J. Bockhorst, M. Craven, D. Page, J. Shavlik & J. Glasner (2003).
A Bayesian Network Approach to Operon Prediction. Bioinformatics, 19, pp. 1227-1235.

This publication is available in PDF and available in postscript.

Abstract:

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 probabilisitic 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 E. 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.

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