Predicting transcription factor binding sites using phylogenetic footprinting and a probabilistic framework for evolutionary turnover

Identifying genomic locations of transcription-factor binding sites (TFBS), particularly in higher eukaryotic genomes, has been an enormous challenge. Computational methods involving identification of sequence conservation between related genomes have been the most successful since sites found in such highly conserved regions are more likely to be functional, i.e. are bound and regulate protein production. In this thesis, we present such a probabilistic algorithm for predicting TFBSs which also takes evolutionary turnovers into account. Our algorithm is validated via simulations and the results of its application on ChIPchip data are presented.