The Basis and Promise of Programmable RNA Editing and Modification
Bibliographic record
Abstract
One key advantage of RNA over genomic editing is its temporary effects. Aside from current use of DNA-targeting CRISPR-Cas9, the more recently discovered CRISPR-Cas13 has been explored as a means of editing due to its RNA-targeting capabilities. Specifically, there has been a recent interest in identifying and functionally characterizing biochemical RNA modifications, which has spurred a new field of research known as “epitranscriptomics”. As one of the most frequently occurring transcriptome modifications, N6-methyladenosine (m6A) has generated much interest. The presence of m6A modifications is under the tight control of a series of regulators, and the ability of fusing these proteins or demethylases to catalytically inactive CRISPR proteins have resulted in a new wave of programmable RNA methylation tools. In addition, studies have been conducted to develop different CRISPR/Cas and base editor systems capable of more efficient editing, and some have explored the effects of in vivo editing for certain diseases. As well, the application of CRISPR and base editors for screening shows promise in revealing the phenotypic outcomes from m6A modification, many of which are linked to physiological, and pathological effects. Thus, the therapeutic potential of CRISPR/Cas and base editors for not only m6A related, but other RNA and DNA related disease has also garnered insight. In this review, we summarize/discuss the recent findings on RNA editing with CRISPR, base editors and non-CRISPR related tools and offer a perspective regarding future applications for basic and clinical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".