The Majority of Primate-Specific Regulatory Sequences Are Derived from Transposable Elements
Bibliographic record
Abstract
Although emerging evidence suggests that transposable elements (TEs) have contributed novel regulatory elements to the human genome, their global impact on transcriptional networks remains largely uncharacterized. Here we show that TEs have contributed to the human genome nearly half of its active elements. Using DNase I hypersensitivity data sets from ENCODE in normal, embryonic, and cancer cells, we found that 44% of open chromatin regions were in TEs and that this proportion reached 63% for primate-specific regions. We also showed that distinct subfamilies of endogenous retroviruses (ERVs) contributed significantly more accessible regions than expected by chance, with up to 80% of their instances in open chromatin. Based on these results, we further characterized 2,150 TE subfamily-transcription factor pairs that were bound in vivo or enriched for specific binding motifs, and observed that TEs contributing to open chromatin had higher levels of sequence conservation. We also showed that thousands of ERV-derived sequences were activated in a cell type-specific manner, especially in embryonic and cancer cells, and we demonstrated that this activity was associated with cell type-specific expression of neighboring genes. Taken together, these results demonstrate that TEs, and in particular ERVs, have contributed hundreds of thousands of novel regulatory elements to the primate lineage and reshaped the human transcriptional landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.004 | 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".