Efficient Splinted Ligation of Synthetic RNA Using RNA Ligase
Bibliographic record
Abstract
RNA ligation allows the creation of large RNA molecules from smaller pieces. This can be useful in a number of contexts: to generate molecules that are larger than can be directly synthesized; to incorporate site-specific changes or RNA modifications within a large RNA in order to facilitate functional and structural studies; to isotopically label segments of large RNAs for NMR structural studies; and to construct libraries of mutant RNAs in which one region is extensively mutagenized or modified. The impediment to widespread use of RNA ligation is the low and variable efficiency of standard ligation strategies, which frequently preclude joining more than two pieces of RNA together.We describe a method using RNA ligase (Rligation), rather than DNA ligase (Dligation), in a splint-mediated ligation reaction that joins RNA molecules with high efficiency. RNA ligase recognizes single-stranded RNA ends, which are held in proximity to one another by the splint. Monitoring the reaction is easily accomplished by denaturing gel electrophoresis and ethidium bromide staining. Using this technique, it is possible to generate a wide range of modified RNAs from synthetic oligoribonucleotides.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".