Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

Anupriya Tripathi; Yoshiki Vázquez‐Baeza; Julia M. Gauglitz; Mingxun Wang; Kai Dührkop; Mélissa Nothias-Esposito; Deepa D. Acharya; Madeleine Ernst; Justin J. J. van der Hooft; Qiyun Zhu; Daniel T. McDonald; Asker Brejnrod; Antonio González; Jo Handelsman; Markus Fleischauer; Marcus Ludwig; Sebastian Böcker; Louis-Félix Nothias-Scaglia; Rob D. Knight; Pieter C. Dorrestein

doi:10.1038/s41589-020-00677-3

BioinformaticsNature Chemical BiologyNov 16, 2020

Peer-reviewed journal article

Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

Anupriya Tripathi iD, Yoshiki Vázquez‐Baeza iD, Julia M. Gauglitz iD, Mingxun Wang iD, Kai Dührkop iD, Mélissa Nothias-Esposito iD, Deepa D. Acharya iD, Madeleine Ernst iD, Justin J. J. van der Hooft iD, Qiyun Zhu iD, Daniel T. McDonald iD, Asker Brejnrod iD, Antonio González iD, Jo Handelsman iD, Markus Fleischauer iD, Marcus Ludwig iD, Sebastian Böcker iD, Louis-Félix Nothias-Scaglia iD, Rob D. Knight iD, Pieter C. Dorrestein iD

Abstract

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.

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