version: "1.0.0" name: bio-atac-seq-atac-qc description: ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling. tool_type: mixed primary_tool: deeptools

Version Compatibility

Reference examples tested with: deepTools 3.5+, Picard 3.1+, samtools 1.19+, bedtools 2.31+, ATACseqQC 1.26+, pysam 0.22+, pyBigWig 0.3+, numpy 1.26+, pandas 2.2+, MultiQC 1.21+.

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.

ATAC-seq Quality Control

"Does my ATAC library pass ENCODE quality criteria?" -> Compute the seven canonical metrics (depth, alignment rate, mitochondrial fraction, library complexity, fragment-size periodicity, TSS enrichment, FRiP) and compare against ENCODE 4 thresholds, then diagnose failures.

• CLI: picard CollectInsertSizeMetrics, samtools flagstat, samtools idxstats
• CLI: deeptools plotFingerprint, computeMatrix reference-point + plotProfile
• R: ATACseqQC::TSSEscore, ATACseqQC::fragSizeDist, ATACseqQC::PTscore
• Python: custom NRF/PBC from coordinate hash; pyBigWig for TSS enrichment

ENCODE 4 ATAC-seq Acceptance Thresholds

ENCODE thresholds are organism-specific. Mouse (mm10, GENCODE M21) TSS enrichment >= 5 is acceptable; non-model organisms have no published threshold (use cohort percentile rank instead). Methodology evolves; verify against the current ENCODE ATAC-seq Standards before reporting.

TSS Enrichment: ENCODE Method vs ATACseqQC Method

The two most common implementations DO NOT produce identical scores.

Trigger: Comparing a TSS score across studies.

Mechanism: Different normalization windows shift the absolute number; ATACseqQC's TSSEscore is typically 2-3x ENCODE's because of the wider flank.

Symptom: Reported score 21 vs ENCODE-ideal 7 mismatch. Likely the calculator was ATACseqQC; the equivalent ENCODE score might be 8.

Fix: State which implementation was used. For ENCODE comparisons, use pyTSSe (Kundaje lab) or implement the ENCODE recipe directly.

import numpy as np
import pyBigWig
def encode_tss_enrichment(bw_path, tss_bed, flank=2000):
    """ENCODE-style TSS enrichment: signal at TSS center / signal at flanks."""
    bw = pyBigWig.open(bw_path)
    profiles = []
    for line in open(tss_bed):
        chrom, start, end, *rest = line.strip().split('\t')
        tss = int(start)
        strand = rest[2] if len(rest) > 2 else '+'
        try:
            vals = bw.values(chrom, tss - flank, tss + flank)
            if vals is None or len(vals) != 2 * flank: continue
            if strand == '-': vals = vals[::-1]
            profiles.append(np.nan_to_num(vals))
        except RuntimeError:
            continue
    avg = np.nanmean(profiles, axis=0)
    flank_signal = np.mean(np.concatenate([avg[:100], avg[-100:]]))
    center_signal = np.mean(avg[flank - 50: flank + 50])
    return center_signal / flank_signal if flank_signal > 0 else 0.0

Fragment-Size Periodicity Patterns

The 10.4 bp helical periodicity is a Buenrostro 2013 hallmark: it reflects the helical pitch of B-form DNA, with Tn5 preferring outward-facing minor grooves on nucleosomal DNA. Its presence is a positive QC indicator but not required.

Per-Metric Failure Modes

Mitochondrial fraction > 50%

Trigger: Standard ATAC-seq protocol on intact cells (no nuclear isolation), or insufficient detergent in lysis.

Mechanism: Mitochondrial DNA is naked (no histones), so Tn5 hyperactively cuts it. Without nuclear-isolation steps (Omni-ATAC pre-spin, OR digitonin lysis with mt removal), chrM dominates the library.

Symptom: samtools idxstats sample.bam | awk '$1=="chrM"' shows >50% of mapped reads on chrM.

Fix: Re-prep with Omni-ATAC (Corces 2017) or fast-ATAC. Re-running QC on chrM-stripped BAM hides the underlying problem; the wasted sequencing remains. If chrM fraction is 30-50%, the library may still be salvageable via chrM removal but yield is reduced.

NRF / PBC1 / PBC2 below threshold

Trigger: Over-amplified library; low input cell count combined with high PCR cycles.

Mechanism: Each PCR cycle doubles starting fragments. With low complexity input (<5000 cells) and >12 cycles, distinct fragments saturate and reads pile up at identical positions. NRF measures unique fragments / total; PBC2 specifically detects multi-copy duplication.

Symptom: NRF < 0.7; PBC2 < 1.0; massive duplicate-removal loss in samtools markdup.

Fix: No fix post-hoc. Re-prep with more starting cells and fewer PCR cycles. Note: ATAC has legitimate duplicates at hyperaccessible sites (Tn5 cuts identically there), so NRF < 0.9 is not by itself fatal. The combined PBC1 < 0.7 + PBC2 < 1.0 + visual coverage pile-ups confirm true bottlenecking.

TSS enrichment < 5

Trigger: Generic chromatin opening throughout the genome (over-transposition), OR genome build mismatch between TSS BED and BAM, OR strand-flip in TSS file.

Mechanism: TSS enrichment requires that signal at TSSs is >> signal in genomic flanks. Over-transposition flattens the signal landscape. Strand-flipped TSSs subtract real signal because TSSs on - strand are calculated from the wrong direction.

Symptom: TSS profile is flat or shows a slight dip at TSS center. Genome browser shows accessibility everywhere, not concentrated at promoters.

Fix: Verify genome build (mm10 vs mm39 differ in TSS positions); verify GTF strand column; confirm signal track was generated post-deduplication. If TSS profile is genuinely flat, library is over-transposed and not recoverable; lower transposition time / Tn5 concentration in next prep.

FRiP < 0.2

Trigger: Signal too diffuse to call peaks (over-transposition), low TSS enrichment, OR peak set is too narrow / restrictive.

Mechanism: FRiP correlates with TSS enrichment because both measure how concentrated the signal is. A diffuse library will have low FRiP regardless of peak count.

Symptom: Peak count looks normal but FRiP < 0.15.

Fix: Check TSS enrichment first. If TSS is also low, the library is over-transposed. If TSS is OK but FRiP is low, the peak caller may be undercalling -- try -p 0.01 (looser) and recalculate FRiP.

Replicate correlation < 0.85

Trigger: Batch effect, technical artefact, or cell-state drift between replicate biological collections.

Mechanism: Pearson correlation on log-scaled binned counts (deepTools multiBamSummary bins -bs 10000) tracks coverage similarity. Below 0.85 indicates non-trivial divergence; ENCODE wants >= 0.9 for biological reps.

Fix: Check PCA; if reps cluster apart from condition, drop the outlier or rerun. If the divergence aligns with batch, add batch as a covariate downstream (DiffBind ~Batch + Condition). Do not silently merge with bad correlation.

Library Complexity (NRF, PBC1, PBC2)

Goal: Detect over-amplification or low-input bottlenecks.

Approach: Hash mapped read positions (or fragment 5' coordinates), tally how many positions have 1, 2, or more reads, and compute the three metrics.

import pysam
from collections import Counter
def library_complexity(bam):
    pos_counts = Counter()
    total = 0
    with pysam.AlignmentFile(bam, 'rb') as bf:
        for r in bf.fetch():
            if r.is_unmapped or r.is_secondary or r.is_supplementary:
                continue
            if r.is_duplicate:                          # Mark, not skip; PBC counts pre-dedup
                pass
            total += 1
            key = (r.reference_name, r.reference_start, r.is_reverse)
            pos_counts[key] += 1
    distinct = len(pos_counts)
    histogram = Counter(pos_counts.values())            # {1: N1, 2: N2, ...}
    n1 = histogram.get(1, 0)
    n2 = histogram.get(2, 0)
    nrf = distinct / total if total else 0.0
    pbc1 = n1 / distinct if distinct else 0.0
    pbc2 = n1 / n2 if n2 else float('inf')
    return {'NRF': nrf, 'PBC1': pbc1, 'PBC2': pbc2, 'total': total, 'distinct': distinct}

r.is_duplicate is informational only here; ENCODE NRF/PBC are computed pre-deduplication on the raw mapped BAM.

Cross-Replicate QC

# Spearman correlation (more robust than Pearson for ATAC)
multiBamSummary bins -bs 10000 -p 8 \
    --bamfiles rep1.bam rep2.bam rep3.bam \
    -o multi.npz
 
plotCorrelation -in multi.npz \
    --corMethod spearman --whatToPlot heatmap --skipZeros \
    -o spearman_heatmap.png
 
# Fingerprint (per-bin signal cumulative -- diagonal = no enrichment, sharp curve = good)
plotFingerprint -p 8 -b rep1.bam rep2.bam rep3.bam \
    --labels rep1 rep2 rep3 \
    --skipZeros --numberOfSamples 50000 \
    -o fingerprint.png \
    --outQualityMetrics fingerprint_metrics.txt

deepTools fingerprint quality metrics report a synthetic JS distance without a reference; the (non-synthetic) Jensen-Shannon distance column is only computed when a reference sample is supplied via --JSDsample. Larger values indicate stronger enrichment.

Library Complexity Extrapolation (preseq)

Goal: Predict whether re-sequencing would rescue a low-NRF library, separating "library is bottlenecked" from "we just sequenced too shallow."

Approach: Fit preseq's rational-function (Pade) approximation of the Good-Toulmin power-series estimator on observed BAM read positions; extrapolate distinct-fragment yield as a function of additional sequencing depth.

# c_curve: observed complexity at current depth
preseq c_curve -B sample.bam -o sample.ccurve.tsv -s 1e6
 
# lc_extrap: predicted complexity at higher depth (extrapolation -e here 200M; preseq default -e is 1e10, step -s default 1M)
preseq lc_extrap -B sample.bam -o sample.lcextrap.tsv -e 200000000 -s 5000000

Interpretation: if lc_extrap shows distinct-fragment count flattening before 100M reads, the library is bottlenecked (re-sequencing won't help; re-prep needed). If it continues to climb, re-sequencing will recover more unique reads. Use alongside NRF/PBC1/PBC2 to decide library re-prep vs deeper sequencing.

Sex-Chromosome QC

Trigger: Clinical-grade ATAC; biobank-scale studies; sample-mix-up detection.

Mechanism: chrY has minimal coverage in female samples; XIST locus (chrX) is highly accessible only in female cells (X-inactivation). Sample-swap or sex-misassignment detectable from these two loci.

# chrY read fraction
samtools idxstats sample.bam | awk '$1=="chrY"{print $3 / $2}'   # reads per bp
 
# XIST locus accessibility (chrX:73820651-73852753 in hg38)
samtools view -c sample.bam chrX:73820651-73852753

Female: chrY reads/bp ~0; XIST count high. Male: chrY reads/bp ~male coverage; XIST count low. Discrepancy with sample metadata flags swap.

Cell-Cycle Effect on Accessibility

Trigger: Proliferating cell lines (K562, HEK293, HeLa); samples with high S/G2M signature.

Mechanism: Replication-associated chromatin opening adds 5-15% global accessibility shift in proliferating cells; without correction, condition-specific cell-cycle differences confound differential analysis.

Detection: Score cells/samples for S-phase signature (Macosko 2015 cell cycle gene set adapted for chromatin: regulated origin loci, replication-stress-response genes); for bulk ATAC, compute per-sample peak intersection with replication-origin atlas (Repli-seq peaks).

Fix for differential: Add S-phase score as covariate in DESeq2 design (~Sphase + Condition); for scATAC, regress on TF-IDF residuals analogous to Seurat CellCycleScoring.

Spike-in QC (Drosophila or E. coli Chromatin)

Trigger: Studies where global accessibility shift is biological (HDAC inhibitor, DNMT inhibitor, differentiation).

Mechanism: Per-library normalization (RPM, CPM) erases global accessibility shifts because total reads are nominally constant. Exogenous chromatin spike-in (Drosophila S2 or E. coli Tn5-naive chromatin added pre-Tn5) provides an external scaling reference.

Pipeline: Align reads to a concatenated human + Drosophila reference; count spike-in reads per sample; normalize by spike-in (not by total reads). Reske 2020 Epigenetics Chromatin shows that normalization-method choice materially changes differential-accessibility results when a global accessibility shift is expected (ARID1A/PIK3CA endometrial-epithelium case study), motivating an external reference such as a chromatin spike-in.

QC threshold: spike-in fraction 0.5-5% of total reads is the workable range. Below 0.1% spike-in is unreliable; above 10% suggests too much spike-in (loss of cellular reads).

Comprehensive QC Aggregation

Goal: Produce a per-sample report card with PASS/FAIL flags against ENCODE thresholds.

Approach: Compute each metric independently, compare to thresholds, write a tab-delimited report consumable by MultiQC.

import json, subprocess, sys
from pathlib import Path
ENCODE_THRESHOLDS = {
    'nuclear_reads_M': (25, 50),                      # (min acceptable, ideal)
    'mt_fraction': (0.5, 0.05),                       # (max acceptable, ideal); inverted
    'NRF': (0.7, 0.9), 'PBC1': (0.7, 0.9), 'PBC2': (1.0, 3.0),
    'TSS_enrichment': (5.0, 7.0), 'FRiP': (0.2, 0.3),
}
def grade(value, thr_acceptable, thr_ideal, inverted=False):
    if inverted:
        return 'FAIL' if value > thr_acceptable else ('PASS' if value <= thr_ideal else 'WARN')
    return 'FAIL' if value < thr_acceptable else ('PASS' if value >= thr_ideal else 'WARN')
def report(metrics, out_tsv):
    rows = []
    for k, (acc, ideal) in ENCODE_THRESHOLDS.items():
        if k not in metrics: continue
        inverted = (k == 'mt_fraction')
        flag = grade(metrics[k], acc, ideal, inverted=inverted)
        rows.append((k, metrics[k], acc, ideal, flag))
    with open(out_tsv, 'w') as f:
        f.write('metric\tvalue\tacceptable\tideal\tflag\n')
        for r in rows: f.write('\t'.join(map(str, r)) + '\n')

MultiQC Aggregation

# Run after generating per-sample QC outputs
multiqc \
    fastqc/ \
    picard/ \
    samtools_stats/ \
    macs2/ \
    deeptools/ \
    -o multiqc_report

MultiQC ingests Picard CollectInsertSizeMetrics, samtools flagstat, deepTools plotFingerprint output, and MACS peaks tables. It does NOT compute TSS enrichment or NRF; pipe a custom _mqc.tsv for those.

Common Errors

References

• Buenrostro JD et al 2013 Nat Methods 10:1213 (ATAC-seq protocol; fragment-size periodicity)
• Corces MR et al 2017 Nat Methods 14:959 (Omni-ATAC; mt fraction reduction protocol)
• Landt SG et al 2012 Genome Res 22:1813 (ENCODE/modENCODE QC framework, NRF/PBC definitions; the PBC1/PBC2 split is a later ENCODE-pipeline refinement)
• ENCODE 4 ATAC-seq Data Standards (encodeproject.org/atac-seq) -- canonical thresholds
• Ou J et al 2018 BMC Genomics 19:169 (ATACseqQC R package; TSSEscore implementation)
• Ramirez F et al 2016 Nucleic Acids Res 44:W160 (deepTools, plotFingerprint JSD)
• Daley T & Smith AD 2013 Nat Methods 10:325 (preseq library-complexity extrapolation model; the lc_extrap re-sequencing decision)

Related Skills

• atac-seq/atac-peak-calling - FRiP requires peaks; QC drives accept/reject before calling
• atac-seq/nucleosome-positioning - Fragment-size analysis
• atac-seq/single-cell-atac - per-cell QC has different thresholds
• read-qc/quality-reports - upstream FastQC
• alignment-files/bam-statistics - samtools flagstat / idxstats
• alignment-files/duplicate-handling - dedup before NRF/PBC computation