Motif Refinement using Hybrid Expectation Maximization based Neighborhood profile Search

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (for eg. transcription factor binding sites in a genome). Most widely used algorithms for finding motifs obtain a generative probabilistic representation of these over-represented signals and try to discover profiles that maximize the information content score. Although these profiles form a very power-ful representation of the signals, the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular al-gorithms like Expectation Maximization (EM) and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results, E