Significance estimation for large scale metabolomics annotations by spectral matching

Kerstin Scheubert, Franziska Hufsky, Daniel Petras, Mingxun Wang, Louis-Félix Nothias, Kai Dührkop, Nuno Bandeira, Pieter C. Dorrestein and Sebastian Böcker ()
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Kerstin Scheubert: Chair for Bioinformatics, Friedrich Schiller University Jena
Franziska Hufsky: Chair for Bioinformatics, Friedrich Schiller University Jena
Daniel Petras: Collaborative Mass Spectrometry Innovation Center, 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
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
Pieter C. Dorrestein: Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
Sebastian Böcker: Chair for Bioinformatics, Friedrich Schiller University Jena

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-01318-5

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