Rapid divergence of codon usage patterns within the rice genome
Bibliographic record
Abstract
BACKGROUND: Synonymous codon usage varies widely between genomes, and also between genes within genomes. Although there is now a large body of data on variations in codon usage, it is still not clear if the observed patterns reflect the effects of positive Darwinian selection acting at the level of translational efficiency or whether these patterns are due simply to the effects of mutational bias. In this study, we have included both intra-genomic and inter-genomic comparisons of codon usage. This allows us to distinguish more efficiently between the effects of nucleotide bias and translational selection. RESULTS: We show that there is an extreme degree of heterogeneity in codon usage patterns within the rice genome, and that this heterogeneity is highly correlated with differences in nucleotide content (particularly GC content) between the genes. In contrast to the situation observed within the rice genome, Arabidopsis genes show relatively little variation in both codon usage and nucleotide content. By exploiting a combination of intra-genomic and inter-genomic comparisons, we provide evidence that the differences in codon usage among the rice genes reflect a relatively rapid evolutionary increase in the GC content of some rice genes. We also noted that the degree of codon bias was negatively correlated with gene length. CONCLUSION: Our results show that mutational bias can cause a dramatic evolutionary divergence in codon usage patterns within a period of approximately two hundred million years. The heterogeneity of codon usage patterns within the rice genome can be explained by a balance between genome-wide mutational biases and negative selection against these biased mutations. The strength of the negative selection is proportional to the length of the coding sequences. Our results indicate that the large variations in synonymous codon usage are not related to selection acting on the translational efficiency of synonymous codons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".