A Phenomic Assessment of Yeast DNA Damage Foci using Synthetic Genetic Array Analysis and High-content Screening
Bibliographic record
Abstract
Aberrant DNA synthesis and maintenance have been implicated in numerous human diseases. I describe here a novel strategy for systematically identifying budding yeast mutants with elevated levels of DNA damage foci, which represent hubs of DNA damage and repair. A previous study manually scored foci in single mutants but was limited in its ability to survey many conditions in large populations. I developed an automated and statistically robust method for identifying aberrant foci phenotypes by combining synthetic genetic array (SGA) and high-content screening (HCS) methodology. Using this approach, I scored thousands of essential and non-essential gene mutants subjected to environmental and genetic perturbations, including the DNA damaging agent, phleomycin, and deletions of DNA repair genes, SGS1 and YKU80. Collectively, I identified a functionally enriched set of 367 mutants that had increased frequencies of DNA damage foci and established SGA-HCS as a powerful tool for investigating the yeast DNA damage response.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".