Elucidation of Codon Usage Signatures across the Domains of Life
Bibliographic record
Abstract
Abstract Due to the degeneracy of the genetic code, multiple codons are translated into the same amino acid. Despite being ‘synonymous’, these codons are not equally used. Selective pressures are thought to drive the choice among synonymous codons within a genome, while GC content, which is generally attributed to mutational drift, is the major determinant of interspecies codon usage bias. Here we find that in addition to the bias caused by GC content, inter-species codon usage signatures can also be detected. More specifically, we show that a single amino acid, arginine, is the major contributor to codon usage bias differences across domains of life. We then exploit this finding, and show that the identified domain-specific codon bias signatures can be used to classify a given sequence into its corresponding domain with high accuracy. Considering that species belonging to the same domain share similar tRNA decoding strategies, we then wondered whether the inclusion of codon autocorrelation patterns might improve the classification performance of our algorithm. However, we find that autocorrelation patterns are not domain-specific, and surprisingly, are unrelated to tRNA reusage, in contrast to the common belief. Instead, our results reveal that codon autocorrelation patterns are a consequence of codon optimality throughout a sequence, where highly expressed genes display autocorrelated ‘optimal’ codons, whereas lowly expressed genes display autocorrelated ‘non-optimal’ codons.
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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 | 0.001 | 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".