Single-molecule identification of the target RNAs of different RNA binding proteins simultaneously in cells
Bibliographic record
Abstract
Abstract RNA-binding proteins (RBPs) regulate nearly every aspect of mRNA processing and are important regulators of gene expression in cells. However, current methods for transcriptome-wide identification of RBP targets are limited since they examine only a single RBP at a time and since they do not provide information on the individual RNA molecules that are bound by a given RBP. Here, we overcome these limitations by developing TRIBE-STAMP, an approach for single-molecule detection of the target RNAs of two RNA binding proteins simultaneously in cells. We apply TRIBE-STAMP to the cytoplasmic m 6 A reader proteins YTHDF1, 2, and 3 and discover that individual mRNA molecules can be bound by more than one YTHDF protein throughout their lifetime, providing new insights into the function of YTHDF proteins in cells. TRIBE-STAMP is a highly versatile approach that enables single-molecule analysis of the targets of RBP pairs simultaneously in the same cells.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".