Operon prediction for sequenced bacterial genomes without experimental information.

ABSTRACT: Various computational approaches have been proposed for operon prediction, but most algorithms rely on experimental or functional data that are only available for a small subset of sequenced genomes. In this study, we explored the possibility of using phylogenetic information to aid in operon prediction, and we constructed a Bayesian hidden Markov model that incorporates comparative genomic data with traditional predictors, such as intergenic distances. The prediction algorithm performs as well as the best previously reported method, with several significant advantages. It uses fewer data sources and so it is easier to implement, and the method is more broadly applicable than previous methods--it can be applied to essentially every gene in any sequenced bacterial genome. Furthermore, we sh

SUBMITTER: Bergman NH 

PROVIDER: S-EPMC1800777 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature