Faculty Opinions recommendation of Regulation of alternative splicing by histone modifications.
Bibliographic record
Abstract
Alternative splicing of pre-mRNA is a prominent mechanism to generate protein diversity, yet its regulation is poorly understood.We demonstrated a direct role for histone modifications in alternative splicing.We found distinctive histone modification signatures that correlate with the splicing outcome in a set of human genes, and modulation of histone modifications causes splice site switching.Histone marks affect splicing outcome by influencing the recruitment of splicing regulators via a chromatin-binding protein.These results outline an adaptor system for the reading of histone marks by the pre-mRNA splicing machinery.Most human genes are alternatively spliced in a cell type-and tissue-specific manner, and defects in alternative splicing (AS) contribute to disease (1-4).Pre-mRNA splicing occurs largely cotranscriptionally, and alternative splice site choice is influenced by RNA polymerase II elongation rate, chromatin remodelers, and histone deacetylase inhibitors (5-14).Genome-wide mapping of histone modifications has revealed nonrandom distributions of nucleosomes and several histone modifications across exons (15)(16)(17)(18)(19).Given these observations, we probed the role of histone modifications in AS.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.066 |
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".