RBPscan: A Quantitative, In Vivo Tool for Profiling RNA-Binding Protein Interactions
Bibliographic record
Abstract
SUMMARY RNA-binding proteins (RBPs) are essential regulators of gene expression at post-transcriptional level, yet obtaining quantitative insights into RBP-RNA interactions in vivo remains a challenge. Here we developed RBPscan, a method that integrates RNA editing with massively parallel reporter assays (MPRAs) to profile RBP binding in vivo . RBPscan fuses the catalytic domain of ADAR to the RBP of interest, using RNA editing of a recorder mRNA as a readout of binding events. We demonstrate its utility in zebrafish embryos, human cells, and yeast, where it quantifies binding strength, resolves dissociation constants, identifies high-specificity motifs for a variety of RBPs, and links binding affinities to their impact on mRNA stability. RBPscan also provides positional information of conserved and novel Pumilio-binding sites in lncRNA NORAD . With its simplicity, scalability, and compatibility across systems, RBPscan offers a versatile tool for investigating RBP-RNA interactions and complements established methods for studying post-transcriptional regulatory networks. GRAPHICAL ABSTARCT
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".