ChIP-seq Analysis Pipeline

A reproducible, modular ChIP-seq analysis pipeline for understanding transcription factor binding, built with Nextflow, designed for alignment, QC, peak calling, annotation, motif analysis, and signal-based visualization. The workflow supports automated processing of multiple replicates using user-provided metadata and reference files.

Table of Contents

1. Features
2. Workflow Visualization
3. Requirements
4. Installation
5. Usage
6. Configuration
7. Output Figures
8. Contributing

Features

• Modular Nextflow pipeline with clear separation of steps:
  • Read QC (FastQC + MultiQC)
  • Adapter trimming
  • Alignment with Bowtie2
  • Sorted BAM generation and indexing
  • Coverage track generation (BigWig)
  • Multi-sample correlation and signal profiling
  • Peak calling (HOMER)
  • Peak intersection across replicates
  • Blacklist removal
  • Peak annotation
  • Motif analysis
• Docker/Singularity container support for reproducibility
• Automatic logging and error handling
• Scalable to large RNA-seq datasets
• Supports both BU SCC and AWS Batch execution

Workflow Visualization

Requirements

• Must have a conda environment with nextflow in order to run nextflow
• Modules already installed on BU Shared Computing cluster (SCC)
• If using aws, see envs file for all packages to install
• If not using BU SCC, see envs directory for software and version information

Installation

• Clone this repository in the SCC
• git clone 'https://github.com/JackSherry6/Waxman-Lab-snRNA-seq-SNP-Calling-Pipeline.git'

Usage

Basic execution:

• module load miniconda
• conda activate <name_of_your_nexflow_conda_env>
• Add your samples to samplesheet in the format specified in csv file
• Set all params in config files to the locations of your files
• nextflow run main.nf -profile conda,cluster (for waxman lab you should always run on the cluster, but if using aws, substitute aws for cluster)

Configuration

• Edit the nextflow.config file to:
  • Set input paths (reads, gtf, blacklist, etc...)
  • Adjust queueSize based on the number of samples
  • Optionally set resume = true to continue interrupted runs

Example Output Figures

Post processing example figures (not generated by pipeline directly):

Contributing

• Email me at jgsherry@bu.edu for additional information or contributing information