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Bioinformatics. 2003 Jul 1;19(10):1227-35.

A Bayesian network approach to operon prediction.

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Department of Biostatistics and Medical Informatics,University of Wisconsin, 1300 University Avenue, Madison, Wisconsin 53706, USA.



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.


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.


Our E.coli K-12 operon predictions are available at

[Indexed for MEDLINE]

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