Building Nucleosome Positioning Maps: Discovering Hidden Gems
Bibliographic record
Abstract
ABSTRACT Nucleosomes serve as fundamental units of chromatin packaging and play a crucial role as central hubs in epigenetic regulation. Their positions throughout the genome are not random and follow certain patterns, influenced by DNA sequence, histone‐DNA interactions, chromatin physical barriers, nucleosome sliding and unwrapping, and chromatin modifications. There are many experimental techniques for identifying nucleosome positions, but these methods often involve a trade‐off between achieving high resolution and covering the entire genome. In this regard, computational approaches may offer a fast alternative, with the benefit of aiding experimental analysis by denoising data, refining nucleosome boundaries, and identifying features critical for nucleosome positioning. Moreover, computational predictions enable the integration of nucleosome positioning data with other genomic and epigenomic datasets, providing a more comprehensive view of chromatin organization and gene regulation. In this review, we focus on various nucleosome positioning methods, including experimental techniques of nucleosome boundaries identification and in silico methods of nucleosome positioning data denoising and prediction of nucleosome positioning from the DNA sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".