Nascent transcription and the associated <i>cis</i> -regulatory landscape in rice
Bibliographic record
Abstract
Abstract Background Plant genomes encode transcripts that require spatio-temporal regulation for proper cellular function, and a large fraction of the regulators can be found in intergenic regions. In animals, distal intergenic regions described as enhancer regions are actively transcribed as enhancer RNAs (eRNAs); the existence of eRNAs in plants has only been fairly recently documented. In this study, we evaluated with high sensitivity the synthesis of eRNAs that arise at genomic elements both distal and proximal to genes by combining PRO-seq with chromatin accessibility, histone modification, and methylation profiles in rice. Results We found that regions defined as transcribed intergenic regions are widespread in the rice genome, and many likely harbor transcribed regulatory elements. In addition to displaying evidence of selective constraint, the presence of these transcribed regulatory elements are correlated with an increase in nearby gene expression. We further identified molecular interactions between genic regions and intergenic transcribed regulatory elements using 3D chromosomal contact data, and found that these interactions were both associated with eQTLs as well as promoting transcription. We also compared the profile of accessible chromatin regions to our identified transcribed regulatory elements, and found less overlap than expected. Finally, we also observed that transcribed intergenic regions that overlapped partially or entirely with repetitive elements had a propensity to be enriched for cytosine methylation, and were likely involved in TE silencing rather than promoting gene transcription. Conclusion The characterization of eRNAs in the rice genome reveals that many share features of enhancers and are associated with transcription regulation, which could make them compelling candidate enhancer elements.
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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".