Affinity Purification of RNA Using an ARiBo Tag
Bibliographic record
Abstract
The increased awareness of the importance of RNA in biology, illustrated by the recent attention given to RNA interference research and applications, has spurred structural and functional investigations of RNA. For these studies, the traditional purification method for in vitro transcribed RNA is denaturing polyacrylamide gel electrophoresis. However, gel-based procedures denature the RNA and can be very tedious and time-consuming. Thus, several alternative schemes have been developed for fast non-denaturing purification of RNA transcribed in vitro. In a recent report, a quick affinity purification procedure was developed for RNAs transcribed with a 3'-ARiBo tag and shown to provide RNA with exceptionally high purity and yield. The ARiBo tag contains the λboxB RNA and the glmS ribozyme, allowing immobilization on GSH-Sepharose resin via a λN-GST fusion protein and elution by activation of the glmS ribozyme with glucosamine-6-phosphate. This Chapter outlines the experimental details for affinity batch purification of RNAs using ARiBo tags. Although the procedure was originally developed for purification of a stable purine riboswitch mutant, it is demonstrated here for purification of the terminal loop of the let-7g precursor miRNA, an important target of the pluripotency factor Lin28.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".