Identification of Chemical–Genetic Interactions via Parallel Analysis of Barcoded Yeast Strains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it