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Bioinformatics Advance Access published online on August 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti628
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received April 29, 2005
Revised July 22, 2005
Accepted August 11, 2005

Article

Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes

Shane T. Jensen 1* *, Lei Shen 2 *, and Jun S. Liu 2

1 Department of Statistics, The Wharton School, University of Pennsylvania
2 Department of Statistics, Harvard University

* To whom correspondence should be addressed.
Shane T. Jensen, E-mail: stjensen{at}wharton.upenn.edu


  Abstract

Motivation: We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown.

Results: We use our framework to predict co-regulated genes in the bacteria Bacillus subtilis using six other closely-related bacterial species. Our predicted motifs and gene clusters are validated using several external sources and significant clusters are examined in detail. An extension to the discovery and clustering of two-block motifs can be used for inference about synergistic binding relationships between transcription factors.

Availability: Software and supplementary materials can be downloaded at http://stat.wharton.upenn.edu/~stjensen/research/phyloclus.html or http://www.fas.harvard.edu/~junliu/phyloclus.html.

*These authors contributed equally to this work.
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