RT Journal Article
SR Electronic
T1 Classes for the masses: Systematic classification of unknowns using fragmentation spectra
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.17.046672
DO 10.1101/2020.04.17.046672
A1 Kai Dührkop
A1 Louis Felix Nothias
A1 Markus Fleischauer
A1 Marcus Ludwig
A1 Martin A. Hoffmann
A1 Juho Rousu
A1 Pieter C. Dorrestein
A1 Sebastian Böcker
YR 2020
UL http://biorxiv.org/content/early/2020/04/18/2020.04.17.046672.abstract
AB Metabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.Competing Interest StatementSB, KD, ML, MF, and MAH are co-founders of Bright Giant GmbH. PCD is scientific advisor for Sirenas LLC.