Transposable Elements Are Major Contributors to the Origin, Diversification, and Regulation of Vertebrate Long Noncoding RNAs
Bibliographic record
Abstract
Advances in vertebrate genomics have uncovered thousands of loci encoding long noncoding RNAs (lncRNAs). While progress has been made in elucidating the regulatory functions of lncRNAs, little is known about their origins and evolution. Here we explore the contribution of transposable elements (TEs) to the makeup and regulation of lncRNAs in human, mouse, and zebrafish. Surprisingly, TEs occur in more than two thirds of mature lncRNA transcripts and account for a substantial portion of total lncRNA sequence (~30% in human), whereas they seldom occur in protein-coding transcripts. While TEs contribute less to lncRNA exons than expected, several TE families are strongly enriched in lncRNAs. There is also substantial interspecific variation in the coverage and types of TEs embedded in lncRNAs, partially reflecting differences in the TE landscapes of the genomes surveyed. In human, TE sequences in lncRNAs evolve under greater evolutionary constraint than their non-TE sequences, than their intronic TEs, or than random DNA. Consistent with functional constraint, we found that TEs contribute signals essential for the biogenesis of many lncRNAs, including ~30,000 unique sites for transcription initiation, splicing, or polyadenylation in human. In addition, we identified ~35,000 TEs marked as open chromatin located within 10 kb upstream of lncRNA genes. The density of these marks in one cell type correlate with elevated expression of the downstream lncRNA in the same cell type, suggesting that these TEs contribute to cis-regulation. These global trends are recapitulated in several lncRNAs with established functions. Finally a subset of TEs embedded in lncRNAs are subject to RNA editing and predicted to form secondary structures likely important for function. In conclusion, TEs are nearly ubiquitous in lncRNAs and have played an important role in the lineage-specific diversification of vertebrate lncRNA repertoires.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".