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Bioinformatics Advance Access published online on April 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth282
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received September 14, 2003
Revised April 4, 2004
Accepted April 19, 2004

Article

Identification of DNA regulatory motifs using Bayesian variable selection

Mahlet G. Tadesse 1, Marina Vannucci 1*, Pietro Liò 2

1 Department of Statistics, Texas A&M University, College Station, TX 77843, USA
2 Computer Laboratory, University of Cambridge, Cambridge CB3 OFD, UK

* To whom correspondence should be addressed. E-mail: mvannucci{at}stat.tamu.edu.


  Abstract

Motivation: Understanding the mechanisms that determine gene expression regulation is an important and challenging problem. A common approach consists of identifying DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Here, we consider a regression model that linearly relates gene expression levels to a sequence matching score of nucleotide patterns. We use Bayesian models and stochastic search techniques to select transcription factor binding site candidates, as an alternative to stepwise regression procedures used by other investigators.

Results: We demonstrate through simulated data the improved performance of the Bayesian variable selection method compared to the stepwise procedure. We then analyze and discuss the results from experiments involving well studied pathways of S. cerevisiae and S. pombe. We identify regulatory motifs known to be related to the experimental conditions considered. Some of our selected motifs are also in agreement with recent findings by other researchers. In addition, our results include some novel motifs that constitute promising sets for further assessment.

Availability: The Matlab code for running the Bayesian variable selection method may be obtained from the corresponding author.


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