Evidence map›Paper›PMID 42745550›Full record

ArticleBioinformatics (Oxford, England)2026

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

C Fearn, J Poulton, C Fratter, C Oliva, C Griguer, R A Baldock, S C Robson, R E McGeehan

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Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

C FearnSchool of Medicine, Pharmacy and Biomedical Sciences, Institute of Life Sciences and Healthcare, University of Portsmouth, Portsmouth, United Kingdom.
J PoultonNuffield Department of Women's Reproductive Health, University of Oxford, Oxford, United Kingdom.
C FratterOxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
C OlivaFree Radical and Radiation Biology Program, University of Iowa, Iowa City, Iowa, United States.
C GriguerFree Radical and Radiation Biology Program, University of Iowa, Iowa City, Iowa, United States.
R A BaldockSchool of Medicine, Pharmacy and Biomedical Sciences, Institute of Life Sciences and Healthcare, University of Portsmouth, Portsmouth, United Kingdom.
S C RobsonSchool of Medicine, Pharmacy and Biomedical Sciences, Institute of Life Sciences and Healthcare, University of Portsmouth, Portsmouth, United Kingdom.
R E McGeehanSchool of Medicine, Pharmacy and Biomedical Sciences, Institute of Life Sciences and Healthcare, University of Portsmouth, Portsmouth, United Kingdom.ORCID 0000-0002-0107-8534

Funding

University of Portsmouth Faculty-PhD studentship
6 · The paper itself

Abstract

motivationTraditional 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.

resultsWe 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. AVAILABILITY AND IMPLEMENTATION: "NanoDel" is an open-source pipeline freely available on GitHub at https://github.com/uopbioinformatics/NanoDel and permanently archived via Zenodo (DOI: 10.5281/zenodo.20119070).

Indexed as

DNA, MitochondrialHigh-Throughput Nucleotide SequencingSequence Analysis, DNASequence DeletionHumansMitochondrial DiseasesDNA, Mitochondrial

Identifiers

PMID42745550
PMCPMC13637604

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.