Genes on Different Strands Mark Boundaries Associated with Co-regulation Domains
Bibliographic record
Abstract
ABSTRACT Gene regulation is influenced by chromatin conformation. Current models suggest that topologically associating domains (TADs) act as regulatory units, which could also include distinct co-expression domains (CODs) favouring correlated gene expression. We integrated publicly available RNA-seq, ChIP-seq and Hi-C data from A549 cells stimulated with the glucocorticoid dexamethasone to explore how differentially expressed genes are co-regulated among TADs and CODs. Interestingly, we found that gene position and orientation also impact co-regulation. Indeed, divergent and convergent pairs of genes we enriched at sub-TAD boundaries, forming distinct CODs. We also found that genes at COD boundaries were less likely to be separated by structural proteins such as Cohesin and CTCF. A complementary analysis of lung expression quantitative trait loci (eQTL) demonstrated that genes affected by the same variant were more likely to be found on the same strand while lacking a TAD boundary. Taken together, these results suggest a model where gene orientation can provide a boundary between CODs, at the sub-TAD level, thus affecting their likelihood of co-regulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".