Retropseudogenes derived from the human Ro/SS-A autoantigen-associated hY RNAs
Bibliographic record
Abstract
We report the characterization in the human genome of 966 pseudogenes derived from the four human Y (hY) RNAs, components of the Ro/SS-A autoantigen. About 95% of the Y RNA pseudogenes are found in corresponding locations on the chimpanzee and human chromosomes. On the contrary, Y pseudogenes in mice are both infrequent and found in different genomic regions. In addition to this rodent/primate discrepancy, the conservation of hY pseudogenes relative to hY genes suggests that they occurred after rodent/primate divergence. Flanking regions of hY pseudogenes contain convincing evidence for involvement of the L1 retrotransposition machinery. Although Alu elements are found in close proximity to most hY pseudogenes, these are not chimeric retrogenes. Point mutations in hY RNA transcripts specifically affecting binding of Ro60 protein likely contributed to their selection for direct trans retrotransposition. This represents a novel requirement for the selection of specific RNAs for their genomic integration by the L1 retrotransposition machinery. Over 40% of the hY pseudogenes are found in intronic regions of protein-coding genes. Considering the functions of proteins known to bind subsets of hY RNAs, hY pseudogenes constitute a new class of L1-dependent non-autonomous retroelements, potentially involved in post-transcriptional regulation of 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 | 0.000 |
| 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) | 0.002 | 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".