Targeted nanopore sequencing using the Flongle device to identify mitochondrial DNA variants
Bibliographic record
Abstract
Variants in mitochondrial genomes (mtDNA) can cause various neurological and mitochondrial diseases such as mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like episodes (MELAS). Given the 16 kb length of mtDNA, continuous sequencing is feasible using long-read sequencing (LRS). Herein, we aimed to show a simple and accessible method for comprehensive mtDNA sequencing with potential diagnostic applications for mitochondrial diseases using the compact and affordable LRS flow cell “Flongle.” Whole mtDNA amplification (WMA) was performed using genomic DNA samples derived from four patients with mitochondrial diseases, followed by LRS using Flongle. We compared these results to those obtained using Cas9 enrichment. Additionally, the accuracy of heteroplasmy rates was assessed by incorporating mtDNA variants at equimolar levels. Finally, mtDNA from 19 patients with Parkinson’s disease (PD) was sequenced using Flongle to identify disease risk-associated variants. mtDNA variants were detected in all four patients with mitochondrial diseases, with results comparable to those obtained from Cas9 enrichment. Heteroplasmy levels were accurately detected (r 2 > 0.99) via WMA using Flongle. A reported variant was identified in three patients with PD. In conclusion, Flongle can simplify the traditionally cumbersome and expensive mtDNA sequencing process, offering a streamlined and accessible approach to diagnosing mitochondrial diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".