Utilising established SDL-screening methods as a tool for the functional genomic characterisation of model and non-model organisms
Bibliographic record
Abstract
The trend for large-scale genetic and phenotypic screens has revealed a wealth of information on biological systems. A major challenge is understanding how genes function and putative roles in networks. The majority of current gene knowledge is garnered from studies utilising the model yeast Saccharomyces cerevisiae. We demonstrate that synthetic dosage lethal genetic array methodologies can be used to study genetic networks in other yeasts, namely the fungal pathogen Candida glabrata, which has limited forward genetic tools, due to the lack of 'natural' mating. We performed two SDL screens in S. cerevisiae, overexpressing the transcriptional regulator UME6 as bait in the first screen and its C. glabrata ortholog CAGL0F05357g in the second. Analysis revealed that SDL maps share 204 common interactors, with 10 genetic interactions unique to C. glabrata indicating a level of genetic rewiring, indicative of linking genotype to phenotype in fungal pathogens. This was further validated by incorporating our results into the global genetic landscape map of the cell from Costanzo et al. to identify common and novel gene attributes. This data demonstrated the utility large data sets and more robust analysis made possible by interrogating exogenous genes in the context of the eukaryotic global genetic landscape.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".