Identification of Transcription Factor Targets by Phenotypic Activation and Microarray Expression Profiling in Yeast
Bibliographic record
Abstract
A major obstacle to identify physiological transcriptional targets is that the conditions that induce the majority of yeast transcription factors (TFs) are unknown. Microarray analyses of deletion mutants indicate that most TFs are inactive under standard growth conditions. To overcome this, we screened an ordered array of yeast open reading frames (ORFs) to identify TFs that confer reduced fitness upon overexpression, suggesting that overexpression results in an activated state (phenotypic activation). Approximately one-third of all yeast TFs exhibited this phenotype. Here, we describe in detail our methodology to characterize these TF overexpression strains including microarray expression profiling, data analysis, and motif searching. Our analyses show that in many cases, the differentially regulated genes correspond to physiological functions and known targets of well-characterized TFs. The expected binding sites of several TFs were also identified in the promoters of these genes. Moreover, novel DNA-binding sequences and putative targets were identified for less-characterized TFs. These results demonstrate that phenotypic activation is an effective approach to rapidly characterize TFs on a large scale, which should also be feasible in other organisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".