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Bioinformatics Advance Access published online on May 8, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm241
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A phylogenetic Gibbs sampler that yields centroid solutions for cis regulatory site prediction

Lee A. Newberg {dagger},1,2, William A. Thompson {dagger},*,3, Sean Conlan 1, Thomas M. Smith 1,2, Lee Ann McCue 1,4 and Charles E. Lawrence 3

1The Wadsworth Center, New York State Department of Health, Albany, NY 12201
2Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180
3Center for Computational Molecular Biology and the Div. Applied Mathematics, Brown University, Providence, RI 02912
4Pacific Northwest National Laboratory, Richland, WA 99352

*To whom correspondence should be addressed. William A. Thompson, E-mail: William_Thompson_1{at}brown.edu


  Abstract

Motivation: Identification of functionally conserved regulatory elements in sequence data from closely related organisms is becoming feasible, due to the rapid growth of public sequence databases. Closely related organisms are most likely to have common regulatory motifs; however, the recent speciation of such organisms results in the high degree of correlation in their genome sequences, confounding the detection of functional elements. Additionally, alignment algorithms that use optimization techniques are limited to the detection of a single alignment that may not be representative. Comparative-genomics studies must be able to address the phylogenetic correlation in the data and efficiently explore the alignment space, in order to make specific and biologically relevant predictions.

Results: We describe here a Gibbs sampler that employs a full phylogenetic model and reports an ensemble centroid solution. We describe regulatory motif detection using both simulated and real data, and demonstrate that this approach achieves improved specificity, sensitivity, and positive predictive value over non-phylogenetic algorithms, and over phylogenetic algorithms that report a maximum likelihood solution.

Availability: The software is freely available at http://bayesweb.wadsworth.org/gibbs/gibbs.html

Associate Editor: Prof. Dmitrij Frishman

{dagger}These authors contributed equally to this work.

Received on January 10, 2007; revised on April 27, 2007; accepted on April 28, 2007

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