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A Bayesian network approach to operon prediction.

Publication ,  Journal Article
Bockhorst, J; Craven, M; Page, D; Shavlik, J; Glasner, J
Published in: Bioinformatics
July 1, 2003

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.

Duke Scholars

Published In

Bioinformatics

DOI

ISSN

1367-4803

Publication Date

July 1, 2003

Volume

19

Issue

10

Start / End Page

1227 / 1235

Location

England

Related Subject Headings

  • Sequence Analysis, DNA
  • Sequence Alignment
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Promoter Regions, Genetic
  • Operon
  • Genome, Bacterial
  • Gene Expression Regulation, Bacterial
  • Gene Expression Profiling
  • Escherichia coli
 

Citation

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Bockhorst, J., Craven, M., Page, D., Shavlik, J., & Glasner, J. (2003). A Bayesian network approach to operon prediction. Bioinformatics, 19(10), 1227–1235. https://doi.org/10.1093/bioinformatics/btg147
Bockhorst, Joseph, Mark Craven, David Page, Jude Shavlik, and Jeremy Glasner. “A Bayesian network approach to operon prediction.Bioinformatics 19, no. 10 (July 1, 2003): 1227–35. https://doi.org/10.1093/bioinformatics/btg147.
Bockhorst J, Craven M, Page D, Shavlik J, Glasner J. A Bayesian network approach to operon prediction. Bioinformatics. 2003 Jul 1;19(10):1227–35.
Bockhorst, Joseph, et al. “A Bayesian network approach to operon prediction.Bioinformatics, vol. 19, no. 10, July 2003, pp. 1227–35. Pubmed, doi:10.1093/bioinformatics/btg147.
Bockhorst J, Craven M, Page D, Shavlik J, Glasner J. A Bayesian network approach to operon prediction. Bioinformatics. 2003 Jul 1;19(10):1227–1235.
Journal cover image

Published In

Bioinformatics

DOI

ISSN

1367-4803

Publication Date

July 1, 2003

Volume

19

Issue

10

Start / End Page

1227 / 1235

Location

England

Related Subject Headings

  • Sequence Analysis, DNA
  • Sequence Alignment
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Promoter Regions, Genetic
  • Operon
  • Genome, Bacterial
  • Gene Expression Regulation, Bacterial
  • Gene Expression Profiling
  • Escherichia coli