A brief histone in time: understanding the combinatorial functions of histone PTMs in the nucleosome context
Bibliographic record
Abstract
It has been over 50 years since Allfrey et al. proposed that histone acetylation regulates RNA synthesis, and the study of histone modifications has progressed at an extraordinary pace for the past two decades. In this review, we provide a perspective on some key events and advances in our understanding of histone modifications. We also highlight reagents and tools from past to present that facilitated progress in this research field. Using histone H3 phosphorylation as an underlying thread, we review the rationale that led to the proposal of the histone code hypothesis, as well as examples that illustrate the concepts of combinatorial histone modifications and cross-talk pathways. We further highlight the importance of investigating these mechanisms in the context of nucleosomes rather than just at the histone level and present current and developing approaches for such studies. Overall, research on histone modifications has yielded great mechanistic insights into the regulation of genomic functions, and extending these studies using nucleosomes will further elucidate the complexity of these pathways in a more physiologically relevant context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".