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Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

Gabi Kastenmüller1*, Maria Elisabeth Schenk1, Johann Gasteiger23 and Hans-Werner Mewes14

Author Affiliations

1 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstraße, D-85764 Neuherberg, Germany

2 Computer-Chemie-Centrum, Universität Erlangen-Nürnberg, Nägelsbachstraße, D-91052 Erlangen, Germany

3 Molecular Networks GmbH, Henkestraße 91, D-91052 Erlangen, Germany

4 Chair for Genome-oriented Bioinformatics, Technische Universität München, Life and Food Science Center Weihenstephan, Am Forum 1, D-85354 Freising-Weihenstephan, Germany

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Genome Biology 2009, 10:R28  doi:10.1186/gb-2009-10-3-r28

Published: 10 March 2009


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.