DMN-seq Data Processing Pipeline

This repository contains comprehensive data processing workflows for DMN-seq analysis. The pipeline provides two distinct analytical approaches for methylation profiling:

1. 5mC Detection (5mC_detection/) - Quantitative detection and analysis of 5-methylcytosine modifications
2. Hypomethylation Enrichment (hypomethylation_enrichment/) - Identification and characterization of hypomethylated regions

Installation

Prerequisites

• Python 3.10
• Snakemake 7.32.4
• Mamba or Conda package manager (Mamba recommended for faster dependency resolution)

Environment Setup

Create and activate the required environment:

# Using mamba (recommended)
mamba env create -f environment.yml
mamba activate dmnseq

# Or using conda
conda env create -f environment.yml
conda activate dmnseq

Configuration

Before running the analysis, configure the pipeline parameters:

1. Update workflow configuration: Edit config.yaml in your chosen analysis directory
2. Configure reference genome paths: Specify genome assembly and annotation files
3. Set software paths: Update paths to required bioinformatics tools
4. Prepare input data: Place raw sequencing files in the raw_data/ directory

Example configuration files are provided in each analysis subdirectory.

Usage

Option 1: Cluster Execution (Recommended for HPC)

The pipeline is optimized for SLURM-based high-performance computing environments (tested on University of Chicago Midway3 cluster):

cd hypomethylation_enrichment/  # or 5mC_detection/
snakemake --profile ../snakemake_config

Note: Modify ./snakemake_config/config.yaml to match your cluster specifications.

Option 2: Local Execution

For local analysis or smaller datasets:

cd hypomethylation_enrichment/  # or 5mC_detection/
snakemake --cores 12  # adjust core count as appropriate

Workflow Overview

Hypomethylation Enrichment Analysis

Identifies and analyzes regions with reduced methylation levels, suitable for:

• Hypomethylated region detection
• Differential methylation analysis
• Methylation landscape characterization

5mC Detection Analysis

Quantitative analysis of 5-methylcytosine modifications, optimized for:

• Single-base resolution methylation calling
• Quantitative methylation profiling
• Context-specific methylation analysis

Output Structure

The pipeline generates structured output directories containing:

• Quality control reports
• Processed alignment files
• Methylation call files
• Statistical analysis results
• Visualization plots and summaries

License

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

Citation

If you use the method, please cite the paper:

@article{wang2026_dmnseq,
  author  = {Wang, Y. and Li, Y. and Ye, C. and others},
  title   = {DMN-seq enriches DNA hypomethylated regions for biomarker discovery using 5-methylcytosine glycosylase},
  journal = {Genome Biology},
  year    = {2026},
  doi     = {10.1186/s13059-026-03991-6},
  url     = {https://doi.org/10.1186/s13059-026-03991-6}
}

If you use the software in this repository, please cite the software:

@software{yangli_2025_dmnseq_zenodo.17211256,
  author       = {Yang Li},
  title        = {v1.0.0},
  publisher = {Zenodo},
  year         = {2025},
  doi          = {10.5281/zenodo.17211256},
  url          = {https://doi.org/10.5281/zenodo.17211256},
}

Support

For questions, issues, and comments, please refer to the repository's issue tracker or contact Yang Li (U Chicago) yliuchicago@uchicago.edu.