Integrative methods for discovering generic CIS-regulatory motifs

Abstract

This thesis presents two classes of algorithms for in silico discovery of regulatory elements. Firstly, we address the problem of motif finding for generic spaced motifs. Spaced motifs, an important class of transcription factors, consists of several short segments separated by spacers of different lengths. To address this issue, we propose a new method called SPACE. They key idea is to obtain the motif as an integration of the submotifs as defined by the frequent pattern. Secondly, we propose a method called MotifVoter to identify regulatory sites by integrating results found by multiple motif finders. It applies a variance based statistical measure to remove the spurious motifs and then refines the prediction by filtering the noisy binding sites from using a novel voting scheme.