A benchmarking of human mitochondrial DNA haplogroup classifiers from whole-genome and whole-exome sequence data

Abstract

The mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. We also assessed the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short-read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort

“…We observed a spike in sequencing depth between 2.5 kbp and 3 kbp on the rCRS (Fig. S11), which has been reported previously [42]. We masked out this region when calculating mean mtDNA depth.…”

HaploCart: Human mtDNA Haplogroup Classification Using a Pangenomic Reference Graph

“…(for BAM input) and (for consensus FASTA input) are the most accurate and robust tools, especially for short reads. For example, was the only algorithm to correctly classify all samples in their whole-exome dataset for BAM input and was the only CLI tool to correctly classify all samples in their whole-genome dataset [22]. The conclusion that is currently the most reliable tool reaffirms a view which had previously been propounded in the literature [23].…”

“…For example, HaploCheck was the only algorithm to correctly classify all samples in their whole-exome dataset for BAM input and HaploGrep2 was the only CLI tool to correctly classify all samples in their whole-genome dataset [21]. The conclusion that HaploGrep2 is currently the most reliable tool reaffirms a view 2 (which was not certified by peer review) is the author/funder.…”

An Upgrade on the Surveillance System of SARS-CoV-2: Deployment of New Methods for Genetic Inspection

“…Other methods such as iterative mapping against references [ 19 ] have been used to analyze closely related organisms whose genomes are mixed within a sample, or as specialized software like SNPGenie [ 16 , 19 , 20 ]. The accuracy of some tools has been analyzed, testing both human WGS and WES [ 21 ], but must be proven valid at classifying data from SARS-CoV-2 genomes. Incorporating tools like FreeBayes in NGS analysis and mutations PCR screening in common practice will increase the information available, for genomic analysis and epidemiology, respectively, and will not represent a significant difference in terms of economy, time, or specialized training.…”