Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

Kai Dührkop, Louis Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A. Hoffmann, Daniel Petras, William H. Gerwick, Juho Rousu, Pieter C. Dorrestein, Sebastian Böcker*

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

67 Sitaatiot (Scopus)

Abstrakti

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

AlkuperäiskieliEnglanti
Sivut462–471
Sivumäärä10
JulkaisuNATURE BIOTECHNOLOGY
Vuosikerta39
Numero4
Varhainen verkossa julkaisun päivämäärä2020
DOI - pysyväislinkit
TilaJulkaistu - huhtikuuta 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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