GC2 Biology Dictates Gene Expressivity in <i>Camellia sinensis</i>
Bibliographic record
Abstract
The effectiveness of the gene expression is influenced by the nature of codons used throughout the gene. This is due to the fact that most genes and organisms do not use synonymous codons uniformly; certain synonymous codons are used preferentially, a phenomenon called codon usage bias (CUB). Based on the hypothesis that highly expressed genes are often characterized by strong compositional bias in terms of codon usage, there are a number of measures currently in use that quantify codon usage bias in genes, and hence provide numerical indices to predict the expression level of genes. We analyzed normalized AT and GC frequency at each codon site. We observed that correlations between gene expression as measured by CAI and GC content at any codon site are very weak. GC2s showed moderate positive correlation with gene expression. We also measured the correlations between CAI and AT content at three codon sites. AT2s showed moderate negative correlation with gene expression. We further observed a strong correlation between RCBS and protein length indicating natural selection operating in favor of shorter genes to be expressed at higher level. Our analysis revealed that the second position of synonymous codons in Camellia sinensis plays a more prominent role than the third position in determining gene expressivity as evident from CUB and correlation analysis on ten genes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".