Iterative learning of single individual haplotypes from high-throughput DNA sequencing data

Abstract

In recent years, advancements in high-throughput DNA sequencing technologies enabled heretofore impractical studies of genetic variations. Cells of diploid organisms, including humans, have a number of chromosome pairs that are homologous — they encode essentially the same genetic information and are almost identical but vary in certain location. These variations are referred to as single nucleotide polymorphisms. The complete information about genetic variations in an individual genome is given by haplotypes, ordered sequences of single nucleotide polymorphisms for each homologous pair of chromosomes. In this paper, we derive a graphical formulation of the haplotype assembly problem, propose an iterative scheme for single individual haplotyping, and demonstrate the performance of the algorithm on experimental data. The results demonstrate that the proposed method has better accuracy than state-of-the-art haplotype assembly techniques.