Double Selection Enhances the Efficiency of Target-AID and Cas9-Based Genome Editing in Yeast
Bibliographic record
Abstract
CRISPR-Cas9 loss of function (LOF) and base editing screens are powerful tools in genetics and genomics. Yeast is one of the main models in these fields, but has only recently started to adopt this new toolkit for high throughput experiments. We developed a double selection strategy based on co-selection that increases LOF mutation rates using the Target-AID base editor. We constructed the pDYSCKO vector, which is amenable to high throughput double selection experiments, and show that the improvement in Target-AID efficiency generalizes across loci. Using modeling, we show that this improvement in efficiency provides the required increased in detection power to measure the fitness effects of thousands of mutations in typical yeast pooled screens. We show that double selection can also improve Cas9 mediated LOF rates, but that this multiplex genome editing causes programmable chromosomal translocations at high frequency. This suggests that multiplex LOF editing should be performed with caution and that base-editors could be preferable tools for some screens in yeast. Base editing using double selection is simple and straightforward and provides an alternative to homology directed repair based high throughput variant strain construction methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".