The Annals of Applied Statistics

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

Qing Zhou and Wing Hung Wong

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat. Volume 1, Number 1 (2007), 36-65.

Dates
First available: 29 June 2007

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1183143728

Digital Object Identifier
doi:10.1214/07-AOAS103

Mathematical Reviews number (MathSciNet)
MR2393840

Zentralblatt MATH identifier
1129.62111

Citation

Zhou, Qing; Wong, Wing Hung. Coupling hidden Markov models for the discovery of Cis -regulatory modules in multiple species. The Annals of Applied Statistics 1 (2007), no. 1, 36--65. doi:10.1214/07-AOAS103. http://projecteuclid.org/euclid.aoas/1183143728.


Export citation

Supplemental materials