A Bayesian network approach to operon prediction.
BACKGROUND
In order to understand transcription regulation in a given prokaryotic genome, it is critical to identify operons, the fundamental units of transcription, in such species. While there are a growing number of organisms whose sequence and gene coordinates are known, by and large their operons are not known.


RESULTS
We present a probabilistic approach to predicting operons using Bayesian networks. Our approach exploits diverse evidence sources such as sequence and expression data. We evaluate our approach on the Escherichia coli K-12 genome where our results indicate we are able to identify over 78% of its operons at a 10% false positive rate. Also, empirical evaluation using a reduced set of data sources suggests that our approach may have significant value for organisms that do not have as rich of evidence sources as E.coli.


BACKGROUND
Our E.coli K-12 operon predictions are available at http://www.biostat.wisc.edu/gene-regulation.
Bockhorst
Joseph
J
Department of Biostatistics and Medical Informatics,University of Wisconsin, 1300 University Avenue, Madison, Wisconsin 53706, USA. joebock@biostat.wisc.edu
Craven
Mark
M
Page
David
D
Shavlik
Jude
J
Glasner
Jeremy
J
D000465:Algorithms; D001499:Bayes Theorem; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics*; D016680:Genome, Bacterial; D009876:Operon / Q000235:genetics*; D011401:Promoter Regions, Genetic / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*