A probabilistic method to detect regulatory modules
- PMID: 12855472
- DOI: 10.1093/bioinformatics/btg1040
A probabilistic method to detect regulatory modules
Abstract
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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