Extensive relationship between antisense transcription and alternative splicing in the human genome
Bibliographic record
Abstract
To analyze the relationship between antisense transcription and alternative splicing, we developed a computational approach for the detection of antisense-correlated exon splicing events using Affymetrix exon array data. Our analysis of expression data from 176 lymphoblastoid cell lines revealed that the majority of expressed sense-antisense genes exhibited alternative splicing events that were correlated to the expression of the antisense gene. Most of these events occurred in areas of sense-antisense (SAS) gene overlap, which were significantly enriched in both exons and nucleosome occupancy levels relative to nonoverlapping regions of the same genes. Nucleosome occupancy was highly correlated with Pol II abundance across overlapping regions and with concomitant increases in local alternative exon usage. These results are consistent with an antisense transcription-mediated mechanism of splicing regulation in normal human cells. A comparison of the prevalence of antisense-correlated splicing events between individuals of Mormon versus African descent revealed population-specific events that may indicate the continued evolution of new SAS loci. Furthermore, the presence of antisense transcription was correlated to alternative splicing across multiple metazoan species, suggesting that it may be a conserved mechanism contributing to splicing regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".