Bioinformatics Vol. 19 Suppl. 1 2003
Pages i292-i301
© 2003 Oxford University Press
A probabilistic method to detect regulatory modules
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 0021, USA
Received on January 6, 2003
; accepted on February 20, 2003
Motivation: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity.
Results: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.
Availability: The source code for the programs is available upon request from the authors.
Contact: saurabh,erik,siggia{at}lonnrot.rockefeller.edu
Keywords: hidden Markov model, cis-regulatory modules, motif correlations, phylogenetic comparison
* To whom correspondence should be addressed.
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