Uncorrected Nucleotide Bias in mtDNA Can Mimic the Effects of Positive Darwinian Selection
Bibliographic record
Abstract
The relative rates of nucleotide substitution at synonymous and nonsynonymous sites within protein-coding regions have been widely used to infer the action of natural selection from comparative sequence data. It is known, however, that mutational and repair biases can affect rates of evolution at both synonymous and nonsynonymous sites. More importantly, it is also known that synonymous sites are particularly prone to the effects of nucleotide bias. This means that nucleotide biases may affect the calculated ratio of substitution rates at synonymous and nonsynonymous sites. Using a large data set of animal mitochondrial sequences, we demonstrate that this is, in fact, the case. Highly biased nucleotide sequences are characterized by significantly elevated dN/dS ratios, but only when the nucleotide frequencies are not taken into account. When the analysis is repeated taking the nucleotide frequencies at each codon position into account, such elevated ratios disappear. These results suggest that the recently reported differences in dN/dS ratios between vertebrate and invertebrate mitochondrial sequences could be explained by variations in mitochondrial nucleotide frequencies rather than the effects of positive Darwinian selection.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".