Codon usage patterns in Nematoda: analysis based on over 25 million codons in thirty-two species
Bibliographic record
Abstract
BACKGROUND: Codon usage has direct utility in molecular characterization of species and is also a arker for molecular evolution. To understand codon usage within the diverse phylum Nematoda,we analyzed a total of 265,494 expressed sequence tags (ESTs) from 30 nematode species. The full genomes of Caenorhabditis elegans and C. briggsae were also examined. A total of 25,871,325 codons ere analyzed and a comprehensive codon usage table for all species was generated. This is the first codon usage table available for 24 of these organisms. RESULTS: Codon usage similarity in Nematoda usually persists over the breadth of a genus but thenrapidly diminishes even within each clade. Globodera, Meloidogyne, Pristionchus, and Strongyloides have the most highly derived patterns of codon usage. The major factor affecting differences in codon usage between species is the coding sequence GC content, which varies in nematodes from 32%to 51%. Coding GC content (measured as GC3) also explains much of the observed variation in the effective number of codons (R = 0.70), which is a measure of codon bias, and it even accounts for differences in amino acid frequency. Codon usage is also affected by neighboring nucleotides(N1 context). Coding GC content correlates strongly with estimated noncoding genomic GC content (R = 0.92). On examining abundant clusters in five species, candidate optimal codons were identified that may be preferred in highly expressed transcripts. CONCLUSION: Evolutionary models indicate that total genomic GC content, probably the product of directional mutation pressure, drives codon usage rather than the converse, a conclusion that is supported by examination of nematode genomes.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".