Background: Mitochondria are the cell organelles that produce most of the chemical energy required to power the cell's biochemical reactions. Despite being a part of a eukaryotic host cell, the mitochondria contain a separate genome whose origin is linked with the endosymbiosis of a prokaryotic cell by the host cell and encode independent genomic information throughout their genomes. Mitochondrial genomes accommodate essential genes and are regularly utilized in biotechnology and phylogenetics. Various assemblers capable of generating complete mitochondrial genomes are being continuously developed. These tools often use whole-genome sequencing data as an input containing reads from the mitochondrial genome. Till now, no published work has explored the systematic comparison of all the available tools for assembling human mitochondrial genomes using short-read sequencing data. This evaluation is required to identify the best tool that can be well-optimized for small-scale projects or even national-level research.
Results: In this study, we have tested the mitochondrial genome assemblers for both simulated datasets and whole genome sequencing (WGS) datasets of humans. For the highest computational setting of 16 computational threads with the simulated dataset having 1000X read depth, MitoFlex took the least execution time of 69 s, and IOGA took the longest execution time of 1278 s. NOVOPlasty utilized the least computational memory of approximately 0.098 GB for the same setting, whereas IOGA utilized the highest computational memory of 11.858 GB. In the case of WGS datasets for humans, GetOrganelle and MitoFlex performed the best in capturing the SNPs information with a mean F1-score of 0.919 at the sequencing depth of 10X. MToolBox and NOVOPlasty performed consistently across all sequencing depths with a mean F1 score of 0.897 and 0.890, respectively.
Conclusions: Based on the overall performance metrics and consistency in assembly quality for all sequencing data, MToolBox performed the best. However, NOVOPlasty was the second fastest tool in execution time despite being single-threaded, and it utilized the least computational resources among all the assemblers when tested on simulated datasets. Therefore, NOVOPlasty may be more practical when there is a significant sample size and a lack of computational resources. Besides, as long-read sequencing gains popularity, mitochondrial genome assemblers must be developed to use long-read sequencing data.
Keywords: Assembly; Benchmark; Genome; Mitochondria.
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