Mapping the Inter- and Intra-genic Codon Usage Landscape in <i>Homo sapiens</i>
Bibliographic record
Abstract
Abstract Although the genetic code is degenerate, codon selection is non-random and reflects significant functional constraints. Codon usage bias (CUB) acts as a layer of post-transcriptional regulation, influencing mRNA stability, translation kinetics, and co-translational protein folding. While CUB is well-characterized in unicellular organisms, its regulatory scope and functional consequences in humans remain complex and less defined. Our study offers a comprehensive evaluation of human codon usage. We report that genes exhibiting the strongest codon bias are enriched in high-stoichiometry biological processes, such as skin development and oxygen/carbon dioxide transport, and harbor significantly fewer synonymous variants than expected (ρ = -0.24, p < 2.2 x 10 -16 ). Furthermore, we find that codon optimization is spatially regulated: it is significantly more pronounced in structured protein domains compared to intrinsically disordered regions (IDRs) (Cliff’s Δ= 0.26, p < 2.2 x 10 -16 ). Consistent with translational selection, the most frequently used codons are supported by higher tRNA gene copy numbers (ρ = 0.49, p < 6.4 x 10 -4 ). Finally, by correcting for GC3 content, we reveal that the apparent correlation between ENC and adaptation indices (CAI/tAI) vanishes, allowing us to disentangle mutational pressure from translational selection. Collectively, our findings position codon usage bias as a central, evolutionarily conserved regulator of translation and protein folding in humans. Our results provide a comprehensive and integrated view of intergenic and intragenic codon usage bias in humans, reinforcing the biological relevance of synonymous codon choice in shaping translational dynamics and protein biogenesis. This provides a refined framework for interpreting synonymous variation and guiding functional genomics.
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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 | 0.001 | 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) | 0.001 | 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".