Complementation of U4 snRNA in S. cerevisiae Splicing Extracts for Biochemical Studies of snRNP Assembly and Function
Bibliographic record
Abstract
Pre-messenger RNA splicing is a surprisingly complex and dynamic process, the details of which remain largely unknown. One important method for studying splicing involves the replacement of endogenous splicing components with their synthetic counterparts. This enables changes in protein or nucleic acid sequence to be tested for functional effects, as well as the introduction of chemical moieties such as cross-linking groups and fluorescent dyes. To introduce the modified component, the endogenous one must be removed and a method found to reconstitute the active splicing machinery. In extracts prepared from S. cerevisiae, reconstitution has been accomplished with the small, nuclear RNAs U6, U2, and U5.We describe a comparable method to reconstitute active U4 small, nuclear RNA (snRNA) into a splicing extract. In order to remove the endogenous U4 it is necessary to target it for oligo-directed RNase H degradation while active splicing is under way, i.e., in the presence of a splicing transcript and ATP. This allows complete degradation of endogenous U4 and subsequent replacement with an exogenous version. In contrast to the procedures described for depletion of U6, U2, or U5 snRNAs, depletion of U4 requires concurrent active splicing. The ability to reconstitute U4 in yeast extract allows a variety of structural and functional studies to be carried out.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".