A comprehensive atlas of pig RNA editome across 23 tissues reveals RNA editing affecting interaction mRNA–miRNAs
Bibliographic record
Abstract
RNA editing is a co-transcriptional/post-transcriptional modification that is mediated by the ADAR enzyme family. Profiling of RNA editing is very limited in pigs. In this study, we collated 3813 RNA-seq data from the public repositories across 23 tissues and carried out comprehensive profiling of RNA editing in pigs. In total, 127,927 A-to-I RNA-editing sites were detected. Our analysis showed that 98.2% of RNA-editing sites were located within repeat regions, primarily within the pig-specific SINE retrotransposon PRE-1/Pre0_SS elements. Subsequently, we focused on analyzing specific RNA-editing sites (SESs) in skeletal muscle tissues. Functional enrichment analyses suggested that they were enriched in signaling pathways associated with muscle cell differentiation, including DMD, MYOD1, and CAV1 genes. Furthermore, we discovered that RNA editing event in the 3'UTR of CFLAR mRNA influenced miR-708-5p binding in this region. In this study, the panoramic RNA-editing landscape of different tissues of pigs was systematically mapped, and RNA-editing sites and genes involved in muscle cell differentiation were identified. In summary, we identified modifications to pig RNA-editing sites and provided candidate targets for further validation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".