ChIP-Seq Analysis Pipeline

This pipeline is designed to perform comprehensive ChIP-Seq analysis, including quality control, alignment, peak calling, blacklist filtering, annotation, motif analysis, and visualization. The pipeline is implemented using Nextflow DSL2 and supports paired-end and single-end sequencing data.

Pipeline Overview

This ChIP-Seq analysis pipeline performs the following steps:

1. Download Blacklist Regions: Downloads the genome-specific blacklist file.
2. Quality Control: Performs read quality control using FastQC.
3. Read Trimming: Uses TrimGalore to trim low-quality reads and adapters.
4. Alignment: Aligns reads to the reference genome using Bowtie2.
5. Post-Alignment Processing:
   • Sorting and indexing of BAM files.
   • Removing duplicate reads using Picard.
   • Generating alignment QC metrics.
6. Peak Calling: Calls peaks using MACS2.
7. Blacklist Filtering: Removes peaks that overlap with blacklist regions.
8. Peak Annotation: Annotates peaks using HOMER.
9. Motif Analysis: Identifies enriched motifs using HOMER.
10. Coverage Analysis:
    • Generates BigWig files for visualization.
    • Computes coverage matrices and plots coverage profiles.
11. Correlation Analysis: Generates correlation matrices and plots.

Installation

1. Clone this repository:

   git clone https://github.com/KavyaBanerj/ChIP-Seq-Nexflow-Pipeline/tree/main
   cd <repository-directory>

2. Install Nextflow:

   curl -s https://get.nextflow.io | bash
   mv nextflow ~/bin/

3. Ensure the necessary software and tools are installed (see Requirements).

Requirements

The following tools are required for running the pipeline:

• Nextflow
• FastQC
• TrimGalore
• Bowtie2
• SAMtools
• Picard
• MACS2
• HOMER
• bedtools
• deepTools
• MultiQC
• wget

Ensure these tools are available in your system PATH or in the container used for the pipeline.

Parameters

Parameter	Description	Default Value
reads	Location of input reads (supports glob patterns).	"$PWD/data/reads/*{1,2}.fastq.gz"
outdir	Output directory.	"$PWD/results"
genome	Path to the genome FASTA file.	"$PWD/data/refGenome/mm10.fa"
gtf	Path to the GTF file (optional for ChIP-Seq).	"$PWD/data/refGenome/mm10.gtf"
blacklist_url	URL to download blacklist regions.	"https://raw.githubusercontent.com/..."
blacklist_path	Local path to the blacklist file.	"$PWD/resources/blacklist/mm10-blacklist.v2.bed"
genome_size	Genome size for MACS2 peak calling.	"mm"
bowtie2_threads	Number of threads for Bowtie2 alignment.	4
bowtie2_index	Bowtie2 index directory.	"$PWD/results/bowtie2_index"
keep_dup	MACS2 keep-dup parameter.	"auto"
skip_alignment	Skip alignment step if set to true.	false
test_mode	Run a test process to verify setup.	false
read_type	Specify read type: paired or single.	"paired"

Pipeline Workflow

The pipeline is divided into several processes, each handling a specific task:

1. Download Blacklist Regions

Downloads the genome-specific blacklist file from the specified URL.

2. Quality Control (FastQC)

Runs FastQC to generate quality control reports for the input reads.

3. Read Trimming (TrimGalore)

Trims low-quality bases and adapters from the reads using TrimGalore.

4. Alignment (Bowtie2)

Aligns the trimmed reads to the reference genome using Bowtie2 and converts the output to BAM format using SAMtools.

5. Post-Alignment Processing

• Sorting and Indexing: Sorts and indexes the aligned BAM files.
• Duplicate Removal: Removes duplicate reads using Picard.
• Alignment QC: Generates alignment statistics and indices.

6. Peak Calling (MACS2)

Identifies enriched regions (peaks) using MACS2 with the specified genome size.

7. Blacklist Filtering (bedtools)

Filters out peaks overlapping with blacklist regions.

8. Peak Annotation (HOMER)

Annotates peaks using HOMER, providing information on genomic features.

9. Motif Analysis (HOMER)

Performs motif analysis to identify enriched motifs in the peak regions.

10. Coverage Analysis (deepTools)

• BigWig Generation: Creates BigWig files for visualization.
• Compute Matrix: Computes coverage matrices.
• Plot Coverage Profile: Generates coverage profile plots.

11. Correlation Analysis (deepTools)

Generates correlation matrices and plots based on the coverage data.

Output Structure

The pipeline generates the following output directories:

results/
├── aligned/            # Aligned BAM files and indices
├── annotated_peaks/    # Annotated peak files
├── blacklist/          # Blacklist regions
├── bigwig/             # BigWig files for coverage visualization
├── correlation/        # Correlation matrices and plots
├── filtered_peaks/     # Blacklist-filtered peak files
├── matrix/             # Coverage matrices
├── motifs/             # HOMER motif analysis results
├── peaks/              # MACS2 peak calling results
├── qc/                 # FastQC quality control reports
├── trimmed/            # Trimmed reads

Usage

Run the pipeline using the following command:

nextflow run main.nf --reads "path/to/reads/*{1,2}.fastq.gz" --genome "path/to/genome.fa" --outdir "path/to/output"

To run in test mode:

nextflow run main.nf --test_mode true

Customizing the Pipeline

You can customize the pipeline by modifying the parameters in the main.nf file or by specifying them at runtime using the -- prefix.

Example:

nextflow run main.nf --reads "data/*.fastq.gz"  --read_type paired

Test Mode

The pipeline includes a test process that can be run independently to validate the setup and ensure that the required tools are available.

Dependencies

The pipeline uses the following software containers from Biocontainers. Please refer to the nextflow.config file for the Docker containers used. Ensure these containers are available in your Docker environment.

Future Scope

This ChIP-Seq analysis pipeline is designed to be modular, scalable, and adaptable. Future enhancements and extensions planned for the pipeline include:

1. Spike-in Normalization
   Add support for spike-in controls to normalize ChIP-Seq signals and ensure accurate comparison across samples.

2. Cloud Deployment
   Improve cloud compatibility by creating profiles for AWS to facilitate large-scale data processing and reproducibility in cloud environments.