A parthenogenetic algorithm for single individual SNP haplotyping

Authors:
Jingli Wu;Jianxin Wang;Jian'er Chen
Affiliations:
School of Information Science and Engineering, Central South University, Changsha 410083, China and Department of Computer Science, Guangxi Normal University, Guilin 541004, China;School of Information Science and Engineering, Central South University, Changsha 410083, China;School of Information Science and Engineering, Central South University, Changsha 410083, China and Department of Computer Science, Texas A&M University, College Station, TX 77843, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

Citing 4
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Abstract

The minimum error correction (MEC) model is one of the important computational models for single individual single nucleotide polymorphism (SNP) haplotyping. Due to the NP-hardness of the model, Qian et al. presented a particle swarm optimization (PSO) algorithm to solve it, and the particle code length is equal to the number of SNP fragments. However, there are hundreds and thousands of SNP fragments in practical applications. The PSO algorithm based on this kind of long particle code cannot obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm PGA-MEC based on parthenogenetic algorithm (PGA) is presented to solve the model. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. The reconstruction rate of PGA-MEC algorithm is higher than that of PSO algorithm and the running time of PGA-MEC algorithm is shorter than that of PSO algorithm, which are proved by a number of experiments.