/bio-atac-seq-atac-qc

name: bio-atac-seq-atac-qc
description: Quality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples.
tool_type: mixed
primary_tool: deeptools

Version Compatibility

Reference examples tested with: bedtools 2.31+, deepTools 3.5+, numpy 1.26+, pandas 2.2+, picard 3.1+, 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.

ATAC-seq Quality Control

**"Check the quality of my ATAC-seq library"** → Evaluate fragment size distribution (nucleosome periodicity), TSS enrichment, FRiP, and library complexity to assess chromatin accessibility experiment quality.

• CLI: `deeptools bamPEFragmentSize`, `picard CollectInsertSizeMetrics`
• Python: `pysam` for custom fragment analysis

Fragment Size Distribution

**Goal:** Assess ATAC-seq library quality by visualizing the characteristic nucleosome periodicity in fragment sizes.

**Approach:** Extract insert sizes from the BAM file using Picard or samtools, producing a distribution that should show NFR (<100 bp) and mono-nucleosome (~200 bp) peaks.

# Using Picard
java -jar picard.jar CollectInsertSizeMetrics \
    I=sample.bam \
    O=insert_sizes.txt \
    H=insert_sizes.pdf \
    M=0.5

# Using samtools
samtools view -f 66 sample.bam | \
    awk '{print sqrt($9^2)}' | \
    sort | uniq -c | \
    awk '{print $2"\t"$1}' > fragment_sizes.txt

TSS Enrichment Score

**Goal:** Quantify signal enrichment at transcription start sites as a key ATAC-seq quality metric.

**Approach:** Create a TSS BED file, compute a signal matrix around TSS positions using deepTools, then plot the enrichment profile.

# Using deepTools
# 1. Create TSS BED file (from GTF)
awk '$3=="transcript" {print $1"\t"$4-1"\t"$4"\t"$14"\t"0"\t"$7}' genes.gtf | \
    tr -d '";' | sort -k1,1 -k2,2n > tss.bed

# 2. Compute matrix around TSS
computeMatrix reference-point \
    -S sample.bw \
    -R tss.bed \
    -a 2000 -b 2000 \
    -o tss_matrix.gz

# 3. Plot TSS enrichment
plotProfile -m tss_matrix.gz \
    -o tss_enrichment.png \
    --perGroup

Calculate TSS Enrichment Score

**Goal:** Compute a numeric TSS enrichment score from a bigWig signal track.

**Approach:** Sample signal values in windows around TSS positions, average across all TSSs, then divide center signal by flanking background.

import numpy as np
import pyBigWig

def calculate_tss_enrichment(bigwig_file, tss_bed, flank=2000):
    '''Calculate TSS enrichment score.'''
    bw = pyBigWig.open(bigwig_file)

    signals = []
    for line in open(tss_bed):
        fields = line.strip().split('\t')
        chrom, tss = fields[0], int(fields[1])
        strand = fields[5] if len(fields) > 5 else '+'

        try:
            vals = bw.values(chrom, max(0, tss - flank), tss + flank)
            if strand == '-':
                vals = vals[::-1]
            signals.append(vals)
        except:
            continue

    avg_signal = np.nanmean(signals, axis=0)

    # TSS enrichment = signal at TSS / background
    background = np.nanmean([avg_signal[:100], avg_signal[-100:]])
    tss_signal = np.nanmean(avg_signal[flank-50:flank+50])

    enrichment = tss_signal / background if background > 0 else 0

    return enrichment, avg_signal

enrichment, signal = calculate_tss_enrichment('sample.bw', 'tss.bed')
print(f'TSS Enrichment Score: {enrichment:.2f}')

FRiP (Fraction of Reads in Peaks)

# Total reads
total=$(samtools view -c -F 4 sample.bam)

# Reads in peaks
in_peaks=$(bedtools intersect -a sample.bam -b peaks.narrowPeak -u | \
    samtools view -c)

# FRiP
frip=$(echo "scale=4; $in_peaks / $total" | bc)
echo "FRiP: $frip"

# Good FRiP for ATAC-seq: >0.2 (20%)

Mitochondrial Read Fraction

# Mitochondrial reads
mt_reads=$(samtools view -c sample.bam chrM)
total_reads=$(samtools view -c sample.bam)

mt_frac=$(echo "scale=4; $mt_reads / $total_reads" | bc)
echo "Mitochondrial fraction: $mt_frac"

# Ideal: <20%, concerning: >50%

Library Complexity (NRF, PBC1, PBC2)

**Goal:** Measure library complexity to detect over-amplification or low-diversity libraries.

**Approach:** Calculate NRF (unique/total reads), PBC1 (1-read locations / all locations), and PBC2 (1-read / 2-read locations) using Picard or custom counting.

# Using Picard EstimateLibraryComplexity
java -jar picard.jar EstimateLibraryComplexity \
    I=sample.bam \
    O=complexity.txt

# Or calculate from BAM
# NRF = unique reads / total reads
# PBC1 = locations with exactly 1 read / locations with >= 1 read
# PBC2 = locations with exactly 1 read / locations with exactly 2 reads

import pysam

def calculate_complexity(bam_file):
    '''Calculate library complexity metrics.'''
    bam = pysam.AlignmentFile(bam_file, 'rb')

    positions = {}
    total = 0
    for read in bam.fetch():
        if read.is_unmapped or read.is_secondary:
            continue
        total += 1
        pos = (read.reference_name, read.reference_start)
        positions[pos] = positions.get(pos, 0) + 1

    distinct = len(positions)
    m1 = sum(1 for v in positions.values() if v == 1)
    m2 = sum(1 for v in positions.values() if v == 2)

    nrf = distinct / total if total > 0 else 0
    pbc1 = m1 / distinct if distinct > 0 else 0
    pbc2 = m1 / m2 if m2 > 0 else 0

    return {'NRF': nrf, 'PBC1': pbc1, 'PBC2': pbc2}

deepTools QC

# Fingerprint plot (asses