Neural network-based approaches, solving haplotype reconstruction in MEC and MEC/GI models

Abstract

Single nucleotide polymorphism (SNP) in human genomes is considered to be highly associated with complex genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies on human genomics. The two sequences of SNPs in diploid human organisms are called haplotypes. In this paper, the problem of haplotype reconstruction from SNP fragments with and without genotype information is studied. Minimum error correction (MEC) is an important model for this problem but only effective when the error rate of the fragments is low. MEC/GI, as an extension to MEC model, employs the related genotype information besides the SNP fragments and, therefore, results in a more accurate inference. We introduce algorithmic neural network-based approaches and experimentally prove that our methods are fast and accurate. Particularly, our approach is faster, more accurate, and also compatible for solving MEC model, in comparison with a feed-forward (and back propagation like) neural network.