Codon Usage Bias of the Wheat Flower Development Gene WAG-2 and Other AGAMOUS Group Genes
Bibliographic record
Abstract
Analyzing codon usage bias of WAG-2 gene in wheat three-pistil (TP) mutant may provide a basis for selecting the appropriate host expression systems to improve the expression of target genes. In the present study, we analyzed the codon bias of the complete coding sequence (CDS) of the WAG-2 gene in TP using Codon W program, and compared the results with AGAMOUS (AG) group genes of other plant species. Results showed that the WAG-2 gene in TP and other monocot AG group genes preferably used codons ending with G/C bases, but Arabidopsis thaliana, Nicotiana tabacum, and other dicot crops were biased toward the synonymous codons with A/T. The clustering results based on codon bias were consistent with those based on CDS of the AG group genes, indicating that the difference in codon preference of AG group genes sequences was closely associated with the genetic relationship of the species. The Euclidean distance coefficients of WAG-2 with A. thaliana and N. tabacum were 9.255 and 5.730, respectively, indicating that N. tabacum may be more suitable for the expression of WAG-2. There were 37 codons showing distinct usage differences between WAG-2 and genome of yeast, 23 between WAG-2 and Escherichia coli. Therefore, the E. coli was the superior protein expression system. These results may improve our understanding of codon usage bias and functional studies of WAG-2.
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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 | 0.000 |
| 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 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".