Bio Atac Seq Nucleosome Positioning

Version Compatibility

Reference examples tested with: Rsamtools 2.18+, matplotlib 3.8+, numpy 1.26+, pyBigWig 0.3+, pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• 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 ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Nucleosome Positioning

"Map nucleosome positions from ATAC-seq" → Separate nucleosome-free and mono-nucleosome fragments by size, then call nucleosome center positions and occupancy scores.

• CLI: nucleoatac run --bed peaks.bed --bam atac.bam --fasta ref.fa
• R: ATACseqQC::splitGAlignmentsByCut() for fragment separation

Extract nucleosome positions and occupancy from ATAC-seq fragment size patterns.

Background

ATAC-seq fragments reflect chromatin structure:

• < 100 bp: Nucleosome-free regions (NFR)
• 180-247 bp: Mono-nucleosome
• 315-473 bp: Di-nucleosome
• 558-615 bp: Tri-nucleosome

ATACseqQC (R)

Installation

BiocManager::install('ATACseqQC')

Fragment Size Distribution

library(ATACseqQC)
library(Rsamtools)

# Read BAM
bamfile <- 'sample.bam'

# Fragment size distribution
fragSize <- fragSizeDist(bamfile, 'sample')

# Nucleosome-free and mono-nucleosome ratios
# Automatic QC metrics

Nucleosome Positioning

Goal: Map nucleosome positions around TSS using ATAC-seq fragment size classes.

Approach: Read BAM, apply Tn5 shift correction, split fragments into NFR and mono-nucleosome classes by size, then compute signal profiles around TSS.

library(ATACseqQC)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)

# Get TSS regions
txs <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene)
tss <- promoters(txs, upstream=1000, downstream=1000)

# Read BAM
gal <- readBamFile(bamfile, asMates=TRUE, bigFile=TRUE)

# Shift reads (Tn5 offset correction)
gal_shifted <- shiftGAlignmentsList(gal)

# Split by nucleosome-free and nucleosomal
objs <- splitGAlignmentsByCut(gal_shifted, txs=txs,
                               genome=BSgenome.Hsapiens.UCSC.hg38)

# nucleosome-free fragments
nfr <- objs$NussomeFree

# Mono-nucleosome fragments
mono <- objs$mononucleosome

# Signal around TSS
sigs <- featureAlignedSignal(cvglist=objs,
                             feature.gr=tss,
                             upstream=1000,
                             downstream=1000)

V-Plot (Fragment Size vs Position)

# V-plot showing nucleosome positioning around TSS
vp <- vPlot(gal_shifted, tss,
            genome=BSgenome.Hsapiens.UCSC.hg38,
            upstream=1000, downstream=1000)

Footprinting

# Transcription factor footprinting
library(MotifDb)

# Get motif
motif <- query(MotifDb, 'CTCF')[[1]]

# Find motif occurrences
library(motifmatchr)
motif_pos <- matchMotifs(motif, BSgenome.Hsapiens.UCSC.hg38,
                         genome='hg38', out='positions')

# Calculate footprint
fp <- factorFootprints(gal_shifted, motif_pos,
                       genome=BSgenome.Hsapiens.UCSC.hg38,
                       upstream=100, downstream=100)

NucleoATAC (Python)

Installation

pip install nucleoatac

Run NucleoATAC

Goal: Call precise nucleosome center positions and occupancy scores from ATAC-seq data.

Approach: Run NucleoATAC on defined genomic regions with a reference genome, producing nucleosome position calls and occupancy tracks.

# Call nucleosomes
nucleoatac run --bed regions.bed --bam sample.bam --fasta reference.fa \
    --out nucleoatac_output --cores 8

Output Files

Visualize Output

# Convert to bigWig for visualization
bedGraphToBigWig nucleoatac_output.occ.bedgraph chrom.sizes nucleosome_occ.bw

Fragment Analysis (Custom)

Extract Fragment Sizes

Goal: Visualize ATAC-seq fragment size distribution to assess nucleosome periodicity.

Approach: Extract template lengths from properly paired reads, then plot the histogram with NFR and mono-nucleosome cutoff markers.

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

bam = pysam.AlignmentFile('sample.bam', 'rb')

fragment_sizes = []
for read in bam.fetch():
    if read.is_proper_pair and read.is_read1:
        frag_size = abs(read.template_length)
        if 0 < frag_size < 1000:
            fragment_sizes.append(frag_size)

bam.close()

# Plot distribution
plt.figure(figsize=(10, 6))
plt.hist(fragment_sizes, bins=200, edgecolor='none', alpha=0.7)
plt.axvline(100, color='red', linestyle='--', label='NFR cutoff')
plt.axvline(180, color='blue', linestyle='--', label='Mono-nuc start')
plt.xlabel('Fragment Size (bp)')
plt.ylabel('Count')
plt.legend()
plt.savefig('fragment_distribution.png', dpi=300)

Split by Fragment Size

# Extract nucleosome-free reads
samtools view -h sample.bam | \
    awk '$9 > -100 && $9 < 100 || $1 ~ /^@/' | \
    samtools view -b > nfr.bam

# Extract mono-nucleosome reads
samtools view -h sample.bam | \
    awk '($9 >= 180 && $9 <= 247) || ($9 <= -180 && $9 >= -247) || $1 ~ /^@/' | \
    samtools view -b > mono_nuc.bam

Signal Around Features

import pysam
import numpy as np
import pyBigWig

def signal_around_sites(bam_file, sites, upstream=1000, downstream=1000):
    bam = pysam.AlignmentFile(bam_file, 'rb')
    window_size = upstream + downstream
    signal = np.zeros(window_size)

    for chrom, pos, strand in sites:
        start = pos - upstream if strand == '+' else pos - downstream
        end = pos + downstream if strand == '+' else pos + upstream

        for read in bam.fetch(chrom, max(0, start), end):
            if read.is_proper_pair and read.is_read1:
                frag_center = read.reference_start + abs(read.template_length) // 2
                rel_pos = frag_center - start
                if 0 <= rel_pos < window_size:
                    signal[rel_pos] += 1

    bam.close()
    return signal / len(sites)

# Load TSS sites
tss_sites = []  # Load from GTF
nfr_signal = signal_around_sites('nfr.bam', tss_sites)
mono_signal = signal_around_sites('mono_nuc.bam', tss_sites)

DANPOS

Installation

conda install -c bioconda danpos

Run DANPOS

# Single sample
danpos.py dpos sample.bam -o danpos_output

# Compare conditions
danpos.py dpeak -b treatment.bam -c control.bam -o danpos_diff

Complete Workflow

Goal: Run end-to-end nucleosome positioning analysis from BAM to heatmaps and V-plots.

Approach: Read BAM, shift reads for Tn5 offset, split fragments by size class, compute signal profiles around TSS, and generate heatmaps and V-plots.

library(ATACseqQC)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)

bamfile <- 'sample.bam'

# 1. Fragment size QC
fragSize <- fragSizeDist(bamfile, 'sample')
pdf('fragment_size.pdf')
plot(fragSize)
dev.off()

# 2. Read and shift
gal <- readBamFile(bamfile, asMates=TRUE, bigFile=TRUE)
gal_shifted <- shiftGAlignmentsList(gal)

# 3. Get TSS regions
txs <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene)
tss <- promoters(txs, upstream=2000, downstream=2000)

# 4. Split by fragment size
objs <- splitGAlignmentsByCut(gal_shifted, txs=txs,
                               genome=BSgenome.Hsapiens.UCSC.hg38)

# 5. Calculate signals
sigs <- featureAlignedSignal(cvglist=objs,
                             feature.gr=tss,
                             upstream=2000,
                             downstream=2000)

# 6. Plot heatmap
pdf('nucleosome_heatmap.pdf', width=8, height=10)
featureAlignedHeatmap(sigs, tss, upstream=2000, downstream=2000)
dev.off()

# 7. V-plot
pdf('vplot.pdf')
vPlot(gal_shifted, tss, genome=BSgenome.Hsapiens.UCSC.hg38,
      upstream=1000, downstream=1000)
dev.off()

# 8. Export nucleosome-free and nucleosomal BAMs
export(objs$NuclsomeFree, 'nfr.bam')
export(objs$mononucleosome, 'mono_nucleosome.bam')

Related Skills

• atac-seq/atac-peak-calling - Call accessibility peaks
• atac-seq/atac-qc - Quality control metrics
• atac-seq/footprinting - TF footprinting
• chip-seq/peak-annotation - Annotate nucleosome positions