ATAC-seq Data Analysis Pipeline with Snakemake

Overview

This pipeline is designed for the analysis of ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) data. It automates the process from raw data processing to the identification of accessible chromatin regions, peak calling, and downstream analysis. Built with Snakemake, this pipeline ensures reproducibility and scalability, accommodating datasets ranging from a few samples to large-scale projects.

This pipeline was created to facilitate learning using data from the ENCODE database, specifically dataset ID ENCSR956DNB.

Features

• Quality Control: Utilizes tools like FastQC and MultiQC for quality assessment of raw sequencing data.
• Alignment: Aligns reads to the reference genome using Bowtie2 or BWA.
• Peak Calling: Identifies regions of increased accessibility using MACS2.
• Visualization: Visualization of Peaks and alignement using IGV.

Installation

Prerequisites

• Conda/Miniconda (recommended for managing environments)
• Snakemake
• Python 3.x
• Singularity (for containerized tool usage)

Setting Up the Environment

1. Install Miniconda (if not already installed):

   • Follow the Miniconda Installation Guide.

2. Clone this repository:

   git clone https://github.com/yourusername/atac-seq-pipeline.git
   cd atac-seq-pipeline

Configuration

The pipeline is configured using a config.yaml file. Here is the provided example configuration:

fastqc_dir: "/home/ubuntu/ATAC-seq-peak-calling/Results/fastqc"
multiqc_dir: "/home/ubuntu/ATAC-seq-peak-calling/Results/multiqc"
trim_dir: "/home/ubuntu/ATAC-seq-peak-calling/Results/trim_reads"
samples:
  sample1:
    pair1: "ENCFF370VYU.fastq.gz"
    pair2: "ENCFF272LNY.fastq.gz"

Snakemake Workflow

The main Snakemake workflow is divided into several rules for different stages of the analysis. The key Snakefiles include:

• data_download_QC: Handles data download and quality control.
• trim_and_filter: Trims and filters raw reads.
• alignment_and_filter_alignment: Aligns reads to the reference genome and filters alignments.
• peak-calling: Performs peak calling using MACS2.

Running the Pipeline

To run the pipeline, execute the following command:

snakemake --use-singularity --cores <number_of_cores> -snakefile <files_mentions_in_workflow>

Note

Replace <number_of_cores> with the number of CPU cores you wish to allocate. Also, make sure to run the command from the directory containing the Snakefile or provide the full path to the Snakefile's directory.

Output

The pipeline generates several output directories as specified in the config.yaml file:

• qc/: Quality control reports from FastQC and MultiQC are stored in the directory specified by fastqc_dir and multiqc_dir.
• alignment/: Aligned reads in BAM format are stored in the directory specified by align_dir.
• peaks/: Peak calling results from MACS2 are stored in the directory specified by peak_dir.
• trim_reads/: Trimmed reads are stored in the directory specified by trim_dir.
• filter_align_reads/: Filtered aligned reads are stored in the directory specified by filter_dir.

Visuslization

Contribution

Contributions are welcome! Please submit a pull request or open an issue to discuss changes.