Recharacterization of ancient DNA miscoding lesions: insights in the era of sequencing-by-synthesis
Bibliographic record
Abstract
Although ancient DNA (aDNA) miscoding lesions have been studied since the earliest days of the field, their nature remains a source of debate. A variety of conflicting hypotheses exist about which miscoding lesions constitute true aDNA damage as opposed to PCR polymerase amplification error. Furthermore, considerable disagreement and speculation exists on which specific damage events underlie observed miscoding lesions. The root of the problem is that it has previously been difficult to assemble sufficient data to test the hypotheses, and near-impossible to accurately determine the specific strand of origin of observed damage events. With the advent of emulsion-based clonal amplification (emPCR) and the sequencing-by-synthesis technology this has changed. In this paper we demonstrate how data produced on the Roche GS20 genome sequencer can determine miscoding lesion strands of origin, and subsequently be interpreted to enable characterization of the aDNA damage behind the observed phenotypes. Through comparative analyses on 390,965 bp of modern chloroplast and 131,474 bp of ancient woolly mammoth GS20 sequence data we conclusively demonstrate that in this sample at least, a permafrost preserved specimen, Type 2 (cytosine-->thymine/guanine-->adenine) miscoding lesions represent the overwhelming majority of damage-derived miscoding lesions. Additionally, we show that an as yet unidentified guanine-->adenine analogue modification, not the conventionally argued cytosine-->uracil deamination, underpins a significant proportion of Type 2 damage. How widespread these implications are for aDNA will become apparent as future studies analyse data recovered from a wider range of substrates.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".