Predicting phenotypic traits of prokaryotes from protein domain frequencies

Background: Establishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel highthroughput and single cell sequencing techniques. Results: We present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains. Conclusions: Fast and ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Thomas Lingner, Stefanie Mühlhausen, Toni Gabaldón, Cédric Notredame, Peter Meinicke
• Keywords: BMCBI 2010, Genome Sequence, Microbial Phenotypes, Phenotype