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Enhancing untargeted metabolomics using metadata-based source annotation
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DOI：10.1038/s41587-022-01368-1.png)
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Abstract
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Human untargeted metabolomics studies annotate only similar to 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.
Keywords:
MOLECULAR NETWORKING
RESOURCE
SOFTWARE
DATABASE
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41.7
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1.2W
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10.1W
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