Abstract

Gene transcription is regulated by interactions between transcription factors and their target binding sites in the genome. A motif is the sequence pattern recognized by a transcription factor to mediate such interactions. With the availability of high-throughput genomic data, computational identification of transcription factor binding motifs has become a major research problem in computational biology and bioinformatics. In this chapter, we present a series of Bayesian approaches to motif discovery. We start from a basic statistical framework for motif finding, extend it to the identification of cis-regulatory modules, and then discuss methods that combine motif finding with phylogenetic footprinting, gene expression or ChIP-chip data, and nucleosome positioning information. Simulation studies and applications to biological data sets are presented to illustrate the utility of these methods.

Citations