Enhancing untargeted metabolomics using metadata-based source annotation.

Gauglitz, Julia M; West, Kiana A; Bittremieux, Wout; Williams, Candace L; Weldon, Kelly C; Panitchpakdi, Morgan; Di Ottavio, Francesca; Aceves, Christine M et al. · Nat Biotechnol · 2022

basic_science · Level V

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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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