Randomized algorithms for motif detection

Authors:
Lusheng Wang;Liang Dong;Hui Fan
Affiliations:
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, Peking University, Beijing, P.R China;School of Information and Electronic Engineering, Institute of Shandong Business and Technology, Yantai, Shandong, P.R China
Venue:
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Year:
2004

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Abstract

Motivation: Motif detection for DNA sequences has many important applications in biological studies, e.g., locating binding sites and regulatory signals, and designing genetic probes etc In this paper, we propose a randomized algorithm, design an improved EM algorithm and combine them to form a software. Results: (1) We design a randomized algorithm for consensus pattern problem We can show that with high probability, our randomized algorithm finds a pattern in polynomial time with cost error at most ε × l for each string, where l is the length of the motif and ε can be any positive number given by the user (2) We design an improved EM (Expectation Maximization) algorithm that outperforms the original EM algorithm (3) We develop a software MotifDetector that uses our randomized algorithm to find good seeds and uses the improved EM algorithm to do local search We compare MotifDetector with Buhler and Tompa's PROJECTION which is considered to be the best known software for motif detection Simulations show that MotifDetector is slower than PROJECTION when the pattern length is relatively small, and outperforms PROJECTION when the pattern length becomes large. Availability: Free from http://www.cs.cityu.edu.hk/~lwang/software/motif/index.html, subject to copyright restrictions.