MAtCHap: an ultra fast algorithm for solving the single individual haplotype assembly problem

Background
Human genomes are diploid, which means they have two homologous copies of each chromosome and the assignment of heterozygous variants to each chromosome copy, the haplotype assembly problem, is of fundamental importance for medical and population genetics.

While short reads from second generation sequencing platforms drastically limit haplotype reconstruction as the great majority of reads do not allow to link many variants together, novel long reads from third generation sequencing can span several variants along the genome allowing to infer much longer haplotype blocks. However, the great majority of haplotype assembly algorithm, originally devised for short sequences, fail when they are applied to noisy long reads data, and although novel algorithm have been properly developed to deal with the properties of this new generation of sequences, these methods are capable to manage only datasets with limited coverages.

Results
To overcome the limits of currently available algorithms, I propose a novel formulation of the single individual haplotype assembly problem, based on maximum allele co-occurrence (MAC) and I develop an ultra-fast algorithm that is capable to reconstruct the haplotype structure of a diploid genome from low- and high-coverage long read datasets with high accuracy.

I test my algorithm (MAtCHap) on synthetic and real PacBio and Nanopore human dataset and I compare its result with other eight state-of-the-art algorithms.

All the results obtained by these analyses show that MAtCHap outperforms other methods in terms of accuracy, contiguity, completeness and computational speed.

Availability
MAtCHap is publicly available at https://sourceforge.net/projects/ matchap/.