Coupling hidden Markov models for the discovery of Cis-regulatory modules in multiple species

TL;DR

This paper introduces MultiModule, a coupled hidden Markov model approach that leverages cross-species conservation to improve the de novo discovery of cis-regulatory modules and motifs in multiple genomes.

Contribution

It presents a novel statistical framework combining module structure and evolutionary conservation using coupled HMMs and MCMC for improved motif discovery across species.

Findings

Significant improvement over existing methods in simulated data.

Effective identification of CRMs in mammalian and Drosophila genomes.

Demonstrates the benefit of cross-species comparison in regulatory element discovery.

Abstract

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.