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Bioinformatics Advance Access originally published online on December 20, 2005
Bioinformatics 2006 22(6):739-746; doi:10.1093/bioinformatics/btk017
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Bayesian sparse hidden components analysis for transcription regulation networks

Chiara Sabatti 1,* and Gareth M. James 2

1Departments of Human Genetics and Statistics, UCLA Los Angeles CA 90095-7088, USA
2Information and Operations Management Department, USC Los Angeles, CA 90089-0809, USA

*To whom correspondence should be addressed.

Motivation: In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert.

Results: Our method integrates literature information, DNA sequences and expression arrays. A set of relevant transcription factors is defined on the basis of literature. Sequence data are used to identify potential target genes and the results are used to define a prior distribution on the topology of the regulatory network. A Bayesian hidden component model for the expression array data allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E.Coli with convincing results.

Availability: www.genetics.ucla.edu/labs/sabatti/software.html

Supplementary information: The supplementary material are available at Bioinformatics online.

Contact: csabatti{at}mednet.ucla.edu

Received on July 14, 2005; revised on December 15, 2005; accepted on December 16, 2005

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