Genome-wide quantification of the effects of DNA methylation on human gene regulation
Bibliographic record
Abstract
Changes in DNA methylation are involved in development, disease, and the response to environmental conditions. However, not all regulatory elements are functionally methylation-dependent (MD). Here, we report a method, mSTARR-seq, that assesses the causal effects of DNA methylation on regulatory activity at hundreds of thousands of fragments (millions of CpG sites) simultaneously. Using mSTARR-seq, we identify thousands of MD regulatory elements in the human genome. MD activity is partially predictable using sequence and chromatin state information, and distinct transcription factors are associated with higher activity in unmethylated versus methylated DNA. Further, pioneer TFs linked to higher activity in the methylated state appear to drive demethylation of experimentally methylated sites. MD regulatory elements also predict methylation-gene expression relationships across individuals, where they are 1.6x enriched among sites with strong negative correlations. mSTARR-seq thus provides a map of MD regulatory activity in the human genome and facilitates interpretation of differential methylation studies.
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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 | 0.000 | 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) | 0.000 | 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".