Similar Selective Factors Affect Both between-Gene and between-Exon Divergence in Drosophila
Bibliographic record
Abstract
As a consequence of alternative splicing, a gene's exons will have different frequencies of inclusion into mature mRNA and different patterns of expression. These differences affect their patterns of evolutionary divergence. Using the recently reannotated genome of Drosophila melanogaster and the genome sequences of four closely related species of the melanogaster subgroup, we investigated the effect of alternative splicing, inclusion level (defined as the number of transcripts an exon is found in), and expression pattern on exon evolution across divergence times ranging from 1 to 12.5 Ma. Genes undergoing alternative splicing have a broader pattern of expression associated with a lower divergence rate in comparison with genes with a single annotated protein isoform. Within genes undergoing alternative splicing, we report a significant effect of inclusion level on exon evolution, as alternatively spliced exons are less conserved than constitutively spliced exons. More generally, there are significant negative correlations between inclusion level and exon evolutionary rates that can be associated with relaxation of selection. A significant effect of expression pattern on evolution rates is also observed. Overall, we found that similar selective factors such as the expression level and the pattern of expression are affecting both gene and exon evolution.
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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 | 0.001 |
| 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 | 0.000 | 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".