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Bioinformatics. 2003;19 Suppl 1:i292-301.

A probabilistic method to detect regulatory modules.

Author information

1
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 0021, USA. saurabh@lonnrot.rockefeller.edu

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.

PMID:
12855472
[Indexed for MEDLINE]

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