Structural and functional relationships shown by genomic analysis of <i>Capra hircus</i> genes
Bibliographic record
Abstract
The effect of base composition biases on codon usage patterns was investigated in the goat species Capra hircus, using custom-designed computational tools available within the public domain. Nucleotide frequencies were nearly equal and a slight increase of adenine–thymine (AT) over guanine–cytosine (GC) was detected throughout the dataset. However, this increase showed no influence on the bases at the third codon position (N3). C3 and G3 were found more often than A/T3, suggesting that there was a small or almost no influence of the general base composition on the N3 base composition. To understand more and analyse in-depth influence and interactions between base compositions and codons, further relative synonymous codon usage (RSCU) was investigated. Amino acid usage and the correlation between its usages were also investigated, using both basic sequence analysis and statistical analysis means (measures of correlation). These analyses were utilized to probe whether there were correlations between genes, genomic characteristics and their function. Genes with high GC and those with low GC were also investigated to see to what extent how a gene functions could influence its sequence structure and impose certain structural modifications. This investigation may shed light on many genomic features of Capra hircus genes and would be of significance for future biotechnology/research projects considering Capra for transgenic and advanced genomic initiatives.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".