Natural Selection for Nucleotide Usage at Synonymous and Nonsynonymous Sites in Influenza A Virus Genes
Bibliographic record
Abstract
Influenza A virus is one of the best-studied viruses and a model organism for the study of molecular evolution; in particular, much research has focused on detecting natural selection on influenza virus proteins. Here, we study the dynamics of the synonymous and nonsynonymous nucleotide composition of influenza A virus genes. In several genes, the nucleotide frequencies at synonymous positions drift away from the equilibria predicted from the synonymous substitution matrices. We investigate possible reasons for this unexpected behavior by fitting several regression models. Relaxation toward a mutation-selection equilibrium following a host jump fails to explain the dynamics of the synonymous nucleotide composition, even if we allow for slow temporal changes in the substitution matrix. Instead, we find that deep internal branches of the phylogeny show distinct patterns of nucleotide substitution and that these branches strongly influence the dynamics of nucleotide composition, suggesting that the observed trends are at least in part a result of natural selection acting on synonymous sites. Moreover, we find that the dynamics of the nucleotide composition at synonymous and nonsynonymous sites are highly correlated, providing evidence that even nonsynonymous sites can be influenced by selection pressure for nucleotide composition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".