Patterns and rates of intron divergence between humans and chimpanzees
Bibliographic record
Abstract
BACKGROUND: Introns, which constitute the largest fraction of eukaryotic genes and which had been considered to be neutral sequences, are increasingly acknowledged as having important functions. Several studies have investigated levels of evolutionary constraint along introns and across classes of introns of different length and location within genes. However, thus far these studies have yielded contradictory results. RESULTS: We present the first analysis of human-chimpanzee intron divergence, in which differences in the number of substitutions per intronic site (Ki) can be interpreted as the footprint of different intensities and directions of the pressures of natural selection. Our main findings are as follows: there was a strong positive correlation between intron length and divergence; there was a strong negative correlation between intron length and GC content; and divergence rates vary along introns and depending on their ordinal position within genes (for instance, first introns are more GC rich, longer and more divergent, and divergence is lower at the 3' and 5' ends of all types of introns). CONCLUSION: We show that the higher divergence of first introns is related to their larger size. Also, the lower divergence of short introns suggests that they may harbor a relatively greater proportion of regulatory elements than long introns. Moreover, our results are consistent with the presence of functionally relevant sequences near the 5' and 3' ends of introns. Finally, our findings suggest that other parts of introns may also be under selective constraints.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".