RNA interference screens to uncover membrane protein biology
Bibliographic record
Abstract
In this review, we discuss the use of RNA interference screens to identify genes involved in the regulation and function of membrane proteins. Briefly, cells expressing the membrane protein of interest can be transduced with a pooled lentiviral short-hairpin RNA (shRNA) library containing tens of thousands of unique shRNAs. Transduced cells are then selected or fractionated based on specific critera, such as membrane protein expression or function. shRNAs from selected cell populations are then deconvoluted and quantified using microarray analyses or high-throughput sequencing technologies. This allows individual shRNAs to be scored and cutoffs can be made to generate a list of shRNA hits. Bioinformatic analyses of gene targets of shRNA hits can be used to identify pathways and processes associated with membrane protein biology. To illustrate this functional genomics approach, we discuss pooled lentiviral shRNA screens that were performed to identify genes that regulate the transcription and cell-surface expression of the cancer stem cell marker CD133. This approach can be adapted to study other membrane proteins, as well as specific aspects of membrane proteins, such as their function or downstream signaling effects.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".