The importance of alternative splicing in adaptive evolution
Bibliographic record
Abstract
Although alternative splicing is a ubiquitous co-transcriptional gene regulatory mechanism in plants, animals and fungi, its contribution to evolutionary transitions is understudied. Alternative splicing enables different mRNA isoforms to be generated from the same gene, expanding transcriptomic and thus proteomic diversity. While the role of gene expression variation in adaptive evolution is widely accepted, biologists still debate the functional impact of alternative isoforms on phenotype. In light of recent empirical research linking splice variation to ecological adaptations, we propose that alternative splicing is an important substrate for adaptive evolution and speciation, particularly at short timescales. In this article we synthesise what is known about the role of alternative splicing in adaptive evolution. We discuss the contribution of standing splice variation to phenotypic plasticity and how hybridisation can produce novel splice forms. Going forwards, we propose that alternative splicing be included as a standard analysis alongside gene expression analysis so we can better understand of how alternative splicing contributes to adaptive divergence at the micro- and macroevolutionary levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".