ATAC-seq Analysis Pipeline

A comprehensive pipeline for processing and analyzing ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) data. This pipeline automates the entire workflow from raw FastQ files to peak calling, including quality control and visualization steps.

Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Pipeline Steps
• Output Structure
• Quality Control
• Configuration
• Troubleshooting

Features

• Automated processing of paired-end ATAC-seq data
• Comprehensive quality control metrics
• Adapter trimming and read preprocessing
• Alignment to reference genome
• Peak calling with MACS2
• Generate genome browser visualization files
• TSS enrichment analysis
• Insert size distribution analysis
• Library complexity estimation

Prerequisites

• Python 2.7+
• FastQC (v0.11.2+)
• Bowtie2 (v2.2.4+)
• MACS2 (v2.1.1+)
• Samtools (v1.3+)
• Picard Tools (v1.92+)
• BedTools (v2.25.0+)
• R (v3.2.2+)
• IGVtools (v2.3.3+)
• Preseq (v1.0.2+)
• NGSplot (v2.47+)

Installation

1. Clone the repository:

git clone https://github.com/rohitrrj/ATACseq_Pipeline.git
cd ATACseq_Pipeline

2. Ensure all required modules are available:

module load python fastqc/0.11.2 bowtie/2.2.4 MACS2/2.1.1 java/latest
module load samtools/1.3 picard-tools/1.92 preseq/1.0.2 bedtools/2.25.0
module load r/3.2.2 igvtools/2.3.3 ngsplot/2.47

3. Configure your project:

cp conf.txt.example conf.txt
# Edit conf.txt with your project-specific paths

Usage

1. Prepare your sample file (one sample per line):

echo "sample1" > samples.txt
echo "sample2" >> samples.txt

2. Structure your input data:

/path/to/data/
├── sample1_R1.fastq
├── sample1_R2.fastq
├── sample2_R1.fastq
└── sample2_R2.fastq

3. Run the pipeline:

./ATAC_seq_preprocess.sh

Pipeline Steps

1. Quality Control (FastQC)

   • Raw read quality assessment
   • Adapter content analysis

2. Read Preprocessing

   • Adapter trimming
   • Quality filtering

3. Alignment (Bowtie2)

   • Mapping to reference genome
   • Remove unmapped/low-quality reads

4. Post-alignment Processing

   • Remove duplicates
   • Remove mitochondrial reads
   • Filter for chromosomes 1-22

5. Peak Calling (MACS2)

   • Identify accessible regions
   • Generate signal tracks

Output Structure

Analysis/
├── trim/                  # Trimmed FastQ files
├── fastqc/                # Quality control reports
├── bowtie/                # Alignment files
├── QC/                    # QC metrics and plots
└── Peaks/                 # MACS2 peak calls

Quality Control

The pipeline generates several QC metrics:

• TSS enrichment scores
• Insert size distributions
• Library complexity estimates
• Alignment statistics
• Peak quality metrics

Configuration

Edit conf.txt to specify:

• Data directory
• Project directory
• Scripts directory
• Sample file location

Example configuration:

myDATADIR="/path/to/data"
myPROJDIR="/path/to/project"
mySCRIPTSDIR="/path/to/scripts"
mySampleFile="samples.txt"

Troubleshooting

Common Issues

1. Module Load Errors

   • Ensure all required modules are available on your system
   • Check module versions match requirements

2. Memory Issues

   • Adjust -l h_vmem in script header
   • Split processing into smaller batches

3. Missing Reference Files

   • Verify reference genome paths
   • Check file permissions

Error Messages

• Error: cannot find input file - Check file paths in conf.txt
• Segmentation fault - Increase memory allocation
• Command not found - Ensure all modules are loaded

License

This project is licensed under the MIT License - see the LICENSE file for details.

Applications

This pipeline has been used in the following publications:

1. "Epigenetic signature of PD-1+ TCF1+ CD8 T cells that act as resource cells during chronic viral infection and respond to PD-1 blockade"

   • Proc Natl Acad Sci U S A. 2022 Feb 22;119(8):e2117314119
   • DOI: 10.1073/pnas.1903520116
   • PMID: 31227606
   • Used for analyzing T cell chromatin accessibility patterns

2. "PD-1 combination therapy with IL-2 modifies CD8+ T cell exhaustion program"

   • Nature. 2022 Oct;610(7933):737-743
   • DOI: 10.1038/s41586-022-05257-0
   • PMID: 36171288
   • Applied for chromatin accessibility analysis in T cell exhaustion studies

Code availability: ⭐ rohitrrj/ATACseq_Pipeline - Comprehensive ATAC-seq data analysis pipeline for chromatin accessibility studies

Contributing

Contributions are welcome! Please read the contributing guidelines before submitting pull requests.