Published on Tue May 05 2020

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.

Qemistree is a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences.

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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.