Improved short adjacent repeat identification using three evolutionary Monte Carlo schemes

Authors:
Jin Xu;Qiwei Li;Victor O. K. Li;Shuo-Yen Robert Li;Xiaodan Fan
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;Department of Statistics, The Chinese University of Hong Kong, Sha Tin, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong/ Department of Computer Engineering, King Saud University, Saudi Arabia;Department of Information Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong;Department of Statistics, The Chinese University of Hong Kong, Sha Tin, Hong Kong
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

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Abstract

This paper employs three Evolutionary Monte Carlo EMC schemes to solve the Short Adjacent Repeat Identification Problem SARIP, which aims to identify the common repeat units shared by multiple sequences. The three EMC schemes, i.e., Random Exchange RE, Best Exchange BE, and crossover are implemented on a parallel platform. The simulation results show that compared with the conventional Markov Chain Monte Carlo MCMC algorithm, all three EMC schemes can not only shorten the computation time via speeding up the convergence but also improve the solution quality in difficult cases. Moreover, we observe that the performances of different EMC schemes depend on the degeneracy degree of the motif pattern.