CODON-BASED DETECTION OF POSITIVE SELECTION CAN BE BIASED BY HETEROGENEOUS DISTRIBUTION OF POLAR AMINO ACIDS ALONG PROTEIN SEQUENCES
Bibliographic record
Abstract
The ratio of the number of nonsynonymous substitutions per site (Ka) over the number of synonymous substitutions per site (Ks) has often been used to detect positive selection.Investigators now commonly generate Ka/Ks ratio profiles in a sliding window to look for peaks and valleys in order to identify regions under positive selection.Here we show that the interpretation of peaks in the Ka/Ks profile as evidence for positive selection can be misleading.Genic regions with Ka/Ks > 1 in the MRG gene family, previously claimed to be under positive selection, are associated with a high frequency of polar amino acids with a high mutability.This association between an increased Ka and a high proportion of polar amino acids appears general and not limited to the MRG gene family or the sliding-window approach.For example, the sites detected to be under positive selection in the HIV1 protein-coding genes with a high posterior probability turn out to be mostly occupied by polar amino acids.These findings caution against invoking positive selection from Ka/Ks ratios and highlight the need for considering biochemical properties of the protein domains showing high Ka/Ks ratios.In short, a high Ka/Ks ratio may arise from the intrinsic properties of amino acids instead of from extrinsic positive 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".