count: 7256
list:
- accessibility: Open access
  additionDate: '2026-02-12T16:06:53.794717Z'
  biotoolsCURIE: biotools:vvv2_display
  biotoolsID: vvv2_display
  collectionID: []
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  credit:
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    fundrefid: null
    gridid: null
    name: Fabrice Touzain
    note: Dr. in Bioinformatics at ANSES ("Swine virology, innovation and genomics"
      Unit, Bioinformatics Team)
    orcidid: https://orcid.org/0000-0002-1103-2425
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://github.com/ANSES-Ploufragan/vvv2_display
  description: Display SNP proportions (vardict outputs), CDS (vadr annotations) of
    an assembly/genome, coverage depth (samtools depth), in png image and a tsv file,
    filtering results to summarize analyses (alignment, variant calling, annotation).
  documentation: []
  download: []
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  function:
  - cmd: vvv2_display.py -p vadr_pass_annotation.tbl -f vadr_fail_annotation.tbl -s
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      -u signif_snp_res.tsv -x signif_snp_res.vcf
    input:
    - data:
        term: Feature table
        uri: http://edamontology.org/data_1270
      format:
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        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: 'Inputs:

      - tbl pass vadr annotation file

      - tbl fail vadr annotation file

      - seqstat vadr output file

      - vcf vardict-java variant calling results (all)

      - samtools depth text file (coverage depth of an alignment)

      Outputs:

      - png summary file with all info

      - tsv file of indexed significant variant (index showed on png figure)

      - [optional] vcf file of significant variants only'
    operation:
    - term: Base position variability plotting
      uri: http://edamontology.org/operation_0287
    - term: Sequence assembly visualisation
      uri: http://edamontology.org/operation_3184
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
    - data:
        term: Annotated text
        uri: http://edamontology.org/data_3779
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
  homepage: https://github.com/ANSES-Ploufragan/vvv2_display
  homepage_status: 0
  language:
  - R
  - Python
  lastUpdate: '2026-02-12T16:07:53.280544Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ANSES-Ploufragan/vvv2_display
  - note: Galaxy wrapper
    type:
    - Other
    url: https://toolshed.g2.bx.psu.edu/view/ftouzain/vvv2_display/fcd3aa68bd39
  - note: 'Listing of Galaxy workflow performing all steps:

      - alignment on ref/assembly

      - annotation of ref/assembly

      - coputation of coverage depth

      - vardict variant calling

      - vvv2_display synthesis


      One can be found for each sequencing technology (Illumina SE/Proton, Illumina
      PE, nanopore, pacbio'
    type:
    - Other
    url: https://workflowhub.eu/search?q=vvv2
  maturity: Mature
  name: vvv2_display
  operatingSystem:
  - Linux
  otherID:
  - type: doi
    value: doi:10.3390/v17101385
    version: 0.2.4.0
  owner: fabrice
  publication:
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    metadata: null
    note: published in Viruses journal
    pmcid: PMC12567792
    pmid: '41157655'
    type:
    - Primary
    version: 0.2.4.0
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    type: uses
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    type: uses
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    type: uses
  toolType:
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  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Virology
    uri: http://edamontology.org/topic_0781
  validated: 0
  version:
  - 0.2.5.0
- accessibility: null
  additionDate: '2016-02-11T12:02:14Z'
  biotoolsCURIE: biotools:motus
  biotoolsID: motus
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit:
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    name: European Molecular Biology Laboratory (EMBL)
    note: null
    orcidid: null
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    typeEntity: Institute
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    url: https://www.embl.de/
  - email: null
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    note: null
    orcidid: null
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    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: http://www.bork.embl.de/software/mOTU/contact.html
  - email: ssunagawa@ethz.ch
    fundrefid: null
    gridid: null
    name: Shinichi Sunagawa
    note: null
    orcidid: http://orcid.org/0000-0003-3065-0314
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Metagenomic operational taxonomic units (mOTUs) enable high-accuracy
    taxonomic profiling of known (sequenced) and unknown microorganisms at species-level
    resolution from shotgun metagenomic or metatranscriptomic data. The method clusters
    single-copy phylogenetic marker gene sequences from metagenomes and reference
    genomes into mOTUs to quantify their abundances in meta-omics data with very high
    precision and recall.
  documentation:
  - note: null
    type:
    - General
    url: https://motu-tool.org/
  download:
  - note: null
    type: Biological data
    url: https://motu-tool.org/download.html
    version: null
  - note: null
    type: Source code
    url: https://github.com/motu-tool
    version: null
  - note: MBT:Container
    type: Container file
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    version: null
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  function:
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    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: Taxonomic profiling from shotgun metagenomes Quality-filtered shotgun metagenomic
      reads A taxonomic profiling matrix with species in rows and samples in columns;
      each entry m_{i,j} indicates the number of reads from sample j mapped to marker
      genes of species i
    operation:
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: DSV
        uri: http://edamontology.org/format_3751
  homepage: https://motu-tool.org/
  homepage_status: 0
  language:
  - Perl
  lastUpdate: '2026-02-12T14:33:33.330556Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/motu-tool/mOTUs
  - note: Database for mOTUs profiler
    type:
    - Other
    url: https://motus-db.org/
  maturity: null
  name: Metagenomic operational taxonomic units (mOTUs)
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: hdhub
  publication:
  - doi: 10.1186/s40168-022-01410-z
    metadata: null
    note: null
    pmcid: PMC9721005
    pmid: '36464731'
    type:
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    version: null
  - doi: 10.1093/nar/gkae1004
    metadata: null
    note: mOTUs-db paper
    pmcid: PMC11701688
    pmid: '39526369'
    type:
    - Primary
    version: null
  relation:
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    type: uses
  - biotoolsID: motus-db
    type: uses
  toolType:
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  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 1
  version:
  - 2.5.1
- accessibility: Open access
  additionDate: '2016-07-11T10:42:10Z'
  biotoolsCURIE: biotools:3dbionotes
  biotoolsID: 3dbionotes
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    name: EOSC-Life
    note: 'EOSC Life [INFRAEOSC-04-2018, Proposal: 824087]'
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    note: CSIC [PIE/COVID-19 number 202020E079]
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    name: Comunidad de Madrid
    note: Comunidad de Madrid through grant CAM [S2017/BMD-3817]
    orcidid: null
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    name: Spanish Ministry of Science and Innovation
    note: Spanish Ministry of Science and Innovation through projects [SEV 2017-0712,
      FPU-2015/264, PID2019-104757RB-I00/AEI/10.13039/501100011033]
    orcidid: null
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    name: Instituto de Salud Carlos III
    note: 'Instituto de Salud Carlos III: PT17/0009/0010 [ISCIII-SGEFI/ERDF]'
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole: []
    url: null
  description: Web based application designed to integrate protein structure, protein
    sequence and protein annotations in a unique graphical environment. The current
    version of the application offers a unified, enriched and interactive view of
    EMDB volumes, PDB structures and Uniprot sequences where the protein annotations
    stored in Uniprot, Immune Epitope DB, Phospho Site Plus, BioMuta and dSysMap can
    be explored interactively at sequence and structural level.
  documentation:
  - note: null
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  download: []
  editPermission:
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  elixir_badge: 0
  function:
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    input:
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        uri: http://edamontology.org/data_1146
      format: []
    - data:
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        uri: http://edamontology.org/data_1127
      format: []
    - data:
        term: UniProt accession
        uri: http://edamontology.org/data_3021
      format: []
    note: Integration of protein structure, protein sequence and protein annotations
      in a unique graphical environment.
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Structure visualisation
      uri: http://edamontology.org/operation_0570
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    output: []
  homepage: http://3dbionotes.cnb.csic.es/ws
  homepage_status: 0
  language:
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  lastUpdate: '2026-02-05T14:26:24.786970Z'
  license: Apache-2.0
  link: []
  maturity: Mature
  name: 3DBIONOTES
  operatingSystem:
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  - Mac
  - Linux
  otherID: []
  owner: coss
  publication:
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    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  - doi: 10.1093/bioinformatics/btx483
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  - doi: 10.1093/bioinformatics/btz118
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  - doi: 10.1093/bioinformatics/btab397
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_3366
  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  validated: 1
  version:
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- accessibility: Open access
  additionDate: '2016-02-03T14:20:28Z'
  biotoolsCURIE: biotools:brenda
  biotoolsID: brenda
  collectionID:
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  - DSMZ Digital Diversity
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
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    name: BMBF
    note: null
    orcidid: null
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    typeEntity: Funding agency
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    url: null
  - email: contact@brenda-enzymes.org
    fundrefid: null
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    name: null
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    typeEntity: Person
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    gridid: grid.420081.f
    name: Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures
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    url: https://www.dsmz.de/
  description: Internationally leading information system on all aspects of enzymes,
    including function, structure, involvement in diseases, application, engineering,
    and molecular properties. The database includes hundreds of milllions of enzyme
    data and is compiled from manual literature annotation, data integration from
    other databases and predicted enzyme functions and locations.
  documentation:
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    url: http://www.brenda-enzymes.org/copy.php
  - note: null
    type:
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  download: []
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      format: []
    - data:
        term: Gene name
        uri: http://edamontology.org/data_2299
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    - data:
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    - data:
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      format: []
    - data:
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    - data:
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    - data:
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    - data:
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    note: null
    operation:
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    - term: Genome visualisation
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    - term: Transmembrane protein visualisation
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    - term: Structural similarity search
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    - term: Transmembrane protein prediction
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    - term: Database search
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    - term: Pathway or network visualisation
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    - term: Structure visualisation
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    - term: Genome feature comparison
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    - term: Literature search
      uri: http://edamontology.org/operation_0305
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      format: []
    - data:
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    - data:
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    - data:
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    - data:
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    - data:
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    - data:
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    - data:
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      format: []
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  description: 'The tool is designed to perform a customisable image pre-processing
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  description: A tool based on artificial intelligence that is able to perform a categorisation
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  description: 'The tool performs an automatic segmentation of the possible glioblastoma
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    note: null
    operation:
    - term: Image annotation
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        uri: http://edamontology.org/format_3548
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      \ \\\n  harbor.eucaim.cancerimage.eu/processing-tools/mr-based_glioblastoma_tumour_detection_and_segmentation:latest\
      \ \\\n  --series-list '[{\"dataset_id\":\"DS001\",\"patient_id\":\"PAT001\"\
      ,\"study_id\":\"ST001\",\"series_path\":\"/input/DICOM/DS001/PAT001/ST001/T1_POST\"\
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    input:
    - data:
        term: Image
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      format:
      - term: nii
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    - data:
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      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
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      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_glioblastoma_tumour_detection_and_segmentation/artifacts-tab
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2026-02-03T11:47:01.186875Z'
  license: CC-BY-NC-ND-4.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_glioblastoma_tumour_detection_and_segmentation/artifacts-tab
  - note: null
    type:
    - Social media
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  maturity: Mature
  name: MR-based glioblastoma tumour detection and segmentation (EUCAIM-SW-021_T-01-02-004)
  operatingSystem:
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  - Mac
  - Linux
  otherID: []
  owner: casopon
  publication:
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    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
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  topic:
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version:
  - 2.1.1
- accessibility: Open access (with restrictions)
  additionDate: '2025-05-29T09:19:51.404419Z'
  biotoolsCURIE: biotools:mr-based_dipg_tumour_detection_and_segmentation
  biotoolsID: mr-based_dipg_tumour_detection_and_segmentation
  collectionID:
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  confidence_flag: null
  cost: Free of charge (with restrictions)
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    url: null
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    url: null
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    rorid: null
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    url: null
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    name: Adrian Galiana-Bordera
    note: null
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    url: null
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    orcidid: https://orcid.org/0009-0000-2991-1391
    rorid: null
    typeEntity: Person
    typeRole:
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    - Support
    url: null
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    fundrefid: null
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    name: Leonor Cerda-Alberich
    note: null
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    rorid: null
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    url: null
  - email: luis_marti@iislafe.es
    fundrefid: null
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    url: null
  description: The tool performs an automatic segmentation of the possible DIPG tumours
    on MR images. DIPG (Diffuse Intrinsic Pontine Glioma), or more recently, DMG (Diffuse
    Midline Glioma) is a H3 K27M–mutant pediatric brainstem cancer detected in T1W
    and Flair/T2-weighted magnetic resonance images. The tool includes a complete
    workflow from DICOM images to DICOM seg tumoral masks.
  documentation:
  - note: null
    type:
    - General
    url: https://link.springer.com/article/10.1007/s10278-025-01557-9
  - note: null
    type:
    - Terms of use
    url: https://drive.eucaim.cancerimage.eu/s/5CL2H8tyMDzFHex
  - note: null
    type:
    - Training material
    url: https://www.youtube.com/watch?v=f3DPS56z6oI&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=36
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  function:
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    input:
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        term: Image
        uri: http://edamontology.org/data_2968
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      - term: nii
        uri: http://edamontology.org/format_3549
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    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
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    output:
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        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  - cmd: "docker run --rm --gpus all \\\n  -v /path/to/input:/input -v /path/to/output:/output\
      \ harbor.eucaim.cancerimage.eu/processing-tools/mr-based_dipg_tumour_detection_and_segmentation:latest\
      \ \\\n  --json-args '{\"dataset_id\":\"DS1\",\"patient_id\":\"P1\",\"study_id\"\
      :\"S1\",\"sequences\":{\"T1w\":\"/input/DICOM/DS1/P1/S1/T1\",\"FLAIR\":\"/input/DICOM/DS1/P1/S1/FLAIR\"\
      }}'"
    input:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
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        uri: http://edamontology.org/format_3548
    note: null
    operation:
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      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
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      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_dipg_tumour_detection_and_segmentation/artifacts-tab
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2026-02-03T11:45:32.223088Z'
  license: CC-BY-NC-ND-4.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_dipg_tumour_detection_and_segmentation/artifacts-tab
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: Mature
  name: MR-based DIPG tumour detection and segmentation (EUCAIM-SW-020_T-01-02-003)
  operatingSystem:
  - Windows
  - Mac
  - Linux
  otherID: []
  owner: casopon
  publication:
  - doi: 10.1007/s10278-025-01557-9
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
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    uri: http://edamontology.org/topic_2640
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    uri: http://edamontology.org/topic_3474
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 2.0.0
- accessibility: Open access (with restrictions)
  additionDate: '2025-05-29T09:08:44.174795Z'
  biotoolsCURIE: biotools:mr-based_neuroblastoma_tumour_detection_and_segmentation
  biotoolsID: mr-based_neuroblastoma_tumour_detection_and_segmentation
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    note: null
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    url: null
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    gridid: null
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    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: dianaveigac@gmail.com
    fundrefid: null
    gridid: null
    name: Diana Veiga Canuto
    note: null
    orcidid: https://orcid.org/0000-0002-6048-2940
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: pedromiguel_martinez@iislafe.es
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    name: Pedro-Miguel Martinez-Girones
    note: null
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    typeEntity: Person
    typeRole:
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    url: null
  - email: carina_soler@iislafe.es
    fundrefid: null
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    name: Carina Soler-Pons
    note: null
    orcidid: https://orcid.org/0009-0000-2991-1391
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    - Documentor
    url: null
  - email: luis_marti@iislafe.es
    fundrefid: null
    gridid: null
    name: Luis Marti-Bonmati
    note: null
    orcidid: https://orcid.org/0000-0002-8234-010X
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    url: null
  description: 'This tool is specifically designed and validated for automated detection
    and segmentation of neuroblastic tumours in T2-weighted magnetic resonance images
    (T2-MR) using deep learning. It processes DICOM or NIfTI input data and outputs
    in NIFTI or DICOM SEG.


    TRAINING & VALIDATION COHORTS:


    Initial Development (Veiga-Canuto 2022):

    -Training: 106 patients, 5-fold CV (median DSC 0.965 ± 0.018).

    -Internal validation: 26 patients (median DSC 0.918 ± 0.067).

    -Sources: La Fe (Spain), SIOPEN HR-NBL1/LINES, St. Anna (Austria), Pisa (Italy).

    -Mean age: 37.6 ± 39.3 months.

    -Median tumor volume: 116,518 mm³.


    External Validation (Veiga-Canuto 2023):

    -300 patients, 535 independent T2 MRI scans (486 at diagnosis, 49 post-chemotherapy).

    -Performance: median DSC 0.997 (0.944–1.000), 94% successful detection.

    -Sources: 12 European countries (HR-NBL1/SIOPEN 119, LINES/SIOPEN 107, German
    Registry 62, others 12).

    -Heterogeneous data: 1.5T (435), 3T (100); Siemens (318), Philips (109), GE (105),
    Canon (3).'
  documentation:
  - note: null
    type:
    - General
    url: https://www.mdpi.com/2072-6694/14/15/3648
  - note: null
    type:
    - Terms of use
    url: https://drive.eucaim.cancerimage.eu/s/44BxMSNpxwgsZiE
  - note: null
    type:
    - Training material
    url: https://www.youtube.com/watch?v=c7XOEGRA9aQ&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=12
  download: []
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  function:
  - cmd: "docker run --rm \\\n  -v \"<input_path>:/input\" \\\n  -v \"<output_path>:/output\"\
      \ \\\n  --gpus all \\\n  harbor.eucaim.cancerimage.eu/processing-tools/mr_based_neuroblastoma_tumour_detection_and_segmentation:2.0.0\
      \ \\\n  --mode dicom-seg \\\n  --series-selector /output/config/series_to_segment.csv\
      \ \\\n  --seg-series-number 2302001 \\\n  --seg-algorithm-name \"nnUNet_Neuroblastoma_Primage_training\"\
      \ \\\n  --seg-coordinating-center \"EUCAIM Consortium\" \\\n  --keep-nifti false"
    input:
    - data:
        term: Image
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      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: JSON
        uri: http://edamontology.org/format_3464
    note: null
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
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      - term: nii
        uri: http://edamontology.org/format_3549
  homepage: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/artifacts-tab
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2026-02-03T11:40:55.741817Z'
  license: CC-BY-NC-ND-4.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/artifacts-tab
  - note: null
    type:
    - Social media
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  maturity: Mature
  name: MR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: casopon
  publication:
  - doi: 10.3390/cancers14153648
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.3390/cancers15051622
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 2.0.0
- accessibility: Open access
  additionDate: '2018-06-21T08:37:35Z'
  biotoolsCURIE: biotools:jmztab-m
  biotoolsID: jmztab-m
  collectionID:
  - de.NBI
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: https://orcid.org/0000-0002-6540-6875
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://github.com/lifs-tools/jmzTab-m
  description: Reference implementation for mzTab 2.0 for metabolomics
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://github.com/lifs-tools/jmzTab-m/blob/master/README.adoc
  download:
  - note: null
    type: Software package
    url: https://github.com/lifs-tools/jmzTab-m/releases
    version: null
  editPermission:
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    type: private
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  homepage: https://github.com/lifs-tools/jmzTab-m
  homepage_status: 0
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  lastUpdate: '2026-02-03T10:26:12.147063Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/lifs-tools/jmzTab-m
  - note: null
    type:
    - Repository
    url: https://anaconda.org/bioconda/jmztab-m
  maturity: Mature
  name: jmztab-m
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: de.NBI_Service_Team
  publication:
  - doi: 10.1021/acs.analchem.9b01987
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: 1.0.4
  relation: []
  toolType:
  - Library
  - Command-line tool
  topic:
  - term: Ontology and terminology
    uri: http://edamontology.org/topic_0089
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 1
  version:
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  - 1.0.1
  - 1.0.2
  - 1.0.4
  - 1.0.5
  - 1.0.6
- accessibility: null
  additionDate: '2019-06-20T14:18:28Z'
  biotoolsCURIE: biotools:decompTumor2Sig
  biotoolsID: decompTumor2Sig
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: r.piro@fu-berlin.de
    fundrefid: null
    gridid: null
    name: Prof. Dr. Rosario M. Piro
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: null
  description: R package for identification of mutational signatures active in individual
    tumors. It decomposes an individual tumor genome into a given set of Alexandrov-type
    or Shiraishi-type signatures, thus quantifying the contribution of the corresponding
    mutational processes to the somatic mutations identified in the tumor.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/rmpiro/decompTumor2Sig/blob/master/decompTumor2Sig-manual.pdf
  download:
  - note: null
    type: Downloads page
    url: http://rmpiro.net/index.html#downloads
    version: null
  editPermission:
    authors:
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    type: group
  elixirCommunity: []
  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence motif recognition
      uri: http://edamontology.org/operation_0239
    - term: Parsing
      uri: http://edamontology.org/operation_1812
    output: []
  homepage: http://rmpiro.net/decompTumor2Sig/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2026-02-02T08:12:42.981321Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/rmpiro/decompTumor2Sig/issues
  - note: null
    type:
    - Repository
    url: https://github.com/rmpiro/decompTumor2Sig
  - note: null
    type:
    - Software catalogue
    url: https://bioconductor.org/packages/release/bioc/html/decompTumor2Sig.html
  maturity: Mature
  name: decompTumor2Sig
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Ruta
  publication:
  - doi: 10.1186/s12859-019-2688-6
    metadata:
      abstract: '© 2019 The Author(s).Background: The somatic mutations found in a
        tumor have in most cases been caused by multiple mutational processes such
        as those related to extrinsic carcinogens like cigarette smoke, and those
        related to intrinsic processes like age-related spontaneous deamination of
        5-methylcytosine. The effect of such mutational processes can be modeled by
        mutational signatures, of which two different conceptualizations exist: the
        model introduced by Alexandrov et al., Nature 500:415-421, 2013, and the model
        introduced by Shiraishi et al., PLoS Genetics 11(12):e1005657, 2015. The initial
        identification and definition of mutational signatures requires large sets
        of tumor samples. Results: Here, we present decompTumor2Sig, an easy to use
        R package that can decompose an individual tumor genome into a given set of
        Alexandrov-type or Shiraishi-type signatures, thus quantifying the contribution
        of the corresponding mutational processes to the somatic mutations identified
        in the tumor. Until now, such tools were available only for Alexandrov signatures.
        We demonstrate the correctness and usefulness of our approach with three test
        cases, using somatic mutations from 21 breast cancer genomes, from 435 tumor
        genomes of ten different tumor entities, and from simulated tumor genomes,
        respectively. Conclusions: The decompTumor2Sig package is freely available
        and has been accepted for inclusion in Bioconductor.'
      authors:
      - name: Kruger S.
      - name: Piro R.M.
      citationCount: 8
      date: '2019-04-18T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'DecompTumor2Sig: Identification of mutational signatures active in individual
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    note: null
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    [v1.4] DrugComb: an integrative cancer drug combination data portal. https://doi.org/10.1093/nar/gkz337'
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  publication:
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  description: Open source Java library for bio- and cheminformatics.
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  homepage: http://github.com/cdk/cdk/
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  language:
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  license: LGPL-2.1
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  name: The Chemistry Development Kit
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  owner: egonw
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    metadata:
      abstract: ''
      authors: []
      citationCount: 381
      date: '2017-06-06T00:00:00Z'
      journal: Journal of Cheminformatics
      title: 'The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular
        formulas, and substructure searching'
    note: null
    pmcid: PMC5461230
    pmid: '29086040'
    type: []
    version: null
  - doi: 10.1021/ci025584y
    metadata:
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      citationCount: 935
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      journal: Journal of Chemical Information and Computer Sciences
      title: 'The Chemistry Development Kit (CDK): An open-source Java library for
        chemo- and bioinformatics'
    note: null
    pmcid: PMC4901983
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    type:
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    version: null
  - doi: 10.2174/138161206777585274
    metadata:
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      citationCount: 445
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      journal: Current Pharmaceutical Design
      title: Recent developments of the Chemistry Development Kit (CDK) - An open-source
        Java library for chemo- and bioinformatics
    note: null
    pmcid: null
    pmid: '16796559'
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    version: null
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  toolType:
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  validated: 1
  version:
  - '1'
- accessibility: null
  additionDate: '2020-06-08T16:17:43Z'
  biotoolsCURIE: biotools:warpp
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  description: 'WarPP places water molecules for the active site of a given PDB file.
    Based on interaction geometries previously derived from protein crystal structures
    potential water positions are generated for free interaction directions (lone
    pairs of acceptors or hydrogen atoms of donors). Every small molecule present
    in the PDB structure is used to generate an active site of 6.5 Å radius around
    each atom. Within these active sites water molecules are placed.

    In the corresponding publication we showed that WarPP is able to place 80% of
    water molecules within 1.0 Å distance to a crystallographically observed one.'
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    note: null
    operation:
    - term: Protein structure analysis
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      format:
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    - data:
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      format:
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  homepage: http://proteinsplus.zbh.uni-hamburg.de/#warpp
  homepage_status: 0
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  name: WarPP
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  publication:
  - doi: 10.1021/acs.jcim.8b00271
    metadata:
      abstract: Water molecules are of great importance for the correct representation
        of ligand binding interactions. Throughout the last years, water molecules
        and their integration into drug design strategies have received increasing
        attention. Nowadays a variety of tools are available to place and score water
        molecules. However, the most frequently applied software solutions require
        substantial computational resources. In addition, none of the existing methods
        has been rigorously evaluated on the basis of a large number of diverse protein
        complexes. Therefore, we present a novel method for placing water molecules,
        called WarPP, based on interaction geometries previously derived from protein
        crystal structures. Using a large, previously compiled, high-quality validation
        set of almost 1500 protein-ligand complexes containing almost 20000 crystallographically
        observed water molecules in their active sites, we validated our placement
        strategy. We correctly placed 80% of the water molecules within 1.0 Å of a
        crystallographically observed one.
      authors:
      - name: Nittinger E.
      - name: Flachsenberg F.
      - name: Bietz S.
      - name: Lange G.
      - name: Klein R.
      - name: Rarey M.
      citationCount: 33
      date: '2018-08-27T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'Placement of Water Molecules in Protein Structures: From Large-Scale
        Evaluations to Single-Case Examples'
    note: null
    pmcid: null
    pmid: '30036062'
    type:
    - Primary
    version: null
  relation: []
  toolType:
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  topic:
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  validated: 1
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- accessibility: Open access
  additionDate: '2019-07-06T06:35:25Z'
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    name: Matthias Rarey
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  description: Tool for 3D protein structure profiling.
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  function:
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    operation:
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  name: StructureProfiler
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  publication:
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    metadata:
      abstract: Motivation Three-dimensional protein structures are important starting
        points for elucidating protein function and applications like drug design.
        Computational methods in this area rely on high quality validation datasets
        which are usually manually assembled. Due to the increase in published structures
        as well as the increasing demand for specially tailored validation datasets,
        automatic procedures should be adopted. Results StructureProfiler is a new
        tool for automatic, objective and customizable profiling of X-ray protein
        structures based on the most frequently applied selection criteria currently
        in use to assemble benchmark datasets. As examples, four dataset configurations
        (Astex, Iridium, Platinum, combined), all results of the combined tests and
        the list of all PDB Ids passing the combined criteria set are attached in
        theSupplementary Material.
      authors:
      - name: Meyder A.
      - name: Kampen S.
      - name: Sieg J.
      - name: Fahrrolfes R.
      - name: Friedrich N.-O.
      - name: Flachsenberg F.
      - name: Rarey M.
      citationCount: 4
      date: '2019-03-01T00:00:00Z'
      journal: Bioinformatics
      title: 'StructureProfiler: An all-in-one tool for 3D protein structure profiling'
    note: null
    pmcid: null
    pmid: '30124779'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: null
  additionDate: '2020-06-08T18:35:57Z'
  biotoolsCURIE: biotools:structurechecker
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  description: StructureProfiler was developed as an all-in-one tool to screen structures
    based on selection criteria typically used upon dataset assembly for structure-based
    design methods. Test configurations based on either the Astex, the Iridium HT,
    the Platinum, or the combination of all three criteria catalogs. (Note that RSCC
    as an electron density validation criteria was replaced by EDIAm scoring).
  documentation:
  - note: null
    type:
    - General
    url: http://proteinsplus.zbh.uni-hamburg.de/help/index
  download: []
  editPermission:
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  function:
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    note: null
    operation:
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    output:
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        uri: http://edamontology.org/data_2087
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: http://proteinsplus.zbh.uni-hamburg.de/#structureprofiler
  homepage_status: 0
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  lastUpdate: '2025-12-23T14:50:36.983259Z'
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  publication:
  - doi: 10.1093/bioinformatics/bty692
    metadata:
      abstract: © The Author(s) 2018.Motivation Three-dimensional protein structures
        are important starting points for elucidating protein function and applications
        like drug design. Computational methods in this area rely on high quality
        validation datasets which are usually manually assembled. Due to the increase
        in published structures as well as the increasing demand for specially tailored
        validation datasets, automatic procedures should be adopted. Results StructureProfiler
        is a new tool for automatic, objective and customizable profiling of X-ray
        protein structures based on the most frequently applied selection criteria
        currently in use to assemble benchmark datasets. As examples, four dataset
        configurations (Astex, Iridium, Platinum, combined), all results of the combined
        tests and the list of all PDB Ids passing the combined criteria set are attached
        in theSupplementary Material.
      authors:
      - name: Meyder A.
      - name: Kampen S.
      - name: Sieg J.
      - name: Fahrrolfes R.
      - name: Friedrich N.-O.
      - name: Flachsenberg F.
      - name: Rarey M.
      citationCount: 2
      date: '2019-03-01T00:00:00Z'
      journal: Bioinformatics
      title: 'StructureProfiler: An all-in-one tool for 3D protein structure profiling'
    note: null
    pmcid: null
    pmid: null
    type:
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    version: null
  relation: []
  toolType:
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  topic:
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  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein binding sites
    uri: http://edamontology.org/topic_3534
  validated: 0
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- accessibility: null
  additionDate: '2016-11-28T10:07:16Z'
  biotoolsCURIE: biotools:protoss
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  description: Fully automated hydrogen prediction tool for protein-ligand complexes.
    It adds missing hydrogen atoms to protein structures and detects reasonable protonation
    states, tautomers, and hydrogen coordinates of both protein and ligand molecules.
    It investigates hydrogen bonds, metal interactions and repulsive atom contacts
    for all possible states and calculates an optimal hydrogen bonding network within
    these degrees of freedom.
  documentation:
  - note: null
    type:
    - General
    url: http://proteinsplus.zbh.uni-hamburg.de/help/index
  download: []
  editPermission:
    authors:
    - Bietz
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  elixirNode:
  - Germany
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  elixir_badge: 0
  function:
  - cmd: null
    input:
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        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
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        uri: http://edamontology.org/format_1476
    - data:
        term: Protein-ligand complex
        uri: http://edamontology.org/data_1461
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Hydrogen bond calculation
      uri: http://edamontology.org/operation_0394
    - term: Protein interaction analysis
      uri: http://edamontology.org/operation_2949
    output:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Protein-ligand complex
        uri: http://edamontology.org/data_1461
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: http://proteinsplus.zbh.uni-hamburg.de/#protoss
  homepage_status: 0
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  lastUpdate: '2025-12-23T14:50:36.482733Z'
  license: null
  link: []
  maturity: null
  name: Protoss
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  publication:
  - doi: 10.1186/1758-2946-6-12
    metadata:
      abstract: The calculation of hydrogen positions is a common preprocessing step
        when working with crystal structures of protein-ligand complexes. An explicit
        description of hydrogen atoms is generally needed in order to analyze the
        binding mode of particular ligands or to calculate the associated binding
        energies. Due to the large number of degrees of freedom resulting from different
        chemical moieties and the high degree of mutual dependence this problem is
        anything but trivial. In addition to an efficient algorithm to take care of
        the complexity resulting from complicated hydrogen bonding networks, a robust
        chemical model is needed to describe effects such as tautomerism and ionization
        consistently. We present a novel method for the placement of hydrogen coordinates
        in protein-ligand complexes which takes tautomers and protonation states of
        both protein and ligand into account. Our method generates the most probable
        hydrogen positions on the basis of an optimal hydrogen bonding network using
        an empirical scoring function. The high quality of our results could be verified
        by comparison to the manually adjusted Astex diverse set and a remarkably
        low rate of undesirable hydrogen contacts compared to other tools. © 2014
        Bietz et al.; licensee Chemistry Central Ltd.
      authors:
      - name: Bietz S.
      - name: Urbaczek S.
      - name: Schulz B.
      - name: Rarey M.
      citationCount: 153
      date: '2014-04-03T00:00:00Z'
      journal: Journal of Cheminformatics
      title: 'Protoss: A holistic approach to predict tautomers and protonation states
        in protein-ligand complexes'
    note: null
    pmcid: PMC4019353
    pmid: '24694216'
    type:
    - Primary
    version: null
  - doi: 10.1186/1758-2946-1-13
    metadata:
      abstract: 'Background. Hydrogen bonds play a major role in the stabilization
        of protein-ligand complexes. The ability of a functional group to form them
        depends on the position of its hydrogen atoms. An accurate knowledge of the
        positions of hydrogen atoms in proteins is therefore important to correctly
        identify hydrogen bonds and their properties. The high mobility of hydrogen
        atoms introduces several degrees of freedom: Tautomeric states, where a hydrogen
        atom alters its binding partner, torsional changes where the position of the
        hydrogen atom is rotated around the last heavy-atom bond in a residue, and
        protonation states, where the number of hydrogen atoms at a functional group
        may change. Also, side-chain flips in glutamine and asparagine and histidine
        residues, which are common crystallographic ambiguities must be identified
        before structure-based calculations can be conducted. Results. We have implemented
        a method to determine the most probable hydrogen atom positions in a given
        protein-ligand complex. Optimality of hydrogen bond geometries is determined
        by an empirical scoring function which is used in molecular docking. This
        allows to evaluate protein-ligand interactions with an established model.
        Also, our method allows to resolve common crystallographic ambiguities such
        as as flipped amide groups and histidine residues. To ensure high speed, we
        make use of a dynamic programming approach. Conclusion. Our results were checked
        against selected high-resolution structures from an external dataset, for
        which the positions of the hydrogen atoms have been validated manually. The
        quality of our results is comparable to that of other programs, with the advantage
        of being fast enough to be applied on-the-fly for interactive usage or during
        score evaluation. © 2009 Lippert and Rarey; licensee BioMed Central Ltd.'
      authors:
      - name: Lippert T.
      - name: Rarey M.
      citationCount: 58
      date: '2009-12-01T00:00:00Z'
      journal: Journal of Cheminformatics
      title: Fast automated placement of polar hydrogen atoms in protein-ligand complexes
    note: null
    pmcid: PMC3225823
    pmid: '20298519'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
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    uri: http://edamontology.org/topic_0128
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 1
  version: []
- accessibility: null
  additionDate: '2016-11-21T10:33:07Z'
  biotoolsCURIE: biotools:poseview
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  description: This tool automatically creates two-dimensional diagrams which depict
    the protein-ligand interactions and conform to chemical drawing conventions. Directed
    interactions between protein and ligand are estimated by an integrated scoring
    scheme and depicted as lines between detailed structure diagrams for both protein
    residues and the ligand molecule.
  documentation:
  - note: null
    type:
    - General
    url: http://proteinsplus.zbh.uni-hamburg.de/help/index
  download: []
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  function:
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    input:
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        uri: http://edamontology.org/format_1476
    note: null
    operation:
    - term: Structure visualisation
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    output:
    - data:
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      format:
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      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: PDF
        uri: http://edamontology.org/format_3508
  homepage: http://proteinsplus.zbh.uni-hamburg.de/#poseview
  homepage_status: 0
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  lastUpdate: '2025-12-23T14:50:36.016352Z'
  license: null
  link: []
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  name: PoseView
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  publication:
  - doi: 10.1093/bioinformatics/btl150
    metadata:
      abstract: 'Motivation: In this paper a new algorithmic approach is presented,
        which automatically generates structure diagrams of molecular complexes. A
        complex diagram contains the ligand, the amino acids of the protein interacting
        with the ligand and the hydrophilic interactions schematized as dashed lines
        between the corresponding atoms. The algorithm is based on a combinatorial
        optimization strategy which solves parts of the layout problem non-heuristically.
        The depicted molecules are represented as structure diagrams according to
        the chemical nomenclature. Due to the frequent usage of complex diagrams in
        the scientific literature as well as in text books dealing with structural
        biology, biochemistry and medicinal chemistry, the new algorithm is a key
        element for computer applications in these areas. Results: The method was
        implemented in the new software tool PoseView. It was tested on a representative
        dataset containing 305 protein - ligand complexes in total from the Brookhaven
        Protein Data Bank. PoseView was able to find collision-free layouts for more
        than three quarters of all complexes. In the following the layout generation
        algorithm is presented and, additional to the statistical results, representative
        test cases demonstrating the challenges of the layout generation will be discussed.
        © 2006 Oxford University Press.'
      authors:
      - name: Stierand K.
      - name: Maass P.C.
      - name: Rarey M.
      citationCount: 349
      date: '2006-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'Molecular complexes at a glance: Automated generation of two-dimensional
        complex diagrams'
    note: null
    pmcid: null
    pmid: '16632493'
    type:
    - Primary
    version: null
  - doi: 10.1002/cmdc.200700010
    metadata:
      abstract: As a result of the increasing application of structure-based drug
        design, the visualization of protein-ligand complexes has become an important
        feature in medicinal chemistry. The large number of experimentally resolved
        complex structures and the further development of computer-aided methods like
        docking or de novo design establishes new possibilities in this field. During
        lead finding and optimization, a manual investigation of many complexes and
        their interaction patterns is typically performed. We present an algorithm
        that automatically generates 2D-protein-ligand diagrams as a possible solution
        for a transparent visualization of the contact partners in a complex and as
        a support for scientists in the evaluation of structure-based design results.
        Running the software on representative test data sets, it generates collision
        free layouts for ∼76% of the cases in the range of tenths of a second per
        complex. The success rate for complexes with ligands which have a molecular
        weight >500 Da is 87 %. © 2007 Wiley-VCH Verlag GmbH & Co. KGaA.
      authors:
      - name: Stierand K.
      - name: Rarey M.
      citationCount: 84
      date: '2007-06-11T00:00:00Z'
      journal: ChemMedChem
      title: 'From modeling to medicinal chemistry: Automatic generation oft wo-dimensional
        complex diagrams'
    note: null
    pmcid: null
    pmid: '17436259'
    type:
    - Primary
    version: null
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  toolType:
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  - term: Data visualisation
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  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 1
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  description: HyPPI classifies a protein-protein complex concerning its interaction
    type into permanent, transient or crystal artifact. Permanent protein-protein
    complexes are only stable in their complexed state and the subunits would denature
    upon complex dissociation. Transient protein-protein complexes are stable in the
    complexed form as well as in the monomeric depending of the necessary function
    of the complex. Crystal artifacts have no biological function and are artifically
    formed during crystallization process. The discrimination is performed using two
    characteristics of the protein-protein complex, the hydrophobicity of the interface
    (ΔGHydrophobic) and the quotient of interface area ratios (IF-quotient). The IF-quotient
    takes the symmetry of the protein-protein interface into account.
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    type:
    - General
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    note: Probability score for interface classification
    operation:
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    output:
    - data:
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  homepage: http://proteinsplus.zbh.uni-hamburg.de/#ppi
  homepage_status: 0
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  publication:
  - doi: 10.1007/s10822-012-9626-2
    metadata:
      abstract: The estimation of free energy of binding is a key problem in structure-based
        design. We developed the scoring function HYDE based on a consistent description
        of HYdrogen bond and DEhydration energies in protein-ligand complexes. HYDE
        is applicable to all types of protein targets since it is not calibrated on
        experimental binding affinity data or protein-ligand complexes. The comprehensible
        atom-based score of HYDE is visualized by applying a very intuitive coloring
        scheme, thereby facilitating the analysis of protein-ligand complexes in the
        lead optimization process. In this paper, we have revised several aspects
        of the former version of HYDE which was described in detail previously. The
        revised HYDE version was already validated in large-scale redocking and screening
        experiments which were performed in the course of the Docking and Scoring
        Symposium at 241st ACS National Meeting. In this study, we additionally evaluate
        the ability of the revised HYDE version to predict binding affinities. On
        the PDBbind 2007 coreset, HYDE achieves a correlation coefficient of 0.62
        between the experimental binding constants and the predicted binding energy,
        performing second best on this dataset compared to 17 other well-established
        scoring functions. Further, we show that the performance of HYDE in large-scale
        redocking and virtual screening experiments on the Astex diverse set and the
        DUD dataset respectively, is comparable to the best methods in this field.
        © 2012 Springer Science+Business Media Dordrecht.
      authors:
      - name: Schneider N.
      - name: Lange G.
      - name: Hindle S.
      - name: Klein R.
      - name: Rarey M.
      citationCount: 218
      date: '2013-01-01T00:00:00Z'
      journal: Journal of Computer-Aided Molecular Design
      title: 'A consistent description of HYdrogen bond and DEhydration energies in
        protein-ligand complexes: Methods behind the HYDE scoring function'
    note: null
    pmcid: null
    pmid: '23269578'
    type:
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    version: null
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  validated: 1
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- accessibility: null
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    name: University of Hamburg - Center for Bioinformatics
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  description: Easily interpretable electron density score for individual atoms. It
    quantifies the electron density support of each single atom by computing a weighted
    sum over an oversampled electron density grid in the proximity of the atom. Atomic
    EDIA values can also be combined with the help of the power mean to compute EDIA_m,
    an electron density score for small molecules, fragments, or residues.
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  - note: null
    type:
    - General
    url: http://proteinsplus.zbh.uni-hamburg.de/help/index
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  function:
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    note: Score for structure quality assessment on a global or atomistic level
    operation:
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  homepage: http://proteinsplus.zbh.uni-hamburg.de/#edia
  homepage_status: 0
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  name: EDIA
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  publication:
  - doi: 10.1021/acs.jcim.7b00391
    metadata:
      abstract: © 2017 American Chemical Society.Macromolecular structures resolved
        by X-ray crystallography are essential for life science research. While some
        methods exist to automatically quantify the quality of the electron density
        fit, none of them is without flaws. Especially the question of how well individual
        parts like atoms, small fragments, or molecules are supported by electron
        density is difficult to quantify. While taking experimental uncertainties
        correctly into account, they do not offer an answer on how reliable an individual
        atom position is. A rapid quantification of this atomic position reliability
        would be highly valuable in structure-based molecular design. To overcome
        this limitation, we introduce the electron density score EDIA for individual
        atoms and molecular fragments. EDIA assesses rapidly, automatically, and intuitively
        the fit of individual as well as multiple atoms (EDIAm) into electron density
        accompanied by an integrated error analysis. The computation is based on the
        standard 2fo - fc electron density map in combination with the model of the
        molecular structure. For evaluating partial structures, EDIAm shows significant
        advantages compared to the real-space R correlation coefficient (RSCC) and
        the real-space difference density Z score (RSZD) from the molecular modeler's
        point of view. Thus, EDIA abolishes the time-consuming step of visually inspecting
        the electron density during structure selection and curation. It supports
        daily modeling tasks of medicinal and computational chemists and enables a
        fully automated assembly of large-scale, high-quality structure data sets.
        Furthermore, EDIA scores can be applied for model validation and method development
        in computer-aided molecular design. In contrast to measuring the deviation
        from the structure model by root-mean-squared deviation, EDIA scores allow
        comparison to the underlying experimental data taking its uncertainty into
        account.
      authors:
      - name: Meyder A.
      - name: Nittinger E.
      - name: Lange G.
      - name: Klein R.
      - name: Rarey M.
      citationCount: 33
      date: '2017-10-23T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: Estimating Electron Density Support for Individual Atoms and Molecular
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    note: null
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    type:
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  relation: []
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  validated: 1
  version: []
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  biotoolsCURIE: biotools:data_integration_quality_check_tool_diqct
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  description: A tool that checks the clinical metadata quality (validity, completeness),
    the integrity between images and clinical metadata provided as well as their accuracy,  the
    de-identification protocol applied, and existence of annotation together with
    the consistency between the images and the annotation files and informs the user
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  - doi: 10.1109/EMBC48229.2022.9871018
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      journal: Proceedings of the Annual International Conference of the IEEE Engineering
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      title: 'Data Quality Check in Cancer Imaging Research: Deploying and Evaluating
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    note: null
    pmcid: null
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  toolType:
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  additionDate: '2016-06-24T11:36:59Z'
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  description: Project that aims to identify orthologs among publicly available, complete
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    type:
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    input:
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    type:
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    note: null
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    type:
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  toolType:
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  topic:
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  - term: Comparative genomics
    uri: http://edamontology.org/topic_0797
  validated: 1
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  additionDate: '2017-03-23T14:24:24Z'
  biotoolsCURIE: biotools:gnpis
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  description: 'GnpIS is an integrative and multi-species information system dedicated
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    to access and cross-reference genetic data (accessions, phenotypes, polymorphisms,
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    Accessible via a public web portal, GnpIS enables different types of data to be
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    GnpIS is used by the French National Research Institute for Agriculture, Food
    and Environment (INRAE) and its partners in major national and international projects.'
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    - data:
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    - data:
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    - data:
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    - data:
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  homepage: https://urgi.versailles.inrae.fr/gnpis
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  language:
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  license: BSD-3-Clause
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    version: null
  owner: cpommier
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    metadata:
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      citationCount: 43
      date: '2013-12-01T00:00:00Z'
      journal: Database
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    note: null
    pmcid: PMC3746681
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      citationCount: 2
      date: '2017-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Mining plant genomic and genetic data using the gnpis information system
    note: null
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    type:
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  - doi: 10.1038/hortres.2016.56
    metadata:
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      citationCount: 38
      date: '2016-11-23T00:00:00Z'
      journal: Horticulture Research
      title: Towards an open grapevine information system
    note: null
    pmcid: PMC5120350
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    type:
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      citationCount: 6
      date: '2016-03-01T00:00:00Z'
      journal: Plant Genome
      title: TransPLANT resources for triticeae genomic data
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    type:
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  - doi: 10.1186/s13059-018-1491-4
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      citationCount: 205
      date: '2018-08-17T00:00:00Z'
      journal: Genome Biology
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    type: []
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      citationCount: 43
      date: '2019-01-01T00:00:00Z'
      journal: Plant Phenomics
      title: Applying FAIR principles to plant phenotypic data management in GnpIS
    note: null
    pmcid: PMC7718628
    pmid: '33313522'
    type: []
    version: null
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  description: MicroMiner assists in identifying single-residue substitutions in protein
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  description: JAMDA enables the preparation of individual protein structures and
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  description: DoGSite3 was developed for predicting robust and reliable small molecule
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next: ?page=2
previous: null
