Chromatin states shape insertion profiles of the piggyBac, Tol2 and Sleeping Beauty transposons and murine leukemia virus
Bibliographic record
Abstract
DNA transposons and retroviruses are versatile tools in functional genomics and gene therapy. To facilitate their application, we conducted a genome-wide insertion site profiling of the piggyBac (PB), Tol2 and Sleeping Beauty (SB) transposons and the murine leukemia virus (MLV) in mouse embryonic stem cells (ESCs). PB and MLV preferred highly expressed genes, whereas Tol2 and SB preferred weakly expressed genes. However, correlations with DNase I hypersensitive sites were different for all vectors, indicating that chromatin accessibility is not the sole determinant. Therefore, we analysed various chromatin states. PB and MLV highly correlated with Cohesin, Mediator and ESC-specific transcription factors. Notably, CTCF sites were correlated with PB but not with MLV, suggesting MLV prefers smaller promoter-enhancer loops, whereas PB insertion encompasses larger chromatin loops termed topologically associating domains. Tol2 also correlated with Cohesin and CTCF. However, correlations with ESC-specific transcription factors were weaker, suggesting that Tol2 prefers transcriptionally weak chromatin loops. Consistently, Tol2 insertions were associated with bivalent histone modifications characteristic of silent and inducible loci. SB showed minimum preference to all chromatin states, suggesting the least adverse effect on adjacent genes. These results will be useful for vector selection for various applications.
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 | 0.000 |
| 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) | 0.001 | 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".