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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Significance estimation for large scale metabolomics annotations by spectral matching.

Kerstin Scheubert1, Franziska Hufsky1,2, Daniel Petras3,4

  • 1Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany.

Nature Communications
|November 15, 2017
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Summary

New methods estimate false discovery rates (FDR) for small molecule identification in mass spectrometry. Adjusting spectral matching parameters significantly increases metabolite annotations, improving data analysis for metabolomics research.

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Area of Science:

  • Metabolomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Small molecule identification in untargeted mass spectrometry depends on matching experimental fragment spectra to spectral libraries.
  • Current methods lack robust statistical approaches for estimating the reliability of these spectral matches, specifically the false discovery rate (FDR).

Purpose of the Study:

  • To develop and validate statistical methods for estimating the FDR in small molecule annotations derived from mass spectrometry data.
  • To demonstrate the impact of optimized spectral matching parameters on the number of metabolite identifications.

Main Methods:

  • Applied empirical Bayes and target-decoy based approaches to estimate FDR across 70 public metabolomics datasets.
  • Systematically adjusted spectral matching scoring parameters and thresholds to evaluate their effect on annotation yield.

Main Results:

  • Developed novel FDR estimation methods applicable to large-scale metabolomics data.
  • Optimizing spectral matching parameters led to an average increase of 139% in metabolite annotations compared to default settings.
  • Parameter adjustments showed a wide range of impact, from -92% to +5705%.

Conclusions:

  • The presented FDR estimation methods provide a crucial tool for assessing the reliability of metabolite annotations in mass spectrometry.
  • Adjusting spectral matching parameters is essential for maximizing the discovery of small molecules in metabolomics studies.
  • These advancements are vital for the progress of metabolomics, mirroring the impact of similar methods in proteomics, transcriptomics, and genomics.