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Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Author

Listed:
  • Wout Bittremieux

    (University of Antwerp)

  • Nicole E. Avalon

    (University of California San Diego)

  • Sydney P. Thomas

    (University of California San Diego
    University of California San Diego)

  • Sarvar A. Kakhkhorov

    (Center for Advanced Technologies
    University of Copenhagen)

  • Alexander A. Aksenov

    (University of California San Diego
    University of California San Diego
    University of Connecticut
    Arome Science inc.)

  • Paulo Wender P. Gomes

    (University of California San Diego
    University of California San Diego)

  • Christine M. Aceves

    (The Scripps Research Institute)

  • Andrés Mauricio Caraballo-Rodríguez

    (University of California San Diego
    University of California San Diego)

  • Julia M. Gauglitz

    (University of California San Diego
    University of California San Diego)

  • William H. Gerwick

    (University of California San Diego
    University of California San Diego)

  • Tao Huan

    (University of British Columbia)

  • Alan K. Jarmusch

    (University of California San Diego
    University of California San Diego
    National Institutes of Health, Research Triangle Park)

  • Rima F. Kaddurah-Daouk

    (Duke University School of Medicine
    Duke University
    Duke Institute of Brain Sciences, Duke University)

  • Kyo Bin Kang

    (Sookmyung Women’s University)

  • Hyun Woo Kim

    (Dongguk University)

  • Todor Kondić

    (Luxembourg Centre for Systems Biomedicine, University of Luxembourg)

  • Helena Mannochio-Russo

    (University of California San Diego
    University of California San Diego
    São Paulo State University)

  • Michael J. Meehan

    (University of California San Diego
    University of California San Diego)

  • Alexey V. Melnik

    (University of Connecticut
    Arome Science inc.)

  • Louis-Felix Nothias

    (CNRS, ICN
    Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d’Azur)

  • Claire O’Donovan

    (Wellcome Genome Campus)

  • Morgan Panitchpakdi

    (University of California San Diego
    University of California San Diego)

  • Daniel Petras

    (University of California San Diego
    University of California San Diego
    University of Tuebingen
    University of California Riverside)

  • Robin Schmid

    (University of California San Diego
    University of California San Diego)

  • Emma L. Schymanski

    (Luxembourg Centre for Systems Biomedicine, University of Luxembourg)

  • Justin J. J. van der Hooft

    (University of California San Diego
    Wageningen University & Research)

  • Kelly C. Weldon

    (University of California San Diego
    University of California San Diego)

  • Heejung Yang

    (Kangwon National University)

  • Shipei Xing

    (University of California San Diego
    University of California San Diego
    University of British Columbia)

  • Jasmine Zemlin

    (University of California San Diego
    University of California San Diego)

  • Mingxun Wang

    (University of California Riverside)

  • Pieter C. Dorrestein

    (University of California San Diego
    University of California San Diego)

Abstract

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or “suspects,” were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Suggested Citation

  • Wout Bittremieux & Nicole E. Avalon & Sydney P. Thomas & Sarvar A. Kakhkhorov & Alexander A. Aksenov & Paulo Wender P. Gomes & Christine M. Aceves & Andrés Mauricio Caraballo-Rodríguez & Julia M. Gaug, 2023. "Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44035-y
    DOI: 10.1038/s41467-023-44035-y
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    References listed on IDEAS

    as
    1. Robin Schmid & Daniel Petras & Louis-Félix Nothias & Mingxun Wang & Allegra T. Aron & Annika Jagels & Hiroshi Tsugawa & Johannes Rainer & Mar Garcia-Aloy & Kai Dührkop & Ansgar Korf & Tomáš Pluskal & , 2021. "Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Florian Huber & Lars Ridder & Stefan Verhoeven & Jurriaan H Spaaks & Faruk Diblen & Simon Rogers & Justin J J van der Hooft, 2021. "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-18, February.
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