Yorzoi: Predicting RNA-seq coverage from DNA sequence in yeast
Bibliographic record
Abstract
Abstract Yeast is one of the principal model organisms in synthetic biology and a widely used chassis for testing heterologous DNA sequences. However, designing DNA constructs correctly is currently limited by our incomplete quantitative understanding of how sequences are transcribed and translated, especially non-native sequences. Here, we present Yorzoi , a sequence-to-expression model for bakers’ yeast that predicts RNA-seq coverage for a 5 kilobase, multi-gene window of DNA at 10 base pair (bp) resolution. To extend its predictive ability beyond native DNA, Yorzoi has been pretrained on a comprehensive dataset not only including native yeast sequences but also human sequences expressed in yeast and structurally rearranged synthetic yeast chromosomes. We demonstrate that our model has learned general rules of yeast transcription by achieving high predictive performance on various downstream tasks. Yorzoi is a powerful tool for in-silico testing of DNA sequences and directly applicable for sequence design. A web application to use our model is available at yorzoi.eu and the code open source on GitHub .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".