Genome-wide quantification of the effects of DNA methylation on human gene regulation
Bibliographic record
Abstract
Abstract Changes in DNA methylation are important in development and disease, but not all regulatory elements act in a methylation-dependent (MD) manner. Here, we developed mSTARR-seq, a high-throughput approach to quantify the effects of DNA methylation on regulatory element function. We assay MD activity in 14% of the euchromatic human genome, identify 2,143 MD regulatory elements, and predict MD activity using sequence and chromatin state information. We identify transcription factors associated with higher activity in unmethylated or methylated states, including an association between pioneer transcription factors and methylated DNA. Finally, we use mSTARR-seq to predict DNA methylation-gene expression correlations in primary cells. Our findings provide a map of MD regulatory activity across the human genome, facilitating interpretation of the many emerging associations between methylation and trait variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".