast
    type: uses
  toolType:
  - Web application
  topic:
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  - term: RNA
    uri: http://edamontology.org/topic_0099
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2016-07-08T14:20:47Z'
  biotoolsCURIE: biotools:ontobrowser
  biotoolsID: ontobrowser
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: grid.419481.1
    name: Carlo Ravagli
    note: null
    orcidid: null
    rorid: 02f9zrr09
    typeEntity: Person
    typeRole:
    - Developer
    url: http://opensource.nibr.com
  - email: null
    fundrefid: null
    gridid: grid.419481.1
    name: Francois Pognan
    note: null
    orcidid: https://orcid.org/0000-0001-7033-2033
    rorid: 02f9zrr09
    typeEntity: Person
    typeRole:
    - Contributor
    url: http://opensource.nibr.com
  - email: philippe.marc@novartis.com
    fundrefid: null
    gridid: grid.419481.1
    name: Philippe Marc
    note: null
    orcidid: https://orcid.org/0000-0002-0064-0572
    rorid: 02f9zrr09
    typeEntity: Person
    typeRole:
    - Contributor
    - Primary contact
    url: http://opensource.nibr.com
  - email: null
    fundrefid: null
    gridid: grid.454814.8
    name: Innovative Medicines Initiative (IMI)
    note: Innovative Medicines Initiative Joint Undertaking under grant agreement
      n° 115002. Part of the eTOX project (http://www.etoxproject.eu)
    orcidid: null
    rorid: 019af4n30
    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: https://www.imi.europa.eu
  description: The tool was developed to manage ontologies (and controlled terminologies
    e.g. CDISC SEND). The primary goal of the tool is to provide an online collaborative
    solution for expert curators to map code list terms (sourced from multiple systems/databases)
    to preferred ontology terms. Other key features include visualisation of ontologies
    in hierarchical/graph format, advanced search capabilities, peer review/approval
    workflow and web service access to data.
  documentation:
  - note: null
    type:
    - Terms of use
    url: https://github.com/Novartis/ontobrowser/blob/master/LICENSE.txt
  - note: null
    type:
    - General
    url: https://github.com/Novartis/ontobrowser/tree/master/doc
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: OBO
        uri: http://edamontology.org/format_2549
    - data:
        term: Ontology term
        uri: http://edamontology.org/data_0966
      format: []
    note: 'Web based collaborative ontology curation

      Interactive hierarchical/graph visualisation

      Cross ontology searching

      Synonym mapping

      Automated mapping of similar matching synonyms

      Central database for all ontologies

      Version tracking

      Review/Approve workflow

      Email notification

      Full curator audit trail/history Ontologies can be loaded into OntoBrowser using
      the /ontobrowser/ontologies RESTful web service. The web service only supports
      the PUT method for loading ontologies and only accepts OBO formatted data.

      https://github.com/Novartis/ontobrowser/blob/master/doc/INSTALL.md Verbatim
      terms (to be reconcilated) are loaded from remote source database.

      Controlled vocabularies are loaded via SQL. The following Internet Media Types
      are supported by the web service for GET requests:

      application/rdf+xml

      application/owl+xml

      text/owl-manchester

      text/turtle

      text/obo

      application/obo

      application/json

      https://github.com/Novartis/ontobrowser/blob/master/doc/web_services.md'
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Ontology visualisation
      uri: http://edamontology.org/operation_3559
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Mapping
      uri: http://edamontology.org/operation_2429
    output:
    - data:
        term: OBO file format name
        uri: http://edamontology.org/data_2254
      format:
      - term: OWL/XML
        uri: http://edamontology.org/format_3262
      - term: Turtle
        uri: http://edamontology.org/format_3255
      - term: Manchester OWL Syntax
        uri: http://edamontology.org/format_3253
      - term: RDF/XML
        uri: http://edamontology.org/format_3261
      - term: JSON
        uri: http://edamontology.org/format_3464
  homepage: https://github.com/Novartis/ontobrowser
  homepage_status: 0
  language:
  - SQL
  lastUpdate: '2021-01-11T08:26:53Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Novartis/ontobrowser
  maturity: Mature
  name: OntoBrowser
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: pmarcpub@free.fr
  publication:
  - doi: 10.1093/bioinformatics/btw579
    metadata:
      abstract: © The Author 2016. Published by Oxford University Press.The lack of
        controlled terminology and ontology usage leads to incomplete search results
        and poor interoperability between databases. One of the major underlying challenges
        of data integration is curating data to adhere to controlled terminologies
        and/or ontologies. Finding subject matter experts with the time and skills
        required to perform data curation is often problematic. In addition, existing
        tools are not designed for continuous data integration and collaborative curation.
        This results in time-consuming curation workflows that often become unsustainable.
        The primary objective of OntoBrowser is to provide an easy-to-use online collaborative
        solution for subject matter experts to map reported terms to preferred ontology
        (or code list) terms and facilitate ontology evolution. Additional features
        include web service access to data, visualization of ontologies in hierarchical/graph
        format and a peer review/approval workflow with alerting.
      authors:
      - name: Ravagli C.
      - name: Pognan F.
      - name: Marc P.
      citationCount: 13
      date: '2017-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'OntoBrowser: A collaborative tool for curation of ontologies by subject
        matter experts'
    note: null
    pmcid: PMC5408772
    pmid: '27605099'
    type:
    - Primary
    version: '1.0'
  relation: []
  toolType:
  - Web API
  - Web application
  topic:
  - term: Ontology and terminology
    uri: http://edamontology.org/topic_0089
  - term: Data identity and mapping
    uri: http://edamontology.org/topic_3345
  validated: 1
  version:
  - '1'
- accessibility: Open access
  additionDate: '2020-12-20T16:40:10Z'
  biotoolsCURIE: biotools:stpeter
  biotoolsID: stpeter
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A tool for analyzing proteomics data acquired by mass spectrometry.
    Quantifies proteins from shotgun MS/MS data using normalized spectral count or
    normalized spectral index techniques. Incorporates distributed techniques to account
    for peptide sequence degeneracy.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: StPeter myProteins.prot.xml
    input:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: protXML
        uri: http://edamontology.org/format_3747
    note: Output file is the same protXML input file, but with protein quantification
      results appended to each protein group.
    operation:
    - term: Spectral counting
      uri: http://edamontology.org/operation_3637
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Protein quantification
      uri: http://edamontology.org/operation_3630
    - term: Label-free quantification
      uri: http://edamontology.org/operation_3634
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: protXML
        uri: http://edamontology.org/format_3747
  homepage: http://tppms.org
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2020-12-21T16:38:53Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: StPeter
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: mhoopmann
  publication:
  - doi: 10.1021/acs.jproteome.7b00786
    metadata:
      abstract: © 2018 American Chemical Society.Label-free quantification has grown
        in popularity as a means of obtaining relative abundance measures for proteomics
        experiments. However, easily accessible and integrated tools to perform label-free
        quantification have been lacking. We describe StPeter, an implementation of
        Normalized Spectral Index quantification for wide availability through integration
        into the widely used Trans-Proteomic Pipeline. This implementation has been
        specifically designed for reproducibility and ease of use. We demonstrate
        that StPeter outperforms other state-of-the art packages using a recently
        reported benchmark data set over the range of false discovery rates relevant
        to shotgun proteomics results. We also demonstrate that the software is computationally
        efficient and supports data from a variety of instrument platforms and experimental
        designs. Results can be viewed within the Trans-Proteomic Pipeline graphical
        user interfaces and exported in standard formats for downstream statistical
        analysis. By integrating StPeter into the freely available Trans-Proteomic
        Pipeline, users can now obtain high-quality label-free quantification of any
        data set in seconds by adding a single command to the workflow.
      authors:
      - name: Hoopmann M.R.
      - name: Winget J.M.
      - name: Mendoza L.
      - name: Moritz R.L.
      citationCount: 11
      date: '2018-03-02T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'StPeter: Seamless Label-Free Quantification with the Trans-Proteomic
        Pipeline'
    note: null
    pmcid: PMC5891225
    pmid: '29400476'
    type:
    - Primary
    version: '1.0'
  relation:
  - biotoolsID: tpp_spc
    type: includedIn
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - 1.4.0
next: ?page=2
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