Pre-mRNA splicing order is predetermined and maintains splicing fidelity across multi-intronic transcripts
Bibliographic record
Abstract
Abstract Combinatorially, intron excision within a given nascent transcript could proceed down any of thousands of paths, each of which would expose different dynamic landscapes of cis-elements and contribute to alternative splicing. In this study, we found that post-transcriptional multi-intron splicing order in human cells is largely predetermined, with most genes spliced in one or a few predominant orders. Strikingly, these orders were conserved across cell types and stages of motor neuron differentiation. Introns flanking alternatively spliced exons were frequently excised last, after their neighboring introns. Perturbations to the spliceosomal U2 snRNA altered the preferred splicing order of many genes, and these alterations were associated with the retention of other introns in the same transcript. In one gene, early removal of specific introns was sufficient to induce delayed excision of three proximal introns, and this delay was caused by two distinct cis-regulatory mechanisms. Together, our results demonstrate that multi-intron splicing order in human cells is predetermined, is influenced by a component of the spliceosome, and ensures splicing fidelity across long pre-mRNAs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".