Enhancing untargeted metabolomics using metadata-based source annotation.

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.

SUBMITTER: Gauglitz JM 

PROVIDER: S-EPMC10277029 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature