High-Throughput RNAi Screening for Germline Apoptosis Genes in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Among the greatest tools that Caenorhabditis elegans can provide researchers are the capabilities to perform high-throughput, genome-wide screens. Using bacterial RNAi libraries, which cover the majority (>85%) of the worm genome, genes can be rapidly and systematically evaluated for apoptosis phenotypes in the germline. Screens can be designed to directly assess the levels of apoptotic corpses under normal physiological conditions using transgenic strains expressing fluorescent reporters that mark apoptotic bodies. Vital dyes that are selectively retained in apoptotic cells, such as acridine orange (AO), can also be used to screen for genes that regulate germline apoptosis. Using these reagents, screens can be performed in wild-type worms or mutant backgrounds that suppress or enhance apoptosis phenotypes. This protocol describes methods for designing and carrying out high-throughput or targeted RNAi screens for germline apoptosis regulators.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".