A Bayesian network approach to operon prediction.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Bockhorst, J; Craven, M; Page, D; Shavlik, J; Glasner, J

Published Date

  • July 1, 2003

Published In

Volume / Issue

  • 19 / 10

Start / End Page

  • 1227 - 1235

PubMed ID

  • 12835266

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btg147


  • eng

Conference Location

  • England