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 high-throughput 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 accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation.
Lingner
Thomas
T
Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de
Mühlhausen
Stefanie
S
Gabaldón
Toni
T
Notredame
Cedric
C
Meinicke
Peter
P
D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry*; D020745:Genome, Archaeal; D016680:Genome, Bacterial; D058977:Molecular Sequence Annotation; D010641:Phenotype; D017434:Protein Structure, Tertiary
D001426:Bacterial Proteins