Nucleosome Positioning

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Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/atac-seq/nucleosome-positioningThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and…

---
name: bio-atac-seq-nucleosome-positioning
description: Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
tool_type: mixed
primary_tool: NucleoATAC
---

## Version Compatibility

Reference examples tested with: NucleoATAC 0.3.4+, ATACseqQC 1.26+, DANPOS 3.1+, samtools 1.19+, pysam 0.22+, pyBigWig 0.3+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+.

NucleoATAC is unmaintained since 2018 but remains the canonical ATAC-specific nucleosome caller; ATACseqQC, DANPOS3, and scprinter are actively developed alternatives. Verify versions before use:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws unexpected errors, introspect the installed package and adapt rather than retrying.

# Nucleosome Positioning

**"Where are the nucleosomes in my ATAC-seq data?"** -> Use fragment-size classes (Tn5 cuts twice through naked DNA generating short fragments; once on each side of a single nucleosome generating ~147+linker fragments) to call nucleosome centers, occupancy scores, and the spacing pattern around regulatory elements.

- CLI: `nucleoatac run --bed regions.bed --bam sample.bam --fasta genome.fa`
- R: `ATACseqQC::splitGAlignmentsByCut()` -> fragment classes; `factorFootprints()` -> per-TF flanking nuc analysis
- CLI: `python danpos.py dpos sample.bam` (alternative; supports MNase, ATAC, DNase)
- Python: `scprinter` for multi-scale nucleosome inference

## Nucleosome Physics for ATAC

A nucleosome wraps ~147 bp DNA in 1.65 turns. Adjacent nucleosomes are separated by 20-50 bp linker; mean **nucleosome repeat length (NRL)** is species-dependent:

| Cell type / organism | NRL | Notes |
|----------------------|-----|-------|
| Yeast S. cerevisiae | 165 bp | Tightly packed; less linker |
| Drosophila S2 | 175-185 bp | |
| Mouse ES cells | 188-196 bp | |
| Human HEK293 / K562 | 196-200 bp | Standard somatic |
| Human cortical neurons | 211 bp | Longer linker |
| Sperm chromatin | 240-250 bp | Tight packaging via protamines |
| Active gene bodies | -10 bp shorter than genome avg | Active transcription disrupts |

NRL determines fragment-size peak positions. ATAC mono-nucleosome peak is at NRL (NOT 147 bp -- that's the protected length; ATAC fragments span the full nucleosome+linker). Di-nuc is at 2x NRL minus a small overlap.

## Fragment-Size Classes (Buenrostro 2013, refined)

| Class | Fragment range | Origin | Use |
|-------|---------------|--------|-----|
| Sub-nucleosomal / NFR | < 100 bp | Two Tn5 cuts in naked accessible DNA | TF binding, footprinting |
| Mono-nucleosomal | 180-247 bp | Tn5 cuts on each side of one nucleosome | Nucleosome positioning |
| Di-nucleosomal | 315-473 bp | Tn5 cuts span two nucleosomes | Phasing, NRL estimation |
| Tri-nucleosomal | 558-615 bp | Three nucleosomes | Heterochromatin / phasing |
| > 700 bp | Rare | Often artefact (chimeric); discard | -- |

Mono-nucleosome window 180-247 bp is the Buenrostro 2013 convention; ATACseqQC uses 180-250. Adjust for the target organism's NRL.

## V-Plot Interpretation

V-plots (fragment-size vs position) are diagnostic. X-axis is position relative to a feature (TSS, motif center); Y-axis is fragment size. Aggregate density forms characteristic patterns:

| Pattern | Visual | Meaning |
|---------|--------|---------|
| V (apex at center, low size at center, increasing flanks) | Classic V | TF or NFR at center, flanking nucleosomes |
| W (two V's flanking center) | W-shape | NFR at center plus +1 / -1 nucleosomes |
| Inverted V (peak at center) | Mountain | Fragment fully enclosed at feature; e.g. nucleosome-bound TF | 
| Flat band at 200 bp | Horizontal line | Constitutive nucleosome (no positioning relative to feature) |
| 10.4 bp helical phasing on V | Sub-peaks at 50, 60, 70, 80 bp size | Tn5 helical preference visible; high-quality library |

V-plots are the primary diagnostic for whether nucleosome-positioning analysis will succeed. Flat-band patterns mean no positioning information; classic V/W patterns mean positioning is recoverable.

## Algorithmic Taxonomy

| Tool | Method | Resolution | Strength | Fails when |
|------|--------|-----------|----------|------------|
| NucleoATAC | Cross-correlation with idealized V-plot template; per-base occupancy + nucleosome calls | Single-bp | ATAC-specific; provides occupancy + fuzziness | Unmaintained since 2018; pegs Python 2/3.6; struggles on chromatin without clear NRL |
| ATACseqQC | Fragment-size split + Tn5-shifted GAlignments + V-plot from BAM | Region-level | R/Bioconductor; integrates with TxDb / motif analysis | No per-base nucleosome calls; visualization-focused |
| DANPOS3 | Smoothing + peak call on cleavage signal; tested on MNase, ATAC, DNase | ~50 bp | Robust differential mode (`dpos`); MNase legacy; broadly maintained | Designed for MNase-Seq; ATAC adaptation needs careful parameter tuning |
| scprinter | CNN multi-scale; resolves co-occurring TF + nucleosome footprints | Single-bp | Modern; single-cell aware; multi-scale | Newer; benchmarks evolving; GPU recommended |
| custom (pysam V-plot) | Fragment counting + 2D density | Region-level | Maximally flexible; reproducible | Requires manual calling logic; slow |

Methodology evolves; verify against current Schep 2015 (NucleoATAC), Chen 2013 (DANPOS), Hu 2025 (scPrinter) before locking pipelines.

## +1 Nucleosome Calling

The +1 nucleosome (first nucleosome downstream of TSS, immediately bordering the NFR) is the most-studied positioning feature. Its position relative to TSS determine

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pysam pyBigWig matplotlib

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

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1. 2026-10-19
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   first audit · source c8c1fc3a2b13

Questions about Nucleosome Positioning

• github.com/GPTomics/bioSkills/tree/main/atac-seq/nucleosome-posi — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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