Unreliable mtDNA data due to nuclear insertions: a cautionary tale from analysis of humans and other great apes
Bibliographic record
Abstract
Analysis of mitochondrial DNA sequence variation has been used extensively to study the evolutionary relationships of individuals and populations, both within and across species. So ubiquitous and easily acquired are mtDNA data that it has been suggested that such data could serve as a taxonomic 'barcode' for an objective species classification scheme. However, there are technical pitfalls associated with the acquisition of mtDNA data. One problem is the presence of translocated pieces of mtDNA in the nuclear genome of many taxa that may be mistaken for authentic organellar mtDNA. We assessed the extent to which such 'numt' sequences may pose an overlooked problem in analyses of mtDNA from humans and apes. Using long-range polymerase chain reaction (PCR), we generated necessarily authentic mtDNA sequences for comparison with sequences obtained using typical methods for a segment of the mtDNA control region in humans, chimpanzees, bonobos, gorillas and orangutans. Results revealed that gorillas are notable for having such a variety of numt sequences bearing high similarity to authentic mtDNA that any analysis of mtDNA using standard approaches is rendered impossible. Studies on humans, chimpanzees, bonobos or orangutans are apparently less problematic. One implication is that explicit measures need to be taken to authenticate mtDNA sequences in newly studied taxa or when any irregularities arise. Furthermore, some taxa may not be amenable to analysis of mtDNA variation at all.
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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 | 0.081 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.009 | 0.042 |
| 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".