Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
| Autor: | Markus Fleischauer, Daniel Petras, Martin Hoffmann, William H. Gerwick, Pieter C. Dorrestein, Juho Rousu, Kai Dührkop, Marcus Ludwig, Sebastian Böcker, Louis-Félix Nothias, Raphael Reher |
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| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Training set Computer science Biomedical Engineering Experimental data High resolution Bioengineering Computational biology Tandem mass spectrometry Mass spectrometry Applied Microbiology and Biotechnology 03 medical and health sciences 0302 clinical medicine Metabolomics Fragmentation (mass spectrometry) Mass spectrum Molecular Medicine 030217 neurology & neurosurgery 030304 developmental biology Biotechnology |
| Zdroj: | Nature Biotechnology. 39:462-471 |
| ISSN: | 1546-1696 1087-0156 |
| Popis: | 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. Unknown metabolites are classified from mass spectrometry data. |
| Databáze: | OpenAIRE |
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