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Self-organizing map approaches for the haplotype assembly problem

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  • Wu, Ling-Yun
  • Li, Zhenping
  • Wang, Rui-Sheng
  • Zhang, Xiang-Sun
  • Chen, Luonan

Abstract

Haplotype assembly is to reconstruct a pair of haplotypes from SNP values observed in a set of individual DNA fragments. In this paper, we focus on studying minimum error correction (MEC) model for the haplotype assembly problem and explore self-organizing map (SOM) methods for this problem. Specifically, haplotype assembly by MEC is formulated into an integer linear programming model. Since the MEC problem is NP-hard and thus cannot be solved exactly within acceptable running time for large-scale instances, we investigate the ability of classical SOMs to solve the haplotype assembly problem with MEC model. Then, aiming to overcome the limits of classical SOMs, a novel SOM approach is proposed for the problem. Extensive computational experiments on both synthesized and real datasets show that the new SOM-based algorithm can efficiently reconstruct haplotype pairs in a very high accuracy under realistic parameter settings. Comparison with previous methods also confirms the superior performance of the new SOM approach.

Suggested Citation

  • Wu, Ling-Yun & Li, Zhenping & Wang, Rui-Sheng & Zhang, Xiang-Sun & Chen, Luonan, 2009. "Self-organizing map approaches for the haplotype assembly problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3026-3037.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:10:p:3026-3037
    DOI: 10.1016/j.matcom.2009.01.021
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    1. Gernot Grabher & Walter W. Powell (ed.), 2004. "Networks," Books, Edward Elgar Publishing, volume 0, number 2771.
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    Cited by:

    1. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.

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