F.C.A: Designing a fuzzy clustering algorithm for haplotype assembly

Reconstructing haplotype in MEC (minimum error correction) model is an important clustering problem which focuses on inferring two haplotypes from SNP fragments (single nucleotide polymorphism) containing gaps and errors. Mutated form of human genome is responsible for genetic diseases which mostly occur in SNP sites. In this paper, a fuzzy clustering approach is performed for haplotype reconstruction or haplotype assembly from a given sample single nucleotide polymorphism (SNP). In the best previous approach based on reconstruction rate (Wang, 2007), all SNP-fragments are considered with equal values. In our proposed method the value of the fragments are based on the degree of membership between SNP-fragments and centers of clusters. Finally, these two approaches are executed on four standard datasets (ACE, Daly, SIM0 and SIM50) and the results show the efficiency of our proposed approach.