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Title
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Enhancing untargeted metabolomics using metadata-based source annotation
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Author
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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. |
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Language
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English
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Source (journal)
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Nature biotechnology. - New York, N.Y., 1996, currens
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Publication
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New York, N.Y.
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2022
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ISSN
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1087-0156
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DOI
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10.1038/S41587-022-01368-1
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Volume/pages
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40
(2022)
, p. 1774-1779
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ISI
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000821717100001
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Pubmed ID
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35798960
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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