Uncovering the multi-layer cis-regulatory landscape of rice via integrative nascent RNA analysis
Bibliographic record
Abstract
BACKGROUND: Efforts to characterize regulatory elements in plant genomes traditionally rely on evolutionary conservation and chromatin accessibility. Recently, intergenic bi-directional nascent transcript has emerged as a putative hallmark of active enhancers. Here, we integrate these approaches to better define the cis-regulatory landscape of the rice genome. RESULTS: In juvenile leaf tissues of the Azucena rice variety, we analyze conserved noncoding sequences, intergenic bi-directional transcripts, and regions of open chromatin. These three features highlight distinct classes of regulatory targets, each exhibiting complexity and regulatory roles. Conserved noncoding sequences are associated with more complex regulatory interactions, while regions marked by chromatin accessibility or bi-directional nascent transcription tend to promote more stable regulatory activity. Some transcribed regulatory sites harbor elements linked to transposable element silencing, whereas others correlate with increased expression of nearby genes, pointing to candidate transcribed regulatory elements. We further identified molecular interactions between genic regions and intergenic transcribed regulatory elements using 3-dimensional chromatin contact data, we identify physical interactions between transcribed intergenic regions and genic regions. These interactions often co-localize with expression quantitative trait loci and coincide with increased transcription, further supporting a regulatory role. CONCLUSIONS: Our integrative analysis reveals multiple distinct classes of regulatory elements in the rice genome, with overlapping but non-identical targets and signatures. Many candidate elements share features consistent with transcriptional enhancement, though the specific criteria for defining active enhancers in plants require further characterization. These findings underscore the importance of using complementary genomic signals to discover and characterize functionally diverse regulatory elements in plant genomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".