Bidirectional promoters are the major source of gene activation-associated non-coding RNAs in mammals
Bibliographic record
Abstract
BACKGROUND: The majority of non-coding RNAs (ncRNAs) involved in mRNA metabolism in mammals have been believed to downregulate the corresponding mRNA expression level in a pre- or post-transcriptional manner by forming short or long ncRNA-mRNA duplex structures. Information on non-duplex-forming long ncRNAs is now also rapidly accumulating. To examine the directional properties of transcription at the whole-genome level, we performed directional RNA-seq analysis of mouse and chimpanzee tissue samples. RESULTS: We found that there is only about 1% of the genome where both the top and bottom strands are utilized for transcription, suggesting that RNA-RNA duplexes are not abundantly formed. Focusing on transcription start sites (TSSs) of protein-coding genes revealed that a significant fraction of them contain switching-points that separate antisense- and sense-biased transcription, suggesting that head-to-head transcription is more prevalent than previously thought. More than 90% of head-to-head type promoters contain CpG islands. Moreover, CCG and CGG repeats are significantly enriched in the upstream regions and downstream regions, respectively, of TSSs located in head-to-head type promoters. Genes with tissue-specific promoter-associated ncRNAs (pancRNAs) show a positive correlation between the expression of their pancRNA and mRNA, which is in accord with the proposed role of pancRNA in facultative gene activation, whereas genes with constitutive expression generally lack pancRNAs. CONCLUSIONS: We propose that single-stranded ncRNA resulting from head-to-head transcription at GC-rich sequences regulates tissue-specific gene expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".