Analysis of codon usage patterns in <i>Morus notabilis</i> based on genome and transcriptome data
Bibliographic record
Abstract
Codons play important roles in regulating gene expression levels and mRNA half-lives. However, codon usage and related studies in multicellular organisms still lag far behind those in unicellular organisms. In this study, we describe for the first time genome-wide patterns of codon bias in Morus notabilis (mulberry tree), and analyze genome-wide codon usage in 12 other species within the order Rosales. The codon usage of M. notabilis was affected by nucleotide composition, mutation pressure, nature selection, and gene expression level. Translational selection optimal codons were identified and highly expressed genes of M. notabilis tended to use the optimal codons. Genes with higher expression levels have shorter coding region and lower amino acid complexity. Housekeeping genes showed stronger translational selection, which, notably, was not caused by the large differences between the expression level of housekeeping genes and other genes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".