Analyze ATAC-seq fragment lengths to estimate nucleosome positions and occupancy, showing where DNA is wrapped around proteins. Use NucleoATAC and ATACseqQC to identify nucleosome-free regions and visualize patterns around gene transcription start sites.
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Extract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis. Use when analyzing chromatin organization, identifying nucleosome-free regions at promoters, or characterizing nucleosome occupancy patterns from ATAC-seq fragment size distributions.
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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:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Map nucleosome positions from ATAC-seq" → Separate nucleosome-free and mono-nucleosome fragments by size, then call nucleosome center positions and occupancy scores.
nucleoatac run --bed peaks.bed --bam atac.bam --fasta ref.faATACseqQC::splitGAlignmentsByCut() for fragment separationExtract nucleosome positions and occupancy from ATAC-seq fragment size patterns.
ATAC-seq fragments reflect chromatin structure:
BiocManager::install('ATACseqQC')
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
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 showing nucleosome positioning around TSS
vp <- vPlot(gal_shifted, tss,
genome=BSgenome.Hsapiens.UCSC.hg38,
upstream=1000, downstream=1000)
# 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)
pip install 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
| File | Description |
|---|---|
.nucpos.bed | Nucleosome positions |
.nucpos.redundant.bed | All nucleosome calls |
.nfrpos.bed | NFR positions |
.occ.bedgraph | Nucleosome occupancy track |
.nucmap_combined.bed | Combined nucleosome map |
# Convert to bigWig for visualization
bedGraphToBigWig nucleoatac_output.occ.bedgraph chrom.sizes nucleosome_occ.bw
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)
# 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
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)
conda install -c bioconda 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
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')
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