Analyses of Codon Usage Patterns and Codon Usage Bias in Peach (<i>Prunus persica</i>)
Bibliographic record
Abstract
To further study the characteristics of peach codon usage, this study analyzed codon usage biases and codon usage patterns of peach genome, based on the statistical calculations of related GC content, effective number of codons (ENC) and relative synonymous codons (RSCU) from 26 873 coding sequences. The results showed that there were obvious biases in codon usage of peach, and 4 out of 61 codons (UCA, ACA, GCA and GAA) were defined as the optimal codons, all of which end with Adenine at the third codon position. Further analyses of codon usage frequency among peach and other 9 relative species in Rosaceae found that the codon usage patterns in the relative genus were similar. These results also suggested that there was a positive correlation between the copy number of tRNA genes and the occurrence frequency of corresponding amino acids (and specific codons) in the peach genome. These results revealed codon usage patterns in Peach and provide an important reference for the relevant studies on codon usage mechanism and the genetic engineering.
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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 | 0.001 | 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 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".