Unique molecular identifier-based single strand mitochondrial DNA sequencing for rare substitution mutations
Bibliographic record
Abstract
Abstract The study of mitochondrial genetics has long been limited to polymorphisms and high frequency mutations owing in part to technical and technological limitations in reliably detecting and quantifying rare somatic mutations. Over the past decade or so, the study of rare somatic mitochondrial DNA (mtDNA) variants has expanded and continues to garner increasing interest in a wide range of research fields. Here, we describe a unique molecular identifier (UMI)-based library preparation and Next Generation Sequencing (NGS) method for the identification and quantification of ultra-rare mutations in the mtDNA control region. Our method exploits degenerate primers to label individual mtDNA molecules. This is followed by several purification, quantification and amplification steps, to obtain high quality amplicons for sequencing on the Illumina MiSeq platform. This method enables the use of total genomic DNA extract as starting point for the assay, overcoming the need for organelle isolation and/or mtDNA enrichment, hence broadening the type of specimen that can be studied, while offering cost and time benefits. The assay described herein has been demonstrated to reliably measure somatic variants present at approximately 0.05% variant allele frequency in a variety of tissues, including fresh and frozen biobanked specimens. Using this protocol, library preparation of 300 specimens can be completed by a single individual with general nucleic acid handling experience in approximately 20 days. Given its flexibility and scalability, this method is particularly well suited for epidemiological studies using a large number of specimens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".