Nucleosome Density ChIP-Seq Identifies Distinct Chromatin Modification Signatures Associated with MNase Accessibility
Bibliographic record
Abstract
Nucleosome position, density, and post-translational modification are widely accepted components of mechanisms regulating DNA transcription but still incompletely understood. We present a modified native ChIP-seq method combined with an analytical framework that allows MNase accessibility to be integrated with histone modification profiles. Application of this methodology to the primitive (CD34+) subset of normal human cord blood cells enabled genomic regions enriched in one versus two nucleosomes marked by histone 3 lysine 4 trimethylation (H3K4me3) and/or histone 3 lysine 27 trimethylation (H3K27me3) to be associated with their transcriptional and DNA methylation states. From this analysis, we defined four classes of promoter-specific profiles and demonstrated that a majority of bivalent marked promoters are heterogeneously marked at a single-cell level in this primitive cell type. Interestingly, extension of this approach to human embryonic stem cells revealed an altered relationship between chromatin modification state and nucleosome content at promoters, suggesting developmental stage-specific organization of histone methylation states.
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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".