Global analysis of alternative splicing differences between humans and chimpanzees
Bibliographic record
Abstract
Alternative splicing is a powerful mechanism affording extensive proteomic and regulatory diversity from a limited repertoire of genes. However, the extent to which alternative splicing has contributed to the evolution of primate species-specific characteristics has not been assessed previously. Using comparative genomics and quantitative microarray profiling, we performed the first global analysis of alternative splicing differences between humans and chimpanzees. Surprisingly, 6%-8% of profiled orthologous exons display pronounced splicing level differences in the corresponding tissues from the two species. Little overlap is observed between the genes associated with alternative splicing differences and the genes that display steady-state transcript level differences, indicating that these layers of regulation have evolved rapidly to affect distinct subsets of genes in humans and chimpanzees. The alternative splicing differences we detected are predicted to affect diverse functions including gene expression, signal transduction, cell death, immune defense, and susceptibility to diseases. Differences in expression at the protein level of the major splice variant of Glutathione S-transferase omega-2 (GSTO2), which functions in the protection against oxidative stress and is associated with human aging-related diseases, suggests that this enzyme is less active in human cells compared with chimpanzee cells. The results of this study thus support an important role for alternative splicing in establishing differences between humans and chimpanzees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".