Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree
Tripathi, A.; Vazquez-Baeza, Y.; Gauglitz, J. M.; Wang, M.; Duhrkop, K.; Esposito-Nothias, M.; Acharya, D.; Ernst, M.; van der Hooft, J. J. J.; Zhu, Q.; McDonald, D.; Gonzalez, A.; Handelsman, J.; Fleischauer, M.; Ludwig, M.; Bocker, S.; NOTHIAS, L. F.; Knight, R.; Dorrestein, P. C.
Show 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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