Faculty Opinions recommendation of RNA targeting with CRISPR-Cas13.
Bibliographic record
Abstract
RNA plays important and diverse roles in biology, but molecular tools to manipulate and measure RNA are limited.For example, RNA interference (RNAi) 1-3 can efficiently knockdown RNAs, but it is prone to off-target effects 4 , and visualizing RNAs typically relies on the introduction of exogenous tags 5 .Here, we demonstrate that the class 2 type VI 6,7 RNA-guided RNA-targeting CRISPR-Cas effector Cas13a 8 (previously known as C2c2) can be engineered for mammalian cell RNA knockdown and binding.After initial screening of fifteen orthologs in E. coli, we identified Cas13a from Leptotrichia wadei (LwaCas13a) as the most effective.LwaCas13a can be heterologously expressed in mammalian and plant cells for targeted knockdown of either reporter or endogenous transcripts.We demonstrate that LwaCas13a is capable of providing comparable levels of knockdown as RNAi, but with dramatically improved specificity.Moreover, catalytically
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.143 | 0.145 |
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".