The high turnover of ribosome-associated transcripts from <i>de novo</i> ORFs produces gene-like characteristics available for <i>de novo</i> gene emergence in wild yeast populations
Bibliographic record
Abstract
Abstract Little is known about the rate of emergence of genes de novo , how they spread in populations and what their initial properties are. We examined wild yeast ( Saccharomyces paradoxus ) populations to characterize the diversity and turnover of intergenic ORFs over short evolutionary time-scales. With ~34,000 intergenic ORFs per individual genome for a total of ~64,000 orthogroups identified, we found de novo ORF formation to have a lower estimated turnover rate than gene duplication. Hundreds of intergenic ORFs show translation signatures similar to canonical genes. However, they have lower translation efficiency, which could reflect a mechanism to reduce their production cost or simply a lack of optimization. We experimentally confirmed the translation of many of these ORFs in laboratory conditions using a reporter assay. Translated intergenic ORFs tend to display low expression levels with sequence properties that generally are close to expectations based on intergenic sequences. However, some of the very recent translated intergenic ORFs, which appeared less than 110 Kya ago, already show gene- like characteristics, suggesting that the raw material for functional innovations could appear over short evolutionary time-scales.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".