Analysis of Codon Usage Bias in the <i>Platycarya</i> Chloroplast Genome
Bibliographic record
Abstract
To detect the codon usage characteristics of chloroplast genomes in Platycarya genus, the CodonW 、 CUSP and SPSSAU software were employed to analyze the codon usage patterns of the chloroplast genomes protein-coding sequence in Platycarya longipes and Platycarya strobilacea in this research. Results show that the GC content of chloroplast genomes was 37.75% and 37.80%, the average GC content in the 3rd position was 27.16% and 27.25%. the range of effective codon number from 35.19 to 56.98, and there were more than 2/3 genes when ENC value greater than 45, which indicated a weak preference. According to the results of neutrality plot analysis, ENC-plot analysis and PR2-plot analysis, codon bias in most Platycarya chloroplast genes were affected by natural selection, while a few were affected by mutations or other factors. And based on the ENC value, five groups of high-expressed and low-expressed genes were identified, 16 codons were ended with A/U among the 18 optimal codons. The research has implications on codon optimization, enhancing the expression efficiency of exogenous gene and phylogenetic analysis.
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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 | 0.002 | 0.002 |
| 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".