Bibliographic record
Abstract
tRNA genes have the fundamental role of translating the genetic code during protein synthesis. Beyond solely a passive decoding role, the tRNA pool exerts selection pressures on the codon usage of organisms and the viruses that infect them because processing codons read by rare tRNAs can be slow or even erroneous. To better understand the interactions of codons and anticodons in eukaryotic species, we first investigated whether tRNAs packaged into HIV-1 particles may relate to the poor codon usage of HIV-1 genes. By comparing the codon usage of HIV-1 genes with that of its human host, we found that tRNAs decoding poorly adapted codons are overrepresented in HIV-1 virions. Because the affinity of Gag-Pol for all tRNAs is non-specific, HIV packaging is most likely passive and reflects the tRNA pool at the time of viral particle formation. Moreover, differences that we found in the codon usage between early and late genes suggest alterations in the tRNA pool are induced late in viral infection. Next, we tested whether a reduced tRNA anticodon pattern, which was called into question by predicted tRNA datasets, is maintained across eukaryotes. tRNA prediction methods are prone to falsely identifying tRNA-derived repetitive sequences as functional tRNA genes. Thus, we proposed and tested a novel approach to identify falsely predicted tRNA genes using phylogenetics. Phylogenetic analysis removed nearly all the genes deviating from the anticodon pattern, therefore the anticodon pattern is reaffirmed across eukaryotes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".