Revisiting the Contribution of cis-Elements to Expression Divergence between Duplicated Genes: The Role of Chromatin Structure
Bibliographic record
Abstract
Although divergence in expression is thought to be a hallmark of functional dispersal between paralogs postduplication, there is currently a limited understanding of the mechanisms underlying the necessary transcriptional alterations as recent studies have suggested that only a very small proportion of expression variation could be explained by transcriptional variation between paralogs. To further this understanding, we examined comprehensively curated regulatory interactions and genomewide nucleosome occupancy in budding yeast to specifically determine the contribution of cis-elements to expression divergence between extant duplicates. We found that divergence in activation by transcription factors plays a more important role in expression divergence of paralogs than previously appreciated; further, analysis of promoter chromatin structure demonstrated that differential nucleosome organization is coupled with divergent expression of paralogs. By incorporating information of cis-elements encoding transcriptional regulation and chromatin structure, we improved the fraction of expression variation that was previously shown to be explained based on known cis-transcriptional effects by approximately 3-fold. Taken together, our analysis highlights the importance of chromatin divergence involved in expression evolution between paralogs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".