An RNA ligase-mediated method for the efficient creation of large, synthetic RNAs
Bibliographic record
Abstract
RNA ligation has been a powerful tool for incorporation of cross-linkers and nonnatural nucleotides into internal positions of RNA molecules. The most widely used method for template-directed RNA ligation uses DNA ligase and a DNA splint. While this method has been used successfully for many years, it suffers from a number of drawbacks, principally, slow and inefficient product formation and slow product release, resulting in a requirement for large quantities of enzyme. We describe an alternative technique catalyzed by T4 RNA ligase instead of DNA ligase. Using a splint design that allows the ligation junction to mimic the natural substrate of RNA ligase, we demonstrate several ligation reactions that appear to go nearly to completion. Furthermore, the reactions generally go to completion within 30 min. We present data evaluating the relative importance of various parameters in this reaction. Finally, we show the utility of this method by generating a 128-nucleotide pre-mRNA from three synthetic oligoribonucleotides. The ability to ligate synthetic or in vitro transcribed RNA with high efficiency has the potential to open up areas of RNA biology to new functional and biophysical investigation. In particular, we anticipate that site-specific incorporation of fluorescent dyes into large RNA molecules will yield a wealth of new information on RNA structure and function.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".