Bioinformatics Vol. 19 no. 10 2003
Pages 1227-1235
© 2003 Oxford University Press
A Bayesian network approach to operon prediction
1 Department of Biostatistics and Medical
Informatics,University of Wisconsin, 1300 University
Avenue, Madison, Wisconsin 53706, USA
2 Department of Computer Sciences,University
of Wisconsin, 1210 West Dayton Street, Madison, Wisconsin
53706, USA
3 Department of Genetics,University
of Wisconsin, 445 Henry Mall, Madison, Wisconsin 53706,
USA
Received on December 2, 2002
; accepted on December 16, 2002
Motivation: 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.
Availability: Our E.coli K-12 operon predictions are available at http://www.biostat.wisc.edu/gene-regulation
Contact: joebock{at}biostat.wisc.edu
* To whom correspondence should be addressed.
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