Conserved expression of transposon-derived non-coding transcripts in primate stem cells
Bibliographic record
Abstract
BACKGROUND: A significant portion of expressed non-coding RNAs in human cells is derived from transposable elements (TEs). Moreover, it has been shown that various long non-coding RNAs (lncRNAs), which come from the human endogenous retrovirus subfamily H (HERVH), are not only expressed but required for pluripotency in human embryonic stem cells (hESCs). RESULTS: To identify additional TE-derived functional non-coding transcripts, we generated RNA-seq data from induced pluripotent stem cells (iPSCs) of four primate species (human, chimpanzee, gorilla, and rhesus) and searched for transcripts whose expression was conserved. We observed that about 30% of TE instances expressed in human iPSCs had orthologous TE instances that were also expressed in chimpanzee and gorilla. Notably, our analysis revealed a number of repeat families with highly conserved expression profiles including HERVH but also MER53, which is known to be the source of a placental-specific family of microRNAs (miRNAs). We also identified a number of repeat families from all classes of TEs, including MLT1-type and Tigger families, that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved. CONCLUSIONS: Together, these results describe TE families and TE-derived lncRNAs whose conserved expression patterns can be used to identify what are likely functional TE-derived non-coding transcripts in primate iPSCs.
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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 | 0.001 | 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".