Bibliographic record
Abstract
Pre-mRNA splicing, the removal of introns from pre-messenger RNA, is an essential step in eukaryotic gene expression. In humans, it has been estimated that 60 % of noninfectious diseases are caused by errors in splicing, making the study of pre-mRNA splicing a high priority from a health perspective. Pre-mRNA splicing is also complicated: the molecular machine that catalyzes the reaction, the spliceosome, is composed of five small nuclear RNAs, and over 100 proteins, making splicing one of the most complex processes in the cell.An important tool for studying pre-mRNA splicing is the in vitro splicing assay. With an in vitro assay, it is possible to test the function of each splicing component by removing the endogenous version and replacing it (or reconstituting it) with a modified one. This assay relies on the ability to produce an extract-either whole cell or nuclear-that contains all of the activities required to convert pre-mRNA to mRNA. To date, splicing extracts have only been produced from human and S. cerevisiae (yeast) cells. We describe a method to produce whole cell extracts from yeast that support splicing with efficiencies up to 90 %. These extracts have been used to reconstitute snRNAs, screen small molecule libraries for splicing inhibitors, and purify a variety of splicing complexes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".