# Optimal Haplotype Assembly from High-Throughput Mate-Pair Reads

## TL;DR

This paper introduces a method to assemble haplotypes from mate-pair reads by determining the required coverage using a formula inspired by convolutional code decoding.

## Contribution

A novel formula for coverage requirements in haplotype assembly, derived from connections to convolutional code decoding.

## Key findings

- The coverage needed for haplotype assembly can be calculated using a simple formula.
- The method leverages insights from convolutional code decoding to model the haplotype assembly problem.
- The approach provides a theoretical foundation for understanding haplotype assembly requirements.

## 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.