Identification of Chemical–Genetic Interactions via Parallel Analysis of Barcoded Yeast Strains
Bibliographic record
Abstract
The Yeast Knockout Collection is a complete set of gene deletion strains for the budding yeast, Saccharomyces cerevisiae In each strain, one of approximately 6000 open-reading frames is replaced with a dominant selectable marker flanked by two DNA barcodes. These barcodes, which are unique to each gene, allow the growth of thousands of strains to be individually measured from a single pooled culture. The collection, and other resources that followed, has ushered in a new era in chemical biology, enabling unbiased and systematic identification of chemical-genetic interactions (CGIs) with remarkable ease. CGIs link bioactive compounds to biological processes, and hence can reveal the mechanism of action of growth-inhibitory compounds in vivo, including those of antifungal, antibiotic, and anticancer drugs. The chemogenomic profiling method described here measures the sensitivity induced in yeast heterozygous and homozygous deletion strains in the presence of a chemical inhibitor of growth (termed haploinsufficiency profiling and homozygous profiling, respectively, or HIPHOP). The protocol is both scalable and amenable to automation. After competitive growth of yeast knockout collection cultures, with and without chemical inhibitors, CGIs can be identified and quantified using either array- or sequencing-based approaches as described here.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".