Selection constraints determine preference for A/U-ending codons in <i>Taxus contorta</i>
Bibliographic record
Abstract
Unequal utilization of synonymous codons is a well-known phenomenon among living organisms. This phenomenon plays a major role in the enhancement of the accuracy and efficiency of translation. Gymnosperms are rarely paid attention in this aspect. Understanding the degree of and determining the forces influencing codon usage bias (CUB) in Taxus contorta, an endangered Himalayan gymnosperm, will prove useful in interpreting the evolutionary characteristics of this species. Using RNAseq data, 93 790 assembled transcripts were clustered into 32 701 unigenes. Around 13 061 full-length sequences were utilized for the analysis of CUB. Compositional properties showed that GC-content ranged from 28.76% to 65.22%, with an average value of 44.28%, suggesting an AT-rich genome. The mean effective number of codons (ENC) value revealed that CUB is not strong in T. contorta. The preferred codons tended to be A/U ending, whereas the avoided codons tended to be G/C ending. A P2 index of 0.54 and a Mutation Responsive Index (MRI) value of –0.02 in addition to the results revealed by the neutrality, ENC, and parity plots showed that natural selection is a predominating factor governing CUB. Mutational pressure, gene length, hydropathiciy, aromaticity, and nucleotide composition influence CUB weakly.
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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 | 0.000 | 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 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".