Neural Network Based Approaches, Solving Haplotype Reconstruction in MEC and MEC/GI Models

Abstract

SSNPs (Single Nucleotide Polymorphism) are different variant positions (1% of DNA sequence) of human genomes which their mutation is 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. Two sequences of mentioned 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 employs the related genotype information besides the SNP fragments and therefore results in a more accurate inference. We introduce algorithmic neural network based approaches (UWNN) and experimentally prove that our methods are fast and accurate. In particular, comparing our approaches with a feed-forward (and back-propagation like) neural network of [2], UWNN is faster, more accurate and also compatible for solving MEC model.