High-confidence structural annotation of metabolites absent from spectral libraries View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-10-14

AUTHORS

Martin A. Hoffmann, Louis-Félix Nothias, Marcus Ludwig, Markus Fleischauer, Emily C. Gentry, Michael Witting, Pieter C. Dorrestein, Kai Dührkop, Sebastian Böcker

ABSTRACT

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries. More... »

PAGES

411-421

References to SciGraph publications

  • 2017-11-14. Significance estimation for large scale metabolomics annotations by spectral matching in NATURE COMMUNICATIONS
  • 2011-11-02. Identification of “Known Unknowns” Utilizing Accurate Mass Data and ChemSpider in JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
  • 2013-09-12. High-fat diet alters gut microbiota physiology in mice in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2014-06-05. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification in METABOLOMICS
  • 2019-04-03. Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics in NATURE COMMUNICATIONS
  • 2020-10-13. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC in NATURE MACHINE INTELLIGENCE
  • 2020-08-24. Feature-based molecular networking in the GNPS analysis environment in NATURE METHODS
  • 2013-06-30. LipidBlast in silico tandem mass spectrometry database for lipid identification in NATURE METHODS
  • 2016-10-31. Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides in NATURE MICROBIOLOGY
  • 2016-04-29. Improved metabolite identification with MIDAS and MAGMa through MS/MS spectral dataset-driven parameter optimization in METABOLOMICS
  • 2010-03-22. In silico fragmentation for computer assisted identification of metabolite mass spectra in BMC BIOINFORMATICS
  • 2021-07-19. Chemical language models enable navigation in sparsely populated chemical space in NATURE MACHINE INTELLIGENCE
  • 2018-08-10. PubChem chemical structure standardization in JOURNAL OF CHEMINFORMATICS
  • 2019-01-05. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification in JOURNAL OF CHEMINFORMATICS
  • 2007-08-08. A note on Platt’s probabilistic outputs for support vector machines in MACHINE LEARNING
  • 2020-02-26. Global chemical effects of the microbiome include new bile-acid conjugations in NATURE
  • 1994-09-01. Optimization and testing of mass spectral library search algorithms for compound identification in JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
  • 2017-08-12. Untargeted metabolomics suffers from incomplete raw data processing in METABOLOMICS
  • 2007-10-21. Semi-supervised learning for peptide identification from shotgun proteomics datasets in NATURE METHODS
  • 2007-02-27. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry in NATURE METHODS
  • 2019-03-18. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information in NATURE METHODS
  • 2015-08-28. MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics in JOURNAL OF CHEMINFORMATICS
  • 2020-01-01. Mass spectrometry searches using MASST in NATURE BIOTECHNOLOGY
  • 2016-08-09. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking in NATURE BIOTECHNOLOGY
  • 2021-06-22. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment in NATURE COMMUNICATIONS
  • 2017-05-25. Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy in JOURNAL OF CHEMINFORMATICS
  • 2020-11-23. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra in NATURE BIOTECHNOLOGY
  • 2017-08-30. Commensal bacteria make GPCR ligands that mimic human signalling molecules in NATURE
  • 2016-11-14. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry in NATURE METHODS
  • 2017-03-27. Critical Assessment of Small Molecule Identification 2016: automated methods in JOURNAL OF CHEMINFORMATICS
  • 2016-01-29. MetFrag relaunched: incorporating strategies beyond in silico fragmentation in JOURNAL OF CHEMINFORMATICS
  • 2020-01-22. In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples in ANALYTICAL AND BIOANALYTICAL CHEMISTRY
  • <error retrieving object. in <ERROR RETRIEVING OBJECT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41587-021-01045-9

    DOI

    http://dx.doi.org/10.1038/s41587-021-01045-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1141876752

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34650271


    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/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Databases, Factual", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metabolome", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metabolomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Molecular Structure", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Tandem Mass Spectrometry", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "International Max Planck Research School \u2018Exploration of Ecological Interactions with Molecular and Chemical Techniques\u2019, Max Planck Institute for Chemical Ecology, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.418160.a", 
              "name": [
                "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany", 
                "International Max Planck Research School \u2018Exploration of Ecological Interactions with Molecular and Chemical Techniques\u2019, Max Planck Institute for Chemical Ecology, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hoffmann", 
            "givenName": "Martin A.", 
            "id": "sg:person.014317746103.61", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014317746103.61"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland", 
              "id": "http://www.grid.ac/institutes/grid.8591.5", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA", 
                "School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland"
              ], 
              "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, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ludwig", 
            "givenName": "Marcus", 
            "id": "sg:person.016017355175.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016017355175.21"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fleischauer", 
            "givenName": "Markus", 
            "id": "sg:person.011271316521.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011271316521.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.266100.3", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gentry", 
            "givenName": "Emily C.", 
            "id": "sg:person.015632357417.61", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015632357417.61"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany", 
              "id": "http://www.grid.ac/institutes/grid.6936.a", 
              "name": [
                "Metabolomics and Proteomics Core, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany", 
                "Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Witting", 
            "givenName": "Michael", 
            "id": "sg:person.0656241330.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656241330.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Departments of Pharmacology and Pediatrics, University of California, San Diego, San Diego, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.266100.3", 
              "name": [
                "Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA", 
                "Departments of Pharmacology and Pediatrics, University of California, San Diego, 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, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 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": "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9613.d", 
              "name": [
                "Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 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.10.1038/s41592-019-0344-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1112858205", 
              "https://doi.org/10.1038/s41592-019-0344-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-021-23953-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1139035094", 
              "https://doi.org/10.1038/s41467-021-23953-9"
            ], 
            "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.10.1038/s41467-019-09550-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1113182145", 
              "https://doi.org/10.1038/s41467-019-09550-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s42256-020-00234-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1131638979", 
              "https://doi.org/10.1038/s42256-020-00234-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-021-23986-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1138940487", 
              "https://doi.org/10.1038/s41467-021-23986-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-015-0087-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049637874", 
              "https://doi.org/10.1186/s13321-015-0087-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-018-0293-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106106906", 
              "https://doi.org/10.1186/s13321-018-0293-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2551", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051249218", 
              "https://doi.org/10.1038/nmeth.2551"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s42256-021-00368-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1139784950", 
              "https://doi.org/10.1038/s42256-021-00368-1"
            ], 
            "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.10.1038/s41587-020-0740-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1132841620", 
              "https://doi.org/10.1038/s41587-020-0740-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-020-2047-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1125136854", 
              "https://doi.org/10.1038/s41586-020-2047-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41587-019-0375-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1123770973", 
              "https://doi.org/10.1038/s41587-019-0375-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-017-0219-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085596302", 
              "https://doi.org/10.1186/s13321-017-0219-x"
            ], 
            "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.10.1038/s41592-020-0933-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1130293863", 
              "https://doi.org/10.1038/s41592-020-0933-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00216-019-02351-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124213828", 
              "https://doi.org/10.1007/s00216-019-02351-7"
            ], 
            "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/nmeth.4072", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023732847", 
              "https://doi.org/10.1038/nmeth.4072"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-017-0207-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084252455", 
              "https://doi.org/10.1186/s13321-017-0207-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature23874", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091381214", 
              "https://doi.org/10.1038/nature23874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13321-018-0324-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111159678", 
              "https://doi.org/10.1186/s13321-018-0324-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-017-01318-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092599528", 
              "https://doi.org/10.1038/s41467-017-01318-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-016-1036-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024865682", 
              "https://doi.org/10.1007/s11306-016-1036-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-017-1246-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091200693", 
              "https://doi.org/10.1007/s11306-017-1246-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2013.155", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003787895", 
              "https://doi.org/10.1038/ismej.2013.155"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth1113", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029861497", 
              "https://doi.org/10.1038/nmeth1113"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13361-011-0265-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053329487", 
              "https://doi.org/10.1007/s13361-011-0265-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10994-007-5018-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039556605", 
              "https://doi.org/10.1007/s10994-007-5018-6"
            ], 
            "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"
          }, 
          {
            "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/nmicrobiol.2016.197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004804034", 
              "https://doi.org/10.1038/nmicrobiol.2016.197"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-10-14", 
        "datePublishedReg": "2021-10-14", 
        "description": "Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.", 
        "genre": "article", 
        "id": "sg:pub.10.1038/s41587-021-01045-9", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2522127", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8631955", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2439746", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.6375598", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1115214", 
            "issn": [
              "1087-0156", 
              "1546-1696"
            ], 
            "name": "Nature Biotechnology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "40"
          }
        ], 
        "keywords": [
          "support vector machine", 
          "vector machine", 
          "database generation", 
          "diverse datasets", 
          "spectral library search", 
          "incorrect annotations", 
          "manual evaluation", 
          "method searches", 
          "annotation", 
          "confidence scores", 
          "structure annotation", 
          "low false discovery rate", 
          "untargeted metabolomics experiments", 
          "structure database", 
          "structural annotation", 
          "value estimation", 
          "spectral library", 
          "library", 
          "metabolomics experiments", 
          "database", 
          "search", 
          "workflow", 
          "machine", 
          "dataset", 
          "library search", 
          "COSMIC", 
          "Human Metabolome Database", 
          "p-value estimation", 
          "experiments", 
          "applications", 
          "false discovery rate", 
          "features", 
          "discovery rate", 
          "Metabolome Database", 
          "estimation", 
          "standards", 
          "data", 
          "generation", 
          "small fraction", 
          "evaluation", 
          "number", 
          "structure", 
          "hits", 
          "directionality", 
          "substantial number", 
          "rate", 
          "scores", 
          "spectra", 
          "samples", 
          "absent", 
          "human samples", 
          "fraction", 
          "molecular structure", 
          "acid", 
          "natural bile acids", 
          "bile acids", 
          "synthetic standards"
        ], 
        "name": "High-confidence structural annotation of metabolites absent from spectral libraries", 
        "pagination": "411-421", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1141876752"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41587-021-01045-9"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "34650271"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41587-021-01045-9", 
          "https://app.dimensions.ai/details/publication/pub.1141876752"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-11-24T21:08", 
        "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_901.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1038/s41587-021-01045-9"
      }
    ]
     

    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/s41587-021-01045-9'

    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/s41587-021-01045-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41587-021-01045-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41587-021-01045-9'


     

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

    355 TRIPLES      21 PREDICATES      121 URIs      80 LITERALS      13 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41587-021-01045-9 schema:about N333bde5a670e42ccba32471dc9972a60
    2 N7e7de0b400c74b03bdcafe55dd014aac
    3 N8b589593f383441a9fd4c490eb9ecdc3
    4 N8bd6a4961bf6416ea266bf8b8ce4f090
    5 Nab9d281ab26c41da940ae59517f28db7
    6 Nc4410a517cee4bf0baf133dd6733fe2c
    7 anzsrc-for:08
    8 anzsrc-for:0806
    9 schema:author Na3c8b9e2b9d745569ef3697c389d1312
    10 schema:citation sg:pub.10.1007/s00216-019-02351-7
    11 sg:pub.10.1007/s10994-007-5018-6
    12 sg:pub.10.1007/s11306-014-0676-4
    13 sg:pub.10.1007/s11306-016-1036-3
    14 sg:pub.10.1007/s11306-017-1246-3
    15 sg:pub.10.1007/s13361-011-0265-y
    16 sg:pub.10.1016/1044-0305(94)87009-8
    17 sg:pub.10.1038/ismej.2013.155
    18 sg:pub.10.1038/nature23874
    19 sg:pub.10.1038/nbt.3597
    20 sg:pub.10.1038/nmeth.2551
    21 sg:pub.10.1038/nmeth.4072
    22 sg:pub.10.1038/nmeth1019
    23 sg:pub.10.1038/nmeth1113
    24 sg:pub.10.1038/nmicrobiol.2016.197
    25 sg:pub.10.1038/s41467-017-01318-5
    26 sg:pub.10.1038/s41467-019-09550-x
    27 sg:pub.10.1038/s41467-021-23953-9
    28 sg:pub.10.1038/s41467-021-23986-0
    29 sg:pub.10.1038/s41586-020-2047-9
    30 sg:pub.10.1038/s41587-019-0375-9
    31 sg:pub.10.1038/s41587-020-0740-8
    32 sg:pub.10.1038/s41592-019-0344-8
    33 sg:pub.10.1038/s41592-020-0933-6
    34 sg:pub.10.1038/s42256-020-00234-6
    35 sg:pub.10.1038/s42256-021-00368-1
    36 sg:pub.10.1186/1471-2105-11-148
    37 sg:pub.10.1186/s13321-015-0087-1
    38 sg:pub.10.1186/s13321-016-0115-9
    39 sg:pub.10.1186/s13321-017-0207-1
    40 sg:pub.10.1186/s13321-017-0219-x
    41 sg:pub.10.1186/s13321-018-0293-8
    42 sg:pub.10.1186/s13321-018-0324-5
    43 schema:datePublished 2021-10-14
    44 schema:datePublishedReg 2021-10-14
    45 schema:description Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.
    46 schema:genre article
    47 schema:isAccessibleForFree true
    48 schema:isPartOf N603b78f158c74cf49f6cbcb2b2aaefcf
    49 Nf7a4d093a7c142f2961273772db0f516
    50 sg:journal.1115214
    51 schema:keywords COSMIC
    52 Human Metabolome Database
    53 Metabolome Database
    54 absent
    55 acid
    56 annotation
    57 applications
    58 bile acids
    59 confidence scores
    60 data
    61 database
    62 database generation
    63 dataset
    64 directionality
    65 discovery rate
    66 diverse datasets
    67 estimation
    68 evaluation
    69 experiments
    70 false discovery rate
    71 features
    72 fraction
    73 generation
    74 hits
    75 human samples
    76 incorrect annotations
    77 library
    78 library search
    79 low false discovery rate
    80 machine
    81 manual evaluation
    82 metabolomics experiments
    83 method searches
    84 molecular structure
    85 natural bile acids
    86 number
    87 p-value estimation
    88 rate
    89 samples
    90 scores
    91 search
    92 small fraction
    93 spectra
    94 spectral library
    95 spectral library search
    96 standards
    97 structural annotation
    98 structure
    99 structure annotation
    100 structure database
    101 substantial number
    102 support vector machine
    103 synthetic standards
    104 untargeted metabolomics experiments
    105 value estimation
    106 vector machine
    107 workflow
    108 schema:name High-confidence structural annotation of metabolites absent from spectral libraries
    109 schema:pagination 411-421
    110 schema:productId N38e90bcda561463cbecad14a08501c91
    111 N73a74b89a6704b8e88c71e74e4d0ab24
    112 N7c3e3743c17841c5ac6591508fd78528
    113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1141876752
    114 https://doi.org/10.1038/s41587-021-01045-9
    115 schema:sdDatePublished 2022-11-24T21:08
    116 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    117 schema:sdPublisher N6e84e0f8be2b4e3ca573994c4976dc92
    118 schema:url https://doi.org/10.1038/s41587-021-01045-9
    119 sgo:license sg:explorer/license/
    120 sgo:sdDataset articles
    121 rdf:type schema:ScholarlyArticle
    122 N333bde5a670e42ccba32471dc9972a60 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    123 schema:name Tandem Mass Spectrometry
    124 rdf:type schema:DefinedTerm
    125 N38e90bcda561463cbecad14a08501c91 schema:name pubmed_id
    126 schema:value 34650271
    127 rdf:type schema:PropertyValue
    128 N46131326147f4f11ba305ba4e0108437 rdf:first sg:person.015632357417.61
    129 rdf:rest Ncc9cc5ac87144de088f6a165258aee52
    130 N603b78f158c74cf49f6cbcb2b2aaefcf schema:volumeNumber 40
    131 rdf:type schema:PublicationVolume
    132 N6e84e0f8be2b4e3ca573994c4976dc92 schema:name Springer Nature - SN SciGraph project
    133 rdf:type schema:Organization
    134 N73a74b89a6704b8e88c71e74e4d0ab24 schema:name doi
    135 schema:value 10.1038/s41587-021-01045-9
    136 rdf:type schema:PropertyValue
    137 N7c3e3743c17841c5ac6591508fd78528 schema:name dimensions_id
    138 schema:value pub.1141876752
    139 rdf:type schema:PropertyValue
    140 N7e7de0b400c74b03bdcafe55dd014aac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    141 schema:name Humans
    142 rdf:type schema:DefinedTerm
    143 N8b589593f383441a9fd4c490eb9ecdc3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    144 schema:name Metabolome
    145 rdf:type schema:DefinedTerm
    146 N8bd6a4961bf6416ea266bf8b8ce4f090 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Metabolomics
    148 rdf:type schema:DefinedTerm
    149 N8c0c0eaa86c04da59303dbf807b92f8e rdf:first sg:person.0751572741.14
    150 rdf:rest rdf:nil
    151 Na3c8b9e2b9d745569ef3697c389d1312 rdf:first sg:person.014317746103.61
    152 rdf:rest Nd63e9087701846b8947115981ed5753e
    153 Na467583f47694d8d8671acb0e5762e49 rdf:first sg:person.011271316521.30
    154 rdf:rest N46131326147f4f11ba305ba4e0108437
    155 Na8c11a975fa343a1a77f71e1bc148ce7 rdf:first sg:person.016017355175.21
    156 rdf:rest Na467583f47694d8d8671acb0e5762e49
    157 Nab9d281ab26c41da940ae59517f28db7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    158 schema:name Databases, Factual
    159 rdf:type schema:DefinedTerm
    160 Naee412f77d0c42348e44b0c2f521f275 rdf:first sg:person.0636577215.98
    161 rdf:rest N8c0c0eaa86c04da59303dbf807b92f8e
    162 Nc4410a517cee4bf0baf133dd6733fe2c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    163 schema:name Molecular Structure
    164 rdf:type schema:DefinedTerm
    165 Nc95e745c9b3447c78b20cd68bedb8643 rdf:first sg:person.01023217043.95
    166 rdf:rest Naee412f77d0c42348e44b0c2f521f275
    167 Ncc9cc5ac87144de088f6a165258aee52 rdf:first sg:person.0656241330.49
    168 rdf:rest Nc95e745c9b3447c78b20cd68bedb8643
    169 Nd63e9087701846b8947115981ed5753e rdf:first sg:person.011720002155.41
    170 rdf:rest Na8c11a975fa343a1a77f71e1bc148ce7
    171 Nf7a4d093a7c142f2961273772db0f516 schema:issueNumber 3
    172 rdf:type schema:PublicationIssue
    173 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    174 schema:name Information and Computing Sciences
    175 rdf:type schema:DefinedTerm
    176 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    177 schema:name Information Systems
    178 rdf:type schema:DefinedTerm
    179 sg:grant.2439746 http://pending.schema.org/fundedItem sg:pub.10.1038/s41587-021-01045-9
    180 rdf:type schema:MonetaryGrant
    181 sg:grant.2522127 http://pending.schema.org/fundedItem sg:pub.10.1038/s41587-021-01045-9
    182 rdf:type schema:MonetaryGrant
    183 sg:grant.6375598 http://pending.schema.org/fundedItem sg:pub.10.1038/s41587-021-01045-9
    184 rdf:type schema:MonetaryGrant
    185 sg:grant.8631955 http://pending.schema.org/fundedItem sg:pub.10.1038/s41587-021-01045-9
    186 rdf:type schema:MonetaryGrant
    187 sg:journal.1115214 schema:issn 1087-0156
    188 1546-1696
    189 schema:name Nature Biotechnology
    190 schema:publisher Springer Nature
    191 rdf:type schema:Periodical
    192 sg:person.01023217043.95 schema:affiliation grid-institutes:grid.266100.3
    193 schema:familyName Dorrestein
    194 schema:givenName Pieter C.
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023217043.95
    196 rdf:type schema:Person
    197 sg:person.011271316521.30 schema:affiliation grid-institutes:grid.9613.d
    198 schema:familyName Fleischauer
    199 schema:givenName Markus
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011271316521.30
    201 rdf:type schema:Person
    202 sg:person.011720002155.41 schema:affiliation grid-institutes:grid.8591.5
    203 schema:familyName Nothias
    204 schema:givenName Louis-Félix
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011720002155.41
    206 rdf:type schema:Person
    207 sg:person.014317746103.61 schema:affiliation grid-institutes:grid.418160.a
    208 schema:familyName Hoffmann
    209 schema:givenName Martin A.
    210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014317746103.61
    211 rdf:type schema:Person
    212 sg:person.015632357417.61 schema:affiliation grid-institutes:grid.266100.3
    213 schema:familyName Gentry
    214 schema:givenName Emily C.
    215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015632357417.61
    216 rdf:type schema:Person
    217 sg:person.016017355175.21 schema:affiliation grid-institutes:grid.9613.d
    218 schema:familyName Ludwig
    219 schema:givenName Marcus
    220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016017355175.21
    221 rdf:type schema:Person
    222 sg:person.0636577215.98 schema:affiliation grid-institutes:grid.9613.d
    223 schema:familyName Dührkop
    224 schema:givenName Kai
    225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636577215.98
    226 rdf:type schema:Person
    227 sg:person.0656241330.49 schema:affiliation grid-institutes:grid.6936.a
    228 schema:familyName Witting
    229 schema:givenName Michael
    230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656241330.49
    231 rdf:type schema:Person
    232 sg:person.0751572741.14 schema:affiliation grid-institutes:grid.9613.d
    233 schema:familyName Böcker
    234 schema:givenName Sebastian
    235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751572741.14
    236 rdf:type schema:Person
    237 sg:pub.10.1007/s00216-019-02351-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124213828
    238 https://doi.org/10.1007/s00216-019-02351-7
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1007/s10994-007-5018-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039556605
    241 https://doi.org/10.1007/s10994-007-5018-6
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1007/s11306-014-0676-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032195585
    244 https://doi.org/10.1007/s11306-014-0676-4
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1007/s11306-016-1036-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024865682
    247 https://doi.org/10.1007/s11306-016-1036-3
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1007/s11306-017-1246-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091200693
    250 https://doi.org/10.1007/s11306-017-1246-3
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1007/s13361-011-0265-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1053329487
    253 https://doi.org/10.1007/s13361-011-0265-y
    254 rdf:type schema:CreativeWork
    255 sg:pub.10.1016/1044-0305(94)87009-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009634640
    256 https://doi.org/10.1016/1044-0305(94)87009-8
    257 rdf:type schema:CreativeWork
    258 sg:pub.10.1038/ismej.2013.155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003787895
    259 https://doi.org/10.1038/ismej.2013.155
    260 rdf:type schema:CreativeWork
    261 sg:pub.10.1038/nature23874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091381214
    262 https://doi.org/10.1038/nature23874
    263 rdf:type schema:CreativeWork
    264 sg:pub.10.1038/nbt.3597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045197127
    265 https://doi.org/10.1038/nbt.3597
    266 rdf:type schema:CreativeWork
    267 sg:pub.10.1038/nmeth.2551 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051249218
    268 https://doi.org/10.1038/nmeth.2551
    269 rdf:type schema:CreativeWork
    270 sg:pub.10.1038/nmeth.4072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023732847
    271 https://doi.org/10.1038/nmeth.4072
    272 rdf:type schema:CreativeWork
    273 sg:pub.10.1038/nmeth1019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009487848
    274 https://doi.org/10.1038/nmeth1019
    275 rdf:type schema:CreativeWork
    276 sg:pub.10.1038/nmeth1113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029861497
    277 https://doi.org/10.1038/nmeth1113
    278 rdf:type schema:CreativeWork
    279 sg:pub.10.1038/nmicrobiol.2016.197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004804034
    280 https://doi.org/10.1038/nmicrobiol.2016.197
    281 rdf:type schema:CreativeWork
    282 sg:pub.10.1038/s41467-017-01318-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092599528
    283 https://doi.org/10.1038/s41467-017-01318-5
    284 rdf:type schema:CreativeWork
    285 sg:pub.10.1038/s41467-019-09550-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1113182145
    286 https://doi.org/10.1038/s41467-019-09550-x
    287 rdf:type schema:CreativeWork
    288 sg:pub.10.1038/s41467-021-23953-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139035094
    289 https://doi.org/10.1038/s41467-021-23953-9
    290 rdf:type schema:CreativeWork
    291 sg:pub.10.1038/s41467-021-23986-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1138940487
    292 https://doi.org/10.1038/s41467-021-23986-0
    293 rdf:type schema:CreativeWork
    294 sg:pub.10.1038/s41586-020-2047-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125136854
    295 https://doi.org/10.1038/s41586-020-2047-9
    296 rdf:type schema:CreativeWork
    297 sg:pub.10.1038/s41587-019-0375-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1123770973
    298 https://doi.org/10.1038/s41587-019-0375-9
    299 rdf:type schema:CreativeWork
    300 sg:pub.10.1038/s41587-020-0740-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1132841620
    301 https://doi.org/10.1038/s41587-020-0740-8
    302 rdf:type schema:CreativeWork
    303 sg:pub.10.1038/s41592-019-0344-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112858205
    304 https://doi.org/10.1038/s41592-019-0344-8
    305 rdf:type schema:CreativeWork
    306 sg:pub.10.1038/s41592-020-0933-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1130293863
    307 https://doi.org/10.1038/s41592-020-0933-6
    308 rdf:type schema:CreativeWork
    309 sg:pub.10.1038/s42256-020-00234-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131638979
    310 https://doi.org/10.1038/s42256-020-00234-6
    311 rdf:type schema:CreativeWork
    312 sg:pub.10.1038/s42256-021-00368-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139784950
    313 https://doi.org/10.1038/s42256-021-00368-1
    314 rdf:type schema:CreativeWork
    315 sg:pub.10.1186/1471-2105-11-148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048363616
    316 https://doi.org/10.1186/1471-2105-11-148
    317 rdf:type schema:CreativeWork
    318 sg:pub.10.1186/s13321-015-0087-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049637874
    319 https://doi.org/10.1186/s13321-015-0087-1
    320 rdf:type schema:CreativeWork
    321 sg:pub.10.1186/s13321-016-0115-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050554722
    322 https://doi.org/10.1186/s13321-016-0115-9
    323 rdf:type schema:CreativeWork
    324 sg:pub.10.1186/s13321-017-0207-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084252455
    325 https://doi.org/10.1186/s13321-017-0207-1
    326 rdf:type schema:CreativeWork
    327 sg:pub.10.1186/s13321-017-0219-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1085596302
    328 https://doi.org/10.1186/s13321-017-0219-x
    329 rdf:type schema:CreativeWork
    330 sg:pub.10.1186/s13321-018-0293-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106106906
    331 https://doi.org/10.1186/s13321-018-0293-8
    332 rdf:type schema:CreativeWork
    333 sg:pub.10.1186/s13321-018-0324-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111159678
    334 https://doi.org/10.1186/s13321-018-0324-5
    335 rdf:type schema:CreativeWork
    336 grid-institutes:grid.266100.3 schema:alternateName Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
    337 Departments of Pharmacology and Pediatrics, University of California, San Diego, San Diego, CA, USA
    338 schema:name Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
    339 Departments of Pharmacology and Pediatrics, University of California, San Diego, San Diego, CA, USA
    340 rdf:type schema:Organization
    341 grid-institutes:grid.418160.a schema:alternateName International Max Planck Research School ‘Exploration of Ecological Interactions with Molecular and Chemical Techniques’, Max Planck Institute for Chemical Ecology, Jena, Germany
    342 schema:name Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
    343 International Max Planck Research School ‘Exploration of Ecological Interactions with Molecular and Chemical Techniques’, Max Planck Institute for Chemical Ecology, Jena, Germany
    344 rdf:type schema:Organization
    345 grid-institutes:grid.6936.a schema:alternateName Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
    346 schema:name Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
    347 Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
    348 rdf:type schema:Organization
    349 grid-institutes:grid.8591.5 schema:alternateName School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    350 schema:name Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
    351 School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    352 rdf:type schema:Organization
    353 grid-institutes:grid.9613.d schema:alternateName Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
    354 schema:name Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
    355 rdf:type schema:Organization
     




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


    ...