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Significance estimation for large scale metabolomics annotations by spectral matching

Author

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  • Kerstin Scheubert

    (Chair for Bioinformatics, Friedrich Schiller University Jena)

  • Franziska Hufsky

    (Chair for Bioinformatics, Friedrich Schiller University Jena
    RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena)

  • Daniel Petras

    (Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Mingxun Wang

    (Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Louis-Félix Nothias

    (Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Kai Dührkop

    (Chair for Bioinformatics, Friedrich Schiller University Jena)

  • Nuno Bandeira

    (Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Pieter C. Dorrestein

    (Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Sebastian Böcker

    (Chair for Bioinformatics, Friedrich Schiller University Jena)

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.

Suggested Citation

  • Kerstin Scheubert & Franziska Hufsky & Daniel Petras & Mingxun Wang & Louis-Félix Nothias & Kai Dührkop & Nuno Bandeira & Pieter C. Dorrestein & Sebastian Böcker, 2017. "Significance estimation for large scale metabolomics annotations by spectral matching," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01318-5
    DOI: 10.1038/s41467-017-01318-5
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    Cited by:

    1. Sasilada Sirirungruang & Vincent Blay & Elys P. Rodriguez & Yasmine F. Scott & Khanh M. Vuu & Collin R. Barnum & Paul H. Opgenorth & Fanzhou Kong & Yuanyue Li & Oliver Fiehn & Patrick M. Shih, 2025. "A substrate-multiplexed platform for profiling enzymatic potential of plant family 1 glycosyltransferases," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    2. Oliver Alka & Premy Shanthamoorthy & Michael Witting & Karin Kleigrewe & Oliver Kohlbacher & Hannes L. Röst, 2022. "DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Daniel G. C. Treen & Mingxun Wang & Shipei Xing & Katherine B. Louie & Tao Huan & Pieter C. Dorrestein & Trent R. Northen & Benjamin P. Bowen, 2022. "SIMILE enables alignment of tandem mass spectra with statistical significance," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Haosong Zhang & Xinhao Zeng & Yandong Yin & Zheng-Jiang Zhu, 2025. "Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics," Nature Communications, Nature, vol. 16(1), pages 1-15, December.

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