A CRISPR-dCas13 RNA-editing tool to study alternative splicing
Bibliographic record
Abstract
Alternative splicing allows multiple transcripts to be generated from the same gene to diversify the protein repertoire and gain new functions despite a limited coding genome. It can impact a wide spectrum of biological processes, including disease. However, its significance has long been underestimated due to limitations in dissecting the precise role of each splicing isoform in a physiological context. Furthermore, identifying key regulatory elements to correct deleterious splicing isoforms has proven equally challenging, increasing the difficulty of tackling the role of alternative splicing in cell biology. In this work, we take advantage of dCasRx, a catalytically inactive RNA targeting CRISPR-dCas13 ortholog, to efficiently switch alternative splicing patterns of endogenous transcripts without affecting overall gene expression levels cost-effectively. Additionally, we demonstrate a new application for the dCasRx splice-editing system to identify key regulatory RNA elements of specific splicing events. With this approach, we are expanding the RNA toolkit to better understand the regulatory mechanisms underlying alternative splicing and its physiological impact in various biological processes, including pathological conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".