A Direct Phenotypic Comparison of siRNA Pools and Multiple Individual Duplexes in a Functional Assay
Bibliographic record
Abstract
BACKGROUND: RNAi is a prominent tool for the identification of novel regulatory elements within complex cellular pathways. In invertebrates, RNAi is a relatively straightforward process, where large double-stranded RNA molecules initiate sequence-specific transcript destruction in target cells. In contrast, RNAi in mammalian cell culture assays requires the delivery of short interfering RNA duplexes to target cells. Due to concerns over off-target phenotypes and extreme variability in duplex efficiency, investigators typically deliver and analyze multiple duplexes per target. Currently, duplexes are delivered and analyzed either individually or as a pool of several independent duplexes. A choice between experiments based on siRNA pools or multiple individual duplexes has considerable implications for throughput, reagent requirements and data analysis in genome-wide surveys, yet there are relatively few data that directly compare the efficiency of the two approaches. METHODOLOGY/PRINCIPAL FINDINGS: To address this critical issue, we conducted a direct comparison of siRNA pools and multiple single siRNAs that target all human phosphatases in a robust functional assay. We determined the frequency with which both approaches uncover loss-of-function phenotypes and compared the phenotypic severity for siRNA pools and the constituent individual duplexes. CONCLUSIONS/SIGNIFICANCE: Our survey indicates that screens with siRNA pools have several significant advantages over identical screens with the corresponding individual siRNA duplexes. Of note, we frequently observed greater phenotypic penetrance for siRNA pools than for the parental individual duplexes. Thus, our data indicate that experiments with siRNA pools have a greater likelihood of generating loss-of-function phenotypes than individual siRNA duplexes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".