A new machine learning-based method is presented here for the identification of metabolic pathways related to specific phenotypes in multiple microbial genomes

Gasteiger, Johann

Kastenmüller, Gabi

Mewes, Hans-Werner

Schenk, Maria Elisabeth

English

PubMed

Gabi Kastenmüller

Maria Schenk

Johann Gasteiger

Hans-Werner Mewes

Crossref

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics. Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale. Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies

Kastenmüller, G.

Schenk, M.E.

Gasteiger, J.

Mewes, H.-W.

PuSH

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.

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Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

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