Cryptic endogenous retrovirus subfamilies in the primate lineage
Bibliographic record
Abstract
ABSTRACT Many endogenous retroviruses (ERVs) in the human genome are primate-specific and have contributed novel cis-regulatory elements and transcripts. However, current approaches for classifying and annotating ERVs and their long terminal repeats (LTRs) have limited resolution and are inaccurate. Here, we developed a new annotation based on phylogenetic analysis and cross-species conservation. Focusing on the evolutionary young MER11A/B/C subfamilies, we revealed the presence of 4 ‘new subfamilies’, that better explained the epigenetic heterogeneity observed within the MER11 instances, suggesting a new annotation for 412 (19.8%) of these repeat elements. Furthermore, we functionally validated the regulatory potential of these four new subfamilies using a massively parallel reporter assay (MPRA), which also identified motifs associated with their differential activities. Combining MPRA with new annotations across primates revealed an apes-specific gain of SOX related motifs through a single-nucleotide deletion. Lastly, by applying our approach across 53 simian-enriched LTR subfamilies, we defined a total of 75 new subfamilies and found that 3,807 (30.0%) instances from 26 LTR subfamilies could be categorized into a novel annotation, many of which with a distinct epigenetic profile. Thus, with our refined annotation of simian-enriched LTRs, it will be possible to better understand the evolution in primate genomes and potentially identify new roles for ERVs and their LTRs in the hosts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".