Statistical Techniques for Examining Gene Regulation

Genes are often regulated in living cells by proteins called transcription factors (TFs) that bind directly to short segments of DNA in close proximity to certain tar-get genes. These short segments have a conserved appearance, which is called a motif. The experimental determination of TF binding sites is expensive and time-consuming. Many motif-finding programs have been developed but no program is clearly superior in all situations, making it difficult to judge which of the motifs predicted by these algorithms is biologically relevant. This thesis provides a review of previous approaches to the problem of motif dis-covery. We derive a comprehensive scoring function based on a full Bayesian model, which can handle unknown site abundance, unknown motif width, and two-block motifs with variable-length gaps. In addition, this scoring function for-mulation enables us to objectively compare different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs. An algorithm, BioOptimizer, is proposed to optimize a scoring function, thereb