<i>Drosophila</i> H2A and H2A.Z Nucleosome Sequences Reveal Different Nucleosome Positioning Sequence Patterns
Bibliographic record
Abstract
Nucleosomes are implicated in transcriptional regulation as well as in packing and stabilizing the DNA. Nucleosome positions affect the transcription by impeding or facilitating the binding of transcription factors. The DNA sequence, especially the periodic occurrences of dinucleotides, is a major factor that affects the nucleosome positioning. We analyzed the Drosophila DNA sequences bound by H2A and H2A.Z nucleosomes. Periodic patterns of dinucleotides (weak-weak/strong-strong or purine-purine/pyrimidine-pyrimidine) were identified as WW/SS and RR/YY nucleosome positioning sequence (NPS) patterns. The WW/SS NPS pattern of the H2A nucleosome has a 10-bp period of weak-weak/strong-strong (W = A or T; S = G or C) dinucleotides. The 10-bp periodicity, however, is disrupted in the middle of the sequence. At the dyad, the SS dinucleotide is preferred. On the other hand, the RR/YY NPS pattern has an 18-bp periodicity of purine-purine/pyrimidine-pyrimidine (R = A or G; Y = T or C) dinucleotides. The NPS patterns from H2A.Z nucleosomes differ from the NPS patterns from H2A nucleosomes. The RR/YY pattern of H2A.Z nucleosomes has major peaks shifted by 10 bp deviated from the H2A nucleosome pattern. The H2A and H2A.Z nucleosomes have different sequence preferences. The shifted peaks coincide with DNA regions interacting with the histone loops.
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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".