AI Article Synopsis

  • Human metabolomics studies typically identify only about 10% of the molecular features present in samples.
  • The authors propose a new method that uses reference data to match metabolomics tandem mass spectrometry (MS/MS) data against annotated source data, effectively creating a pseudo-MS/MS library.
  • This new approach significantly improves the usage of MS/MS spectra by 5.1 times compared to traditional methods, enabling better analysis of dietary patterns from untargeted metabolomic data.

Article Abstract

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277029PMC
http://dx.doi.org/10.1038/s41587-022-01368-1DOI Listing

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