MitSorter: a standalone tool for accurate discrimination of mtDNA and NuMT ONT reads based on differential methylation

TL;DR

MitSorter is a new tool that accurately separates mitochondrial DNA from nuclear mitochondrial DNA segments using methylation patterns in long-read sequencing data.

Contribution

MitSorter introduces a novel method for distinguishing mtDNA and NuMTs using differential CpG methylation in ONT sequencing data.

Findings

MitSorter partitions reads into unmethylated (mtDNA) and methylated (NuMTs) categories with high accuracy.

The tool improves the accuracy of mtDNA mutation profiling by reducing NuMT contamination.

MitSorter is validated and applicable to various genomic research areas.

Abstract

The accurate differentiation between mitochondrial DNA (mtDNA) and nuclear mitochondrial DNA segments (NuMTs) is a critical challenge in studies involving mitochondrial disorders. Mapping the mtDNA mutation spectrum and quantifying heteroplasmy are complex tasks when using next-generation sequencing methods, mostly due to NuMTs contamination in data analysis.

Here, we present a novel, easy-to-use standalone command-line tool designed to reliably discriminate long reads originated by either mtDNA or NuMTs and generated by Oxford Nanopore Technologies (ONT) sequencing based on the known lack of CpG methylation in human mtDNA. MitSorter aligns the reads to the mitochondrial genome incorporating base modification calls directly from raw POD5 files. The resulting BAM file is then partitioned into two separate BAM files: one containing unmethylated reads and the other containing methylated reads. We show that MitSorter analysis can provide a more accurate landscape of the mtDNA mutation profile. We describe here the tool's features, computational framework, validation approach, and its potential applications in other genomic research areas.

Source code and documentation, are available at https://github.com/asvarvara/MitSorter.

Figures