pipeline-atacseq

ENCODE ATAC-seq Pipeline

When to Use

• User wants to run an ATAC-seq processing pipeline from FASTQ to peaks and signal tracks
• User asks about "ATAC-seq pipeline", "Tn5 shift", "chromatin accessibility pipeline", or "Bowtie2 for ATAC"
• User needs to process ATAC-seq data with proper Tn5 insertion site correction
• Example queries: "process my ATAC-seq FASTQs", "run ENCODE ATAC-seq pipeline", "call accessibility peaks from ATAC-seq"

Execute the ENCODE ATAC-seq processing pipeline from raw FASTQ files through Tn5 offset correction, peak calling, IDR analysis, and signal track generation. This skill provides a complete Nextflow DSL2 implementation following ENCODE uniform analysis standards.

Overview

ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) uses the Tn5 transposase to probe open chromatin regions. The ENCODE pipeline processes ATAC-seq data through quality control, alignment with Bowtie2, Tn5 insertion site correction (+4/-5 bp offset), mitochondrial read removal, nucleosome-free fragment selection, peak calling with MACS2, and IDR-based replicate consistency analysis.

Key differences from ChIP-seq: Bowtie2 aligner (optimized for short fragments), Tn5 transposase shift correction, aggressive mitochondrial read filtering (can be 30-80% of reads), nucleosomal fragment size distribution as a QC metric, and TSS enrichment score as the primary quality indicator.

Key Literature

| Reference | Journal | Year | DOI | Relevance | |-----------|---------|------|-----|-----------| | Buenrostro et al. "Transposition of native chromatin (ATAC-seq)" | Nature Methods | 2013 | 10.1038/nmeth.2688 | Original ATAC-seq method (~5,000 citations) | | Corces et al. "An improved ATAC-seq protocol" | Nature Methods | 2017 | 10.1038/nmeth.4396 | Omni-ATAC improvements (~2,500 citations) | | ENCODE Project Consortium "Expanded encyclopaedias" | Nature | 2020 | 10.1038/s41586-020-2493-4 | ENCODE Phase 3 standards | | Amemiya et al. "ENCODE Blacklist" | Scientific Reports | 2019 | 10.1038/s41598-019-45839-z | Artifact regions (~1,372 citations) | | Langmead & Salzberg "Fast gapped-read alignment with Bowtie 2" | Nature Methods | 2012 | 10.1038/nmeth.1923 | Aligner (~30,000 citations) | | Yan et al. "From reads to insight: ATAC-seq analysis" | Genome Biology | 2020 | 10.1186/s13059-020-1929-3 | Analysis best practices |

Pipeline Stages

FASTQ ──> FastQC / Trim Galore ──> Bowtie2 ──> Mito Removal + Tn5 Shift
  │                                                       │
  │           ┌──────────────────────────────────────────┘
  │           v
  │     Picard MarkDup ──> Blacklist Filter ──> Size Selection
  │                                                   │
  │                    ┌─────────────────┬────────────┘
  │                    v                 v
  │             NFR Fragments     Mono-Nucleosome
  │                    │
  │                    v
  │           MACS2 Peak Calling ──> IDR Analysis
  │                    │                    │
  │                    v                    v
  │             Signal Tracks         QC Report (MultiQC + ataqv)
  v
 Raw QC Report

Stage Summary

| Stage | Tool | Input | Output | Reference | |-------|------|-------|--------|-----------| | 1. QC & Trimming | FastQC, Trim Galore | Raw FASTQ | Trimmed FASTQ | references/01-qc-trimming.md | | 2. Alignment | Bowtie2 | Trimmed FASTQ | Sorted BAM | references/02-alignment.md | | 3. Tn5 Shift & Filtering | Samtools, bedtools, Picard | Sorted BAM | Shifted, filtered BAM | references/03-tn5-filtering.md | | 4. Peak Calling & IDR | MACS2, IDR | Filtered BAM | Peaks (narrowPeak) | references/04-peak-calling.md | | 5. QC & Signal | deeptools, ataqv, MultiQC | Filtered BAM, Peaks | bigWig, QC report | references/05-qc-metrics.md |

Input Requirements

Required Files

• ATAC-seq FASTQ: Paired-end reads (strongly recommended; single-end supported)
• Reference genome: Bowtie2-indexed genome (GRCh38 for human, mm10 for mouse)

Sample Sheet Format

sample_id,read1,read2,replicate
SAMPLE1_rep1,atac_R1.fq.gz,atac_R2.fq.gz,1
SAMPLE1_rep2,atac_R1.fq.gz,atac_R2.fq.gz,2

No input control needed: Unlike ChIP-seq, ATAC-seq does not require a separate input or IgG control. MACS2 calls peaks against a local background model.

Tn5 Transposase Offset Correction

The Tn5 transposase inserts sequencing adapters with a 9-bp duplication. To center reads on the actual cut site:

• Forward strand (+): shift +4 bp
• Reverse strand (-): shift -5 bp

This correction is essential for accurate footprinting and motif analysis.

Fragment Size Distribution

ATAC-seq produces a characteristic nucleosomal ladder pattern:

| Fragment Class | Size Range | Biological Meaning | |---------------|------------|-------------------| | Nucleosome-free (NFR) | <150 bp | Open chromatin / TF binding | | Mono-nucleosome | 150-300 bp | Single nucleosome wrapping | | Di-nucleosome | 300-500 bp | Two nucleosomes | | Tri-nucleosome | 500-700 bp | Three nucleosomes |

For peak calling, use nucleosome-free reads (<150 bp) only.

QC Thresholds

| Metric | Threshold | Category | Source | |--------|-----------|----------|--------| | Total sequenced reads | >=50M (recommended) | Read depth | ENCODE | | Mapping rate | >80% | Alignment | ENCODE | | Mitochondrial fraction | <20% (ideal <5%) | Sample quality | ENCODE | | NRF (non-redundant fraction) | >=0.8 | Library complexity | ENCODE | | PBC1 | >=0.8 | Library complexity | ENCODE | | TSS enrichment score | >=5 | Signal quality | ENCODE standard | | FRiP | >=0.3 | Peak quality | ENCODE | | NFR fraction | >0.4 of fragments <150bp | Fragment distribution | Buenrostro 2013 | | IDR optimal peaks | >50,000 | Reproducibility | ENCODE |

TSS Enrichment Score

The TSS enrichment score measures the fold enrichment of ATAC-seq signal at transcription start sites compared to flanking regions. It is the single most informative QC metric for ATAC-seq:

| Score | Quality | Interpretation | |-------|---------|---------------| | >=7 | Excellent | High signal-to-noise | | 5-7 | Good | Acceptable for most analyses | | 3-5 | Marginal | Review other metrics carefully | | <3 | Poor | Likely failed; consider re-doing |

Execution

Quick Start (Local Docker)

nextflow run scripts/main.nf \
  -profile local \
  --reads 'fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir results/

SLURM HPC

nextflow run scripts/main.nf \
  -profile slurm \
  --reads 'fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir results/

Google Cloud

nextflow run scripts/main.nf \
  -profile gcp \
  --reads 'gs://bucket/fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir 'gs://bucket/results/'

AWS Batch

nextflow run scripts/main.nf \
  -profile aws \
  --reads 's3://bucket/fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir 's3://bucket/results/'

Cloud Cost Estimates

| Platform | Instance | Cost/Sample | Time/Sample | Notes | |----------|----------|-------------|-------------|-------| | GCP | n1-standard-8 | ~$2-4 | 2-3 hours | Preemptible recommended | | AWS | m5.2xlarge | ~$2-4 | 2-3 hours | Spot instances recommended | | Local | 8 cores, 32GB | $0 | 3-5 hours | Docker required | | SLURM | 8 cores, 32GB | Varies | 2-3 hours | Singularity recommended |

Output Directory Structure

results/
  fastqc/                   # Raw and trimmed QC reports
  trimmed/                  # Trimmed FASTQ files
  aligned/                  # Sorted BAM files (pre-filtering)
  filtered/
    shifted/                # Tn5-corrected BAM files
    nfr/                    # Nucleosome-free fragments (<150 bp)
    mononuc/                # Mono-nucleosome fragments (150-300 bp)
  peaks/
    narrow/                 # MACS2 narrowPeak files
    idr/                    # IDR-filtered reproducible peaks
  signal/                   # bigWig signal tracks
  qc/
    tss_enrichment