Expression Divergence Is Correlated with Sequence Evolution but Not Positive Selection in Conifers
Bibliographic record
Abstract
The evolutionary and genomic determinants of sequence evolution in conifers are poorly understood, and previous studies have found only limited evidence for positive selection. Using RNAseq data, we compared gene expression profiles to patterns of divergence and polymorphism in 44 seedlings of lodgepole pine (Pinus contorta) and 39 seedlings of interior spruce (Picea glauca × engelmannii) to elucidate the evolutionary forces that shape their genomes and their plastic responses to abiotic stress. We found that rapidly diverging genes tend to have greater expression divergence, lower expression levels, reduced levels of synonymous site diversity, and longer proteins than slowly diverging genes. Similar patterns were identified for the untranslated regions, but with some exceptions. We found evidence that genes with low expression levels had a larger fraction of nearly neutral sites, suggesting a primary role for negative selection in determining the association between evolutionary rate and expression level. There was limited evidence for differences in the rate of positive selection among genes with divergent versus conserved expression profiles and some evidence supporting relaxed selection in genes diverging in expression between the species. Finally, we identified a small number of genes that showed evidence of site-specific positive selection using divergence data alone. However, estimates of the proportion of sites fixed by positive selection (α) were in the range of other plant species with large effective population sizes suggesting relatively high rates of adaptive divergence among conifers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".