Journal
BIOINFORMATICS
Volume 19, Issue -, Pages i292-i301Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btg1040
Keywords
hidden Markov model; cis-regulatory modules; motif correlations; phylogenetic comparison
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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.
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