GENOME ANALYSIS OF GENBANK KNOWN RABBIT (Oryctolagus cuniculus) GENES
Bibliographic record
Abstract
After downloading all known rabbit genes, key words specific to complete coding sequences were used to filter out partial coding sequences. As a result, 160 full-length, nuclear, protein-coding and functionally annotated genes were extracted from GenBank database. These genes were subjected to synonymous codon and amino acid usage analysis. The results showed a clear base composition bias in the genes analyzed. The effective number of codons used (Nc values) ranged between 30.07 and 59.98 with a mean of 51.31 with a standard deviation (SD) of 5.71. The frequency of G + C at the synonymous third position of codons (GC3s) varied between 0.3 and 0.96 with a mean of 0.55 and a SD of 0.14, clearly indicating marked variation and heterogeneity in codon usage patterns among the different genes. The distribution of relative synonymous codon usage (RSCU) values calculated for all the genes indicated that codons with a G or C in the third position are widely employed, and although RCSU values for A/T or G/C ending codons are almost equal, G/C appears to play a dominant role. This pattern of dominance was confirmed by the distribution of amino acid occurrence. Leucine (CUN) and serine (UCN) were the most frequent amino acids, followed by arginine (CGN), proline (CCN), glycine (GGN) and alanine (GCN), in relatively equal proportions. The heterogeneity observed in the analyzed genes was then further probed by multivariate statistical analyses. A major trend in codon usage that correlated with gene expression values was revealed. These findings suggest that translational efficiency exerts a stronger influence on codon usage preference than compositional bias in the sampled rabbit 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.002 | 0.001 |
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".