Pooled Lentiviral shRNA Screening for Functional Genomics in Mammalian Cells
Bibliographic record
Abstract
Genome sequencing efforts have reformed the nature of biological inquiry, prompting the development of technologies for the functional annotation of mammalian genes. Based on methodologies originally discovered in plants and Caenorhabditis elegans, RNA interference has offered cell biologists an effective and reproducible approach to perturb gene function in mammalian cells and whole organisms. Initial application of RNA interference libraries targeting the human and mouse genomes relied on arrayed screening approaches, whereby each unique RNA interference reagent is arrayed into individual wells of a microtiter plate. These screens are not trivial to perform, requiring a substantial investment in infrastructure. In the past decade, many technological advances have been made that make genome-wide RNA interference screening more accessible to researchers and more feasible to perform in nonspecialized laboratories. Here, we describe a comprehensive protocol for pooled short-hairpin RNA screening, including methodologies for pooled lentivirus production, cell infection, genome-wide negative selection screening and resources for pooled screen deconvolution, and data analysis. As a technique, pooled shRNA screening is still in its infancy, but the methodology has already been successfully applied to probe diverse signaling pathways, as a means of drug target identification, and to identify essential genes in normal and cancer cell lines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".