DNA accessibility of chromatosomes quantified by automated image analysis of AFM data
Bibliographic record
Abstract
DNA compaction and accessibility in eukaryotes are governed by nucleosomes and orchestrated through interactions between DNA and DNA-binding proteins. Using QuantAFM, a method for automated image analysis of atomic force microscopy (AFM) data, we performed a detailed statistical analysis of structural properties of mono-nucleosomes. QuantAFM allows fast analysis of AFM images, including image preprocessing, object segmentation, and quantification of different structural parameters to assess DNA accessibility of nucleosomes. A comparison of nucleosomes reconstituted with and without linker histone H1 quantified H1's already described ability of compacting the nucleosome. We further employed nucleosomes bearing two charge-modifying mutations at position R81 and R88 in histone H2A (H2A R81E/R88E) to characterize DNA accessibility under destabilizing conditions. Upon H2A mutation, even in presence of H1, the DNA opening angle at the entry/exit site was increased and the DNA wrapping length around the histone core was reduced. Interestingly, a distinct opening of the less bendable DNA side was observed upon H2A mutation, indicating an enhancement of the intrinsic asymmetry of the Widom-601 nucleosomes. This study validates AFM as a technique to investigate structural parameters of nucleosomes and highlights how the DNA sequence, together with nucleosome modifications, can influence the DNA accessibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".