count: 8958
list:
- accessibility: Open access
  additionDate: '2019-11-14T19:27:27Z'
  biotoolsCURIE: biotools:PolySTest
  biotoolsID: PolySTest
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: Robust statistical testing of quantitative proteomics data. The tool
    comprises and combines different tests and includes a new method for combined
    statistics of quantitative and missing values.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode:
  - Denmark
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: DSV
        uri: http://edamontology.org/format_3751
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Statistical modelling
      uri: http://edamontology.org/operation_3664
    - term: Differential protein expression profiling
      uri: http://edamontology.org/operation_3741
    output:
    - data:
        term: Q-value
        uri: http://edamontology.org/data_3932
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: P-value
        uri: http://edamontology.org/data_1669
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Histogram
        uri: http://edamontology.org/data_3905
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Gene expression profile
        uri: http://edamontology.org/data_0928
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: http://computproteomics.bmb.sdu.dk/app_direct/PolySTest
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-08T12:32:48.900143Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/computproteomics/PolySTest
  maturity: Mature
  name: PolySTest
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: veits@bmb.sdu.dk
  publication:
  - doi: 10.1074/mcp.RA119.001777
    metadata:
      abstract: ''
      authors: []
      citationCount: 26
      date: '2020-08-01T00:00:00Z'
      journal: Molecular and Cellular Proteomics
      title: 'PolySTest: Robust statistical testing of proteomics data with missing
        values improves detection of biologically relevant features'
    note: null
    pmcid: PMC8015005
    pmid: '32424025'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2018-06-02T07:03:53Z'
  biotoolsCURIE: biotools:vsclust
  biotoolsID: vsclust
  collectionID:
  - Proteomics
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: veits@bmb.sdu.dk
    fundrefid: null
    gridid: null
    name: Veit Schwämmle
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Feature-based variance-sensitive clustering of omics data. Optimizes
    cluster assignment by taking into account individual feature variance .
  documentation:
  - note: null
    type:
    - General
    url: https://bitbucket.org/veitveit/vsclust
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Expression profile pathway mapping
      uri: http://edamontology.org/operation_0533
    - term: Expression profile clustering
      uri: http://edamontology.org/operation_0313
    - term: Statistical modelling
      uri: http://edamontology.org/operation_3664
    - term: Principal component analysis
      uri: http://edamontology.org/operation_3960
    output:
    - data:
        term: Clustered expression profiles
        uri: http://edamontology.org/data_3768
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Q-value
        uri: http://edamontology.org/data_3932
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: http://computproteomics.bmb.sdu.dk/app_direct/VSClust/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-08T12:32:14.933981Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://bitbucket.org/veitveit/vsclust/src/master/
  maturity: null
  name: VSClust
  operatingSystem:
  - Windows
  - Mac
  - Linux
  otherID: []
  owner: veits@bmb.sdu.dk
  publication:
  - doi: 10.1093/bioinformatics/bty224
    metadata:
      abstract: ''
      authors: []
      citationCount: 27
      date: '2018-09-01T00:00:00Z'
      journal: Bioinformatics
      title: 'VSClust: Feature-based variance-sensitive clustering of omics data'
    note: null
    pmcid: null
    pmid: '29635359'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web service
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  validated: 1
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2025-11-07T18:15:49.962044Z'
  biotoolsCURIE: biotools:prostate_zone_segmentation_tool
  biotoolsID: prostate_zone_segmentation_tool
  collectionID:
  - EUCAIM
  confidence_flag: tool
  cost: Free of charge (with restrictions)
  credit:
  - email: jose.almeida@research.fchampalimaud.org
    fundrefid: null
    gridid: null
    name: José Guilherme de Almeida
    note: null
    orcidid: https://orcid.org/0000-0002-1887-0157
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Documentor
    - Maintainer
    url: https://josegcpa.net
  - email: null
    fundrefid: null
    gridid: null
    name: Nickolas Papanikolaou
    note: null
    orcidid: https://orcid.org/0000-0003-3298-2072
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Nuno Rodrigues
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'This tool automatically segments the the prostate into two zones:
    the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes
    as input a T2-weighted image and produces a segmentation in the same input format.


    Three datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119)
    and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images
    available in each dataset and trained a standard three-class nnU-Net model with
    three classes: background, peripheral zone and the combination of the transition
    and central zones as annotated by radiologists. Using 149 cases from the three
    previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92])
    for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using
    ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77,
    respectively, comparable to the observed between-radiologist DSC for a subset
    of cases (DSC=0.61 and DSC=0.73, respectively).'
  documentation:
  - note: This is the repository containing all of the code together with a comprehensive
      README for deployment and examplar files.
    type:
    - API documentation
    url: https://github.com/josegcpa/nnunet_serve
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw
      --gpus="all" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders
      <series-folder> --output_dir /data/output
    input:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    note: 'Image annotation for individual DICOM and Nifti images: Please note that
      it is easier to mount the input and output paths path and keep `--study_path`
      always as `/data/input` and `--output_dir` always as `/data/output`. This way
      only `--series_folders` requires any change. `--series_folders` can refer both
      to a Nifti file (if the input is Nifti) or the a DICOM series folder.


      If the input is DICOM, `--is_dicom` should be used.'
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  - cmd: docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw
      --gpus="all" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir
      /data/input --output_dir /data/output}}
    input:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    note: "Batch image annotation for DICOM and Nifti images using dataset JSON and\
      \ data directory supports two methods: using a dataset JSON and using a data\
      \ directory. \n\nThe dataset JSON-based method relies on having a dataset JSON\
      \ which has a list of dictionaries that follow this structure: `{\"study_path\"\
      : str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous\
      \ to the previous modes. Be mindful that paths should be specified accordingly\
      \ for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\
      \nThe data directory-based method relies on having a directory structured as\
      \ patient/study/series, where each series follows an nnU-Net-like convention\
      \ (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should\
      \ be `<series-id>_0000`. The data directory should be specified using `--data_dir`\
      \ and requires the specification of an `--output_dir`.\n\nIf the input is DICOM,\
      \ `--is_dicom` should be specified."
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://github.com/josegcpa/nnunet_serve
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-11-07T18:17:59.196564Z'
  license: CC-BY-NC-4.0
  link:
  - note: This is the nnunet_serve repository, containing the generic nnU-Net building
      and serving functionalities used by the Computational Clinical Imaging Group
      at Champalimaud Foundation.
    type:
    - Repository
    url: https://github.com/josegcpa/nnunet_serve
  maturity: Emerging
  name: Prostate zone segmentation tool
  operatingSystem:
  - Linux
  otherID: []
  owner: josegcpa
  publication:
  - doi: 10.1016/j.compbiomed.2024.108216
    metadata:
      abstract: ''
      authors: []
      citationCount: 9
      date: '2024-03-01T00:00:00Z'
      journal: Computers in Biology and Medicine
      title: Analysis of domain shift in whole prostate gland, zonal and lesions segmentation
        and detection, using multicentric retrospective data
    note: null
    pmcid: null
    pmid: '38442555'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: nnunet
    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  validated: 0
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2025-11-04T10:22:47.687623Z'
  biotoolsCURIE: biotools:hcc_segmentation_tool_for_contrast-enhanced_abdominal_ct
  biotoolsID: hcc_segmentation_tool_for_contrast-enhanced_abdominal_ct
  collectionID:
  - EUCAIM
  confidence_flag: tool
  cost: Free of charge (with restrictions)
  credit:
  - email: jose.almeida@research.fchampalimaud.org
    fundrefid: null
    gridid: null
    name: José Guilherme de Almeida
    note: null
    orcidid: https://orcid.org/0000-0002-1887-0157
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Documentor
    - Maintainer
    url: https://josegcpa.net
  - email: null
    fundrefid: null
    gridid: null
    name: Nickolas Papanikolaou
    note: null
    orcidid: https://orcid.org/0000-0003-3298-2072
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Nuno Rodrigues
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Teresa Sotto-Mayor
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'Features an nnU-Net based segmentation tool capable of performing
    HCC segmentation for both arterial and portal venous phase in polyphasic contrast-enhanced
    abdominal CT.


    Please be mindful that the usage instructions below are for the image in the EUCAIM
    Harbor.'
  documentation:
  - note: This is the repository containing all of the code together with a comprehensive
      README for deployment and examplar files.
    type:
    - API documentation
    url: https://github.com/josegcpa/nnunet_serve
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw
      --gpus="all" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders
      <series-folder> --output_dir /data/output
    input:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    note: 'Image annotation for individual DICOM and Nifti images: Please note that
      it is easier to mount the input and output paths path and keep `--study_path`
      always as `/data/input` and `--output_dir` always as `/data/output`. This way
      only `--series_folders` requires any change. `--series_folders` can refer both
      to a Nifti file (if the input is Nifti) or the a DICOM series folder.


      If the input is DICOM, `--is_dicom` should be used.'
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  - cmd: docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw
      --gpus="all" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir
      /data/input --output_dir /data/output}}
    input:
    - data:
        term: MRI image
        uri: http://edamontology.org/data_3442
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
    note: "Batch image annotation for DICOM and Nifti images using dataset JSON and\
      \ data directory supports two methods: using a dataset JSON and using a data\
      \ directory. \n\nThe dataset JSON-based method relies on having a dataset JSON\
      \ which has a list of dictionaries that follow this structure: `{\"study_path\"\
      : str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous\
      \ to the previous modes. Be mindful that paths should be specified accordingly\
      \ for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\
      \nThe data directory-based method relies on having a directory structured as\
      \ patient/study/series, where each series follows an nnU-Net-like convention\
      \ (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should\
      \ be `<series-id>_0000`. The data directory should be specified using `--data_dir`\
      \ and requires the specification of an `--output_dir`.\n\nIf the input is DICOM,\
      \ `--is_dicom` should be specified."
    operation:
    - term: Image annotation
      uri: http://edamontology.org/operation_3553
    output:
    - data:
        term: Image
        uri: http://edamontology.org/data_2968
      format:
      - term: nii
        uri: http://edamontology.org/format_3549
      - term: DICOM format
        uri: http://edamontology.org/format_3548
  homepage: https://github.com/josegcpa/nnunet_serve
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-11-07T18:15:59.638977Z'
  license: CC-BY-NC-4.0
  link:
  - note: This is the nnunet_serve repository, containing the generic nnU-Net building
      and serving functionalities used by the Computational Clinical Imaging Group
      at Champalimaud Foundation.
    type:
    - Repository
    url: https://github.com/josegcpa/nnunet_serve
  maturity: Emerging
  name: HCC segmentation tool for contrast-enhanced abdominal CT
  operatingSystem:
  - Linux
  otherID: []
  owner: josegcpa
  publication: []
  relation:
  - biotoolsID: nnunet
    type: uses
  toolType:
  - Command-line tool
  topic:
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-11-05T14:50:14.996722Z'
  biotoolsCURIE: biotools:spectra
  biotoolsID: spectra
  collectionID:
  - BioConductor
  - Bioconductor
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: The Spectra package defines an efficient infrastructure for storing
    and handling mass spectrometry spectra and functionality to subset, process, visualize
    and compare spectra data. It provides different implementations (backends) to
    store mass spectrometry data. These comprise backends tuned for fast data access
    and processing and backends for very large data sets ensuring a small memory footprint.
  documentation:
  - note: null
    type:
    - API documentation
    url: https://rformassspectrometry.github.io/Spectra/
  - note: null
    type:
    - Training material
    url: https://jorainer.github.io/SpectraTutorials/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: MSP
        uri: http://edamontology.org/format_4039
      - term: MGF
        uri: http://edamontology.org/format_3651
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: NetCDF
        uri: http://edamontology.org/format_3650
      - term: mzXML
        uri: http://edamontology.org/format_3654
    note: null
    operation:
    - term: Spectral analysis
      uri: http://edamontology.org/operation_3214
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: MSP
        uri: http://edamontology.org/format_4039
      - term: MGF
        uri: http://edamontology.org/format_3651
      - term: mzML
        uri: http://edamontology.org/format_3244
      - term: SQLite format
        uri: http://edamontology.org/format_3621
      - term: NetCDF
        uri: http://edamontology.org/format_3650
      - term: mzXML
        uri: http://edamontology.org/format_3654
  homepage: https://github.com/RforMassSpectrometry/Spectra
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-07T11:38:15.960655Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Rformassspectrometry/Spectra
  - note: Main documentation
    type:
    - Other
    url: https://rformassspectrometry.github.io/Spectra/
  maturity: Mature
  name: Spectra
  operatingSystem:
  - Windows
  - Linux
  - Mac
  otherID: []
  owner: jorainer
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2019-06-18T19:35:41Z'
  biotoolsCURIE: biotools:ComplexBrowser
  biotoolsID: ComplexBrowser
  collectionID:
  - Proteomics
  confidence_flag: null
  cost: null
  credit:
  - email: veits@bmb.sdu.dk
    fundrefid: null
    gridid: null
    name: Veit Schwämmle
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: ComplexBrowser is a R based software for supervised analysis of changes
    in protein complex abundance and subunit co-expression in proteomic datasets.
    It uses information contained in CORUM and EBI Complex Portal databases to provide
    the user with visualizations helping with biological interpretation of obtained
    results. ComplexBrowser also incorporates a normalization and quality control
    steps.
  documentation:
  - note: null
    type:
    - User manual
    url: https://bitbucket.org/michalakw/complexbrowser/raw/dc9d8f0c8dff610e2f58e3cb5f5087d050df26f2/Manual.pdf
  download:
  - note: null
    type: Source code
    url: https://bitbucket.org/michalakw/complexbrowser/
    version: '1.0'
  editPermission:
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    type: private
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  function:
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    input:
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      format:
      - term: CSV
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      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Protein interaction data
        uri: http://edamontology.org/data_0906
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    - term: Principal component visualisation
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    - term: Expression analysis
      uri: http://edamontology.org/operation_2495
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Validation
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    - term: Pathway or network visualisation
      uri: http://edamontology.org/operation_3083
    - term: Scatter plot plotting
      uri: http://edamontology.org/operation_2940
    - term: ID mapping
      uri: http://edamontology.org/operation_3282
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Heat map generation
      uri: http://edamontology.org/operation_0531
    - term: Regression analysis
      uri: http://edamontology.org/operation_3659
    - term: Expression correlation analysis
      uri: http://edamontology.org/operation_3463
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Dendrogram visualisation
      uri: http://edamontology.org/operation_2938
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: PDF
        uri: http://edamontology.org/format_3508
  homepage: http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-07T08:52:31.717711Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://computproteomics.bmb.sdu.dk/
  maturity: null
  name: ComplexBrowser
  operatingSystem:
  - Windows
  - Mac
  - Linux
  otherID: []
  owner: vtsiamis
  publication:
  - doi: 10.1101/573774
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-02-28T15:04:33.594183Z'
  biotoolsCURIE: biotools:prolfquapp
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  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A command-line tool for differential expression analysis in quantitative
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  documentation:
  - note: null
    type:
    - General
    url: https://github.com/prolfqua/prolfquapp/blob/master/README.md
  download:
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  function:
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      # and again you run the version within the docker container with

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      config.yaml -w NameOfAnalysis -s DIANN'
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    - data:
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      format:
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    note: null
    operation:
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    output:
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        term: Expression data
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      format:
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      - term: HTML
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  - cmd: null
    input:
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        term: Expression data
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      format:
      - term: xlsx
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    note: null
    operation:
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output:
    - data:
        term: Quality control report
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      format:
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  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
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        uri: http://edamontology.org/format_3475
    - data:
        term: Protein sequence
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      format:
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        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: PDF
        uri: http://edamontology.org/format_3508
  homepage: https://github.com/prolfqua/prolfquapp
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-06T14:37:12.116175Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/prolfqua/prolfquapp
  - note: null
    type:
    - Issue tracker
    url: https://github.com/prolfqua/prolfquapp/issues
  maturity: Emerging
  name: prolfquapp
  operatingSystem:
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  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
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    metadata:
      abstract: ''
      authors: []
      citationCount: 1
      date: '2025-02-07T00:00:00Z'
      journal: Journal of Proteome Research
      title: prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential
        Expression Analysis in Quantitative Proteomics
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: 0.0.6
  relation: []
  toolType:
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  topic:
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  validated: 0
  version:
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- accessibility: Open access (with restrictions)
  additionDate: '2025-11-05T13:55:16.717008Z'
  biotoolsCURIE: biotools:cobrar
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    name: Silvio Waschina
    note: null
    orcidid: https://orcid.org/0000-0002-6290-3593
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    typeEntity: Person
    typeRole:
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    url: https://www.uni-kiel.de/en/person/waschina-silvio-44493
  description: The R package cobrar provides structures and functions for constraint-based
    metabolic network analysis, e.g. the prediction of metabolic fluxes using fluxes
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  documentation: []
  download:
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  license: GPL-3.0
  link:
  - note: Link to the GitHub repo added
    type:
    - Other
    url: https://waschina.github.io/cobrar/
  maturity: Emerging
  name: COBRAR
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: rsmsheriff
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 0.2.2
- accessibility: Open access
  additionDate: '2017-01-17T15:04:54Z'
  biotoolsCURIE: biotools:xcms
  biotoolsID: xcms
  collectionID:
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  confidence_flag: null
  cost: null
  credit:
  - email: sneumann@ipb-halle.de
    fundrefid: null
    gridid: null
    name: Steffen Neumann
    note: null
    orcidid: https://orcid.org/0000-0002-7899-7192
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Framework for processing and visualization of chromatographically separated
    and single-spectra mass spectral data.  The packages enables imports from AIA/ANDI
    NetCDF, mzXML, mzData and mzML files and preprocesses data for high-throughput,
    untargeted analyte profiling.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/html/xcms.html
  - note: Galaxy training material for LC-MS data preprocessing with XCMS
    type:
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    url: https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/tutorial.html
  - note: Galaxy training for GC-MS data preprocessing with XCMS
    type:
    - Training material
    url: https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gc_ms_with_xcms/tutorial.html
  - note: Tutorial on using the XCMS R library
    type:
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    url: https://jorainer.github.io/xcmsTutorials/
  download:
  - note: null
    type: Source code
    url: http://bioconductor/packages/release/bioc/src/contrib/xcms_1.50.1.tar.gz
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  - note: null
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    version: null
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  elixirCommunity:
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  elixirNode:
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  - Czech Republic
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  function:
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    input:
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    - data:
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    - term: Label-free quantification
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    - term: Validation
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    - term: Data filtering
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    - term: Peak detection
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    output:
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      format:
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  - cmd: null
    input:
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  - note: null
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  - note: Link to the repository hosting the R code.
    type:
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    url: https://github.com/sneumann/xcms
  - note: null
    type:
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  publication:
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    metadata:
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      authors: []
      citationCount: 165
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      journal: Bioinformatics
      title: Correction of mass calibration gaps in liquid chromatography-mass spectrometry
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    note: null
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    pmid: '20671148'
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  relation:
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  toolType:
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  topic:
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  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  validated: 1
  version:
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  additionDate: '2020-04-22T11:39:28Z'
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    name: Tobias Rausch
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    typeEntity: Person
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  description: BAM Statistics, Feature Counting and Annotation
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  publication:
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    metadata:
      abstract: '© 2018 The Author(s) 2018. Published by Oxford University Press.Summary:
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        sequencing datasets is key for enabling downstream computational and biological
        analyses. We present Alfred, an efficient and versatile command-line application
        that computes multi-sample QC metrics in a read-group aware manner, across
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      authors:
      - name: Rausch T.
      - name: Hsi-Yang Fritz M.
      - name: Korbel J.O.
      - name: Benes V.
      citationCount: 14
      date: '2019-07-15T00:00:00Z'
      journal: Bioinformatics
      title: 'Alfred: Interactive multi-sample BAM alignment statistics, feature counting
        and feature annotation for long- and short-read sequencing'
    note: null
    pmcid: PMC6612896
    pmid: '30520945'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2023-07-13T08:14:40.190855Z'
  biotoolsCURIE: biotools:nlmced_filter
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  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Feriel Romdhane
    note: null
    orcidid: https://orcid.org/0000-0002-5854-9341
    rorid: null
    typeEntity: null
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    url: null
  description: The NLmCED (Non Local mean Coherence Enhancing Diffusion) filter is
    a denoising tool aimed to reduce Rician Noise in 3D MR images. It is a combination
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    from the noise reduction capabilities of the NLM filter while maintaining the
    edge-preserving characteristics of anisotropic diffusion.
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    input:
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    note: null
    operation:
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    output:
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        term: Image
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      format:
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      - term: DICOM format
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      - term: MAT
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  homepage: https://github.com/Feriel87/Denoising-CEST-MRI/tree/main/NLmCED_Filter
  homepage_status: 0
  language:
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  - MATLAB
  lastUpdate: '2025-11-05T12:10:02.656053Z'
  license: CC-BY-NC-ND-4.0
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  - note: Github Link
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  name: NLmCED Filter
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  owner: Feriel
  publication:
  - doi: 10.1504/IJCVR.2018.090012
    metadata:
      abstract: ''
      authors: []
      citationCount: 3
      date: '2018-01-01T00:00:00Z'
      journal: International Journal of Computational Vision and Robotics
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  description: MS2DeepScore is a deep learning similarity measure for mass fragmentation
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  description: MS-based metabolomics data processing and compound annotation pipeline.
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    metadata:
      abstract: Mapping the chemical space of compounds to chemical structures remains
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        automate the complex annotation by combining tandem mass spectrometry (MS2)
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        which contributes to an efficient candidate selection procedure. The results
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        Taken together, MAW could greatly facilitate automated metabolite characterization
        in diverse fields such as clinical metabolomics and natural product discovery.
      authors:
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      - name: Gadelha L.
      - name: Steinbeck C.
      - name: Sorokina M.
      - name: Peters K.
      citationCount: 10
      date: '2023-12-01T00:00:00Z'
      journal: Journal of Cheminformatics
      title: 'MAW: the reproducible Metabolome Annotation Workflow for untargeted
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    note: null
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  description: Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine
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    type:
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    url: https://github.com/ElucidataInc/ElMaven
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ElucidataInc/ElMaven/issues
  maturity: Mature
  name: El-MAVEN
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: hans
  publication:
  - doi: 10.1007/978-1-4939-9236-2_19
    metadata:
      abstract: ''
      authors: []
      citationCount: 106
      date: '2019-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: 'EL-MAVEN: A fast, robust, and user-friendly mass spectrometry data processing
        engine for metabolomics'
    note: null
    pmcid: null
    pmid: '31119671'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-09-05T21:19:03.847868Z'
  biotoolsCURIE: biotools:msnovelist
  biotoolsID: msnovelist
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: zamboni@imsb.biol.ethz.ch
    fundrefid: null
    gridid: null
    name: Nicola Zamboni
    note: null
    orcidid: https://orcid.org/0000-0003-1271-1021
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sebastian Böcker
    note: null
    orcidid: https://orcid.org/0000-0002-9304-8091
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Kai Dührkop
    note: null
    orcidid: https://orcid.org/0000-0002-9056-0540
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Michael A. Stravs
    note: null
    orcidid: https://orcid.org/0000-0002-1426-8572
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: De novo structure generation from mass spectra.
  documentation: []
  download: []
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Ab initio structure prediction
      uri: http://edamontology.org/operation_0476
    - term: Spectrum calculation
      uri: http://edamontology.org/operation_3860
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Small molecule design
      uri: http://edamontology.org/operation_4009
    output: []
  homepage: https://github.com/meowcat/MSNovelist
  homepage_status: 0
  language:
  - Python
  - Java
  lastUpdate: '2025-11-03T16:47:18.329938Z'
  license: AGPL-3.0
  link: []
  maturity: null
  name: MSNovelist
  operatingSystem:
  - Mac
  - Linux
  - Windows
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  owner: Jennifer
  publication:
  - doi: 10.1038/S41592-022-01486-3
    metadata:
      abstract: © 2022, The Author(s).Current methods for structure elucidation of
        small molecules rely on finding similarity with spectra of known compounds,
        but do not predict structures de novo for unknown compound classes. We present
        MSNovelist, which combines fingerprint prediction with an encoder–decoder
        neural network to generate structures de novo solely from tandem mass spectrometry
        (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural
        Product Social Molecular Networking site, MSNovelist predicted 25% of structures
        correctly on first rank, retrieved 45% of structures overall and reproduced
        61% of correct database annotations, without having ever seen the structure
        in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist
        correctly predicted 26% and retrieved 57% of structures, recovering 64% of
        correct database annotations. Finally, we illustrate the application of MSNovelist
        in a bryophyte MS2 dataset, in which de novo structure prediction substantially
        outscored the best database candidate for seven spectra. MSNovelist is ideally
        suited to complement library-based annotation in the case of poorly represented
        analyte classes and novel compounds.
      authors:
      - name: Stravs M.A.
      - name: Duhrkop K.
      - name: Bocker S.
      - name: Zamboni N.
      citationCount: 1
      date: '2022-07-01T00:00:00Z'
      journal: Nature Methods
      title: 'MSNovelist: de novo structure generation from mass spectra'
    note: null
    pmcid: PMC9262714
    pmid: '35637304'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Lipids
    uri: http://edamontology.org/topic_0153
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2021-10-28T20:02:19.622064Z'
  biotoolsCURIE: biotools:peakpanther
  biotoolsID: peakpanther
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: phenomecentre@imperial.ac.uk
    fundrefid: null
    gridid: null
    name: Arnaud M Wolfer
    note: null
    orcidid: https://orcid.org/0000-0001-5856-3218
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Goncalo Correia
    note: null
    orcidid: https://orcid.org/0000-0001-8271-9294
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jake Pearce
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Caroline Sands
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: R package for large-scale targeted extraction and integration of annotated
    metabolic features in LC-MS profiling datasets.
  documentation:
  - note: null
    type:
    - General
    url: https://phenomecentre.github.io/peakPantheR.github.io/
  download:
  - note: null
    type: Source code
    url: https://github.com/phenomecentre/peakPantheR/
    version: null
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    - term: Feature extraction
      uri: http://edamontology.org/operation_3937
    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    output: []
  homepage: https://phenomecentre.github.io/peakPantheR.github.io/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-03T16:47:17.380949Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://bioconductor.org/packages/peakPantheR/
  - note: null
    type:
    - Repository
    url: https://github.com/phenomecentre/metabotyping-dementia-urine
  maturity: null
  name: peakPantheR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1093/bioinformatics/btab433
    metadata:
      abstract: 'Summary: Untargeted liquid chromatography-mass spectrometry (LC-MS)
        profiling assays are capable of measuring thousands of chemical compounds
        in a single sample, but unreliable feature extraction and metabolite identification
        remain considerable barriers to their interpretation and usefulness. peakPantheR
        (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R
        package for the targeted extraction and integration of annotated features
        from LC-MS profiling experiments. It takes advantage of chromatographic and
        spectral databases and prior information of sample matrix composition to generate
        annotated and interpretable metabolic phenotypic datasets and power workflows
        for real-time data quality assessment.'
      authors:
      - name: Wolfer A.M.
      - name: Correia G.D.S.
      - name: Sands C.J.
      - name: Camuzeaux S.
      - name: Yuen A.H.Y.
      - name: Chekmeneva E.
      - name: Takats Z.
      - name: Pearce J.T.M.
      - name: Lewis M.R.
      citationCount: 21
      date: '2021-12-15T00:00:00Z'
      journal: Bioinformatics
      title: Extraction and integration of annotated metabolic features in LC-MS profiling
        datasets
    note: null
    pmcid: PMC8665750
    pmid: '34125879'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Endocrinology and metabolism
    uri: http://edamontology.org/topic_3407
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-09-04T23:10:37.868759Z'
  biotoolsCURIE: biotools:peakforest
  biotoolsID: peakforest
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: Franck.Giacomoni@inrae.fr
    fundrefid: null
    gridid: null
    name: Franck Giacomoni
    note: null
    orcidid: https://orcid.org/0000-0001-6063-4214
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Estelle Pujos-Guillot
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Christophe Junot
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: François Fenaille
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A multi-platform digital infrastructure for interoperable metabolite
    spectral data and metadata management.
  documentation: []
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    - term: Spectral library search
      uri: http://edamontology.org/operation_3801
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  homepage: https://peakforest.org
  homepage_status: 0
  language:
  - Java
  - JavaScript
  lastUpdate: '2025-11-03T16:47:12.424836Z'
  license: Other
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/peakforest
  maturity: null
  name: PeakForest
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1007/S11306-022-01899-3
    metadata:
      abstract: '© 2022, The Author(s).Introduction: Accuracy of feature annotation
        and metabolite identification in biological samples is a key element in metabolomics
        research. However, the annotation process is often hampered by the lack of
        spectral reference data in experimental conditions, as well as logistical
        difficulties in the spectral data management and exchange of annotations between
        laboratories. Objectives: To design an open-source infrastructure allowing
        hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with
        an ergonomic Web interface and Web services to support metabolite annotation
        and laboratory data management. Methods: We developed the PeakForest infrastructure,
        an open-source Java tool with automatic programming interfaces that can be
        deployed locally to organize spectral data for metabolome annotation in laboratories.
        Standardized operating procedures and formats were included to ensure data
        quality and interoperability, in line with international recommendations and
        FAIR principles. Results: PeakForest is able to capture and store experimental
        spectral MS and NMR metadata as well as collect and display signal annotations.
        This modular system provides a structured database with inbuilt tools to curate
        information, browse and reuse spectral information in data treatment. PeakForest
        offers data formalization and centralization at the laboratory level, facilitating
        shared spectral data across laboratories and integration into public databases.
        Conclusion: PeakForest is a comprehensive resource which addresses a technical
        bottleneck, namely large-scale spectral data annotation and metabolite identification
        for metabolomics laboratories with multiple instruments. PeakForest databases
        can be used in conjunction with bespoke data analysis pipelines in the Galaxy
        environment, offering the opportunity to meet the evolving needs of metabolomics
        research. Developed and tested by the French metabolomics community, PeakForest
        is freely-available at https://github.com/peakforest.'
      authors:
      - name: Paulhe N.
      - name: Canlet C.
      - name: Damont A.
      - name: Peyriga L.
      - name: Durand S.
      - name: Deborde C.
      - name: Alves S.
      - name: Bernillon S.
      - name: Berton T.
      - name: Bir R.
      - name: Bouville A.
      - name: Cahoreau E.
      - name: Centeno D.
      - name: Costantino R.
      - name: Debrauwer L.
      - name: Delabriere A.
      - name: Duperier C.
      - name: Emery S.
      - name: Flandin A.
      - name: Hohenester U.
      - name: Jacob D.
      - name: Joly C.
      - name: Jousse C.
      - name: Lagree M.
      - name: Lamari N.
      - name: Lefebvre M.
      - name: Lopez-Piffet C.
      - name: Lyan B.
      - name: Maucourt M.
      - name: Migne C.
      - name: Olivier M.-F.
      - name: Rathahao-Paris E.
      - name: Petriacq P.
      - name: Pinelli J.
      - name: Roch L.
      - name: Roger P.
      - name: Roques S.
      - name: Tabet J.-C.
      - name: Tremblay-Franco M.
      - name: Traikia M.
      - name: Warnet A.
      - name: Zhendre V.
      - name: Rolin D.
      - name: Jourdan F.
      - name: Thevenot E.
      - name: Moing A.
      - name: Jamin E.
      - name: Fenaille F.
      - name: Junot C.
      - name: Pujos-Guillot E.
      - name: Giacomoni F.
      citationCount: 0
      date: '2022-06-01T00:00:00Z'
      journal: Metabolomics
      title: 'PeakForest: a multi-platform digital infrastructure for interoperable
        metabolite spectral data and metadata management'
    note: null
    pmcid: PMC9197906
    pmid: '35699774'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: NMR
    uri: http://edamontology.org/topic_0593
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Data submission, annotation and curation
    uri: http://edamontology.org/topic_0219
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2023-12-01T15:46:02.196263Z'
  biotoolsCURIE: biotools:buddy
  biotoolsID: buddy
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: thuan@chem.ubc.ca
    fundrefid: null
    gridid: null
    name: Tao Huan
    note: null
    orcidid: http://orcid.org/0000-0001-6295-2435
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Open-source cheminformatic software platform developed for MS-based
    metabolomics research.
  documentation:
  - note: null
    type:
    - User manual
    url: https://philipbear.github.io/BUDDY_Metabolomics
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Peptide database search
      uri: http://edamontology.org/operation_3646
    - term: de Novo sequencing
      uri: http://edamontology.org/operation_3644
    - term: Optimisation and refinement
      uri: http://edamontology.org/operation_2425
    output: []
  homepage: https://github.com/HuanLab/BUDDY
  homepage_status: 0
  language:
  - C#
  lastUpdate: '2025-11-03T16:45:48.375514Z'
  license: MIT
  link: []
  maturity: null
  name: BUDDY
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/s41592-023-01850-x
    metadata:
      abstract: A substantial fraction of metabolic features remains undetermined
        in mass spectrometry (MS)-based metabolomics, and molecular formula annotation
        is the starting point for unraveling their chemical identities. Here we present
        bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation.
        Our approach prioritizes MS/MS-explainable formula candidates, implements
        machine-learned ranking and offers false discovery rate estimation. Compared
        with the mathematically exhaustive formula enumeration, our approach shrinks
        the formula candidate space by 42.8% on average. Method benchmarking on annotation
        accuracy was systematically carried out on reference MS/MS libraries and real
        metabolomics datasets. Applied on 155,321 recurrent unidentified spectra,
        our approach confidently annotated >5,000 novel molecular formulae absent
        from chemical databases. Beyond the level of individual metabolic features,
        we combined bottom-up MS/MS interrogation with global optimization to refine
        formula annotations while revealing peak interrelationships. This approach
        allowed the systematic annotation of 37 fatty acid amide molecules in human
        fecal data. All bioinformatics pipelines are available in a standalone software,
        BUDDY (https://github.com/HuanLab/BUDDY).
      authors:
      - name: Xing S.
      - name: Shen S.
      - name: Xu B.
      - name: Li X.
      - name: Huan T.
      citationCount: 21
      date: '2023-06-01T00:00:00Z'
      journal: Nature Methods
      title: 'BUDDY: molecular formula discovery via bottom-up MS/MS interrogation'
    note: null
    pmcid: null
    pmid: '37055660'
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Cheminformatics
    uri: http://edamontology.org/topic_2258
  - term: Endocrinology and metabolism
    uri: http://edamontology.org/topic_3407
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-11-25T21:43:37.501728Z'
  biotoolsCURIE: biotools:tidymass
  biotoolsID: tidymass
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: caroline.johnson@yale.edu
    fundrefid: null
    gridid: null
    name: Caroline H. Johnson
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: mpsnyder@stanford.edu
    fundrefid: null
    gridid: null
    name: Michael P. Snyder
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Xiaotao Shen
    note: null
    orcidid: http://orcid.org/0000-0002-9608-9964
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Hong Yan
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: An Object-oriented Reproducible Analysis Framework for LC-MS Data.
  documentation: []
  download:
  - note: null
    type: Container file
    url: https://hub.docker.com/r/jaspershen/tidymass-case-study
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/jaspershen/tidymass
    version: null
  - note: null
    type: Container file
    url: https://www.tidymass.org/start/tidymass_docker/
    version: null
  editPermission:
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    type: group
  elixirCommunity: []
  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://www.tidymass.org/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-11-03T16:45:44.856713Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/tidymass
  - note: null
    type:
    - Other
    url: https://tidymass.tidymass.org/
  maturity: null
  name: tidyMass
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1038/s41467-022-32155-w
    metadata:
      abstract: Reproducibility, traceability, and transparency have been long-standing
        issues for metabolomics data analysis. Multiple tools have been developed,
        but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/),
        a comprehensive R-based computational framework that can achieve the traceable,
        shareable, and reproducible workflow needs of data processing and analysis
        for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages
        that share an underlying design philosophy, grammar, and data structure, which
        provides a comprehensive, reproducible, and object-oriented computational
        framework. The modular architecture makes tidyMass a highly flexible and extensible
        tool, which other users can improve and integrate with other tools to customize
        their own pipeline.
      authors:
      - name: Shen X.
      - name: Yan H.
      - name: Wang C.
      - name: Gao P.
      - name: Johnson C.H.
      - name: Snyder M.P.
      citationCount: 28
      date: '2022-12-01T00:00:00Z'
      journal: Nature Communications
      title: TidyMass an object-oriented reproducible analysis framework for LC–MS
        data
    note: null
    pmcid: PMC9334349
    pmid: '35902589'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 1
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2021-05-26T20:04:08Z'
  biotoolsCURIE: biotools:gnps
  biotoolsID: gnps
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: miw023@ucsd.edu
    fundrefid: null
    gridid: null
    name: Mingxun Wang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: GNPS is a web-based mass spectrometry ecosystem that aims to be an
    open-access knowledge base for community-wide organization and sharing of raw,
    processed, or annotated fragmentation mass spectrometry data (MS/MS). GNPS aids
    in identification and discovery throughout the entire life cycle of data; from
    initial data acquisition/analysis to post publication.
  documentation:
  - note: null
    type:
    - General
    url: https://ccms-ucsd.github.io/GNPSDocumentation/
  - note: null
    type:
    - Quick start guide
    url: https://ccms-ucsd.github.io/GNPSDocumentation/quickstart/
  - note: null
    type:
    - API documentation
    url: https://ccms-ucsd.github.io/GNPSDocumentation/api/
  - note: null
    type:
    - FAQ
    url: https://ccms-ucsd.github.io/GNPSDocumentation/faq/
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
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  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Deisotoping
      uri: http://edamontology.org/operation_3629
    - term: Spectral counting
      uri: http://edamontology.org/operation_3637
    - term: Spectral library search
      uri: http://edamontology.org/operation_3801
    output: []
  homepage: https://gnps.ucsd.edu/
  homepage_status: 0
  language: []
  lastUpdate: '2025-11-03T16:45:43.961672Z'
  license: Other
  link:
  - note: null
    type:
    - Discussion forum
    url: https://groups.google.com/forum/#!forum/molecular_networking_bug_reports
  maturity: null
  name: GNPS
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1038/NBT.3597
    metadata:
      abstract: © 2016 Nature America, Inc. All rights reserved.The potential of the
        diverse chemistries present in natural products (NP) for biotechnology and
        medicine remains untapped because NP databases are not searchable with raw
        data and the NP community has no way to share data other than in published
        papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput
        characterization of NP, there is a pressing need for an infrastructure to
        enable sharing and curation of data. We present Global Natural Products Social
        Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge
        base for community-wide organization and sharing of raw, processed or identified
        tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely
        available community-wide reference MS libraries will underpin improved annotations.
        Data-driven social-networking should facilitate identification of spectra
        and foster collaborations. We also introduce the concept of 'living data'
        through continuous reanalysis of deposited data.
      authors:
      - name: Wang M.
      - name: Carver J.J.
      - name: Phelan V.V.
      - name: Sanchez L.M.
      - name: Garg N.
      - name: Peng Y.
      - name: Nguyen D.D.
      - name: Watrous J.
      - name: Kapono C.A.
      - name: Luzzatto-Knaan T.
      - name: Porto C.
      - name: Bouslimani A.
      - name: Melnik A.V.
      - name: Meehan M.J.
      - name: Liu W.-T.
      - name: Crusemann M.
      - name: Boudreau P.D.
      - name: Esquenazi E.
      - name: Sandoval-Calderon M.
      - name: Kersten R.D.
      - name: Pace L.A.
      - name: Quinn R.A.
      - name: Duncan K.R.
      - name: Hsu C.-C.
      - name: Floros D.J.
      - name: Gavilan R.G.
      - name: Kleigrewe K.
      - name: Northen T.
      - name: Dutton R.J.
      - name: Parrot D.
      - name: Carlson E.E.
      - name: Aigle B.
      - name: Michelsen C.F.
      - name: Jelsbak L.
      - name: Sohlenkamp C.
      - name: Pevzner P.
      - name: Edlund A.
      - name: McLean J.
      - name: Piel J.
      - name: Murphy B.T.
      - name: Gerwick L.
      - name: Liaw C.-C.
      - name: Yang Y.-L.
      - name: Humpf H.-U.
      - name: Maansson M.
      - name: Keyzers R.A.
      - name: Sims A.C.
      - name: Johnson A.R.
      - name: Sidebottom A.M.
      - name: Sedio B.E.
      - name: Klitgaard A.
      - name: Larson C.B.
      - name: Boya C.A.P.
      - name: Torres-Mendoza D.
      - name: Gonzalez D.J.
      - name: Silva D.B.
      - name: Marques L.M.
      - name: Demarque D.P.
      - name: Pociute E.
      - name: O'Neill E.C.
      - name: Briand E.
      - name: Helfrich E.J.N.
      - name: Granatosky E.A.
      - name: Glukhov E.
      - name: Ryffel F.
      - name: Houson H.
      - name: Mohimani H.
      - name: Kharbush J.J.
      - name: Zeng Y.
      - name: Vorholt J.A.
      - name: Kurita K.L.
      - name: Charusanti P.
      - name: McPhail K.L.
      - name: Nielsen K.F.
      - name: Vuong L.
      - name: Elfeki M.
      - name: Traxler M.F.
      - name: Engene N.
      - name: Koyama N.
      - name: Vining O.B.
      - name: Baric R.
      - name: Silva R.R.
      - name: Mascuch S.J.
      - name: Tomasi S.
      - name: Jenkins S.
      - name: Macherla V.
      - name: Hoffman T.
      - name: Agarwal V.
      - name: Williams P.G.
      - name: Dai J.
      - name: Neupane R.
      - name: Gurr J.
      - name: Rodriguez A.M.C.
      - name: Lamsa A.
      - name: Zhang C.
      - name: Dorrestein K.
      - name: Duggan B.M.
      - name: Almaliti J.
      - name: Allard P.-M.
      - name: Phapale P.
      - name: Nothias L.-F.
      - name: Alexandrov T.
      - name: Litaudon M.
      - name: Wolfender J.-L.
      - name: Kyle J.E.
      - name: Metz T.O.
      - name: Peryea T.
      - name: Nguyen D.-T.
      - name: VanLeer D.
      - name: Shinn P.
      - name: Jadhav A.
      - name: Muller R.
      - name: Waters K.M.
      - name: Shi W.
      - name: Liu X.
      - name: Zhang L.
      - name: Knight R.
      - name: Jensen P.R.
      - name: Palsson B.O.
      - name: Pogliano K.
      - name: Linington R.G.
      - name: Gutierrez M.
      - name: Lopes N.P.
      - name: Gerwick W.H.
      - name: Moore B.S.
      - name: Dorrestein P.C.
      - name: Bandeira N.
      citationCount: 1024
      date: '2016-09-08T00:00:00Z'
      journal: Nature Biotechnology
      title: Sharing and community curation of mass spectrometry data with Global
        Natural Products Social Molecular Networking
    note: null
    pmcid: PMC5321674
    pmid: '27504778'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Workbench
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Lipids
    uri: http://edamontology.org/topic_0153
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2019-07-04T14:05:39Z'
  biotoolsCURIE: biotools:CliqueMS
  biotoolsID: CliqueMS
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: oriol.senan@urv.cat
    fundrefid: null
    gridid: null
    name: Oriol Senan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Computational tool for annotating in-source metabolite ions from LC-MS
    untargeted metabolomics data based on a coelution similarity network.
  documentation:
  - note: Tutorial material
    type:
    - Training material
    url: https://github.com/osenan/cliqueMS/blob/master/vignettes/annotate_features.Rmd
  download:
  - note: null
    type: Source code
    url: https://cran.r-project.org/src/contrib/Archive/cliqueMS/
    version: null
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Read binning
      uri: http://edamontology.org/operation_3798
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    output: []
  homepage: https://github.com/osenan/cliqueMS
  homepage_status: 0
  language:
  - R
  - C++
  lastUpdate: '2025-11-03T16:44:03.166147Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/osenan/cliqueMS/issues
  maturity: Mature
  name: CliqueMS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: Ruta
  publication:
  - doi: 10.1093/bioinformatics/btz207
    metadata:
      abstract: 'The analysis of biological samples in untargeted metabolomic studies
        using LC-MS yields tens of thousands of ion signals. Annotating these features
        is of the utmost importance for answering questions as fundamental as, e.g.
        how many metabolites are there in a given sample.Results: Here, we introduce
        CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is
        based on the similarity between coelution profiles and therefore, as opposed
        to most methods, allows for the annotation of a single spectrum. Furthermore,
        CliqueMS improves upon the state of the art in several dimensions: (i) it
        uses a more discriminatory feature similarity metric; (ii) it treats the similarities
        between features in a transparent way by means of a simple generativemodel;
        (iii) it uses a well-grounded maximum likelihood inference approach to group
        features; (iv) it uses empirical adduct frequencies to identify the parental
        mass and (v) it deals more flexibly with the identification of the parental
        mass by proposing and ranking alternative annotations. We validate our approach
        with simple mixtures of standards and with real complex biological samples.
        CliqueMS reduces the thousands of features typically obtained in complex samples
        to hundreds of metabolites, and it is able to correctly annotate more metabolites
        and adducts from a single spectrum than available tools.'
      authors:
      - name: Senan O.
      - name: Aguilar-Mogas A.
      - name: Navarro M.
      - name: Capellades J.
      - name: Noon L.
      - name: Burks D.
      - name: Yanes O.
      - name: Guimera R.
      - name: Sales-Pardo M.
      citationCount: 48
      date: '2019-10-15T00:00:00Z'
      journal: Bioinformatics
      title: 'CliqueMS: A computational tool for annotating in-source metabolite ions
        from LC-MS untargeted metabolomics data based on a coelution similarity network'
    note: null
    pmcid: PMC6792096
    pmid: '30903689'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - 0.3.2
- accessibility: Open access
  additionDate: '2022-07-06T15:00:55.171504Z'
  biotoolsCURIE: biotools:neatms
  biotoolsID: neatms
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: dieter.beule@bih-charite.de
    fundrefid: null
    gridid: null
    name: Dieter Beule
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Yoann Gloaguen
    note: null
    orcidid: https://orcid.org/0000-0003-0493-8592
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jennifer A. Kirwan
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: NeatMS is an open source python package for untargeted LCMS signal
    labelling and filtering. NeatMS enables automated filtering of false positive
    MS peaks reported by commonly used LCMS data processing pipelines. NeatMS relies
    on neural network based classification.
  documentation:
  - note: null
    type:
    - User manual
    url: https://neatms.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    output: []
  homepage: https://anaconda.org/bioconda/neatms
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-11-03T16:32:44.163513Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/bihealth/NeatMS
  maturity: null
  name: NeatMS
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1021/ACS.ANALCHEM.1C02220
    metadata:
      abstract: © 2022 American Chemical Society. All rights reserved.Available automated
        methods for peak detection in untargeted metabolomics suffer from poor precision.
        We present NeatMS, which uses machine learning based on a convoluted neural
        network to reduce the number and fraction of false peaks. NeatMS comes with
        a pre-trained model representing expert knowledge in the differentiation of
        true chemical signal from noise. Furthermore, it provides all necessary functions
        to easily train new models or improve existing ones by transfer learning.
        Thus, the tool improves peak curation and contributes to the robust and scalable
        analysis of large-scale experiments. We show how to integrate it into different
        liquid chromatography-mass spectrometry (LC-MS) analysis workflows, quantify
        its performance, and compare it to various other approaches. NeatMS software
        is available as open source on github under permissive MIT license and is
        also provided as easy-to-install PyPi and Bioconda packages.
      authors:
      - name: Gloaguen Y.
      - name: Kirwan J.A.
      - name: Beule D.
      citationCount: 0
      date: '2022-03-29T00:00:00Z'
      journal: Analytical Chemistry
      title: Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics
    note: null
    pmcid: PMC8969107
    pmid: '35290737'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2024-01-02T02:11:50.145026Z'
  biotoolsCURIE: biotools:umetaflow
  biotoolsID: umetaflow
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: tiwe@biosustain.dtu.dk
    fundrefid: null
    gridid: null
    name: Tilmann Weber
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Untargeted metabolomics workflow for high-throughput data processing
    and analysis.
  documentation: []
  download: []
  editPermission:
    authors:
    - recetox-specdatri
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Imputation
      uri: http://edamontology.org/operation_3557
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Deisotoping
      uri: http://edamontology.org/operation_3629
    output: []
  homepage: https://github.com/biosustain/snakemake_UmetaFlow
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-11-03T16:16:27.787938Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/biosustain/pyOpenMS_UmetaFlow
  maturity: null
  name: UmetaFlow
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s13321-023-00724-w
    metadata:
      abstract: 'Metabolomics experiments generate highly complex datasets, which
        are time and work-intensive, sometimes even error-prone if inspected manually.
        Therefore, new methods for automated, fast, reproducible, and accurate data
        processing and dereplication are required. Here, we present UmetaFlow, a computational
        workflow for untargeted metabolomics that combines algorithms for data pre-processing,
        spectral matching, molecular formula and structural predictions, and an integration
        to the GNPS workflows Feature-Based Molecular Networking and Ion Identity
        Molecular Networking for downstream analysis. UmetaFlow is implemented as
        a Snakemake workflow, making it easy to use, scalable, and reproducible. For
        more interactive computing, visualization, as well as development, the workflow
        is also implemented in Jupyter notebooks using the Python programming language
        and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally,
        UmetaFlow is also offered as a web-based Graphical User Interface for parameter
        optimization and processing of smaller-sized datasets. UmetaFlow was validated
        with in-house LC–MS/MS datasets of actinomycetes producing known secondary
        metabolites, as well as commercial standards, and it detected all expected
        features and accurately annotated 76% of the molecular formulas and 65% of
        the structures. As a more generic validation, the publicly available MTBLS733
        and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more
        than 90% of all ground truth features and performed exceptionally well in
        quantification and discriminating marker selection. We anticipate that UmetaFlow
        will provide a useful platform for the interpretation of large metabolomics
        datasets. Graphical Abstract: [Figure not available: see fulltext.]'
      authors:
      - name: Kontou E.E.
      - name: Walter A.
      - name: Alka O.
      - name: Pfeuffer J.
      - name: Sachsenberg T.
      - name: Mohite O.S.
      - name: Nuhamunada M.
      - name: Kohlbacher O.
      - name: Weber T.
      citationCount: 7
      date: '2023-12-01T00:00:00Z'
      journal: Journal of Cheminformatics
      title: 'UmetaFlow: an untargeted metabolomics workflow for high-throughput data
        processing and analysis'
    note: null
    pmcid: PMC10176759
    pmid: '37173725'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  validated: 0
  version: []
next: ?page=2
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