Effects of Gene Expression on Molecular Evolution in Arabidopsis thaliana and Arabidopsis lyrata
Bibliographic record
Abstract
We analyzed the complete genome sequence of Arabidopsis thaliana and sequence data from 83 genes in the outcrossing A. lyrata, to better understand the role of gene expression on the strength of natural selection on synonymous and replacement sites in Arabidopsis. From data on tRNA gene abundance, we find a good concordance between codon preferences and the relative abundance of isoaccepting tRNAs in the complete A. thaliana genome, consistent with models of translational selection. Both EST-based and new quantitative measures of gene expression (MPSS) suggest that codon preferences derived from information on tRNA abundance are more strongly associated with gene expression than those obtained from multivariate analysis, which provides further support for the hypothesis that codon bias in Arabidopsis is under selection mediated by tRNA abundance. Consistent with previous results, analysis of protein evolution reveals a significant correlation between gene expression level and amino acid substitution rate. Analysis by MPSS estimates of gene expression suggests that this effect is primarily the result of a correlation between the number of tissues in which a gene is expressed and the rate of amino acid substitution, which indicates that the degree of tissue specialization may be an important determinant of the rate of protein evolution in Arabidopsis.
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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 | 0.000 | 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".