Minimum error correction-based haplotype assembly: considerations for long read data

TL;DR

This paper critically examines the MEC-based haplotype assembly method, revealing its limitations with error-prone long reads from various sequencing devices and suggesting coverage requirements to improve accuracy.

Contribution

It demonstrates that MEC can produce incorrect haplotypes with long read data and provides coverage guidelines to mitigate this issue.

Findings

MEC may lead to incorrect haplotypes with long reads.

Coverage of 25 is recommended for Pacific BioSciences RS data.

MEC performance varies with error rates and coverage levels.

Abstract

The single nucleotide polymorphism (SNP) is the most widely studied type of genetic variation. A haplotype is defined as the sequence of alleles at SNP sites on each haploid chromosome. Haplotype information is essential in unravelling the genome-phenotype association. Haplotype assembly is a well-known approach for reconstructing haplotypes, exploiting reads generated by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often used for reconstruction of haplotypes from reads. However, problems with the MEC metric have been reported. Here, we investigate the MEC approach to demonstrate that it may result in incorrectly reconstructed haplotypes for devices that produce error-prone long reads. Specifically, we evaluate this approach for devices developed by Illumina, Pacific BioSciences and Oxford Nanopore Technologies. We show that imprecise haplotypes may be reconstructed with a lower MEC than that of the exact haplotype. The performance of MEC is explored for different coverage levels and error rates of data. Our simulation results reveal that in order to avoid incorrect MEC-based haplotypes, a coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.