"NanoDel": Identification of large-scale mitochondrial DNA deletions using long-read sequencing.

Abstract

Traditional methods for detecting large-scale mitochondrial DNA (mtDNA) deletions (LSMDs) in cells present challenges, i.e. requiring a priori information, high DNA inputs, and are not always sensitive and/or quantitative. Mitigation can be achieved through high-throughput DNA sequencing using e.g. Illumina and Oxford Nanopore Technologies (ONT), in combination with LSMD breakpoint identification and quantification using bioinformatics. Splice-aware RNA alignment tools increase the sensitivity for detecting LSMD breakpoints compared with DNA aligners. Long-read sequencing (LRS) also offers potential advantages over short-read sequencing (SRS), e.g. greater read lengths and capturing variants on single reads. Here we aimed to capture the benefits of both a splice-aware alignment tool and LRS. We developed "NanoDel", a LRS pipeline, to sensitively and accurately detect cellular LSMDs. Using artificial datasets, "NanoDel" was more sensitive and accurate than other pipelines. In samples diagnosed with mitochondrial disease, it identified both known and previously uncharacterised (including mixtures) of LSMDs, without a priori information. Analysis of selected LSMDs revealed proximity to repeat, putative G-quadruplex motifs, and the "contact zone". Together with occurrence in a range of healthy and pathological tissues, indicates potential for a shared vulnerability landscape in mtDNA, shaped by sequence motifs and structural constraints. This proof-of-concept study shows that "NanoDel" combined with one-amplicon LR-PCR offers a robust strategy for detecting LSMDs across a variety of cell/tissue samples. Applying "NanoDel" to a larger and broader range of samples would confirm this, yielding new mechanistic insights into LSMD formation, and further our understanding of mtDNA instability in the future. "NanoDel" is available at https://github.com/uopbioinformatics/NanoDel (DOI: 10.5281/zenodo.20119070) and raw read data are available through the NCBI Sequence Read Archive (SRA) under BioProject accession code PRJNA1369153 (https://www.ncbi.nlm.nih.gov/bioproject/1369153). Supplementary data are available at Bioinformatics online.