  Cis-regulatory modules (CRMs) composed of multiple transcription factor
binding sites (TFBSs) control gene expression in eukaryotic genomes.
Comparative genomic studies have shown that these regulatory elements are more
conserved across species due to evolutionary constraints. We propose a
statistical method to combine module structure and cross-species orthology in
de novo motif discovery. We use a hidden Markov model (HMM) to capture the
module structure in each species and couple these HMMs through multiple-species
alignment. Evolutionary models are incorporated to consider correlated
structures among aligned sequence positions across different species. Based on
our model, we develop a Markov chain Monte Carlo approach, MultiModule, to
discover CRMs and their component motifs simultaneously in groups of
orthologous sequences from multiple species. Our method is tested on both
simulated and biological data sets in mammals and Drosophila, where significant
improvement over other motif and module discovery methods is observed.
