Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.
Computational models
Machine learning
Metabolomics
Network topology
Plant biotechnology
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
2019
2019
Historique:
received:
27
11
2018
accepted:
18
04
2019
entrez:
27
6
2019
pubmed:
27
6
2019
medline:
27
6
2019
Statut:
epublish
Résumé
The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.
Identifiants
pubmed: 31240252
doi: 10.1038/s42003-019-0440-4
pii: 440
pmc: PMC6581905
doi:
Types de publication
Journal Article
Langues
eng
Pagination
214Déclaration de conflit d'intérêts
Competing interestsThe authors declare no competing interests.
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