Systematic identification and characterization of regulatory elements derived from human endogenous retroviruses
Bibliographic record
Abstract
Human endogenous retroviruses (HERVs) and other long terminal repeat (LTR)-type retrotransposons (HERV/LTRs) have regulatory elements that possibly influence the transcription of host genes. We systematically identified and characterized these regulatory elements based on publicly available datasets of ChIP-Seq of 97 transcription factors (TFs) provided by ENCODE and Roadmap Epigenomics projects. We determined transcription factor-binding sites (TFBSs) using the ChIP-Seq datasets and identified TFBSs observed on HERV/LTR sequences (HERV-TFBSs). Overall, 794,972 HERV-TFBSs were identified. Subsequently, we identified "HERV/LTR-shared regulatory element (HSRE)," defined as a TF-binding motif in HERV-TFBSs, shared within a substantial fraction of a HERV/LTR type. HSREs could be an indication that the regulatory elements of HERV/LTRs are present before their insertions. We identified 2,201 HSREs, comprising specific associations of 354 HERV/LTRs and 84 TFs. Clustering analysis showed that HERV/LTRs can be grouped according to the TF binding patterns; HERV/LTR groups bounded to pluripotent TFs (e.g., SOX2, POU5F1, and NANOG), embryonic endoderm/mesendoderm TFs (e.g., GATA4/6, SOX17, and FOXA1/2), hematopoietic TFs (e.g., SPI1 (PU1), GATA1/2, and TAL1), and CTCF were identified. Regulatory elements of HERV/LTRs tended to locate nearby and/or interact three-dimensionally with the genes involved in immune responses, indicating that the regulatory elements play an important role in controlling the immune regulatory network. Further, we demonstrated subgroup-specific TF binding within LTR7, LTR5B, and LTR5_Hs, indicating that gains or losses of the regulatory elements occurred during genomic invasions of the HERV/LTRs. Finally, we constructed dbHERV-REs, an interactive database of HERV/LTR regulatory elements (http://herv-tfbs.com/). This study provides fundamental information in understanding the impact of HERV/LTRs on host transcription, and offers insights into the transcriptional modulation systems of HERV/LTRs and ancestral HERVs.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".