In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles.

ABSTRACT:

Background

In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task.

Results

Using gene occurrence patterns in sample genomes, we developed two CGP methods (statistical and inductive CGP) to assist with the discovery of bacterial gene functions. Statistical CGP exploits the differences in gene frequency against phenotypic groups, while inductive CGP applies supervised machine learning to identify gene occurrence pattern across genomes.

SUBMITTER: Lin FP 

PROVIDER: S-EPMC2669486 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature