Significance estimation for large scale metabolomics annotations by spectral matching View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-11-14

AUTHORS

Kerstin Scheubert, Franziska Hufsky, Daniel Petras, Mingxun Wang, Louis-Félix Nothias, Kai Dührkop, Nuno Bandeira, Pieter C. Dorrestein, Sebastian Böcker

ABSTRACT

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science. More... »

PAGES

1494

References to SciGraph publications

  • 2007-03-27. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry in BMC BIOINFORMATICS
  • 2014-06-05. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification in METABOLOMICS
  • 2016-11-08. SPLASH, a hashed identifier for mass spectra in NATURE BIOTECHNOLOGY
  • 2007-09-27. Analysis and validation of proteomic data generated by tandem mass spectrometry in NATURE METHODS
  • 2016-08-09. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking in NATURE BIOTECHNOLOGY
  • 2006-08-03. Combined use of ESI–QqTOF-MS and ESI–QqTOF-MS/MS with mass-spectral library search for qualitative analysis of drugs in ANALYTICAL AND BIOANALYTICAL CHEMISTRY
  • 1994-09-01. Optimization and testing of mass spectral library search algorithms for compound identification in JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
  • 2016-02-01. Fragmentation trees reloaded in JOURNAL OF CHEMINFORMATICS
  • 2016-09-02. Metabolomics enables precision medicine: “A White Paper, Community Perspective” in METABOLOMICS
  • 2007-02-27. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry in NATURE METHODS
  • 2016-01-18. vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available in JOURNAL OF CHEMINFORMATICS
  • 2016-10-31. Dereplication of peptidic natural products through database search of mass spectra in NATURE CHEMICAL BIOLOGY
  • 2016-01-29. MetFrag relaunched: incorporating strategies beyond in silico fragmentation in JOURNAL OF CHEMINFORMATICS
  • 2007-09-12. Proposed minimum reporting standards for chemical analysis in METABOLOMICS
  • 2010-03-22. In silico fragmentation for computer assisted identification of metabolite mass spectra in BMC BIOINFORMATICS
  • 2017-07-05. Global chemical analysis of biology by mass spectrometry in NATURE REVIEWS CHEMISTRY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-017-01318-5

    DOI

    http://dx.doi.org/10.1038/s41467-017-01318-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092599528

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29133785


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Statistics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chromatography, Liquid", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Databases, Protein", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metabolomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Tandem Mass Spectrometry", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Scheubert", 
            "givenName": "Kerstin", 
            "id": "sg:person.01252516512.79", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01252516512.79"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, 07743, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany", 
                "RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, 07743, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hufsky", 
            "givenName": "Franziska", 
            "id": "sg:person.01312635241.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312635241.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
                "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Petras", 
            "givenName": "Daniel", 
            "id": "sg:person.01114355122.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114355122.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Mingxun", 
            "id": "sg:person.0577427451.94", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577427451.94"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
                "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nothias", 
            "givenName": "Louis-F\u00e9lix", 
            "id": "sg:person.011720002155.41", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011720002155.41"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "D\u00fchrkop", 
            "givenName": "Kai", 
            "id": "sg:person.0636577215.98", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636577215.98"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
                "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bandeira", 
            "givenName": "Nuno", 
            "id": "sg:person.01165537115.94", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01165537115.94"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA", 
                "Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dorrestein", 
            "givenName": "Pieter C.", 
            "id": "sg:person.01023217043.95", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023217043.95"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "B\u00f6cker", 
            "givenName": "Sebastian", 
            "id": "sg:person.0751572741.14", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751572741.14"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s11306-007-0082-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023480149", 
              "https://doi.org/10.1007/s11306-007-0082-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3597", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045197127", 
              "https://doi.org/10.1038/nbt.3597"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nchembio.2219", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043661254", 
              "https://doi.org/10.1038/nchembio.2219"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth1019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009487848", 
              "https://doi.org/10.1038/nmeth1019"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-016-0116-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011550150", 
              "https://doi.org/10.1186/s13321-016-0116-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-014-0676-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032195585", 
              "https://doi.org/10.1007/s11306-014-0676-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-016-0112-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042277193", 
              "https://doi.org/10.1186/s13321-016-0112-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth1088", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051930348", 
              "https://doi.org/10.1038/nmeth1088"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-8-105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022661426", 
              "https://doi.org/10.1186/1471-2105-8-105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-016-1094-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041025435", 
              "https://doi.org/10.1007/s11306-016-1094-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41570-017-0054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090344463", 
              "https://doi.org/10.1038/s41570-017-0054"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00216-006-0634-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014793355", 
              "https://doi.org/10.1007/s00216-006-0634-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-016-0115-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050554722", 
              "https://doi.org/10.1186/s13321-016-0115-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3689", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008733036", 
              "https://doi.org/10.1038/nbt.3689"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-11-148", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048363616", 
              "https://doi.org/10.1186/1471-2105-11-148"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1016/1044-0305(94)87009-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009634640", 
              "https://doi.org/10.1016/1044-0305(94)87009-8"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-11-14", 
        "datePublishedReg": "2017-11-14", 
        "description": "The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from \u221292 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.", 
        "genre": "article", 
        "id": "sg:pub.10.1038/s41467-017-01318-5", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2439746", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.6375598", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1043282", 
            "issn": [
              "2041-1723"
            ], 
            "name": "Nature Communications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "8"
          }
        ], 
        "keywords": [
          "mass spectrometry", 
          "reference library spectra", 
          "advancement of proteomics", 
          "FDR estimation methods", 
          "small molecules", 
          "fragment spectra", 
          "untargeted mass spectrometry", 
          "spectrometry", 
          "library spectra", 
          "metabolomics data sets", 
          "spectra", 
          "molecules", 
          "GNPS", 
          "spectral matching", 
          "false discovery rate", 
          "statistical methods", 
          "metabolomics data", 
          "empirical Bayes", 
          "proteomics", 
          "significance estimation", 
          "method", 
          "estimation method", 
          "discovery rate", 
          "number of annotations", 
          "parameters", 
          "data sets", 
          "large-scale analysis", 
          "scale analysis", 
          "default parameters", 
          "genomic science", 
          "rate", 
          "estimation", 
          "advancement", 
          "field", 
          "analysis", 
          "matching", 
          "Bayes", 
          "set", 
          "match scores", 
          "science", 
          "number", 
          "criteria", 
          "data", 
          "transcriptomics", 
          "users", 
          "setting", 
          "project", 
          "annotation", 
          "scores"
        ], 
        "name": "Significance estimation for large scale metabolomics annotations by spectral matching", 
        "pagination": "1494", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1092599528"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41467-017-01318-5"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "29133785"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41467-017-01318-5", 
          "https://app.dimensions.ai/details/publication/pub.1092599528"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-11-24T21:01", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_717.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1038/s41467-017-01318-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41467-017-01318-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41467-017-01318-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41467-017-01318-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41467-017-01318-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    264 TRIPLES      21 PREDICATES      96 URIs      72 LITERALS      13 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41467-017-01318-5 schema:about N3b21890e20f24ab5a985e0b45dc03251
    2 N3b227798a7444a61a4752e0f6cae3be1
    3 N7f1ac5440018401499cef086045d9ec1
    4 N93f374c97178412a88e3684adb576b68
    5 Nb7709e1a4b3e4ab78543556217fa3651
    6 Nb90f108fc9884cb28ff219c4e06d7918
    7 anzsrc-for:01
    8 anzsrc-for:0104
    9 schema:author Nc27b2b38b536445bb4692fe3ac0899ee
    10 schema:citation sg:pub.10.1007/s00216-006-0634-8
    11 sg:pub.10.1007/s11306-007-0082-2
    12 sg:pub.10.1007/s11306-014-0676-4
    13 sg:pub.10.1007/s11306-016-1094-6
    14 sg:pub.10.1016/1044-0305(94)87009-8
    15 sg:pub.10.1038/nbt.3597
    16 sg:pub.10.1038/nbt.3689
    17 sg:pub.10.1038/nchembio.2219
    18 sg:pub.10.1038/nmeth1019
    19 sg:pub.10.1038/nmeth1088
    20 sg:pub.10.1038/s41570-017-0054
    21 sg:pub.10.1186/1471-2105-11-148
    22 sg:pub.10.1186/1471-2105-8-105
    23 sg:pub.10.1186/s13321-016-0112-z
    24 sg:pub.10.1186/s13321-016-0115-9
    25 sg:pub.10.1186/s13321-016-0116-8
    26 schema:datePublished 2017-11-14
    27 schema:datePublishedReg 2017-11-14
    28 schema:description The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
    29 schema:genre article
    30 schema:isAccessibleForFree true
    31 schema:isPartOf Ndcfd09ff269e4f85a2d645f3882dfe91
    32 Ne1bb0357bc4b42f3a5ae9f57a1c4e4d1
    33 sg:journal.1043282
    34 schema:keywords Bayes
    35 FDR estimation methods
    36 GNPS
    37 advancement
    38 advancement of proteomics
    39 analysis
    40 annotation
    41 criteria
    42 data
    43 data sets
    44 default parameters
    45 discovery rate
    46 empirical Bayes
    47 estimation
    48 estimation method
    49 false discovery rate
    50 field
    51 fragment spectra
    52 genomic science
    53 large-scale analysis
    54 library spectra
    55 mass spectrometry
    56 match scores
    57 matching
    58 metabolomics data
    59 metabolomics data sets
    60 method
    61 molecules
    62 number
    63 number of annotations
    64 parameters
    65 project
    66 proteomics
    67 rate
    68 reference library spectra
    69 scale analysis
    70 science
    71 scores
    72 set
    73 setting
    74 significance estimation
    75 small molecules
    76 spectra
    77 spectral matching
    78 spectrometry
    79 statistical methods
    80 transcriptomics
    81 untargeted mass spectrometry
    82 users
    83 schema:name Significance estimation for large scale metabolomics annotations by spectral matching
    84 schema:pagination 1494
    85 schema:productId N6317929e85ed46deb1430ecfe2bfd15d
    86 N912d624f2fb0438a97f958d1ca244cc6
    87 Nee92f23c22814f279cf72ea25941b03d
    88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092599528
    89 https://doi.org/10.1038/s41467-017-01318-5
    90 schema:sdDatePublished 2022-11-24T21:01
    91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    92 schema:sdPublisher Nbe35dbb30c05445587e52e50a7c236e4
    93 schema:url https://doi.org/10.1038/s41467-017-01318-5
    94 sgo:license sg:explorer/license/
    95 sgo:sdDataset articles
    96 rdf:type schema:ScholarlyArticle
    97 N0a5029e9cd3047c29d68215af7786a5d rdf:first sg:person.0577427451.94
    98 rdf:rest N96d7964bd3b54c5e94bb38c9bc8b7a65
    99 N17e9aa666597451989227fe327376c7a rdf:first sg:person.01114355122.40
    100 rdf:rest N0a5029e9cd3047c29d68215af7786a5d
    101 N2ec1a0af10c84a778c8fc22503a4ec02 rdf:first sg:person.0636577215.98
    102 rdf:rest N746dd4a6ab3a44889cb402bc4fe49868
    103 N33163cd3803c4e648f25da1e3f858af2 rdf:first sg:person.01312635241.44
    104 rdf:rest N17e9aa666597451989227fe327376c7a
    105 N3b21890e20f24ab5a985e0b45dc03251 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    106 schema:name Chromatography, Liquid
    107 rdf:type schema:DefinedTerm
    108 N3b227798a7444a61a4752e0f6cae3be1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    109 schema:name Metabolomics
    110 rdf:type schema:DefinedTerm
    111 N620ca222f73446b6bb2d81b9e1f51a19 rdf:first sg:person.01023217043.95
    112 rdf:rest Nb2d1c744022f40a29418faf1ff18b0cb
    113 N6317929e85ed46deb1430ecfe2bfd15d schema:name pubmed_id
    114 schema:value 29133785
    115 rdf:type schema:PropertyValue
    116 N746dd4a6ab3a44889cb402bc4fe49868 rdf:first sg:person.01165537115.94
    117 rdf:rest N620ca222f73446b6bb2d81b9e1f51a19
    118 N7f1ac5440018401499cef086045d9ec1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    119 schema:name Databases, Protein
    120 rdf:type schema:DefinedTerm
    121 N912d624f2fb0438a97f958d1ca244cc6 schema:name dimensions_id
    122 schema:value pub.1092599528
    123 rdf:type schema:PropertyValue
    124 N93f374c97178412a88e3684adb576b68 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Algorithms
    126 rdf:type schema:DefinedTerm
    127 N96d7964bd3b54c5e94bb38c9bc8b7a65 rdf:first sg:person.011720002155.41
    128 rdf:rest N2ec1a0af10c84a778c8fc22503a4ec02
    129 Nb2d1c744022f40a29418faf1ff18b0cb rdf:first sg:person.0751572741.14
    130 rdf:rest rdf:nil
    131 Nb7709e1a4b3e4ab78543556217fa3651 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    132 schema:name Tandem Mass Spectrometry
    133 rdf:type schema:DefinedTerm
    134 Nb90f108fc9884cb28ff219c4e06d7918 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    135 schema:name Computational Biology
    136 rdf:type schema:DefinedTerm
    137 Nbe35dbb30c05445587e52e50a7c236e4 schema:name Springer Nature - SN SciGraph project
    138 rdf:type schema:Organization
    139 Nc27b2b38b536445bb4692fe3ac0899ee rdf:first sg:person.01252516512.79
    140 rdf:rest N33163cd3803c4e648f25da1e3f858af2
    141 Ndcfd09ff269e4f85a2d645f3882dfe91 schema:issueNumber 1
    142 rdf:type schema:PublicationIssue
    143 Ne1bb0357bc4b42f3a5ae9f57a1c4e4d1 schema:volumeNumber 8
    144 rdf:type schema:PublicationVolume
    145 Nee92f23c22814f279cf72ea25941b03d schema:name doi
    146 schema:value 10.1038/s41467-017-01318-5
    147 rdf:type schema:PropertyValue
    148 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    149 schema:name Mathematical Sciences
    150 rdf:type schema:DefinedTerm
    151 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    152 schema:name Statistics
    153 rdf:type schema:DefinedTerm
    154 sg:grant.2439746 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-017-01318-5
    155 rdf:type schema:MonetaryGrant
    156 sg:grant.6375598 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-017-01318-5
    157 rdf:type schema:MonetaryGrant
    158 sg:journal.1043282 schema:issn 2041-1723
    159 schema:name Nature Communications
    160 schema:publisher Springer Nature
    161 rdf:type schema:Periodical
    162 sg:person.01023217043.95 schema:affiliation grid-institutes:None
    163 schema:familyName Dorrestein
    164 schema:givenName Pieter C.
    165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023217043.95
    166 rdf:type schema:Person
    167 sg:person.01114355122.40 schema:affiliation grid-institutes:None
    168 schema:familyName Petras
    169 schema:givenName Daniel
    170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114355122.40
    171 rdf:type schema:Person
    172 sg:person.01165537115.94 schema:affiliation grid-institutes:None
    173 schema:familyName Bandeira
    174 schema:givenName Nuno
    175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01165537115.94
    176 rdf:type schema:Person
    177 sg:person.011720002155.41 schema:affiliation grid-institutes:None
    178 schema:familyName Nothias
    179 schema:givenName Louis-Félix
    180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011720002155.41
    181 rdf:type schema:Person
    182 sg:person.01252516512.79 schema:affiliation grid-institutes:grid.9613.d
    183 schema:familyName Scheubert
    184 schema:givenName Kerstin
    185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01252516512.79
    186 rdf:type schema:Person
    187 sg:person.01312635241.44 schema:affiliation grid-institutes:grid.9613.d
    188 schema:familyName Hufsky
    189 schema:givenName Franziska
    190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312635241.44
    191 rdf:type schema:Person
    192 sg:person.0577427451.94 schema:affiliation grid-institutes:None
    193 schema:familyName Wang
    194 schema:givenName Mingxun
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577427451.94
    196 rdf:type schema:Person
    197 sg:person.0636577215.98 schema:affiliation grid-institutes:grid.9613.d
    198 schema:familyName Dührkop
    199 schema:givenName Kai
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636577215.98
    201 rdf:type schema:Person
    202 sg:person.0751572741.14 schema:affiliation grid-institutes:grid.9613.d
    203 schema:familyName Böcker
    204 schema:givenName Sebastian
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751572741.14
    206 rdf:type schema:Person
    207 sg:pub.10.1007/s00216-006-0634-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014793355
    208 https://doi.org/10.1007/s00216-006-0634-8
    209 rdf:type schema:CreativeWork
    210 sg:pub.10.1007/s11306-007-0082-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023480149
    211 https://doi.org/10.1007/s11306-007-0082-2
    212 rdf:type schema:CreativeWork
    213 sg:pub.10.1007/s11306-014-0676-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032195585
    214 https://doi.org/10.1007/s11306-014-0676-4
    215 rdf:type schema:CreativeWork
    216 sg:pub.10.1007/s11306-016-1094-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041025435
    217 https://doi.org/10.1007/s11306-016-1094-6
    218 rdf:type schema:CreativeWork
    219 sg:pub.10.1016/1044-0305(94)87009-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009634640
    220 https://doi.org/10.1016/1044-0305(94)87009-8
    221 rdf:type schema:CreativeWork
    222 sg:pub.10.1038/nbt.3597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045197127
    223 https://doi.org/10.1038/nbt.3597
    224 rdf:type schema:CreativeWork
    225 sg:pub.10.1038/nbt.3689 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008733036
    226 https://doi.org/10.1038/nbt.3689
    227 rdf:type schema:CreativeWork
    228 sg:pub.10.1038/nchembio.2219 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043661254
    229 https://doi.org/10.1038/nchembio.2219
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1038/nmeth1019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009487848
    232 https://doi.org/10.1038/nmeth1019
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1038/nmeth1088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051930348
    235 https://doi.org/10.1038/nmeth1088
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1038/s41570-017-0054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090344463
    238 https://doi.org/10.1038/s41570-017-0054
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1186/1471-2105-11-148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048363616
    241 https://doi.org/10.1186/1471-2105-11-148
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1186/1471-2105-8-105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022661426
    244 https://doi.org/10.1186/1471-2105-8-105
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1186/s13321-016-0112-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1042277193
    247 https://doi.org/10.1186/s13321-016-0112-z
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1186/s13321-016-0115-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050554722
    250 https://doi.org/10.1186/s13321-016-0115-9
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1186/s13321-016-0116-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011550150
    253 https://doi.org/10.1186/s13321-016-0116-8
    254 rdf:type schema:CreativeWork
    255 grid-institutes:None schema:alternateName Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA
    256 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA
    257 schema:name Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA
    258 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, 92093, La Jolla, San Diego, CA, USA
    259 rdf:type schema:Organization
    260 grid-institutes:grid.9613.d schema:alternateName Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany
    261 RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, 07743, Jena, Germany
    262 schema:name Chair for Bioinformatics, Friedrich Schiller University Jena, 07743, Jena, Germany
    263 RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, 07743, Jena, Germany
    264 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...