Cis-regulatory modules composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new de novo motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module binding sites. We illustrate the use of CisModule by its application to the discovery of a novel tissue-specific regulatory module in Ciona savignyi. In addition, comparative genomic studies show that regulatory elements are more conserved across species due to evolutionary constraints. Thus we further extend our approach to combine both module structures and cross-species orthology in 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 for background nucleotides and TFBSs are incorporated to capture correlated structures between aligned sequence bases. Based on the full model, we develop a Gibbs sampler to discover CRMs and their component motifs simultaneously in the sequence context of multiple species. Our new method has been tested on well-annotated biological data sets, where significant improvement over other module discovery and phylogenetic motif discovery methods is observed.
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Joel Bader Seminar
Andrew Smith Seminar
Gene Regulatory Networks Special Feature (PNAS)
Abstract
Joint work with Wing Hung Wong
Cis-regulatory modules composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new de novo motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module binding sites. We illustrate the use of CisModule by its application to the discovery of a novel tissue-specific regulatory module in Ciona savignyi. In addition, comparative genomic studies show that regulatory elements are more conserved across species due to evolutionary constraints. Thus we further extend our approach to combine both module structures and cross-species orthology in 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 for background nucleotides and TFBSs are incorporated to capture correlated structures between aligned sequence bases. Based on the full model, we develop a Gibbs sampler to discover CRMs and their component motifs simultaneously in the sequence context of multiple species. Our new method has been tested on well-annotated biological data sets, where significant improvement over other module discovery and phylogenetic motif discovery methods is observed.