Revealing Hidden Relationships Among Yeast Genes Involved in Chromosome Segregation Using Systematic Synthetic Lethal and Synthetic Dosage Lethal Screens
Bibliographic record
Abstract
The vast accumulation of knowledge from genome sequencing projects and studies with model organisms has presented a remarkable challenge to biologists: to understand the functions of thousands of highly conserved genes and how they work together to regulate fundamental cellular processes. This challenge is compounded by the inescapable reality that most genes are 'buffered' by other genes that contribute to the same biological processes, limiting the impact of phenotypic studies with single mutants. In budding yeast, functional genomic methods have been developed for the systematic application of established genetic techniques. In particular, the Synthetic Genetic Array (SGA) method allows genome-wide synthetic lethal (SL) and synthetic dosage lethal (SDL) screens thus enabling an unbiased survey of genetic interactions. We have used genes encoding components of the yeast kinetochore as a biological testbed for assaying the utility of SGA-based SL and SDL screens for revealing new pathways and genes involved in chromosome segregation. We identified 211 nonessential deletion mutants that were unable to tolerate either overexpression or loss of function of kinetochore genes. Our study uncovered a wealth of relationships between gene products that functionally interact with the kinetochore, and also highlighted the value of performing genome-wide screens with both hypomorphic and hypermorphic alleles of query genes. Here, we will highlight our recent kinetochore SGA genomic screens, in the broader context of applying complementary genetic screening approaches in the systematic exploration of biological pathways or functional complexes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".