Improving synthetic lethal screens by regulating the yeast centromere sequence
Bibliographic record
Abstract
The synthetic lethal screen is a useful method in identifying novel genes functioning in an alternative pathway to the gene of interest. The current synthetic lethal screen protocol in yeast is based on a colony-sectoring assay that allows direct visualization of mutant colonies among a large population by their inability to afford plasmid loss. This method demands an appropriate level of stability of the plasmid carrying the gene of interest. YRp-based plasmids are extremely unstable and complete plasmid loss occurs within a few generations. Consequently, YCp plasmids are the vector of choice for synthetic lethal screens. However, we found that the high-level stability of YCp plasmids resulted in a large number of false positives that must be further characterized. In this study, we attempt to improve the existing synthetic lethal screen protocol by regulating the plasmid stability and copy number. It was found that by placing a yeast centromere sequence under the control of either inducible or constitutive promoters, plasmid stability can be significantly decreased. Hence, altering the conditions under which yeast cells carrying the plasmid PGAL1-CEN4 were cultivated allowed us to develop a method that eliminated virtually 100% of false positives and drastically reduced the time required to carry out a synthetic lethal screen.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".