bio-atac-seq-nucleosome-positioning

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 determines transcription initiation kinetics.

Canonical +1 position: +50 to +60 bp from TSS in metazoa; -100 to -120 bp from TATA in yeast; varies by gene type (Pol II vs Pol III, housekeeping vs developmental).

Calling strategy:

Goal: Identify each gene's +1 nucleosome, the first nucleosome downstream of the TSS that flanks the NFR.

Approach: Build gene-body intervals slopped around TSSs, run NucleoATAC over them to call per-base nucleosome positions, then pick the most-downstream-of-TSS nucleosome per gene.

# 1. Define gene-body intervals
bedtools slop -i genes.bed -g chrom.sizes -l 200 -r 1000 > gene_bodies.bed

# 2. Run NucleoATAC
nucleoatac run --bed gene_bodies.bed --bam sample.dedup.bam --fasta genome.fa \
    --out tss_nuc/ --cores 8

# 3. The first nucleosome downstream of each TSS in nucpos.bed is +1

A failure to detect a clear +1 peak in aggregate V-plot suggests TSS annotation is wrong or library is over-transposed.

Per-Tool Failure Modes

NucleoATAC -- Region size and depth dependence

Trigger: Short region BED (< 1 kb per region); shallow library (< 25M nuclear reads).

Mechanism: NucleoATAC fits an idealized V-plot template per region. Short regions provide too few fragments for stable correlation; shallow data provides noisy templates.

Symptom: No nucleosome calls in shallow regions; "occupancy" track is flat at zero.

Fix: Use regions >= 500 bp; merge adjacent peaks via bedtools to ensure region size; require >= 30M nuclear reads.

NucleoATAC -- Maintenance status

Trigger: Installing NucleoATAC in 2025+.

Mechanism: Last release 2018; pegs Python 3.6 in some installs; depends on outdated NumPy API.

Fix: Use a dedicated conda env (conda create -n nucleoatac python=3.7 numpy=1.18 scipy=1.5 pysam); accept it works but is no longer updated. Consider scprinter or DANPOS3 alternatives for new projects.

ATACseqQC factorFootprints -- Asymmetric nucleosome flanks

Trigger: Pioneer-factor binding sites where one face is on a nucleosome.

Mechanism: factorFootprints assumes symmetric flanking nucleosomes. Pioneer TFs (FOXA1, GATA) only have nucleosome on one side -> asymmetric output.

Symptom: Single shoulder in flanking signal; unbalanced V-plot.

Fix: Treat asymmetry as biological signal, not artefact. For pioneers, use stranded analysis.

DANPOS dpos with default parameters -- ATAC mismatch

Trigger: Running python danpos.py dpos with MNase defaults on ATAC.

Mechanism: DANPOS3's smoothing window and peak-calling defaults are tuned for MNase signal (smoother coverage). ATAC's sharper signal requires --smooth_width 80 --width 145 or similar; otherwise calls are over-smoothed.

Fix: Use ATAC-tuned parameters. See DANPOS docs for ATAC-specific recipe; or use NucleoATAC instead.

Mono-nucleosome filter window mis-set

Trigger: Using strict 147 bp filter for mono-nuc fraction; using 100-180 bp instead of 180-247.

Mechanism: Mono-nuc fragments are 180-247 bp because they span the nucleosome AND a linker. Filtering tighter excludes the legitimate signal.

Symptom: Mono-nuc count is much lower than expected (< 30% of NFR count).

Fix: Use Buenrostro 2013 windows: NFR < 100, mono 180-247, di 315-473.

Decision Tree by Goal

Goal	Recommended workflow
Per-base nucleosome occupancy track	NucleoATAC (with caveat about maintenance); or scprinter
V-plot at TSS or motif center	ATACseqQC vPlot
Differential nucleosome positioning between conditions	DANPOS3 dpos
+1 nucleosome calling at all genes	NucleoATAC + post-process to first nuc downstream of TSS
Single-cell nucleosome positioning	scprinter
Quick fragment-size QC plot	ATACseqQC fragSizeDist
NRL estimation	Custom Fourier / autocorrelation on fragment-end coverage
Nucleosome-aware peak calling	MACS3 hmmratac (peak-calling skill)

Estimating NRL from Fragment-Size Distribution

Goal: Estimate the nucleosome repeat length from ATAC fragment-size periodicity.

Approach: Collect proper-pair fragment lengths from the BAM, build a histogram, find density peaks via scipy find_peaks, and read off the mono-nucleosome peak position within the 150-250 bp window.

import numpy as np, pysam
from scipy.signal import find_peaks

bam = pysam.AlignmentFile('sample.bam', 'rb')
frag_lengths = [abs(r.template_length) for r in bam.fetch()
                if r.is_proper_pair and r.is_read1 and 0 < abs(r.template_length) < 1500]
hist, edges = np.histogram(frag_lengths, bins=300, range=(0, 1500))
peaks, _ = find_peaks(hist, distance=50, prominence=hist.max() * 0.05)
peak_positions = edges[peaks] + (edges[1] - edges[0]) / 2
# Mono peak should be ~NRL; di peak ~2*NRL
mono = peak_positions[(peak_positions > 150) & (peak_positions < 250)][0]
print(f'Estimated NRL: {mono:.0f} bp')

NRL inferred this way is approximate; for precision use autocorrelation on cumulative cleavage coverage instead.

V-Plot in Python

Goal: Build a fragment-size-by-position density plot to diagnose nucleosome positioning around a feature.

Approach: Iterate proper-pair fragments in a flank window around each feature center, accumulate counts into a (fragment_size x position) grid, and render the 2D density.

import numpy as np, pysam, matplotlib.pyplot as plt

def vplot(bam_path, regions_bed, max_size=600, flank=1000):
    bam = pysam.AlignmentFile(bam_path, 'rb')
    grid = np.zeros((max_size, 2 * flank))
    for line in open(regions_bed):
        chrom, start, *_ = line.strip().split('\t')
        center = int(start)
        for r in bam.fetch(chrom, max(0, center - flank), center + flank):
            if not r.is_proper_pair or not r.is_read1: continue
            size = abs(r.template_length)
            if size <= 0 or size >= max_size: continue
            frag_center = r.reference_start + size // 2
            x = frag_center - center + flank
            if 0 <= x < 2 * flank:
                grid[size, x] += 1
    return grid

g = vplot('sample.bam', 'tss.bed')
plt.imshow(g, aspect='auto', origin='lower', cmap='magma',
           extent=[-1000, 1000, 0, 600])
plt.xlabel('Distance from feature (bp)')
plt.ylabel('Fragment size (bp)')
plt.savefig(, dpi=, bbox_inches=)

V-plot quality is the most useful diagnostic before nucleosome calling. Classic V at TSS = positioning info recoverable; flat band = not.

Differential Nucleosome Positioning (DANPOS3 dpos)

# Compare control vs treatment nucleosome positions.
# The sample pair is the POSITIONAL argument (a:b means a minus b); -b is for background/input to
# subtract, and -c specifies a read-count to normalize to (an integer, NOT a control BAM path).
python danpos.py dpos condition2.bam:condition1.bam \
    -o danpos_diff/ \
    --paired 1 \
    --smooth_width 80

DANPOS reports four event types: shifted nucleosomes, gained, lost, fuzziness change. ENCODE has no official threshold; require >= 30 bp shift and FDR < 0.05 for nucleosome shift calls.

Full ATAC-tuned DANPOS3 recipe:

# --width 145: summit-scan window (DANPOS -jw/--width; default 40)
# --smooth_width 80: smoothing kernel width (DANPOS -z; default 20, widened for ATAC)
# -jd 145: min distance between adjacent nuc calls (single-dash short flag)
# --pheight 1e-5: occupancy P-value cutoff (DANPOS -p; dpos default 0). -q/--height is the separate density cutoff
# --frsz 200: fragment size used (mono-nuc)
python danpos.py dpos sample.bam \
    --paired 1 \
    --width 145 \
    --smooth_width 80 \
    -jd 145 \
    --pheight 1e-5 \
    --frsz 200 \
    --out danpos_out/

Verify exact flags with python danpos.py dpos --help; DANPOS3 (github.com/sklasfeld/DANPOS3) is invoked as python danpos.py, not a danpos3 executable, and installs from GitHub (the bioconda danpos package is DANPOS2). Its documentation has been spotty and flag names can drift across releases.

Adapted from DANPOS3 docs for ATAC; --smooth_width 80 widens the smoothing kernel to match ATAC's sharper signal vs MNase's broader cleavage. -jd 145 (single-dash short, alternative --distance 145) enforces nucleosome spacing >= 145 bp (one nucleosome footprint).

Histone Variant Detection from Fragment Size

Trigger: Suspected H2A.Z- or H3.3-containing nucleosomes; differential nucleosome composition between conditions.

Mechanism: H2A.Z replacement of H2A produces nucleosomes with weaker DNA-histone interaction (lower thermal stability); the H2A.Z population tends toward shorter fragment sizes than canonical H2A nucleosomes. H3.3 replacement is more subtle, but H3.3-H2A.Z double-variant nucleosomes are particularly destabilized at active promoters (Jin 2009 Nat Genet 41:941-945).

Detection: Aggregate fragment-size distribution at H2A.Z ChIP-seq peaks vs H3K4me3-only peaks; the H2A.Z population shows mean fragment size ~10 bp shorter. ATAC alone CANNOT definitively call H2A.Z; H2A.Z ChIP-seq is needed for ground truth. ATAC fragment-size analysis is a hypothesis generator.

# Per-region fragment-size mean as H2A.Z indicator
def region_frag_size(bam, region):
    sizes = [abs(r.template_length) for r in bam.fetch(*region)
             if r.is_proper_pair and r.is_read1 and 100 < abs(r.template_length) < 300]
    return np.mean(sizes) if sizes else np.nan

# Compare H2A.Z-positive vs H2A.Z-negative TSSs

Long-Read Single-Molecule Chromatin (Fiber-seq, NanoNOMe)

Alternative to short-read ATAC for nucleosome positioning:

Method	Tech	Resolution	Strength
Fiber-seq (Stergachis 2020)	PacBio HiFi + DNA methylation footprinting	Per-molecule single-bp	Reads continuous chromatin fiber up to 20 kb; resolves haplotype-specific positioning
NanoNOMe (Lee 2020 Nat Methods 17:1191-1199)	Nanopore + GpC methyltransferase	Per-molecule single-bp	Same single-molecule but cheaper than PacBio

Fiber-seq can detect nucleosome occupancy directly per single chromatin molecule (no aggregation needed). Resolves cell-cycle-dependent and stochastic positioning that bulk ATAC averages out. Preferred for fine-structure analysis of regulatory elements.

For most labs, short-read ATAC + NucleoATAC remains primary; Fiber-seq is special-purpose when single-molecule resolution is essential.

Nucleosome Fuzziness

Fuzziness measures how sharply positioned a nucleosome is across cells. Defined as the standard deviation of per-cell nucleosome center positions.

Fuzziness range	Interpretation
< 20 bp	Sharply positioned (rare in metazoa; common at +1 in yeast)
20-50 bp	Standard well-positioned
50-100 bp	Fuzzy; constitutive but non-stable
> 100 bp	Effectively unpositioned

These ranges are field-convention bands (drawn from NucleoATAC / DANPOS practice); no single primary paper prescribes them -- verify against tool-specific documentation when reporting.

NucleoATAC reports per-nucleosome fuzziness in the fuzziness column (column 13) of .nucpos.bed -- a measure of how wide the signal peak is; well-positioned nucleosomes show low fuzziness (~20-50 bp), consistent with the table above.

Common Errors

Error / symptom	Cause	Solution
nucleoatac run ImportError on numpy	Python 3.6 incompatibility	Use dedicated conda env with pinned versions
Empty .nucpos.bed output	Region BED too short or library too shallow	Verify region size >= 500 bp; depth >= 30M
V-plot shows horizontal band, no V	No positioning info; library over-transposed or wrong feature center	Check feature BED; verify TSS positions are correct
Mono-nuc count very low	Wrong fragment-size window (used 100-180 instead of 180-247)	Use Buenrostro windows
factorFootprints asymmetric	Pioneer TF; this is biological	Treat as signal, not artefact
DANPOS calls many shifts	MNase parameters used on ATAC	Tune --smooth_width 80 --width 145 for ATAC
splitGAlignmentsByCut error in ATACseqQC	BAM is single-end	Mono-nuc analysis requires paired-end
+1 nucleosome not visible at TSS aggregate	TSS list mixes coding + non-coding strands; or wrong genome build	Restrict to protein-coding TSSs in matched build

References

• Schep AN et al 2015 Genome Res 25:1757 (NucleoATAC)
• Chen K et al 2013 Genome Res 23:341 (DANPOS)
• Buenrostro JD et al 2013 Nat Methods 10:1213 (ATAC fragment-size classes)
• Ou J et al 2018 BMC Genomics 19:169 (ATACseqQC)
• Hu Y et al 2025 Nature 638:779 (scPrinter/PRINT; multiscale footprints)
• Mavrich TN et al 2008 Nature 453:358 (+1 nucleosome positioning)
• Voong LN et al 2016 Cell 167:1555-1570 (high-resolution chemical nucleosome mapping)
• Jin C et al 2009 Nat Genet 41:941 (H3.3/H2A.Z double-variant nucleosome instability at active regions)
• Teif VB et al 2012 Nat Struct Mol Biol 19:1185 (NRL variation across cell types)

Related Skills

• atac-seq/atac-qc - Fragment-size periodicity QC
• atac-seq/atac-peak-calling - Nucleosome-aware MACS3 hmmratac
• atac-seq/footprinting - Per-TF flanking nucleosome analysis
• atac-seq/single-cell-atac - scprinter for sc nucleosome positioning
• chip-seq/peak-annotation - Annotate nucleosome positions to genes
• alignment-files/bam-statistics - Insert-size statistics upstream