The Competition between Splicing and 3′ Processing Shapes the Human Transcriptome
Bibliographic record
Abstract
Eukaryotic pre-mRNA processing steps, including splicing and 3' processing, are tightly coordinated, yet the underlying mechanisms remain incompletely understood. U1 snRNP has been proposed to inhibit 3' processing at intronic polyadenylation (IPA) sites through a splicing-independent mechanism termed telescripting. In contrast, we discovered that disrupting splicing by using six different methods-targeting various key components such as U1 snRNP, U2 snRNP, U2AF, and SF3b-activates 3' processing at thousands of IPA sites. Notably, splicing inhibition, especially of U1 snRNP, induced widespread premature transcription termination within gene bodies through both IPA-coupled and IPA-independent mechanisms. Inhibition of different splicing factors activated overlapping and distinct sets of IPA sites, reflecting their specific contributions to transcription and spliceosome function. Conversely, inhibition of 3' processing enhanced splicing globally. These findings support a model in which splicing and 3' processing are competing processes that intersect with transcription to shape the transcriptome landscape.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".