Improved Approach for Haplotype Inference Based on Markov Chain
Variable-order Markov model (VMM) is an important statistical method for haplotype inference problem. It is well-suited for sparse marker maps and large-scale data. The existing algorithm, HaploRec, solves VMM by a greedy algorithm with pruning strategy. We present an improved Expectation-Maximization (EM) algorithm for VMM, which is based on dynamic programming (DP). The computational experimental results with simulated and real data show that the proposed algorithm can greatly improve the accuracy of VMM with an acceptable running time.The methods described in this paper are implemented in a software package, HMC, which is available from the internet.
Haplotype Inference SNP Markov Chain Dynamic Programming
Ling-Yun Wu Ji-Hong Zhang Raymond Chan
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sci School of International Business, Beijing Foreign Studies University, Beijing 100089, China Department of Mathematics, Chinese University of Hong Kong, Hong Kong
国际会议
The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)
云南丽江
英文
212-223
2008-10-31（万方平台首次上网日期，不代表论文的发表时间）