Oligoarray Comparative Genomic Hybridization-Mediated Mapping of Suppressor Mutations Generated in a Deletion-Biased Mutagenesis Screen
Bibliographic record
Abstract
Suppressor screens are an invaluable method for identifying novel genetic interactions between genes in the model organism Caenorhabditis elegans. However, traditionally this approach has suffered from the laborious and protracted process of mapping mutations at the molecular level. Using a mutagen known to generate small deletions, coupled with oligoarray comparative genomic hybridization (aCGH), we have identified mutations in two genes that suppress the lethality associated with a mutation of the essential receptor tyrosine kinase rol-3. First, we find that deletion of the Bicaudal-C ortholog, bcc-1, suppresses rol-3-associated lethality. Second, we identify several duplications that also suppress rol-3-associated lethality. We establish that overexpression of srap-1, a single gene present in these duplications, mediates the suppression. This study demonstrates the suitability of deletion-biased mutagenesis screening in combination with aCGH characterization for the rapid identification of novel suppressor mutations. In addition to detecting small deletions, this approach is suitable for identifying copy number suppressor mutations, a class of suppressor not easily characterized using alternative approaches.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".