ATAC-seq Best Practices (2025/26): From FASTQ to Publication-Ready Figures

The Core Analysis Pipeline: Building a Solid Foundation

Every robust bulk ATAC-seq analysis starts with a standardized series of data processing steps. The goal here is to clean the raw data and align it to a reference genome, preparing it for the critical step of identifying accessible chromatin regions, or "peaks."

• Adapter Trimming & Alignment: The first step is to remove adapter sequences from the raw reads using tools like Trimmomatic, Trim Galore! or fastp. The cleaned reads are then aligned to the appropriate reference genome (e.g., hg38, mm10) using an aligner like BWA-MEM or Bowtie2.
• Mitochondrial & Low-MAPQ Filtering: Remove reads aligning to the mitochondrial genome (chrM) and drop alignments with low mapping quality (e.g., MAPQ < 30) to prevent high-background loci from dominating downstream signal.
• The Critical Tn5 Offset: Because the Tn5 transposase binds as a dimer and inserts adapters with a 9-base pair stagger, all reads must be computationally shifted to center the signal precisely where the enzyme cut the DNA. Reads mapping to the positive strand are shifted +4 bp, and those on the negative strand are shifted -5 bp. This step is absolutely essential for accurate transcription factor footprinting and motif analysis.
• Deduplication, Complexity Checks & Peak Calling: PCR duplicates arising during library preparation are removed, typically with Picard Tools. This step is also informative; a very high duplication rate can indicate a low-complexity library, which may be a red flag, so we summarize the non-redundant fraction (NRF) and PCR bottlenecking coefficient (PBC) alongside duplication percentages. Finally, peaks are "called" to identify regions with a statistically significant enrichment of signal over the background.
  • MACS3 remains the most widely used and versatile peak caller.
  • Genrich is an excellent alternative that performs particularly well with datasets that have low read counts or high background noise.
  • SEACR is optimized for CUT&RUN/CUT&Tag; for bulk ATAC-seq we default to MACS3 and often cross-check with Genrich.
• ENCODE Blacklist Filtering: Peaks and coverage tracks are intersected with the ENCODE blacklist (hg38 or mm10) to suppress recurrent artifact regions before visualization or differential testing.

---

The Four Pillars of ATAC-seq Quality Control (QC)

This is the most important part of the analysis. QC isn't just a final check; it's a series of gates that your data must pass at each stage. Skipping these steps is like building a house on a shaky foundation.

Gate 1: Fragment Length Distribution

The pattern of fragment lengths reveals the quality of the nuclear preparation and tagmentation. A good ATAC-seq library will show a distinct periodic pattern, with a strong peak at <100 bp (nucleosome-free regions, NFRs) followed by decaying peaks at ~200 bp intervals (mono-, di-, and tri-nucleosomes).

Pass/Fail: Pass: Clear periodicity with a dominant NFR peak. Fail: A smear of fragment lengths or a dominant peak >200 bp, suggesting poor nuclear isolation or DNA degradation.

Gate 2: Transcription Start Site (TSS) Enrichment

Active gene promoters are typically highly accessible. Therefore, we expect to see a strong enrichment of ATAC-seq reads centered directly on known TSSs. This is a powerful measure of signal-to-noise.

Pass/Fail: Pass: A TSS enrichment score of > 6 is acceptable; a score of > 10 indicates excellent quality. Fail: A low or flat score suggests that the signal is not concentrated in expected regulatory regions and may be driven by noise.

Gate 3: Fraction of Reads in Peaks (FRiP)

This metric calculates the proportion of all usable reads that fall within the called peak regions. It's a simple but effective way to measure how much of your sequencing budget was spent on capturing true signal versus background noise.

FRiP = (Number of Reads in Called Peaks) / (Total Number of Usable Reads)

Pass/Fail: Pass: For bulk ATAC-seq, a FRiP score > 0.3 (30%) is considered good. Fail: A score < 0.15 is a major red flag, indicating low signal enrichment.

We report FRiP alongside library complexity metrics such as the non-redundant fraction (NRF) and PCR bottleneck coefficients (PBC1/PBC2) to confirm that unique fragments, not duplicates, are driving peak enrichment.

Gate 4: Signal-to-Noise Metrics (NSC/RSC)

Developed by the ENCODE consortium, the Normalized Strand Cross-correlation (NSC) and Relative Strand Cross-correlation (RSC) are powerful QC metrics. They measure signal quality based on the clustering of reads on the positive and negative strands around enriched regions, providing a peak-caller-independent assessment of signal quality.

Pass/Fail: Pass: An NSC > 1.05 and RSC > 0.8 are the minimum thresholds for a high-quality dataset. Fail: Values below this indicate low signal-to-noise and potentially unreliable peak calls.

Before moving to differential analysis, we quantify replicate concordance via irreproducible discovery rate (IDR) analysis or naive overlap thresholds, flagging any samples that fail to produce consistent peak sets.

---

From Peaks to Pathways: Downstream Biological Interpretation

Once your data has passed all QC gates, you can confidently proceed to biological interpretation.

1. Differential Accessibility Analysis: Once replicate concordance passes IDR or naive overlap thresholds, use tools like DESeq2 or edgeR to identify statistically significant changes in peak accessibility between your experimental conditions.
2. Peak-to-Gene Linkage & RNA-seq Integration: The ultimate goal is often to understand how changes in chromatin accessibility affect gene expression. By integrating your ATAC-seq data with a matched RNA-seq dataset, you can directly link differentially accessible regions (DARs) to differentially expressed genes (DEGs), building a powerful, multi-omics narrative.
3. Motif Enrichment & TF Footprinting: To uncover the upstream regulators driving the observed changes, you can perform motif analysis using tools like HOMER or MEME-ChIP. This identifies which transcription factor binding motifs are enriched in your DARs, allowing you to infer which TFs are most active.

---

The Deliverable Blueprint: Creating Reviewer-Proof Figures

A successful analysis culminates in a set of clear, high-quality figures that tell a cohesive story. For any ATAC-seq project destined for publication or an internal report, we recommend a standard figure set:

• QC Summary: A multi-panel plot showing key metrics for all samples—FRiP, TSS enrichment, NRF/PBC, NSC/RSC, and fragment length periodicity.
• Sample Clustering: A PCA plot and correlation heatmap to confirm that biological replicates cluster together and to visualize experiment-wide variance.
• Differential Accessibility Plot: A volcano plot or MA plot highlighting the most significant DARs.
• Genome Browser Tracks: Visualizations of key gene loci showing Tn5-shifted bigWig coverage that is normalized (e.g., CPM or RPGC) and the corresponding peak calls for each sample group.
• Motif Enrichment Results: A plot showing the top TF motifs enriched in up- or down-regulated peaks, along with their significance.

Coupled with a detailed methods section that explicitly states the software, versions, and parameters used, this deliverable blueprint is designed to pass the scrutiny of peer review and give you confidence in your conclusions. We execute every project in containerized Nextflow or Snakemake workflows so tool versions and environments are locked and reproducible.

Conclusion

A successful ATAC-seq analysis is a journey from raw reads to biological insight, guided at every step by rigorous quality control. By adopting a systematic approach—from the initial pipeline to the final figures—you can ensure your data is robust, your interpretations are sound, and your project delivers maximum impact.

Navigating the complexities of ATAC-seq requires both state-of-the-art pipelines and deep scientific expertise. If you're planning a new project or have existing data that isn't meeting QC thresholds, our team of Ph.D. scientists is here to help you turn your raw reads into a compelling biological story.

If you're ready to turn your raw reads into a compelling biological story, let's start a conversation.