gynecoloji/chipseq_pipeline repository overview

⁠ChIP-seq Analysis Pipeline

A comprehensive, production-ready Snakemake workflow for ChIP-seq data analysis, fully containerized and optimized for both local Docker execution and HPC deployment via Singularity.

⁠🚀 Quick Start

Pull the image:

docker pull gynecoloji/chipseq_pipeline:v1.0

Run the pipeline:

docker run --rm \
    -v $(pwd)/data:/pipeline/data:ro \
    -v $(pwd)/ref:/pipeline/ref:ro \
    -v $(pwd)/results:/pipeline/results \
    -v $(pwd)/logs:/pipeline/logs \
    gynecoloji/chipseq_pipeline:v1.0 \
    --cores 8 --use-conda -s /pipeline/snakefile_tolerant_ChIPseq -p

For HPC (Singularity):

singularity pull chipseq_pipeline.sif docker://gynecoloji/chipseq_pipeline:v1.0
singularity run --bind $(pwd)/data:/pipeline/data chipseq_pipeline.sif --cores 8 --use-conda -s /pipeline/snakefile_tolerant_ChIPseq -p

⁠✨ Features

• Complete ChIP-seq workflow: FastQC → Fastp → HISAT2 → Filtering → MACS2 → BigWig
• Flexible peak calling: Supports both narrow (TFs) and broad (histone marks) modes
• Input control support: Optional normalization for accurate peak calling
• Quality control: Comprehensive QC at multiple pipeline steps
• Blacklist filtering: Removes reads from ENCODE blacklist regions
• PCR duplicate removal: Picard-based deduplication
• Visualization ready: Generates BigWig tracks for genome browsers
• HPC compatible: Fully tested with Singularity on SLURM clusters

⁠🔧 Included Tools

⁠📋 Requirements

Input files:

• Paired-end FASTQ files (.fastq.gz)
• Sample metadata CSV (samples.csv)
• HISAT2 genome index
• ENCODE blacklist BED file
• Configuration file (config.yaml)

System requirements:

• 16GB+ RAM recommended
• 8+ CPU cores for optimal performance
• 50GB+ storage for intermediate files

⁠📂 Directory Structure

Mount these directories when running:

/pipeline/data     - Input FASTQ files
/pipeline/ref      - Reference files (index, blacklist, config)
/pipeline/results  - Output directory
/pipeline/logs     - Log files

⁠🎯 Use Cases

1. Transcription Factor ChIP-seq - Narrow peak calling mode
2. Histone Modification ChIP-seq - Broad peak calling mode
3. With/without input controls - Flexible normalization
4. Multi-sample comparisons - Batch processing support

⁠📖 Documentation

Full documentation: https://github.com/gynecoloji/Snakemake_ChIP_seq_containerization⁠

Includes:

• Detailed installation instructions
• Sample configuration examples
• SLURM job submission scripts
• Troubleshooting guide
• Advanced usage options

⁠💡 Example Workflow

1. Prepare your data and reference files
2. Create samples.csv with sample metadata
3. Configure ref/config.yaml with paths
4. Run pipeline with desired number of cores
5. Find results in organized output directories

⁠🔬 Output

The pipeline generates:

• Quality control reports (FastQC, Fastp)
• Alignment statistics
• Deduplicated BAM files
• Peak calls (narrowPeak/broadPeak formats)
• RPGC-normalized BigWig tracks
• Input-normalized BigWig files (when controls available)
• Comprehensive QC summary

⁠🏆 Highlights

• Production-ready: Extensively tested on real ChIP-seq datasets
• Reproducible: Containerized for consistency across systems
• HPC-optimized: Designed for high-performance computing environments
• Well-documented: Comprehensive README and inline documentation
• Actively maintained: Regular updates and bug fixes

⁠📞 Support

• GitHub Issues: https://github.com/gynecoloji/Snakemake_ChIP_seq_containerization/issues⁠
• Email: [email protected]⁠
• Documentation: Full README with troubleshooting guide

⁠📜 License

MIT License - Free for academic and commercial use

⁠🙏 Acknowledgments

Built with tools from the Bioconda community and ENCODE Consortium resources.

Tags: chipseq bioinformatics snakemake peak-calling macs2 hisat2 genomics ngs hpc singularity

Image Size: ~4 GB (includes all bioinformatics tools and conda environments)

Last Updated: January 2026