Refining motifs by improving information content scores using neighborhood profile search

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these overrepresented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful 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 algorithms 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, EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses (like projection algorithms). Global meth...

• Type: Journal
• Year: 2006
• Where: ALMOB
• Authors: Chandan K. Reddy, Yao-Chung Weng, Hsiao-Dong Chiang
• Keywords: ALMOB 2006, Global Methods, Initial Guesses, Local Maximum