Genome-wide identification of functional enhancers and their potential roles in pig breeding
Bibliographic record
Abstract
BACKGROUND: The pig is an economically important livestock species and is a widely applied large animal model in medical research. Enhancers are critical regulatory elements that have fundamental functions in evolution, development and disease. Genome-wide quantification of functional enhancers in the pig is needed. RESULTS: We performed self-transcribing active regulatory region sequencing (STARR-seq) in the porcine kidney epithelial PK15 and testicular ST cell lines, and reliably identified 2576 functional enhancers. Most of these enhancers were located in repetitive sequences and were enriched within silent and lowly expressed genes. Enhancers poorly overlapped with chromatin accessibility regions and were highly enriched in chromatin with the repressive histone modification H3K9me3, which is different from predicted pig enhancers detected using ChIP-seq for H3K27ac or/and H3K4me1 modified histones. This suggests that most pig enhancers identified with STARR-seq are endogenously repressed at the chromatin level and may function during cell type-specific development or at specific developmental stages. Additionally, the PPP3CA gene is associated with the loin muscle area trait and the QKI gene is associated with alkaline phosphatase activity that may be regulated by distal functional enhancers. CONCLUSIONS: In summary, we generated the first functional enhancer map in PK15 and ST cells for the pig genome and highlight its potential roles in pig breeding.
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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) | 0.001 | 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".