Reporter-Based Synthetic Genetic Array Analysis: A Functional Genomics Approach for Investigating the Cell Cycle in Saccharomyces cerevisiae
Bibliographic record
Abstract
Temporal control of gene expression is a widespread feature of cell cycles, with clear transcriptional programs in bacteria, yeast, and metazoans. In budding yeast, approximately 1,000 genes are transcribed during a specific interval of the cell cycle. Although a number of factors that contribute to this periodic pattern of gene expression have been studied in Saccharomyces cerevisiae, pathways of cell cycle-regulated transcription remain largely undefined. To identify regulators of genes exhibiting cell cycle periodicity, we have developed a functional genomics approach termed reporter-based synthetic genetic array (R-SGA) analysis. Based on synthetic genetic array (SGA) analysis, R-SGA allows rapid and easily automated incorporation of a cell cycle reporter gene into the array of viable haploid yeast gene-deletion mutants. Scoring of reporter activity in mutant strains compared to wild type identifies candidate regulators of the cell cycle gene of interest. In contrast to microarrays, which generally provide information about the expression of all genes under a particular condition (for example, a single gene deletion), R-SGA analysis facilitates the study of the expression of a single gene in all deletion mutants. Our system can be adapted to examine the expression of any gene not only in the context of haploid deletion mutants but also using other array-based strain collections available to the yeast community.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".