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
Gabi
G
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de
Schenk
Maria Elisabeth
ME
Gasteiger
Johann
J
Mewes
Hans-Werner
HW
D001105:Archaea / Q000235:genetics / Q000378:metabolism; D001185:Artificial Intelligence; D001419:Bacteria / Q000235:genetics*; D016680:Genome, Bacterial / Q000235:genetics*; D006639:Histidine / Q000378:metabolism; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008697:Methane / Q000378:metabolism; D005810:Multigene Family; D010510:Periodontal Diseases / Q000382:microbiology; D010641:Phenotype; D015203:Reproducibility of Results
D006639:Histidine; D008697:Methane