Optimal Haplotype Assembly from High-Throughput Mate-Pair Reads

TL;DR

This paper investigates the problem of reconstructing human haplotypes from high-throughput mate-pair sequencing reads, providing a formula for the required coverage and connecting the problem to convolutional code decoding.

Contribution

It introduces a simple formula for haplotype assembly coverage and links the problem to convolutional code decoding, offering new insights into sequencing data analysis.

Findings

Derived a formula for haplotype assembly coverage

Connected haplotype inference to convolutional code decoding

Provided theoretical analysis under a generative model

Abstract

Humans have $23$ pairs of homologous chromosomes. The homologous pairs are almost identical pairs of chromosomes. For the most part, differences in homologous chromosome occur at certain documented positions called single nucleotide polymorphisms (SNPs). A haplotype of an individual is the pair of sequences of SNPs on the two homologous chromosomes. In this paper, we study the problem of inferring haplotypes of individuals from mate-pair reads of their genome. We give a simple formula for the coverage needed for haplotype assembly, under a generative model. The analysis here leverages connections of this problem with decoding convolutional codes.