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Bioinformatics Advance Access published online on December 20, 2005
Bioinformatics, 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
Received July 14, 2005
Revised December 15, 2005
Accepted December 16, 2005

Article

Bayesian sparse hidden components analysis for transcription regulation networks

Chiara Sabatti 1 * and Gareth M. James 2

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

* To whom correspondence should be addressed.
Chiara Sabatti, E-mail: csabatti{at}mednet.ucla.edu


  Abstract

Motivation: In systems like E. 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 is 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.


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