bio-atac-seq-nucleosome-positioning

1

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsReview advisedSnapshot 7df91cb49554

Summary

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.

SKILL.md

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

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

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

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

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

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')