Randomized Algorithms for Motif Detection

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 sof...

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Bibliographic Details
Published inAlgorithms and Computation pp. 884 - 895
Main Authors Wang, Lusheng, Dong, Liang, Fan, Hui
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 01.01.2004
Springer
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN9783540241317
3540241310
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-30551-4_75

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Summary: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.
ISBN:9783540241317
3540241310
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-30551-4_75