count: 30464
list:
- accessibility: null
  additionDate: '2022-05-16T13:26:15.720803Z'
  biotoolsCURIE: biotools:skm
  biotoolsID: skm
  collectionID:
  - ELIXIR-SI
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: National Institute of Biology, Department of Biotechnology and Systems Biology
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://www.nib.si/
  - email: carissarobyn.bleker@nib.si
    fundrefid: null
    gridid: null
    name: Carissa Bleker
    note: null
    orcidid: https://orcid.org/0000-0003-1617-7145
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Kristina Gruden
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Živa Ramšak
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Vid Podpečan
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: 'Stress Knowledge Map:  A compilation of knowledge on mechanisms underlying
    responses of plants to stress, the so called stress signalling network.'
  documentation:
  - note: null
    type:
    - General
    url: https://skm.nib.si/documentation
  download: []
  editPermission:
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    - zagor
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  - Slovenia
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  function:
  - cmd: null
    input:
    - data:
        term: Gene name
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      format: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output:
    - data:
        term: Database search results
        uri: http://edamontology.org/data_2080
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: sif
        uri: http://edamontology.org/format_3619
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Mathematical model
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      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: SBGN-ML
        uri: http://edamontology.org/format_3692
      - term: sif
        uri: http://edamontology.org/format_3619
  homepage: https://skm.nib.si/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-06T16:36:05.864955Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://skm.nib.si/contact
  maturity: Emerging
  name: SKM
  operatingSystem: []
  otherID: []
  owner: carissa
  publication:
  - doi: 10.1016/j.xplc.2024.100920
    metadata:
      abstract: 'Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available
        resource containing two complementary knowledge graphs that describe the current
        knowledge of biochemical, signaling, and regulatory molecular interactions
        in plants: a highly curated model of plant stress signaling (PSS; 543 reactions)
        and a large comprehensive knowledge network (488 390 interactions). Both were
        constructed by domain experts through systematic curation of diverse literature
        and database resources. SKM provides a single entry point for investigations
        of plant stress response and related growth trade-offs, as well as interactive
        explorations of current knowledge. PSS is also formulated as a qualitative
        and quantitative model for systems biology and thus represents a starting
        point for a plant digital twin. Here, we describe the features of SKM and
        show, through two case studies, how it can be used for complex analyses, including
        systematic hypothesis generation and design of validation experiments, or
        to gain new insights into experimental observations in plant biology.'
      authors:
      - name: Bleker C.
      - name: Ramsak
      - name: Bittner A.
      - name: Podpecan V.
      - name: Zagorscak M.
      - name: Wurzinger B.
      - name: Baebler
      - name: Petek M.
      - name: Kriznik M.
      - name: van Dieren A.
      - name: Gruber J.
      - name: Afjehi-Sadat L.
      - name: Weckwerth W.
      - name: Zupanic A.
      - name: Teige M.
      - name: Vothknecht U.C.
      - name: Gruden K.
      citationCount: 8
      date: '2024-06-10T00:00:00Z'
      journal: Plant Communications
      title: 'Stress Knowledge Map: A knowledge graph resource for systems biology
        analysis of plant stress responses'
    note: 'Stress Knowledge Map: A knowledge graph resource for systems biology analysis
      of plant stress responses'
    pmcid: null
    pmid: '38616489'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: gomapman
    type: uses
  - biotoolsID: newt-ve
    type: uses
  toolType:
  - Web application
  - Database portal
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-06-06T11:29:48.238556Z'
  biotoolsCURIE: biotools:flumut
  biotoolsID: flumut
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: FluMut is an open-source tool designed to search for molecular markers
    with potential impact on the biological characteristics of Influenza A viruses
    of the A(H5N1) subtype, starting from complete or partial nucleotide genome sequences.
  documentation: []
  download: []
  editPermission:
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  homepage: https://izsvenezie-virology.github.io/FluMut/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-06T12:05:28.550810Z'
  license: gSOAP-1.3b
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/izsvenezie-virology/FluMut
  - note: Website of the tool.
    type:
    - Repository
    url: https://izsvenezie-virology.github.io/FluMut/
  - note: Publication link.
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    - Other
    url: https://doi.org/10.1093/ve/veaf011
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  name: flumut
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  owner: evezeyl
  publication:
  - doi: 10.1093/ve/veaf011
    metadata:
      abstract: Over the past century, Influenza A virus (IAV) has caused four of
        the five reported pandemics, all of which originated from viruses possessing
        genome segments of avian origin. The recent spread of highly pathogenic avian
        influenza (HPAI) viruses, particularly the clade 2.3.4.4b A(H5N1) subtype,
        has led to an alarming increase in mammalian infections, raising concerns
        about the potential for future pandemics. In response to this, we developed
        FluMut, an open-source, cross-platform tool designed to identify molecular
        markers with potential impacts on H5N1 virus phenotypes. FluMut leverages
        an up-To-date database, FluMutDB, to rapidly analyze thousands of nucleotide
        sequences, identifying mutations associated with host adaptation, increased
        virulence, and antiviral resistance. The tool is available both as a command-line
        interface and a user-friendly graphical interface, making it accessible to
        researchers with varying levels of computational expertise. FluMut provides
        comprehensive outputs, including tables of detected markers, their biological
        effects, and corresponding literature references. This tool fills a critical
        gap in the genomic surveillance of HPAI H5N1, facilitating real-Time monitoring
        of viral evolution and aiding in the identification of mutations that may
        signal increased pandemic potential. Future updates will extend FluMut's capabilities
        to other influenza subtypes.
      authors:
      - name: Giussani E.
      - name: Sartori A.
      - name: Salomoni A.
      - name: Cavicchio L.
      - name: De Battisti C.
      - name: Pastori A.
      - name: Varotto M.
      - name: Zecchin B.
      - name: Hughes J.
      - name: Monne I.
      - name: Fusaro A.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
      journal: Virus Evolution
      title: 'FluMut: A tool for mutation surveillance in highly pathogenic H5N1 genomes'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 0.6.4
- accessibility: Open access
  additionDate: '2023-06-14T13:47:49.064543Z'
  biotoolsCURIE: biotools:virtual_imaging_platform
  biotoolsID: virtual_imaging_platform
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: VIP is a web portal for medical imaging applications. It allows users
    to access scientific applications as a service (directly through the web browser
    with no installation required), as well as distributed computing resources in
    a transparent manner. It exploits the resources available in the biomed virtual
    organization of the EGI e-infrastructure to offer an open service to researchers
    worldwide.
  documentation:
  - note: null
    type:
    - Other
    url: https://github.com/virtual-imaging-platform/VIP-portal/wiki
  download:
  - note: null
    type: Source code
    url: https://github.com/virtual-imaging-platform/
    version: 3.0.1
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
    note: null
    operation:
    - term: Image analysis
      uri: http://edamontology.org/operation_3443
    output: []
  homepage: https://vip.creatis.insa-lyon.fr
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2025-06-06T11:51:24.125664Z'
  license: CECILL-B
  link:
  - note: null
    type:
    - Service
    url: https://vip.creatis.insa-lyon.fr/
  maturity: Mature
  name: Virtual Imaging Platform
  operatingSystem: []
  otherID: []
  owner: sorina.pop
  publication:
  - doi: 10.1109/tmi.2012.2220154
    metadata:
      abstract: This paper presents the Virtual Imaging Platform (VIP), a platform
        accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of
        object models and medical image simulators, and to provide access to distributed
        computing and storage resources. A complete overview is presented, describing
        the ontologies designed to share models in a common repository, the workflow
        template used to integrate simulators, and the tools and strategies used to
        exploit computing and storage resources. Simulation results obtained in four
        image modalities and with different models show that VIP is versatile and
        robust enough to support large simulations. The platform currently has 200
        registered users who consumed 33 years of CPU time in 2011. © 1982-2012 IEEE.
      authors:
      - name: Glatard T.
      - name: Lartizien C.
      - name: Gibaud B.
      - name: Da Silva R.F.
      - name: Forestier G.
      - name: Cervenansky F.
      - name: Alessandrini M.
      - name: Benoit-Cattin H.
      - name: Bernard O.
      - name: Camarasu-Pop S.
      - name: Cerezo N.
      - name: Clarysse P.
      - name: Gaignard A.
      - name: Hugonnard P.
      - name: Liebgott H.
      - name: Marache S.
      - name: Marion A.
      - name: Montagnat J.
      - name: Tabary J.
      - name: Friboulet D.
      citationCount: 88
      date: '2013-01-10T00:00:00Z'
      journal: IEEE Transactions on Medical Imaging
      title: A virtual imaging platform for multi-modality medical image simulation
    note: null
    pmcid: null
    pmid: '23014715'
    type: []
    version: null
  relation: []
  toolType:
  - Bioinformatics portal
  topic: []
  validated: 0
  version:
  - 3.0.1
- accessibility: null
  additionDate: '2025-06-06T10:00:26.469620Z'
  biotoolsCURIE: biotools:2d_digital_mammography_harmonization
  biotoolsID: 2d_digital_mammography_harmonization
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: GIBI230 - HULAFE
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: This preprocessing tool is design for 2D digital mammograms in NIfTI
    format. It standardizes and harmonizes images through a configurable pipeline
    that includes spatial reorientation, pseudo-3D stacking, isotropic resampling,
    intensity normalization, optional denoising, contrast enhancement, and mask processing
    (if available).
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
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  elixir_badge: 0
  function: []
  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-06T10:00:26.471908Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: 2D Digital Mammography Harmonization (EUCAIM-SW-046_T-01-03-008)
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: pedromiguel_martinez_HULAFE
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-06-06T07:21:14.735086Z'
  biotoolsCURIE: biotools:genin2
  biotoolsID: genin2
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Genin2 is a lightining-fast bioinformatic tool to predict genotypes
    for clade 2.3.4.4b H5Nx viruses collected in Europe since October 2020. Genin2
    identifies only epidemiologically relevant European genotypes, i.e., detected
    in at least 3 viruses collected from at least 2 countries.
  documentation: []
  download: []
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  homepage: https://github.com/izsvenezie-virology/genin2
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-06T07:22:04.679127Z'
  license: null
  link:
  - note: Article describing the method
    type:
    - Other
    url: https://doi.org/10.1093/ve/veae027
  - note: null
    type:
    - Repository
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  name: Genin2
  operatingSystem: []
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  owner: evezeyl
  publication:
  - doi: 10.1093/ve/veae027
    metadata:
      abstract: Since 2016, A(H5Nx) high pathogenic avian influenza (HPAI) virus of
        clade 2.3.4.4b has become one of the most serious global threats not only
        to wild and domestic birds, but also to public health. In recent years, important
        changes in the ecology, epidemiology, and evolution of this virus have been
        reported, with an unprecedented global diffusion and variety of affected birds
        and mammalian species. After the two consecutive and devastating epidemic
        waves in Europe in 2020-2021 and 2021-2022, with the second one recognized
        as one of the largest epidemics recorded so far, this clade has begun to circulate
        endemically in European wild bird populations. This study used the complete
        genomes of 1,956 European HPAI A(H5Nx) viruses to investigate the virus evolution
        during this varying epidemiological outline. We investigated the spatiotemporal
        patterns of A(H5Nx) virus diffusion to/from and within Europe during the 2020-2021
        and 2021-2022 epidemic waves, providing evidence of ongoing changes in transmission
        dynamics and disease epidemiology. We demonstrated the high genetic diversity
        of the circulating viruses, which have undergone frequent reassortment events,
        providing for the first time a complete overview and a proposed nomenclature
        of the multiple genotypes circulating in Europe in 2020-2022. We described
        the emergence of a new genotype with gull adapted genes, which offered the
        virus the opportunity to occupy new ecological niches, driving the disease
        endemicity in the European wild bird population. The high propensity of the
        virus for reassortment, its jumps to a progressively wider number of host
        species, including mammals, and the rapid acquisition of adaptive mutations
        make the trend of virus evolution and spread difficult to predict in this
        unfailing evolving scenario.
      authors:
      - name: Fusaro A.
      - name: Zecchin B.
      - name: Giussani E.
      - name: Palumbo E.
      - name: Aguero-Garcia M.
      - name: Bachofen C.
      - name: Balint A.
      - name: Banihashem F.
      - name: Banyard A.C.
      - name: Beerens N.
      - name: Bourg M.
      - name: Briand F.-X.
      - name: Brojer C.
      - name: Brown I.H.
      - name: Brugger B.
      - name: Byrne A.M.P.
      - name: Cana A.
      - name: Christodoulou V.
      - name: Dirbakova Z.
      - name: Fagulha T.
      - name: Fouchier R.A.M.
      - name: Garza-Cuartero L.
      - name: Georgiades G.
      - name: Gjerset B.
      - name: Grasland B.
      - name: Groza O.
      - name: Harder T.
      - name: Henriques A.M.
      - name: Hjulsager C.K.
      - name: Ivanova E.
      - name: Janeliunas Z.
      - name: Krivko L.
      - name: Lemon K.
      - name: Liang Y.
      - name: Lika A.
      - name: Malik P.
      - name: Mcmenamy M.J.
      - name: Nagy A.
      - name: Nurmoja I.
      - name: Onita I.
      - name: Pohlmann A.
      - name: Revilla-Fernandez S.
      - name: Sanchez-Sanchez A.
      - name: Savic V.
      - name: Slavec B.
      - name: Smietanka K.
      - name: Snoeck C.J.
      - name: Steensels M.
      - name: Svansson V.
      - name: Swieton E.
      - name: Tammiranta N.
      - name: Tinak M.
      - name: Van Borm S.
      - name: Zohari S.
      - name: Adlhoch C.
      - name: Baldinelli F.
      - name: Terregino C.
      - name: Monne I.
      citationCount: 33
      date: '2024-01-01T00:00:00Z'
      journal: Virus Evolution
      title: High pathogenic avian influenza A(H5) viruses of clade 2.3.4.4b in Europe
        - Why trends of virus evolution are more difficult to predict
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 2.1.2
- accessibility: null
  additionDate: '2023-08-30T09:06:30.320364Z'
  biotoolsCURIE: biotools:pym2aia
  biotoolsID: pym2aia
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Jonas Cordes
    note: null
    orcidid: https://orcid.org/0000-0003-3148-4282
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: pyM²aia is a Python package for memory-efficient access and processing
    of mass spectrometry image data. The I/O functionality is derived from the interactive
    desktop application M²aia. Special features are the batch generator utilities
    for deep learning applications.
  documentation:
  - note: null
    type:
    - General
    url: https://data.jtfc.de/pym2aia/sphinx-build/html/index.html
  download:
  - note: null
    type: Source code
    url: https://github.com/m2aia/pym2aia
    version: null
  editPermission:
    authors: []
    type: public
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://m2aia.de/pym2aia.html
  homepage_status: 0
  language:
  - Python
  - C++
  lastUpdate: '2025-06-05T12:19:20.571346Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/m2aia/pym2aia
  maturity: null
  name: pyM2aia
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: jtfc
  publication:
  - doi: 10.1093/gigascience/giab049
    metadata:
      abstract: 'Background: Mass spectrometry imaging (MSI) is a label-free analysis
        method for resolving bio-molecules or pharmaceuticals in the spatial domain.
        It offers unique perspectives for the examination of entire organs or other
        tissue specimens. Owing to increasing capabilities of modern MSI devices,
        the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting
        in hundreds of gigabytes of data. To fully leverage such MSI acquisitions,
        interactive tools for 3D image reconstruction, visualization, and analysis
        are required, which preferably should be open-source to allow scientists to
        develop custom extensions. Findings: We introduce M2aia (MSI applications
        for interactive analysis in MITK), a software tool providing interactive and
        memory-efficient data access and signal processing of multiple large MSI datasets
        stored in imzML format. M2aia extends MITK, a popular open-source tool in
        medical image processing. Besides the steps of a typical signal processing
        workflow, M2aia offers fast visual interaction, image segmentation, deformable
        3D image reconstruction, and multi-modal registration. A unique feature is
        that fused data with individual mass axes can be visualized in a shared coordinate
        system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse
        kidney dataset and 3D reconstruction and multi-modal image registration of
        a lipid and peptide dataset of a mouse brain, which we make publicly available.
        Conclusions: To our knowledge, M2aia is the first extensible open-source application
        that enables a fast, user-friendly, and interactive exploration of large datasets.
        M2aia is applicable to a wide range of MSI analysis tasks.'
      authors:
      - name: Cordes J.
      - name: Enzlein T.
      - name: Marsching C.
      - name: Hinze M.
      - name: Engelhardt S.
      - name: Hopf C.
      - name: Wolf I.
      citationCount: 17
      date: '2021-07-01T00:00:00Z'
      journal: GigaScience
      title: M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal
        mass spectrometry imaging data
    note: 'Cordes J; Enzlein T; Marsching C; Hinze M; Engelhardt S; Hopf C; Wolf I
      (July, 2021): M²aia - Interactive, fast and memory efficient analysis of 2D
      and 3D multi-modal mass spectrometry imaging data https://doi.org/10.1093/gigascience/giab049'
    pmcid: PMC8290197
    pmid: '34282451'
    type: []
    version: null
  relation:
  - biotoolsID: m2aia
    type: uses
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- accessibility: null
  additionDate: '2021-03-19T09:12:19Z'
  biotoolsCURIE: biotools:msi-r
  biotoolsID: msi-r
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: 'Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based
    on Locally Endogenous Biological Signals.


    Using Biological Signals for Mass Recalibration of Mass Spectrometry Imaging Data.


    This repository contains the python script performing the recalibration of mass
    spectrometry images (MSI) from the associated paper Using Biological Signals for
    Mass Recalibration of Mass Spectrometry Imaging Data.'
  documentation: []
  download: []
  editPermission:
    authors:
    - sizhengZhao
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    - data:
        term: Data reference
        uri: http://edamontology.org/data_2093
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Mass spectra calibration
      uri: http://edamontology.org/operation_3627
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://github.com/LaRoccaRaphael/MSI_recalibration
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T11:49:34.683716Z'
  license: Apache-2.0
  link: []
  maturity: null
  name: MSI-R
  operatingSystem: []
  otherID: []
  owner: Niclaskn
  publication:
  - doi: 10.1021/acs.analchem.0c05071
    metadata:
      abstract: Mass spectrometry imaging (MSI) is a powerful and convenient method
        for revealing the spatial chemical composition of different biological samples.
        Molecular annotation of the detected signals is only possible if a high mass
        accuracy is maintained over the entire image and the m/z range. However, the
        change in the number of ions from pixel-to-pixel of the biological samples
        could lead to small fluctuations in the detected m/z-values, called mass shift.
        The use of internal calibration is known to offer the best solution to avoid,
        or at least to reduce, mass shifts. Their “a priori” selection for a global
        MSI acquisition is prone to false positive detection and therefore to poor
        recalibration. To fill this gap, this work describes an algorithm that recalibrates
        each spectrum individually by estimating its mass shift with the help of a
        list of pixel-specific internal calibrating ions, automatically generated
        in a data-adaptive manner (https://github.com/ LaRoccaRaphael/MSI_recalibration).
        Through a practical example, we applied the methodology to a zebrafish whole-body
        section acquired at a high mass resolution to demonstrate the impact of mass
        shift on data analysis and the capability of our algorithm to recalibrate
        MSI data. In addition, we illustrate the broad applicability of the method
        by recalibrating 31 different public MSI data sets from METASPACE from various
        samples and types of MSI and show that our recalibration significantly increases
        the numbers of METASPACE annotations (gaining from 20 up to 400 additional
        annotations), particularly the high-confidence annotations with a low false
        discovery rate.
      authors:
      - name: Rocca R.L.
      - name: Kune C.
      - name: Tiquet M.
      - name: Stuart L.
      - name: Eppe G.
      - name: Alexandrov T.
      - name: de Pauw E.
      - name: Quinton L.
      citationCount: 14
      date: '2021-03-02T00:00:00Z'
      journal: Analytical Chemistry
      title: Adaptive pixel mass recalibration for mass spectrometry imaging based
        on locally endogenous biological signals
    note: null
    pmcid: null
    pmid: '33583182'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Transcription factors and regulatory sites
    uri: http://edamontology.org/topic_0749
  validated: 0
  version: []
- accessibility: null
  additionDate: '2021-01-18T12:22:18Z'
  biotoolsCURIE: biotools:msiwarp
  biotoolsID: msiwarp
  collectionID: []
  confidence_flag: high
  cost: null
  credit:
  - email: p.l.horvatovich@rug.nl
    fundrefid: null
    gridid: null
    name: Peter Horvatovich
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'A General Approach to Mass Alignment in Mass Spectrometry Imaging.


    MSIWarp is a flexible tool to perform mass alignment of Mass Spectrometry Imaging
    (MSI) spectra. A key feature of MSIWarp is its compatibility with centroid spectra.'
  documentation: []
  download: []
  editPermission:
    authors:
    - sizhengZhao
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Alignment
      uri: http://edamontology.org/operation_2928
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://github.com/horvatovichlab/MSIWarp
  homepage_status: 0
  language:
  - C++
  - Python
  lastUpdate: '2025-06-05T11:41:35.873717Z'
  license: MIT
  link: []
  maturity: null
  name: MSIWarp
  operatingSystem: []
  otherID: []
  owner: Niclaskn
  publication:
  - doi: 10.1021/acs.analchem.0c03833
    metadata:
      abstract: Mass spectrometry imaging (MSI) is a technique that provides comprehensive
        molecular information with high spatial resolution from tissue. Today, there
        is a strong push toward sharing data sets through public repositories in many
        research fields where MSI is commonly applied; yet, there is no standardized
        protocol for analyzing these data sets in a reproducible manner. Shifts in
        the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle
        that can make it impossible to distinguish one compound from another. Here,
        we present a label-free m/z alignment approach that is compatible with multiple
        instrument types and makes no assumptions on the sample's molecular composition.
        Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an
        m/z recalibration function by maximizing a similarity score that considers
        both the intensity and m/z position of peaks matched between two spectra.
        MSIWarp requires only centroid spectra to find the recalibration function
        and is thereby readily applicable to almost any MSI data set. To deal with
        particularly misaligned or peak-sparse spectra, we provide an option to detect
        and exclude spurious peak matches with a tailored random sample consensus
        (RANSAC) procedure. We evaluate our approach with four publicly available
        data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate
        up to 88% improvement in m/z alignment.
      authors:
      - name: Eriksson J.O.
      - name: Sanchez Brotons A.
      - name: Rezeli M.
      - name: Suits F.
      - name: Marko-Varga G.
      - name: Horvatovich P.
      citationCount: 15
      date: '2020-12-15T00:00:00Z'
      journal: Analytical Chemistry
      title: 'MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging'
    note: null
    pmcid: PMC7745203
    pmid: '33317272'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2021-05-26T20:42:18Z'
  biotoolsCURIE: biotools:maldi_quant
  biotoolsID: maldi_quant
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: mail@sebastiangibb.de
    fundrefid: null
    gridid: null
    name: Sebastian Gibb
    note: null
    orcidid: https://orcid.org/0000-0001-7406-4443
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Korbinian Strimmer
    note: null
    orcidid: https://orcid.org/0000-0001-7917-2056
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: MALDIquant is a complete analysis pipeline for matrix-assisted laser
    desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass
    spectrometry data. In addition to commonly used plotting and processing methods
    it includes distinctive features, namely baseline subtraction methods such as
    morphological filters (TopHat) or the statistics-sensitive non-linear iterative
    peak-clipping algorithm (SNIP), peak alignment using warping functions, handling
    of replicated measurements as well as allowing spectra with different resolutions.
  documentation:
  - note: null
    type:
    - General
    url: https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf
  download: []
  editPermission:
    authors:
    - sizhengZhao
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
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      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
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      format:
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    note: null
    operation:
    - term: Quantification
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    - term: Spectrum calculation
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    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Plot
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      format:
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  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Mass spectra calibration
      uri: http://edamontology.org/operation_3627
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://cran.r-project.org/package=MALDIquant
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-06-05T11:28:57.497366Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/sgibb/MALDIquant/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/sgibb/MALDIquant/issues
  - note: null
    type:
    - Other
    url: http://strimmerlab.org/software/maldiquant/
  maturity: null
  name: MALDIquant
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1093/bioinformatics/bts447
    metadata:
      abstract: MALDIquant is an R package providing a complete and modular analysis
        pipeline for quantitative analysis of mass spectrometry data. MALDIquant is
        specifically designed with application in clinical diagnostics in mind and
        implements sophisticated routines for importing raw data, preprocessing, non-linear
        peak alignment and calibration. It also handles technical replicates as well
        as spectra with unequal resolution. © The Author 2012. Published by Oxford
        University Press. All rights reserved.
      authors:
      - name: Gibb S.
      - name: Strimmer K.
      citationCount: 499
      date: '2012-09-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Maldiquant: A versatile R package for the analysis of mass spectrometry
        data'
    note: null
    pmcid: null
    pmid: '22796955'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  - Workflow
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-05-29T10:58:25.526645Z'
  biotoolsCURIE: biotools:volumetry_analysis
  biotoolsID: volumetry_analysis
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: GIBI230 - HULAFE
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Performs volumetric analysis of brain structures by segmenting and
    calculating the volume of grey matter, white matter, and CSF. Results support
    studies on neurodegeneration, development, or disease progression.
  documentation: []
  download: []
  editPermission:
    authors:
    - pedromiguel_martinez_HULAFE
    type: group
  elixirCommunity: []
  elixirNode: []
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  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-05T11:09:36.363997Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Volumetry analysis (EUCAIM-SW-060_T-02-01-009)
  operatingSystem: []
  otherID: []
  owner: casopon
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T10:56:54.107090Z'
  biotoolsCURIE: biotools:deep_features_extraction
  biotoolsID: deep_features_extraction
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  confidence_flag: null
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  - email: null
    fundrefid: null
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    name: GIBI230 - HULAFE
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Extracts deep features from MR images using pretrained neural networks.
    These features can be used for classification, clustering, or survival prediction
    tasks in medical imaging.
  documentation: []
  download: []
  editPermission:
    authors:
    - pedromiguel_martinez_HULAFE
    type: group
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  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Deep features extraction (EUCAIM-SW-058_T-02-01-007)
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  topic:
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
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  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T10:54:55.801249Z'
  biotoolsCURIE: biotools:r1_and_t1_maps_extraction
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    typeRole: []
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  description: Computes R1 and T1 maps from MR images, showing the rate and time of
    longitudinal relaxation. These are key quantitative biomarkers for tissue characterization.
  documentation: []
  download: []
  editPermission:
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    - pedromiguel_martinez_HULAFE
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  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
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    uri: http://edamontology.org/topic_3365
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  additionDate: '2025-05-29T10:53:18.816046Z'
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  - email: null
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    name: GIBI230 - HULAFE
    note: null
    orcidid: null
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    typeEntity: null
    typeRole: []
    url: null
  description: Extracts diffusion-related maps (e.g., ADC, IVIM, Kurtosis) from DWI
    sequences to evaluate microstructural properties of tissues, commonly used in
    oncology and neurology.
  documentation: []
  download: []
  editPermission:
    authors:
    - pedromiguel_martinez_HULAFE
    type: group
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  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Diffusion maps extraction (EUCAIM-SW-056_T-02-01-005)
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  topic:
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    uri: http://edamontology.org/topic_3365
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- accessibility: null
  additionDate: '2025-05-29T10:51:47.444616Z'
  biotoolsCURIE: biotools:r2_maps_extraction
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  description: Tool for calculating R2 maps from T2*-weighted images. These maps reflect
    tissue relaxation rates and can be used to assess tissue properties and detect
    abnormalities.
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    type:
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    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
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  credit: []
  description: "Implemented by GIBI230, this tool is a Docker-based software designed\
    \ for extracting radiomic features from 3D medical images in NIfTI format using\
    \ the PyRadiomics library (if DICOM images, the DICOM to NIFTI converter must\
    \ be run before using this tool). It streamlines the radiomics calculation process\
    \ by generating a structured CSV file containing all extracted variables from\
    \ medical images.\nThe dockerized software enables users to configure parameters\
    \ like filters, bin width, resampling spacing, and normalization settings can\
    \ be specified. The output radiomic variables provide quantitative information\
    \ for further analysis in medical imaging research and machine learning applications.\
    \ \nSpecially important the parameter selection of the band width. For robust\
    \ and reproducible results, a bin width of 5 is commonly recommended, but it should\
    \ be adjusted based on image resolution, modality, and noise levels."
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  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T11:07:12.219171Z'
  license: null
  link: []
  maturity: null
  name: Radiomic features extraction (EUCAIM-SW-054_T-02-01-003)
  operatingSystem: []
  otherID: []
  owner: pedromiguel_martinez_HULAFE
  publication:
  - doi: 10.1148/ryai.230208
    metadata:
      abstract: 'Purpose: To evaluate the reproducibility of radiomics features extracted
        from T2-weighted MR images in patients with neuroblastoma. Materials and Methods:
        A retrospective study included 419 patients (mean age, 29 months ± 34 [SD];
        220 male, 199 female) with neu-roblastic tumors diagnosed between 2002 and
        2023, within the scope of the PRedictive In-silico Multiscale Analytics to
        support cancer personalized diaGnosis and prognosis, Empowered by imaging
        biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences
        at diagnosis and/or after initial chemotherapy. Images underwent processing
        steps (denoising, inhomogeneity bias field cor-rection, normalization, and
        resampling). Tumors were automatically segmented, and 107 shape, first-order,
        and second-order radiomics features were extracted, considered as the reference
        standard. Subsequently, the previous image processing settings were modified,
        and volumetric masks were applied. New radiomics features were extracted and
        compared with the reference standard. Reproducibility was assessed using the
        concordance correlation coefficient (CCC); intrasubject repeatability was
        measured using the coefficient of variation (CoV). Results: When normalization
        was omitted, only 5% of the radiomics features demonstrated high reproducibility.
        Statistical analysis revealed significant changes in the normalization and
        resampling processes (P < .001). Inhomogeneities removal had the least impact
        on radiomics (83% of parameters remained stable). Shape features remained
        stable after mask modifications, with a CCC greater than 0.90. Mask modifications
        were the most favorable changes for achieving high CCC values, with a radiomics
        features stability of 70%. Only 7% of second-order radiomics features showed
        an excellent CoV of less than 0.10. Conclusion: Modifications in the T2-weighted
        MRI preparation process in patients with neuroblastoma resulted in changes
        in radiomics features, with normalization identified as the most influential
        factor for reproducibility. Inhomogeneities removal had the least impact on
        radiomics features.'
      authors:
      - name: Veiga-Canuto D.
      - name: Fernandez-Paton M.
      - name: Alberich L.C.
      - name: Pastor A.J.
      - name: Maya A.G.
      - name: Sierra J.M.C.
      - name: Nebot C.S.
      - name: de las Heras B.M.
      - name: Potschger U.
      - name: Taschner-Mandl S.
      - name: Neri E.
      - name: Canete A.
      - name: Ladenstein R.
      - name: Hero B.
      - name: Alberich-Bayarri A.
      - name: Marti-Bonmati L.
      citationCount: 1
      date: '2024-07-01T00:00:00Z'
      journal: 'Radiology: Artificial Intelligence'
      title: Reproducibility Analysis of Radiomic Features on T2-weighted MR Images
        after Processing and Segmentation Alterations in Neuroblastoma Tumors
    note: null
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    pmid: null
    type:
    - Primary
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  description: This tool extracts perfusion maps from dynamic imaging data (e.g.,
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  description: Tool designed to analyze and visualize patterns of functional connectivity
    between different brain regions based on fMRI data. It provides correlation matrices
    and derived metrics useful for neuroscience and clinical applications.
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  toolType:
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  description: This tool standardizes slice thickness in CT scans by resampling them
    to a common slice spacing. It is intended as a preprocessing step to harmonize
    CT data, improving consistency across scans acquired with varying slice thicknesses.
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  editPermission:
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  function:
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    input:
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        term: Raw image
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      format:
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        uri: http://edamontology.org/format_3548
    note: null
    operation:
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      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Raw image
        uri: http://edamontology.org/data_3424
      format:
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://quibim.com/products/qp-lung/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T10:41:22.023530Z'
  license: CC-BY-NC-ND-4.0
  link: []
  maturity: Emerging
  name: CT Slice thickness normalization
  operatingSystem:
  - Linux
  otherID: []
  owner: xrafael
  publication: []
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  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  validated: 0
  version:
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  additionDate: '2025-05-29T11:02:38.369484Z'
  biotoolsCURIE: biotools:overall_survival_in_neuroblastoma
  biotoolsID: overall_survival_in_neuroblastoma
  collectionID:
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    note: null
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    typeEntity: null
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    url: null
  description: A machine learning-based tool to estimate the overall survival probability
    in patients with neuroblastoma, supporting clinical decision-making and prognosis.
  documentation: []
  download: []
  editPermission:
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  license: null
  link:
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    type:
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    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Overall survival in neuroblastoma by GIBI230 (EUCAIM-SW-078_T-02-04-008)
  operatingSystem: []
  otherID: []
  owner: casopon
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  relation: []
  toolType:
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  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 1.0.0
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  additionDate: '2025-05-29T11:01:13.461164Z'
  biotoolsCURIE: biotools:overall_survival_and_treatment_response_in_glioblastoma_multiforme
  biotoolsID: overall_survival_and_treatment_response_in_glioblastoma_multiforme
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    note: null
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  description: A machine learning model that predicts overall survival and treatment
    response in patients with glioblastoma, using radiomic, clinical, or genomic features.
  documentation: []
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  link:
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    type:
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  maturity: null
  name: Overall survival and treatment response in glioblastoma multiforme (EUCAIM-SW-077_T-02-04-007)
  operatingSystem: []
  otherID: []
  owner: casopon
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  relation: []
  toolType:
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  topic:
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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:
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  additionDate: '2025-05-29T10:41:57.535794Z'
  biotoolsCURIE: biotools:cluster_based_harmonization
  biotoolsID: cluster_based_harmonization
  collectionID:
  - EUCAIM
  confidence_flag: null
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    name: GIBI230 - HULAFE
    note: null
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  description: 'The tool is designed to perform radiomics harmonization on large and
    heterogeneous datasets, where the risk of over-harmonization is present. Instead
    of directly applying harmonization based on predefined batch labels, the tool
    first identifies groups of batches that share similar characteristics through
    clustering of the radiomics data. It then performs harmonization using these cluster-derived
    labels. The tool allows the harmonization of radiomics variables using two methods:
    (1) original ComBat (Rabinovic, 2007) method, where each original batch group
    is considered for the harmonization process and (2) cluster-based ComBat method,
    where batch groups with similar radiomics characteristics form clusters and the
    latter are being considered for the harmonization process.'
  documentation: []
  download: []
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    type: group
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  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
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  link:
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    type:
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    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Cluster based harmonization (EUCAIM-SW-044_T-01-03-006)
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  owner: casopon
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  toolType:
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  topic:
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
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  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T10:38:08.771699Z'
  biotoolsCURIE: biotools:ct-based_neuroblastoma_tumour_detection_and_segmentation
  biotoolsID: ct-based_neuroblastoma_tumour_detection_and_segmentation
  collectionID:
  - EUCAIM
  confidence_flag: null
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    name: GIBI230 - HULAFE
    note: null
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  description: The tool performs an automatic segmentation of the possible neuroblastoma
    tumours on CT images.
  documentation: []
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    type: group
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  maturity: null
  name: CT-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-022_T-01-02-005)
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  toolType:
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  topic:
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    uri: http://edamontology.org/topic_2640
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
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  biotoolsCURIE: biotools:mr-based_glioblastoma_tumour_detection_and_segmentation
  biotoolsID: mr-based_glioblastoma_tumour_detection_and_segmentation
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  - EUCAIM
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    note: null
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  description: The tool performs an automatic segmentation of the possible glioblastoma
    tumours on MRI images and its subregions
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    type: group
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  name: MR-based glioblastoma tumour detection and segmentation (EUCAIM-SW-021_T-01-02-004)
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  publication:
  - doi: 10.3390/cancers14153648
    metadata:
      abstract: 'Tumor segmentation is one of the key steps in imaging processing.
        The goals of this study were to assess the inter-observer variability in manual
        segmentation of neuroblastic tumors and to analyze whether the state-of-the-art
        deep learning architecture nnU-Net can provide a robust solution to detect
        and segment tumors on MR images. A retrospective multicenter study of 132
        patients with neuroblastic tumors was performed. Dice Similarity Coefficient
        (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC)
        were used to compare segmentation sets. Two more metrics were elaborated to
        understand the direction of the errors: the modified version of False Positive
        (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented
        46 tumors and a comparative study was performed. nnU-Net was trained-tuned
        with 106 cases divided into five balanced folds to perform cross-validation.
        The five resulting models were used as an ensemble solution to measure training
        (n = 106) and validation (n = 26) performance, independently. The time needed
        by the model to automatically segment 20 cases was compared to the time required
        for manual segmentation. The median DSC for manual segmentation sets was 0.969
        (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR).
        The automatic segmentation model achieved a better performance regarding the
        FPRm. MR images segmentation variability is similar between radiologists and
        nnU-Net. Time leverage when using the automatic model with posterior visual
        validation and manual adjustment corresponds to 92.8%.'
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Sanguesa Nebot C.
      - name: Martinez de las Heras B.
      - name: Potschger U.
      - name: Gabelloni M.
      - name: Carot Sierra J.M.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Canete A.
      - name: Ladenstein R.
      - name: Neri E.
      - name: Marti-Bonmati L.
      citationCount: 25
      date: '2022-08-01T00:00:00Z'
      journal: Cancers
      title: Comparative Multicentric Evaluation of Inter-Observer Variability in
        Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance
        Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.3390/cancers15051622
    metadata:
      abstract: Objectives. To externally validate and assess the accuracy of a previously
        trained fully automatic nnU-Net CNN algorithm to identify and segment primary
        neuroblastoma tumors in MR images in a large children cohort. Methods. An
        international multicenter, multivendor imaging repository of patients with
        neuroblastic tumors was used to validate the performance of a trained Machine
        Learning (ML) tool to identify and delineate primary neuroblastoma tumors.
        The dataset was heterogeneous and completely independent from the one used
        to train and tune the model, consisting of 300 children with neuroblastic
        tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and
        49 after finalization of the first phase of chemotherapy). The automatic segmentation
        algorithm was based on a nnU-Net architecture developed within the PRIMAGE
        project. For comparison, the segmentation masks were manually edited by an
        expert radiologist, and the time for the manual editing was recorded. Different
        overlaps and spatial metrics were calculated to compare both masks. Results.
        The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median;
        Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify
        nor segment the tumor. No differences were found regarding the MR magnetic
        field, type of T2 sequence, or tumor location. No significant differences
        in the performance of the net were found in patients with an MR performed
        after chemotherapy. The time for visual inspection of the generated masks
        was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where
        manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The
        automatic CNN was able to locate and segment the primary tumor on the T2-weighted
        images in 94% of cases. There was an extremely high agreement between the
        automatic tool and the manually edited masks. This is the first study to validate
        an automatic segmentation model for neuroblastic tumor identification and
        segmentation with body MR images. The semi-automatic approach with minor manual
        editing of the deep learning segmentation increases the radiologist’s confidence
        in the solution with a minor workload for the radiologist.
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Jimenez-Pastor A.
      - name: Carot Sierra J.M.
      - name: Gomis-Maya A.
      - name: Sanguesa-Nebot C.
      - name: Fernandez-Paton M.
      - name: Martinez de las Heras B.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Potschger U.
      - name: Simon T.
      - name: Neri E.
      - name: Alberich-Bayarri A.
      - name: Canete A.
      - name: Hero B.
      - name: Ladenstein R.
      - name: Marti-Bonmati L.
      citationCount: 11
      date: '2023-03-01T00:00:00Z'
      journal: Cancers
      title: Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma
        Detection and Segmentation in MR Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
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  - term: Data architecture, analysis and design
    uri: http://edamontology.org/topic_3365
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T09:19:51.404419Z'
  biotoolsCURIE: biotools:mr-based_dipg_tumour_detection_and_segmentation
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  description: The tool performs an automatic segmentation of the possible DIPG tumours
    on MR images.
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  - note: null
    type:
    - General
    url: https://link.springer.com/article/10.1007/s10278-025-01557-9
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  maturity: null
  name: MR-based DIPG tumour detection and segmentation (EUCAIM-SW-020_T-01-02-003)
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  publication:
  - doi: 10.1007/s10278-025-01557-9
    metadata: null
    note: null
    pmcid: null
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    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
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T09:08:44.174795Z'
  biotoolsCURIE: biotools:mr-based_neuroblastoma_tumour_detection_and_segmentation
  biotoolsID: mr-based_neuroblastoma_tumour_detection_and_segmentation
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    name: GIBI230 - HULAFE
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    url: null
  description: The tool performs an automatic segmentation of the possible neuroblastoma
    tumours on MR images.
  documentation:
  - note: null
    type:
    - General
    url: https://www.mdpi.com/2072-6694/14/15/3648
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  link:
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  maturity: null
  name: MR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)
  operatingSystem: []
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  owner: casopon
  publication:
  - doi: 10.3390/cancers14153648
    metadata:
      abstract: 'Tumor segmentation is one of the key steps in imaging processing.
        The goals of this study were to assess the inter-observer variability in manual
        segmentation of neuroblastic tumors and to analyze whether the state-of-the-art
        deep learning architecture nnU-Net can provide a robust solution to detect
        and segment tumors on MR images. A retrospective multicenter study of 132
        patients with neuroblastic tumors was performed. Dice Similarity Coefficient
        (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC)
        were used to compare segmentation sets. Two more metrics were elaborated to
        understand the direction of the errors: the modified version of False Positive
        (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented
        46 tumors and a comparative study was performed. nnU-Net was trained-tuned
        with 106 cases divided into five balanced folds to perform cross-validation.
        The five resulting models were used as an ensemble solution to measure training
        (n = 106) and validation (n = 26) performance, independently. The time needed
        by the model to automatically segment 20 cases was compared to the time required
        for manual segmentation. The median DSC for manual segmentation sets was 0.969
        (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR).
        The automatic segmentation model achieved a better performance regarding the
        FPRm. MR images segmentation variability is similar between radiologists and
        nnU-Net. Time leverage when using the automatic model with posterior visual
        validation and manual adjustment corresponds to 92.8%.'
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Sanguesa Nebot C.
      - name: Martinez de las Heras B.
      - name: Potschger U.
      - name: Gabelloni M.
      - name: Carot Sierra J.M.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Canete A.
      - name: Ladenstein R.
      - name: Neri E.
      - name: Marti-Bonmati L.
      citationCount: 25
      date: '2022-08-01T00:00:00Z'
      journal: Cancers
      title: Comparative Multicentric Evaluation of Inter-Observer Variability in
        Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance
        Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.3390/cancers15051622
    metadata:
      abstract: Objectives. To externally validate and assess the accuracy of a previously
        trained fully automatic nnU-Net CNN algorithm to identify and segment primary
        neuroblastoma tumors in MR images in a large children cohort. Methods. An
        international multicenter, multivendor imaging repository of patients with
        neuroblastic tumors was used to validate the performance of a trained Machine
        Learning (ML) tool to identify and delineate primary neuroblastoma tumors.
        The dataset was heterogeneous and completely independent from the one used
        to train and tune the model, consisting of 300 children with neuroblastic
        tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and
        49 after finalization of the first phase of chemotherapy). The automatic segmentation
        algorithm was based on a nnU-Net architecture developed within the PRIMAGE
        project. For comparison, the segmentation masks were manually edited by an
        expert radiologist, and the time for the manual editing was recorded. Different
        overlaps and spatial metrics were calculated to compare both masks. Results.
        The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median;
        Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify
        nor segment the tumor. No differences were found regarding the MR magnetic
        field, type of T2 sequence, or tumor location. No significant differences
        in the performance of the net were found in patients with an MR performed
        after chemotherapy. The time for visual inspection of the generated masks
        was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where
        manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The
        automatic CNN was able to locate and segment the primary tumor on the T2-weighted
        images in 94% of cases. There was an extremely high agreement between the
        automatic tool and the manually edited masks. This is the first study to validate
        an automatic segmentation model for neuroblastic tumor identification and
        segmentation with body MR images. The semi-automatic approach with minor manual
        editing of the deep learning segmentation increases the radiologist’s confidence
        in the solution with a minor workload for the radiologist.
      authors:
      - name: Veiga-Canuto D.
      - name: Cerda-Alberich L.
      - name: Jimenez-Pastor A.
      - name: Carot Sierra J.M.
      - name: Gomis-Maya A.
      - name: Sanguesa-Nebot C.
      - name: Fernandez-Paton M.
      - name: Martinez de las Heras B.
      - name: Taschner-Mandl S.
      - name: Duster V.
      - name: Potschger U.
      - name: Simon T.
      - name: Neri E.
      - name: Alberich-Bayarri A.
      - name: Canete A.
      - name: Hero B.
      - name: Ladenstein R.
      - name: Marti-Bonmati L.
      citationCount: 11
      date: '2023-03-01T00:00:00Z'
      journal: Cancers
      title: Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma
        Detection and Segmentation in MR Images
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: 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:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T08:55:46.088021Z'
  biotoolsCURIE: biotools:denoising-inhomogeneity_correction_tool
  biotoolsID: denoising-inhomogeneity_correction_tool
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
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    gridid: null
    name: GIBI230 - HULAFE
    note: null
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    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A customisable image pre-processing tool that performs 5 of the most
    common denoising filters and ANTS N4 bias field correction filter.
  documentation:
  - note: null
    type:
    - General
    url: https://drive.google.com/file/d/1W0aHAIG_bdU9Z1J3aDNsb1r39WBuX9Ot/view?usp=drive_link
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  homepage_status: 0
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  lastUpdate: '2025-06-05T08:49:36.367527Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Denoising-Inhomogeneity Correction Tool (EUCAIM-SW-015_T-01-01-015)
  operatingSystem: []
  otherID: []
  owner: casopon
  publication:
  - doi: 10.1007/s10278-021-00512-8
    metadata:
      abstract: 'Several noise sources, such as the Johnson–Nyquist noise, affect
        MR images disturbing the visualization of structures and affecting the subsequent
        extraction of radiomic data. We evaluate the performance of 5 denoising filters
        (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian
        filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)),
        with 33 different settings, in T2-weighted MR images of phantoms (N = 112)
        and neuroblastoma patients (N = 25). Filters were discarded until the most
        optimal solutions were obtained according to 3 image quality metrics: peak
        signal-to-noise ratio (PSNR), edge-strength similarity–based image quality
        metric (ESSIM), and noise (standard deviation of the signal intensity of a
        region in the background area). The selected filters were ADFs and UNLMs.
        From them, 107 radiomics features preservation at 4 progressively added noise
        levels were studied. The ADF with a conductance of 1 and 2 iterations standardized
        the radiomic features, improving reproducibility and quality metrics.'
      authors:
      - name: Fernandez Paton M.
      - name: Cerda Alberich L.
      - name: Sanguesa Nebot C.
      - name: Martinez de las Heras B.
      - name: Veiga Canuto D.
      - name: Canete Nieto A.
      - name: Marti-Bonmati L.
      citationCount: 10
      date: '2021-10-01T00:00:00Z'
      journal: Journal of Digital Imaging
      title: MR Denoising Increases Radiomic Biomarker Precision and Reproducibility
        in Oncologic Imaging
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T08:06:46.381063Z'
  biotoolsCURIE: biotools:time_coherence_tool
  biotoolsID: time_coherence_tool
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: GIBI230 - HULAFE
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: "Tool that aims to validate visually the chronological order and logical\
    \ consistency of dates associated with a patient's medical history. It generates\
    \ a timeline visualization for each patient from an Excel file and highlights\
    \ rule violations.\n \nStatus : Containerized"
  documentation: []
  download: []
  editPermission:
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  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T08:48:31.083164Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: Time Coherence Tool (EUCAIM-SW-001_T-01-01-001)
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: pedromiguel_martinez_HULAFE
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-29T08:46:55.065078Z'
  biotoolsCURIE: biotools:ml_model_for_mr_series_categorisation
  biotoolsID: ml_model_for_mr_series_categorisation
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
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    name: GIBI230 - HULAFE
    note: null
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    typeEntity: null
    typeRole: []
    url: null
  description: AI tool that is able to perform a categorisation of MR series by using
    standardized DICOM tags. The categorisation includes the type of sequence (e.g.
    spin echo, gradient echo), the weighting (e.g. T1W, T2W, DCE, ...), the presence
    of fat suppression and the detection of non-relevant / junk series (e.g. localizers,
    calibrations, screenshots...).
  documentation:
  - note: null
    type:
    - General
    url: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01086-w
  download: []
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  homepage: https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T08:47:44.651751Z'
  license: null
  link:
  - note: null
    type:
    - Social media
    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: ML model for MR series categorisation (EUCAIM-SW-011_T-01-01-011)
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: casopon
  publication:
  - doi: 10.1186/s40537-025-01086-w
    metadata:
      abstract: Large medical image repositories present challenges related to unstructured
        data. A data enrichment process allows the storage of additional information
        for fast identification of the content and properties of medical imaging studies.
        The aim of this study is to develop a metadata enrichment pipeline to facilitate
        the secondary use of medical images in a high-throughput environment. Our
        aim was to develop a categorization tool for the MR series to generate standardized
        tags that identify relevant image characteristics such as patient orientation,
        sequence type, weighting type, or the presence of fat suppression. Three models
        that make use of machine learning (ML) and DICOM tags are proposed. The dataset
        for their development consists of 4666 MR series from cancer patients, labeled
        by expert radiologists and acquired from different manufacturers, clinical
        centers and anatomical regions, covering as much variability as possible with
        the aim of making the models generalizable to other databases. Moreover, the
        inference performance of the end system has been evaluated on 25,596 MR series
        as well as the final model outputs with an external evaluation set of 1286
        MR series. The weighting model achieves very reliable results with a f1 score
        of 88% in the validation set. Junk and chemical shift models achieved scores
        of 82% and 83% respectively. These results open the door to the automatic
        application of image post-processing and deep learning algorithms after accurate
        labeling, minimizing human intervention. Furthermore, the proposed solution
        can infer thousands of DICOM series in less than one minute. Thanks to the
        fast inference times provided by this solution, it fits well in a big data
        ecosystem, eliminating any performance issues on ingestion in a semi-real-time
        environment.
      authors:
      - name: Gomis-Maya A.
      - name: Cerda-Alberich L.
      - name: Veiga-Canuto D.
      - name: Claudio-Fanni S.
      - name: Ten-Steve A.
      - name: Ribas-Despuig G.
      - name: Mallol-Rosello P.J.
      - name: Vila-Frances J.
      - name: Marti-Bonmati L.
      citationCount: 0
      date: '2025-12-01T00:00:00Z'
      journal: Journal of Big Data
      title: Automatic magnetic resonance imaging series labelling for large repositories
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Data identity and mapping
    uri: http://edamontology.org/topic_3345
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-05-30T16:38:57.306019Z'
  biotoolsCURIE: biotools:mri_image_intensity_normalization
  biotoolsID: mri_image_intensity_normalization
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit: []
  description: This tool enables image intensity harmonization across T2-weighted
    (T2w) MRI datasets from various anatomical regions, including the prostate, breast,
    and rectum.
  documentation:
  - note: null
    type:
    - General
    url: https://quibim.com/news/understanding-mri-image-harmonization/
  download: []
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  function:
  - cmd: null
    input:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    note: null
    operation:
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://quibim.com/news/understanding-mri-image-harmonization/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T07:33:17.653864Z'
  license: CC-BY-NC-ND-4.0
  link: []
  maturity: Emerging
  name: MRI Image intensity normalization
  operatingSystem:
  - Linux
  otherID: []
  owner: xrafael
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: MRI
    uri: http://edamontology.org/topic_3444
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  validated: 0
  version:
  - '0.1'
- accessibility: null
  additionDate: '2025-05-21T12:27:35.032026Z'
  biotoolsCURIE: biotools:qp-insights_uploader
  biotoolsID: qp-insights_uploader
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Quibim
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://quibim.com/
  description: This desktop application enables users to upload DICOM data along with
    associated clinical information to QP-Insights—the data management platform of
    the UPV Reference Node within EUCAIM.
  documentation:
  - note: null
    type:
    - User manual
    url: https://eucaim.gitbook.io/enduserguide/6-userguide4members
  download:
  - note: null
    type: Downloads page
    url: https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab
    version: null
  editPermission:
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    type: private
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Raw image
        uri: http://edamontology.org/data_3424
      format:
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    - data:
        term: Data index report
        uri: http://edamontology.org/data_0956
      format:
      - term: rcc
        uri: http://edamontology.org/format_3580
      - term: xls
        uri: http://edamontology.org/format_3468
    note: null
    operation:
    - term: Data deposition
      uri: http://edamontology.org/operation_3431
    output: []
  homepage: https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-05T07:29:04.541438Z'
  license: CC-BY-NC-ND-4.0
  link: []
  maturity: null
  name: QP-Insights Uploader
  operatingSystem:
  - Mac
  otherID: []
  owner: celia_martin
  publication: []
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  - term: Data integration and warehousing
    uri: http://edamontology.org/topic_3366
  validated: 0
  version:
  - 1.0.0
- accessibility: null
  additionDate: '2025-04-18T15:04:18.411979Z'
  biotoolsCURIE: biotools:dicom_tags_extractor
  biotoolsID: dicom_tags_extractor
  collectionID:
  - EUCAIM
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: BC|Platforms
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: 'The tool scans the imaging DICOM files of a defined directory at the
    series level, and produces as output one single JSON file containing all the DICOM
    tags for each detected series.

    If a list of selected DICOM tags is provided, the tool can also produce one single
    csv file containing the listed DICOM tags.

    To use this tool, the user can download an executable file (.exe). Two execution
    modes are available : manual execution and command line execution.

    For the tool to scan data successfully, it is important that : 1/ the extension
    “.dcm” is visible, as the tool only scans those files, and discard any other;
    2/ each DICOM series has its own folder, as the tool gets a single .dcm file from
    each folder and extracts the tags.'
  documentation: []
  download:
  - note: '1- Install ORAS tool (https://oras.land/docs/installation).

      2- Login in the registry using the command oras login harbor.eucaim.cancerimage.eu,
      and provide a username and a token

      3- Download the file by using the command oras pull harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_tag_extraction:1.0'
    type: Software package
    url: https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories
    version: '1.0'
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    metadata:
      abstract: In large scale biological experiments, like high-throughput or high-content
        cellular screening, the amount and the complexity of images to be analyzed
        are steadily increasing. To handle and process these images, well defined
        image processing and analysis steps need to be performed by applying dedicated
        workflows. Multiple software tools have emerged with the aim to facilitate
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      authors:
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      - name: Erfle H.
      - name: Eils R.
      - name: Rohr K.
      - name: Gunkel M.
      citationCount: 22
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: Workflows for microscopy image analysis and cellular phenotyping
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  - term: Bioinformatics
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  validated: 1
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  description: AITANA is a platform to facilitate the investigation, development,
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    and methodologies. AITANA wants to minimize the times of training and find the
    solution of a Machine Learning project with the use of a complete platform to
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  description: An elastic platform for Big Data Analytics in the cloud that facilitates
    the configuration and deployment of robust, efficient, fault-tolerant and elastic
    services to undertake the different phases of exploiting large amounts of data,
    from model creation to putting them into production, decisively increasing accessibility
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  - term: Machine learning
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  additionDate: '2025-05-28T23:55:08.439649Z'
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  credit: []
  description: This module provides tools for analyzing and processing DICOM serial
    files with a focus on image comparison and deduplication. It searches directories
    for DICOM series, organizes them by patient ID and SeriesInstanceUID and excludes
    certain modalities. It detects duplicate files within series using metadata checks
    and performs perceptual matching by comparing perceptual hashes at the series
    level. For identified duplicate candidates, it performs a deeper content-based
    analysis using SSIM and cosine similarity metrics to confirm visual similarity.
    The module provides a detailed report on duplicates and potential matches, helping
    to ensure the consistency and integrity of DICOM records
  documentation:
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  homepage: https://www.med.auth.gr/en/content/laboratory-computing-medical-informatics-and-biomedical-imaging-technologies
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  lastUpdate: '2025-06-04T09:02:13.793866Z'
  license: Proprietary
  link: []
  maturity: Mature
  name: DICOM image similarity-duplicate checker
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  owner: difoto
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  toolType:
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    uri: http://edamontology.org/topic_3572
  validated: 0
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- accessibility: Restricted access
  additionDate: '2025-05-28T23:59:16.112917Z'
  biotoolsCURIE: biotools:dicom-seg_annotation
  biotoolsID: dicom-seg_annotation
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  cost: Commercial
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  description: This module provides a command line tool to validate DICOM SEG files
    against predefined requirements specified in an Excel file. It contains components
    for finding relevant DICOM files, loading and parsing validation requests and
    applying validation rules. The main validation process checks each DICOM file
    for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements
    file. A detailed report is generated highlighting issues such as missing, invalid
    or conditionally required attributes, including file paths and affected DICOM
    tags. The tool is designed to ensure data integrity and compliance with DICOM
    standards.
  documentation:
  - note: null
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    - User manual
    url: https://docs.google.com/document/d/1EnkkmFOGAo-WhPwx3OQ8vxSb3FKFn4b-/edit?rtpof=true&sd=true&tab=t.0
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  maturity: Mature
  name: DICOM-SEG Annotation
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  additionDate: '2025-04-15T17:42:40.968987Z'
  biotoolsCURIE: biotools:dicom_file_integrity_checker_by_gibi230
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    name: GIBI230 - HULAFE
    note: null
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  description: 'The tool performs a DICOM quality check in terms of correct number
    of files per sequence, corrupted files, precise directory hierarchy, separated
    dynamic series merging them, interest series filtering/selection by specific series
    description lists and diffusion sequence identification by b-values. It applies
    the desired changes to the dataset and generates a report containing information
    about the selected sequences, corrupted files, missing files and merged files.


    Status : Containerized'
  documentation:
  - note: "Pedro-Miguel Martinez-Girones 1,*, Adrian Galiana-Bordera 1, Carina Soler-Pons\
      \ 1, Ignacio Gomez-Rico-Junquero 1, Ignacio Blanquer-Espert 3, Leonor Cerda-Alberich\
      \ 1 and Luis Marti-Bonmati 1,2\n1\tBiomedical Imaging Research Group (GIBI230),\
      \ La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell,\
      \ València, 46026, Spain.\n2\tLa Fe Medical Imaging Department, Avenida Fernando\
      \ Abril Martorell, València, 46026, Spain.\n3\tComputing Science Department,\
      \ Universitat Politècnica de València, Cami de Vera s/n, València, 46022, Spain."
    type:
    - General
    url: https://drive.google.com/file/d/1s8-nVAvSuAGTHWwun4AQcgHtRWclsFg2/view?usp=sharing
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  homepage: https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tab
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  lastUpdate: '2025-06-04T08:07:34.085530Z'
  license: null
  link:
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    type:
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    url: https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/
  maturity: null
  name: DICOM File Integrity Checker (EUCAIM-SW-002_T-01-01-002)
  operatingSystem:
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  - Windows
  otherID: []
  owner: pedromiguel_martinez_HULAFE
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Computer science
    uri: http://edamontology.org/topic_3316
  - term: Data acquisition
    uri: http://edamontology.org/topic_3077
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 0
  version:
  - 1.2.9
  - 2.0.0
- accessibility: Open access
  additionDate: '2017-02-10T14:14:47Z'
  biotoolsCURIE: biotools:act
  biotoolsID: act
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: D.R.Westhead@leeds.ac.uk
    fundrefid: null
    gridid: null
    name: David R Westhead
    note: null
    orcidid: https://orcid.org/0000-0002-0519-3820
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/154/professor-david-r-westhead
  - email: imichalop@bioacademy.gr
    fundrefid: null
    gridid: null
    name: Ioannis Michalopoulos
    note: null
    orcidid: https://orcid.org/0000-0001-8991-8712
    rorid: null
    typeEntity: Person
    typeRole:
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    url: https://www.michalopoulos.net/
  description: The Arabidopsis Coexpression Tool (ACT) is based on the coexpression
    analysis of 21273 Arabidopsis thaliana genes from gene pair correlation data of
    3500 Affymetrix Arabidopsis ATH1 Genome Array Chip microarray samples.
  documentation:
  - note: null
    type:
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    url: https://www.michalopoulos.net/act2.6/help.php
  download: []
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    input:
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        term: Gene identifier
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      format: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_2938
    output:
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        term: Gene tree
        uri: http://edamontology.org/data_3271
      format: []
  homepage: https://www.michalopoulos.net/act/
  homepage_status: 0
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  lastUpdate: '2025-06-04T05:50:14.941224Z'
  license: null
  link: []
  maturity: Mature
  name: Arabidopsis Co-expression Tool (ACT)
  operatingSystem:
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  - Windows
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  owner: imichalop@bioacademy.gr
  publication:
  - doi: 10.3390/genes16030258
    metadata:
      abstract: 'Background/Objectives: Genes with similar expression patterns across
        multiple samples are considered coexpressed, and they may participate in similar
        biological processes or pathways. Gene coexpression networks depict the degree
        of similarity between the expression profiles of all genes in a set of samples.
        Gene coexpression tools allow for the prediction of functional gene partners
        or the assignment of roles to genes of unknown function. Weighted Gene Correlation
        Network Analysis (WGCNA) is an R package that provides a multitude of functions
        for constructing and analyzing a weighted or unweighted gene coexpression
        network. Methods: Previously preprocessed, high-quality gene expression data
        of 3500 samples of Affymetrix microarray technology from various tissues of
        the Arabidopsis thaliana plant model species were used to construct a weighted
        gene coexpression network, using WGCNA. Results: The gene dendrogram was used
        as the basis for the creation of a new Arabidopsis coexpression tool (ACT)
        version (ACT2.6). The dendrogram contains 21,273 leaves, each one corresponding
        to a single gene. Genes that are clustered in the same clade are coexpressed.
        WGCNA grouped the genes into 27 functional modules, all of which were positively
        or negatively correlated with specific tissues. Discussion: Genes known to
        be involved in common metabolic pathways were discovered in the same module.
        By comparing the current ACT version with the previous one, it was shown that
        the new version outperforms the old one in discovering the functional connections
        between gene partners. ACT2.6 is a major upgrade over the previous version
        and a significant addition to the collection of public gene coexpression tools.'
      authors:
      - name: Zogopoulos V.L.
      - name: Papadopoulos K.
      - name: Malatras A.
      - name: Iconomidou V.A.
      - name: Michalopoulos I.
      citationCount: 0
      date: '2025-03-01T00:00:00Z'
      journal: Genes
      title: 'ACT2.6: Global Gene Coexpression Network in Arabidopsis thaliana Using
        WGCNA'
    note: null
    pmcid: PMC11942487
    pmid: '40149410'
    type:
    - Primary
    version: null
  - doi: 10.1016/j.xpro.2022.101208
    metadata:
      abstract: Coexpressed genes tend to participate in related biological processes.
        Gene coexpression analysis allows the discovery of functional gene partners
        or the assignment of biological roles to genes of unknown function. In this
        protocol, we describe the steps necessary to create a gene coexpression tree
        for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray
        data. Because the computational analysis described here is highly dependent
        on sample quality, we detail an automatic quality control approach. For complete
        details on the use and execution of this protocol, please refer to Zogopoulos
        et al. (2021).
      authors:
      - name: Zogopoulos V.L.
      - name: Malatras A.
      - name: Michalopoulos I.
      citationCount: 6
      date: '2022-03-18T00:00:00Z'
      journal: STAR Protocols
      title: Gene coexpression analysis in Arabidopsis thaliana based on public microarray
        data
    note: null
    pmcid: PMC8885756
    pmid: '35243384'
    type:
    - Method
    version: null
  - doi: 10.3390/biology11071019
    metadata:
      abstract: Gene coexpression analysis constitutes a widely used practice for
        gene partner identification and gene function prediction, consisting of many
        intricate procedures. The analysis begins with the collection of primary transcriptomic
        data and their preprocessing, continues with the calculation of the similarity
        between genes based on their expression values in the selected sample dataset
        and results in the construction and visualisation of a gene coexpression network
        (GCN) and its evaluation using biological term enrichment analysis. As gene
        coexpression analysis has been studied ex-tensively, we present most parts
        of the methodology in a clear manner and the reasoning behind the selection
        of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble
        account of the steps required for performing a complete gene coexpression
        analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays,
        as well as the best practices for GCN construction. Furthermore, we recount
        the most popular webtools and standalone applications performing gene coexpression
        analysis, with details on their methods, features and outputs.
      authors:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Papadopoulos K.
      - name: Tsotra I.
      - name: Iconomidou V.A.
      - name: Michalopoulos I.
      citationCount: 11
      date: '2022-07-01T00:00:00Z'
      journal: Biology
      title: Approaches in Gene Coexpression Analysis in Eukaryotes
    note: null
    pmcid: PMC9312353
    pmid: '36101400'
    type:
    - Review
    version: null
  - doi: 10.1093/nar/gkl204
    metadata:
      abstract: The Arabidopsis Co-expression Tool, ACT, ranks the genes across a
        large microarray dataset according to how closely their expression follows
        the expression of a query gene. A database stores pre-calculated co-expression
        results for ∼21 800 genes based on data from over 300 arrays. These results
        can be corroborated by calculation of co-expression results for user-defined
        sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique
        Finder (CF) identifies groups of genes which are consistently co-expressed
        with each other across a user-defined co-expression list. The parameters can
        be altered easily to adjust cluster size and the output examined for optimal
        inclusion of genes with known biological roles. Alternatively, a Scatter Plot
        tool displays the correlation coefficients for all genes against two user-selected
        queries on a scatter plot which can be useful for visual identification of
        clusters of genes with similar r-values. User-input groups of genes can be
        highlighted on the scatter plots. Inclusion of genes with known biology in
        sets of genes identified using CF and Scatter Plot tools allows inferences
        to be made about the roles of the other genes in the set and both tools can
        therefore be used to generate short lists of genes for further characterization.
        ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT. © The Author 2006.
        Published by Oxford University Press. All rights reserved.
      authors:
      - name: Manfield I.W.
      - name: Jen C.-H.
      - name: Pinney J.W.
      - name: Michalopoulos I.
      - name: Bradford J.R.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 130
      date: '2006-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based
        gene expression analysis'
    note: null
    pmcid: PMC1538833
    pmid: '16845059'
    type:
    - Other
    version: null
  - doi: 10.1111/j.1365-313x.2006.02681.x
    metadata:
      abstract: 'We present a new WWW-based tool for plant gene analysis, the Arabidopsis
        Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray
        data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression
        analysis tool allows users to identify genes whose expression patterns are
        correlated across selected experiments or the complete data set. Results are
        accompanied by estimates of the statistical significance of the correlation
        relationships, expressed as probability (P) and expectation (E) values. Additionally,
        highly ranked genes on a correlation list can be examined using the novel
        CLIQUE FINDER tool to determine the sets of genes most likely to be regulated
        in a similar manner. In combination, these tools offer three levels of analysis:
        creation of correlation lists of co-expressed genes, refinement of these lists
        using two-dimensional scatter plots, and dissection into cliques of co-regulated
        genes. We illustrate the applications of the software by analysing genes encoding
        functionally related proteins, as well as pathways involved in plant responses
        to environmental stimuli. These analyses demonstrate novel biological relationships
        underlying the observed gene co-expression patterns. To demonstrate the ability
        of the software to develop testable hypotheses on gene function within a defined
        biological process we have used the example of cell wall biosynthesis genes.
        The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/.
        © 2006 The Authors.'
      authors:
      - name: Jen C.-H.
      - name: Manfield I.W.
      - name: Michalopoulos I.
      - name: Pinney J.W.
      - name: Willats W.G.T.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 65
      date: '2006-04-01T00:00:00Z'
      journal: Plant Journal
      title: 'The Arabidopsis co-expression tool (ACT): A WWW-based tool and database
        for microarray-based gene expression analysis'
    note: null
    pmcid: null
    pmid: '16623895'
    type:
    - Other
    version: null
  - doi: 10.1016/j.isci.2021.102848
    metadata:
      abstract: Gene coexpression analysis refers to the discovery of sets of genes
        which exhibit similar expression patterns across multiple transcriptomic data
        sets, such as microarray experiment data of public repositories. Arabidopsis
        Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis
        thaliana, identifies genes which are correlated to a driver gene. Primary
        microarray data from ATH1 Affymetrix platform were processed with Single-Channel
        Array Normalization algorithm and combined to produce a coexpression tree
        which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades
        of coexpressed genes, as well as to perform gene set enrichment analysis,
        being unique in revealing enriched transcription factors targeting coexpressed
        genes. ACT offers a simple and user-friendly interface producing working hypotheses
        which can be experimentally verified for the discovery of gene partnership,
        pathway membership, and transcriptional regulation. ACT analyses have been
        successful in identifying not only genes with coordinated ubiquitous expressions
        but also genes with tissue-specific expressions.
      authors:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Angelopoulou A.
      - name: Jen C.-H.
      - name: Duddy W.J.
      - name: Daras G.
      - name: Hatzopoulos P.
      - name: Westhead D.R.
      - name: Michalopoulos I.
      citationCount: 15
      date: '2021-08-20T00:00:00Z'
      journal: iScience
      title: 'Arabidopsis Coexpression Tool: a tool for gene coexpression analysis
        in Arabidopsis thaliana'
    note: null
    pmcid: PMC8334378
    pmid: '34381973'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - '2.6'
- accessibility: Open access
  additionDate: '2025-06-03T11:42:31.433057Z'
  biotoolsCURIE: biotools:cnsistent
  biotoolsID: cnsistent
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: adam.streck@gmail.com
    fundrefid: null
    gridid: null
    name: Adam Streck
    note: null
    orcidid: https://orcid.org/0000-0002-7302-0147
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: Imputation, consistent segmentation, statistical analysis, and visualization
    of copy number profiles.
  documentation:
  - note: null
    type:
    - Command-line options
    - API documentation
    - Quick start guide
    url: https://cnsistent.readthedocs.io/
  download:
  - note: null
    type: Source code
    url: https://bitbucket.org/schwarzlab/cnsistent.git
    version: null
  - note: null
    type: Software package
    url: https://pypi.org/project/CNSistent/
    version: null
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  language:
  - Python
  lastUpdate: '2025-06-03T11:54:45.306699Z'
  license: MIT
  link: []
  maturity: Emerging
  name: CNSistent
  operatingSystem: []
  otherID: []
  owner: AdamStreck
  publication:
  - doi: 10.1101/2024.12.23.630118
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Copy number variation
    uri: http://edamontology.org/topic_3958
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  validated: 0
  version:
  - 0.7.4
- accessibility: Open access
  additionDate: '2017-01-13T13:16:12Z'
  biotoolsCURIE: biotools:samtools
  biotoolsID: samtools
  collectionID:
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  - Animal and Crop Genomics
  - SAMtools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: rd@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Richard Durbin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
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    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
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    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: SAMtools and BCFtools are widely used programs for processing and analysing
    high-throughput sequencing data. They include tools for file format conversion
    and manipulation, sorting, querying, statistics, variant calling, and effect analysis
    amongst other methods.
  documentation:
  - note: HowTos for samtools
    type:
    - Other
    url: http://www.htslib.org/doc/#howtos
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  - note: null
    type:
    - Installation instructions
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  function:
  - cmd: null
    input:
    - data:
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      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
    note: null
    operation:
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Data editing
      uri: http://edamontology.org/operation_3096
    - term: Data parsing
      uri: http://edamontology.org/operation_1812
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Data formatting
      uri: http://edamontology.org/operation_0335
    - term: Data sorting
      uri: http://edamontology.org/operation_3802
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T09:08:10.199446Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/samtools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/samtools/issues
  maturity: Mature
  name: SAMtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 42239
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 6224
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btr509
    metadata:
      abstract: 'Motivation: Most existing methods for DNA sequence analysis rely
        on accurate sequences or genotypes. However, in applications of the next-generation
        sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample
        low-coverage sequencing or somatic mutation discovery). These applications
        press for the development of new methods for analyzing sequence data with
        uncertainty. Results: We present a statistical framework for calling SNPs,
        discovering somatic mutations, inferring population genetical parameters and
        performing association tests directly based on sequencing data without explicit
        genotyping or linkage-based imputation. On real data, we demonstrate that
        our method achieves comparable accuracy to alternative methods for estimating
        site allele count, for inferring allele frequency spectrum and for association
        mapping. We also highlight the necessity of using symmetric datasets for finding
        somatic mutations and confirm that for discovering rare events, mismapping
        is frequently the leading source of errors. © The Author 2011. Published by
        Oxford University Press. All rights reserved.'
      authors:
      - name: Li H.
      citationCount: 4405
      date: '2011-11-01T00:00:00Z'
      journal: Bioinformatics
      title: A statistical framework for SNP calling, mutation discovery, association
        mapping and population genetical parameter estimation from sequencing data
    note: null
    pmcid: PMC3198575
    pmid: '21903627'
    type: []
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Suite
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - 1.16.1
  - '1.17'
  - '1.18'
  - '1.19'
  - 1.19.1
  - 1.19.2
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: null
  additionDate: '2020-01-14T09:05:51Z'
  biotoolsCURIE: biotools:BlobToolKit
  biotoolsID: BlobToolKit
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: BlobToolKit Viewer is a genome-scale dataset visualistion tool developed
    as part of the blobtoolkit project to allow browser-based identification and filtering
    of target and non-target data in genome assemblies.
  documentation: []
  download:
  - note: null
    type: Downloads page
    url: https://blobtoolkit.genomehubs.org/download
    version: null
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Data deposition
      uri: http://edamontology.org/operation_3431
    - term: Data parsing
      uri: http://edamontology.org/operation_1812
    output: []
  homepage: https://blobtoolkit.genomehubs.org/view
  homepage_status: 0
  language:
  - JavaScript
  - Python
  lastUpdate: '2025-06-03T09:01:03.782648Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/blobtoolkit
  - note: null
    type:
    - Repository
    url: https://github.com/blobtoolkit/specification
  - note: null
    type:
    - Repository
    url: https://github.com/blobtoolkit/viewer
  maturity: null
  name: BlobToolKit
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1101/844852
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1534/g3.119.400908
    metadata:
      abstract: Reconstruction of target genomes from sequence data produced by instruments
        that are agnostic as to the species-of-origin may be confounded by contaminant
        DNA. Whether introduced during sample processing or through co-extraction
        alongside the target DNA, if insufficient care is taken during the assembly
        process, the final assembled genome may be a mixture of data from several
        species. Such assemblies can confound sequence-based biological inference
        and, when deposited in public databases, may be included in downstream analyses
        by users unaware of underlying problems. We present BlobToolKit, a software
        suite to aid researchers in identifying and isolating non-target data in draft
        and publicly available genome assemblies. BlobToolKit can be used to process
        assembly, read and analysis files for fully reproducible interactive exploration
        in the browser-based Viewer. BlobToolKit can be used during assembly to filter
        non-target DNA, helping researchers produce assemblies with high biological
        credibility. We have been running an automated BlobToolKit pipeline on eukaryotic
        assemblies publicly available in the International Nucleotide Sequence Data
        Collaboration and are making the results available through a public instance
        of the Viewer at https://blobtoolkit.genomehubs.org/view. We aim to complete
        analysis of all publicly available genomes and then maintain currency with
        the flow of new genomes. We have worked to embed these views into the presentation
        of genome assemblies at the European Nucleotide Archive, providing an indication
        of assembly quality alongside the public record with links out to allow full
        exploration in the Viewer.
      authors:
      - name: Challis R.
      - name: Richards E.
      - name: Rajan J.
      - name: Cochrane G.
      - name: Blaxter M.
      citationCount: 994
      date: '2020-04-01T00:00:00Z'
      journal: 'G3: Genes, Genomes, Genetics'
      title: BlobToolKit - interactive quality assessment of genome assemblies
    note: null
    pmcid: PMC7144090
    pmid: '32071071'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Phylogenomics
    uri: http://edamontology.org/topic_0194
  - term: Zoology
    uri: http://edamontology.org/topic_3500
  - term: Biodiversity
    uri: http://edamontology.org/topic_3050
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2017-08-20T16:07:58Z'
  biotoolsCURIE: biotools:htslib
  biotoolsID: htslib
  collectionID:
  - Animal and Crop Genomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    - Primary contact
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: The main purpose of HTSlib is to provide access to genomic information
    files, both alignment data (SAM, BAM, and CRAM formats) and variant data (VCF
    and BCF formats). The library also provides interfaces to access and index genome
    reference data in FASTA format and tab-delimited files with genomic coordinates.
    It is utilized and incorporated into both SAMtools and BCFtools.
  documentation:
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
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  function:
  - cmd: null
    input:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: CRAM
        uri: http://edamontology.org/format_3462
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
    note: null
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: CRAM
        uri: http://edamontology.org/format_3462
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T09:00:20.313887Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/htslib
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/htslib/issues
  maturity: Mature
  name: HTSlib
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/gigascience/giab007
    metadata:
      abstract: 'Background: Since the original publication of the VCF and SAM formats,
        an explosion of software tools have been created to process these data files.
        To facilitate this a library was produced out of the original SAMtools implementation,
        with a focus on performance and robustness. The file formats themselves have
        become international standards under the jurisdiction of the Global Alliance
        for Genomics and Health. Findings: We present a software library for providing
        programmatic access to sequencing alignment and variant formats. It was born
        out of the widely used SAMtools and BCFtools applications. Considerable improvements
        have been made to the original code plus many new features including newer
        access protocols, the addition of the CRAM file format, better indexing and
        iterators, and better use of threading. Conclusion: Since the original Samtools
        release, performance has been considerably improved, with a BAM read-write
        loop running 5 times faster and BAM to SAM conversion 13 times faster (both
        using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen
        HTSlib downloaded >1 million times from GitHub and conda. The C library has
        been used directly by an estimated 900 GitHub projects and has been incorporated
        into Perl, Python, Rust, and R, significantly expanding the number of uses
        via other languages. HTSlib is open source and is freely available from htslib.org
        under MIT/BSD license.'
      authors:
      - name: Bonfield J.K.
      - name: Marshall J.
      - name: Danecek P.
      - name: Li H.
      - name: Ohan V.
      - name: Whitwham A.
      - name: Keane T.
      citationCount: 189
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: 'HTSlib: C library for reading/writing high-Throughput sequencing data'
    note: 'HTSlib: C library for reading/writing high-throughput sequencing data.'
    pmcid: PMC7931820
    pmid: '33594436'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: samtools
    type: usedBy
  - biotoolsID: bcftools
    type: usedBy
  toolType:
  - Library
  topic:
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - 1.2.1
  - '1.3'
  - 1.3.1
  - 1.3.2
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - 1.10.1
  - 1.10.2
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - '1.17'
  - '1.18'
  - '1.19'
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: Open access
  additionDate: '2015-08-24T08:35:55Z'
  biotoolsCURIE: biotools:bcftools
  biotoolsID: bcftools
  collectionID:
  - BCFtools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: BCFtools is a set of utilities that manipulate variant calls in the
    Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently
    with both VCFs and BCFs, both uncompressed and BGZF-compressed.
  documentation:
  - note: null
    type:
    - General
    url: http://www.htslib.org/doc/bcftools.html
  - note: A workflow for BCFtools.
    type:
    - Other
    url: http://www.htslib.org/workflow/#mapping_to_variant
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
    note: Multiple data munging operations.
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T08:59:30.745713Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/bcftools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/bcftools/issues
  maturity: Mature
  name: BCFtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 42239
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 6224
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Command-line tool
  - Suite
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: Genotyping experiment
    uri: http://edamontology.org/topic_3516
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - 1.10.1
  - 1.10.2
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - '1.17'
  - '1.18'
  - '1.19'
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: null
  additionDate: '2016-06-24T11:36:59Z'
  biotoolsCURIE: biotools:oma
  biotoolsID: oma
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: SIB
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: contact@omabrowser.org
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: Christophe.Dessimoz@unil.ch
    fundrefid: null
    gridid: null
    name: Christophe Dessimoz
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Project that aims to identify orthologs among publicly available, complete
    genomes. With many hundreds of genomes analyzed to date, it is one of the largest
    projects of its kind.
  documentation:
  - note: null
    type:
    - General
    url: https://omabrowser.org/oma/about/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Switzerland
  elixirPlatform:
  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: UniProt accession
        uri: http://edamontology.org/data_3021
      format: []
    - data:
        term: Text
        uri: http://edamontology.org/data_3671
      format: []
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: orthology prediction and paralogy OMA group
    operation:
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    output: []
  homepage: http://omabrowser.org/oma/home/
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-03T08:27:37.764262Z'
  license: null
  link: []
  maturity: null
  name: OMA
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: adrian.altenhoff
  publication:
  - doi: 10.1093/nar/gku1158
    metadata:
      abstract: 'The Orthologous Matrix (OMA) project is a method and associated database
        inferring evolutionary relationships amongst currently 1706 complete proteomes
        (i.e. the protein sequence associated for every protein-coding gene in all
        genomes). In this update article, we present six major new developments in
        OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part
        of the OMA pipeline; (iii) better support for plant genomes and in particular
        homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic
        context of orthologs; (v) statically computed hierarchical orthologous groups
        subsets downloadable in OrthoXML format; and (vi) possibility to export parts
        of the all-against-all computations and to combine them with custom data for
        ''client-side'' orthology prediction. OMA can be accessed through the OMA
        Browser and various programmatic interfaces at http://omabrowser.org.'
      authors:
      - name: Altenhoff A.M.
      - name: Sunca N.
      - name: Glover N.
      - name: Train C.-M.
      - name: Sueki A.
      - name: Pilizota I.
      - name: Gori K.
      - name: Tomiczek B.
      - name: Muller S.
      - name: Redestig H.
      - name: Gonnet G.H.
      - name: Dessimoz C.
      citationCount: 160
      date: '2015-01-28T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The OMA orthology database in 2015: Function predictions, better plant
        support, synteny view and other improvements'
    note: null
    pmcid: PMC4383958
    pmid: '25399418'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkad1020
    metadata:
      abstract: In this update paper, we present the latest de v elopments in the
        OMA browser knowledgebase, whichãims to provide high-quality orthology inferencesãnd
        facilitate the study of gene families, genomesãnd their evolution. First,
        we discuss theãddition of new species in the database, particularlyãn expanded
        representation of prokaryotic species. The OMA browser now offers Ancestral
        Genome pagesãndãn Ancestral Gene Order vie w er,ãllo wing users to e xplore
        the e v olutionar y histor yãnd gene content ofãncestral genomes. Weãlso introduceã
        re vãmped L ocal Synten y Vie w er to compare genomic neighborhoodsãcross
        both e xtantãndãncestral genomes. Hierarchical Orthologous Groups (H O Gs)ãre
        nowãnnotated with Gene Ontologyãnnotations,ãnd users can easily perform extant
        orãncestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem,
        including OMAmer for proteome mapping, OMArk for proteome qualityãssessment,
        OMAMO for model organism selectionãnd Read2Tree for phylogenetic species tree
        construction from reads. These new features provide exciting opportunities
        for orthologyãnalysisãnd comparative genomics. OMA isãccessibleãt https://omabrowser.org
        .
      authors:
      - name: Altenhoff A.M.
      - name: Vesztrocy A.W.
      - name: Bernard C.
      - name: Train C.-M.
      - name: Nicheperovich A.
      - name: Banos S.P.
      - name: Julca I.
      - name: Moi D.
      - name: Nevers Y.
      - name: Majidian S.
      - name: Dessimoz C.
      - name: Glover N.M.
      citationCount: 24
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'OMA or tholog y in 2024: impro v ed prokary ot e co v erãg e,ãncestralãnd
        extant GO enrichment,ã revamped synteny viewerãnd more in the OMA Ecosystem'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkaa1007
    metadata:
      abstract: OMA is an established resource to elucidate evolutionary relationships
        among genes from currently 2326 genomes covering all domains of life. OMA
        provides pairwise and groupwise orthologs, functional annotations, local and
        global gene order conservation (synteny) information, among many other functions.
        This update paper describes the reorganisation of the database into gene-,
        group- and genome-centric pages. Other new and improved features are detailed,
        such as reporting of the evolutionarily best conserved isoforms of alternatively
        spliced genes, the inferred local order of ancestral genes, phylogenetic profiling,
        better cross-references, fast genome mapping, semantic data sharing via RDF,
        as well as a special coronavirus OMA with 119 viruses from the Nidovirales
        order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude
        with improvements to the documentation of the resource through primers, tutorials
        and short videos. OMA is accessible at https://omabrowser.org.
      authors:
      - name: Altenhoff A.M.
      - name: Train C.-M.
      - name: Gilbert K.J.
      - name: Mediratta I.
      - name: de Farias T.M.
      - name: Moi D.
      - name: Nevers Y.
      - name: Radoykova H.-S.
      - name: Rossier V.
      - name: Vesztrocy A.W.
      - name: Glover N.M.
      - name: Dessimoz C.
      citationCount: 135
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'OMA orthology in 2021: Website overhaul, conserved isoforms, ancestral
        gene order and more'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkx1019
    metadata:
      abstract: 'The Orthologous Matrix (OMA) is a leading resource to relate genes
        across many species from all of life. In this update paper, we review the
        recent algorithmic improvements in the OMA pipeline, describe increases in
        species coverage (particularly in plants and early-branching eukaryotes) and
        introduce several new features in the OMA web browser. Notable improvements
        include: (i) a scalable, interactive viewer for hierarchical orthologous groups;
        (ii) protein domain annotations and domain-based links between orthologous
        groups; (iii) functionality to retrieve phylogenetic marker genes for a subset
        of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul
        of the programmatic access (REST API and semantic web), which will facilitate
        incorporation of OMA analyses in computational pipelines and integration with
        other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org.'
      authors:
      - name: Altenhoff A.M.
      - name: Glover N.M.
      - name: Train C.-M.
      - name: Kaleb K.
      - name: Warwick Vesztrocy A.
      - name: Dylus D.
      - name: De Farias T.M.
      - name: Zile K.
      - name: Stevenson C.
      - name: Long J.
      - name: Redestig H.
      - name: Gonnet G.H.
      - name: Dessimoz C.
      citationCount: 168
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The OMA orthology database in 2018: Retrieving evolutionary relationships
        among all domains of life through richer web and programmatic interfaces'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Database portal
  - Desktop application
  topic:
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  - term: Comparative genomics
    uri: http://edamontology.org/topic_0797
  validated: 1
  version: []
- accessibility: null
  additionDate: '2024-04-25T12:18:28.403118Z'
  biotoolsCURIE: biotools:tooldistillator
  biotoolsID: tooldistillator
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: 'ToolDistillator is a tool to extract information from output files
    of specific tools, expose it as JSON files, and aggregate over several tools.

    It can produce both a single file to each tool or a summarized file from a set
    of reports.'
  documentation:
  - note: null
    type:
    - General
    url: https://gitlab.com/ifb-elixirfr/abromics/tooldistillator/-/blob/main/README.md?ref_type=heads
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data parsing
      uri: http://edamontology.org/operation_1812
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: JSON
        uri: http://edamontology.org/format_3464
  homepage: https://gitlab.com/ifb-elixirfr/abromics/tooldistillator
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-03T06:31:14.660645Z'
  license: null
  link: []
  maturity: null
  name: ToolDistillator
  operatingSystem: []
  otherID: []
  owner: bebatut
  publication:
  - doi: 10.5281/ZENODO.8282656
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType: []
  topic:
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 0.8.4.1
  - 0.8.5.0
  - 0.9.0
  - 0.9.1
  - 0.9.2
  - 0.9.3
- accessibility: null
  additionDate: '2024-09-06T14:19:58.602565Z'
  biotoolsCURIE: biotools:bdpn
  biotoolsID: bdpn
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: anna.zhukova@pasteur.fr
    fundrefid: null
    gridid: null
    name: Anna Zhukova
    note: null
    orcidid: https://orcid.org/0000-0003-2200-7935
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://research.pasteur.fr/en/member/anna-zhukova/
  description: Estimator of epidemiological parameters under the Birth-Death with
    Contact Tracing (BD-CT) model from phylogenetic trees and a non-parametric CT
    detection test.
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://pypi.org/project/bdct
  download:
  - note: null
    type: Source code
    url: https://github.com/evolbioinfo/bdct
    version: null
  - note: null
    type: Downloads page
    url: https://pypi.org/project/bdct
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/evolbioinfo/bdct
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Phylogenetic tree
        uri: http://edamontology.org/data_0872
      format:
      - term: newick
        uri: http://edamontology.org/format_1910
    note: null
    operation:
    - term: Statistical inference
      uri: http://edamontology.org/operation_3658
    output:
    - data:
        term: Text data
        uri: http://edamontology.org/data_2526
      format:
      - term: DSV
        uri: http://edamontology.org/format_3751
  homepage: https://github.com/evolbioinfo/bdct
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-02T14:34:59.109392Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/evolbioinfo/bdct
  maturity: null
  name: bdct
  operatingSystem: []
  otherID: []
  owner: azhukova
  publication:
  - doi: 10.1371/journal.pcbi.1012461
    metadata: null
    note: null
    pmcid: null
    pmid: '40440423'
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Public health and epidemiology
    uri: http://edamontology.org/topic_3305
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-06-02T12:24:30.535794Z'
  biotoolsCURIE: biotools:wtv
  biotoolsID: wtv
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: "A CLI/python library that selects characteristic qualitative ions\
    \ based on \nhttps://github.com/yuanhonglun/WTV_2.0"
  documentation:
  - note: null
    type:
    - User manual
    url: https://recetox.github.io/wtv/
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://recetox.github.io/wtv/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-02T13:00:49.495177Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: wtv
  operatingSystem: []
  otherID: []
  owner: acquayefrank
  publication:
  - doi: 10.1016/j.molp.2024.04.012
    metadata:
      abstract: Volatilomics is essential for understanding the biological functions
        and fragrance contributions of plant volatiles. However, the annotation coverage
        achieved using current untargeted and widely targeted volatomics (WTV) methods
        has been limited by low sensitivity and/or low acquisition coverage. Here,
        we introduce WTV 2.0, which enabled the construction of a high-coverage library
        containing 2111 plant volatiles, and report the development of a comprehensive
        selective ion monitoring (cSIM) acquisition method, including the selection
        of characteristic qualitative ions with the minimal ion number for each compound
        and an optimized segmentation method, that can acquire the smallest but sufficient
        number of ions for most plant volatiles, as well as the automatic qualitative
        and semi-quantitative analysis of cSIM data. Importantly, the library and
        acquisition method we developed can be self-expanded by incorporating compounds
        not present in the library, utilizing the obtained cSIM data. We showed that
        WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with
        the untargeted method, doubled the annotation coverage compared with the untargeted
        and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran
        as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with
        a user-friendly interface and is applicable to profiling of volatiles and
        primary metabolites in any species.
      authors:
      - name: Yuan H.
      - name: Jiangfang Y.
      - name: Liu Z.
      - name: Su R.
      - name: Li Q.
      - name: Fang C.
      - name: Huang S.
      - name: Liu X.
      - name: Fernie A.R.
      - name: Luo J.
      citationCount: 12
      date: '2024-06-03T00:00:00Z'
      journal: Molecular Plant
      title: 'WTV2.0: A high-coverage plant volatilomics method with a comprehensive
        selective ion monitoring acquisition mode'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: wtv
    type: isNewVersionOf
  toolType:
  - Command-line tool
  topic: []
  validated: 0
  version:
  - 0.0.2
- accessibility: Open access
  additionDate: '2025-05-31T07:43:57.081940Z'
  biotoolsCURIE: biotools:syngenes
  biotoolsID: syngenes
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: mvallino@ufpa.br
    fundrefid: null
    gridid: null
    name: Luan Pinto Rabelo
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: SynGenes is a Python class for standardizing Mitochondrial or Chloroplast
    gene nomenclatures, this class is capable of recognizing and converting the different
    nomenclature variations into a standardized form.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  homepage: https://luanrabelo.github.io/SynGenes/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-05-31T07:43:57.084502Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://pypi.org/project/SynGenes/
  maturity: null
  name: SynGenes
  operatingSystem:
  - Mac
  - Windows
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12859-024-05781-y
    metadata:
      abstract: 'Background: The reconstruction of the evolutionary history of organisms
        has been greatly influenced by the advent of molecular techniques, leading
        to a significant increase in studies utilizing genomic data from different
        species. However, the lack of standardization in gene nomenclature poses a
        challenge in database searches and evolutionary analyses, impacting the accuracy
        of results obtained. Results: To address this issue, a Python class for standardizing
        gene nomenclatures, SynGenes, has been developed. It automatically recognizes
        and converts different nomenclature variations into a standardized form, facilitating
        comprehensive and accurate searches. Additionally, SynGenes offers a web form
        for individual searches using different names associated with the same gene.
        The SynGenes database contains a total of 545 gene name variations for mitochondrial
        and 2485 for chloroplasts genes, providing a valuable resource for researchers.
        Conclusions: The SynGenes platform offers a solution for standardizing gene
        nomenclatures of mitochondrial and chloroplast genes and providing a standardized
        search solution for specific markers in GenBank. Evaluation of SynGenes effectiveness
        through research conducted on GenBank and PubMedCentral demonstrated its ability
        to yield a greater number of outcomes compared to conventional searches, ensuring
        more comprehensive and accurate results. This tool is crucial for accurate
        database searches, and consequently, evolutionary analyses, addressing the
        challenges posed by non-standardized gene nomenclature. Graphical abstract:
        (Figure presented.)'
      authors:
      - name: Rabelo L.P.
      - name: Sodre D.
      - name: de Sousa R.P.C.
      - name: Watanabe L.
      - name: Gomes G.
      - name: Sampaio I.
      - name: Vallinoto M.
      citationCount: 1
      date: '2024-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'SynGenes: a Python class for standardizing nomenclatures of mitochondrial
        and chloroplast genes and a web form for enhancing searches for evolutionary
        analyses'
    note: null
    pmcid: PMC11036621
    pmid: '38649820'
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Ontology and terminology
    uri: http://edamontology.org/topic_0089
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-05-30T12:24:45.929338Z'
  biotoolsCURIE: biotools:fastspar
  biotoolsID: fastspar
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: FastSpar is a C++ implementation of the SparCC algorithm for fast correlation
    estimation from compositional count data such as microbiome OTU tables.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/scwatts/fastspar
  homepage_status: 0
  language: []
  lastUpdate: '2025-05-30T12:53:30.248280Z'
  license: MIT
  link: []
  maturity: null
  name: FastSpar
  operatingSystem: []
  otherID: []
  owner: minaHA
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  validated: 0
  version: []
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