HiC-sm

A Snakemake workflow for processing Hi-C sequencing data from raw FASTQ files to analysis-ready contact matrices and downstream analyses.

Overview

HiC-sm is a comprehensive, modular Snakemake pipeline designed for Hi-C data processing. It supports multiple alignment strategies, quality control, contact matrix generation, and downstream analyses including loop detection and distance-dependent contact analysis.

Features

• Multiple Alignment Strategies: Supports BWA-MEM, BWA-MEM2, BWA-MEME, Bowtie2 (with rescue mapping), and Chromap
• Quality Control: Integrated fastp for read trimming and MultiQC for comprehensive QC reporting
• Flexible Processing:
  • Automatic aggregation of multiple runs per sample
  • Support for paired-end and single-end reads
  • Configurable ligation site handling
• Contact Matrix Generation:
  • Cooler format (.mcool) with multiple resolutions
  • Optional .hic format output
  • Multiple filtering strategies
• Downstream Analysis:
  • Mustache loop detection
  • Distance-dependent contact analysis
  • Scaling analysis
• Modular Architecture: Clean separation of concerns with domain-specific rule files
• PEP Integration: Uses Portable Encapsulated Project (PEP) for sample metadata management

Requirements

• Snakemake >= 8.20.1
• Conda or Mamba for environment management
• Python 3.11+
• Genome reference files (FASTA, BWA/Bowtie2 indices, chromosome sizes)

Installation

1. Clone the repository:

git clone <repository-url>
cd hic_sm

2. Install Snakemake (if not already installed):

conda install -c conda-forge -c bioconda snakemake
# or
mamba install -c conda-forge -c bioconda snakemake

3. The workflow uses conda environments defined in workflow/envs/. These will be automatically created when you run the workflow.

Quick Start

1. Prepare Sample Sheet

Create a CSV file with sample information (see config/hg38_test_sample.csv for example):

sample_name,run,R1,R2,ligation_site,skip_ligation
sample1,1,/path/to/sample1_R1.fastq.gz,/path/to/sample1_R2.fastq.gz,GATCGATC,false
sample2,1,/path/to/sample2_R1.fastq.gz,/path/to/sample2_R2.fastq.gz,GATCGATC,false

Required columns:

• sample_name: Unique sample identifier
• run: Run number (integer)
• R1, R2: Paths to forward and reverse FASTQ files (or fastq1, fastq2)

Optional columns:

• ligation_site: Ligation site sequence (overrides config default)
• skip_ligation: Boolean to skip ligation site processing
• passqc: QC flag (1 = pass, 0 = fail)

2. Configure Workflow

Edit config/test_config.yml to set:

• Genome assembly and reference paths
• Mapper selection (bwa-mem, bwa-mem2, bwa-meme, bowtie2, chromap)
• Output directories
• Resolution bins
• Filtering options

3. Set Up PEP Configuration

Configure your PEP project file (default: pep/project_config.yaml) to define:

• Sample table path
• Path variables (e.g., database_dir, process_dir)

4. Run the Workflow

Dry-run to check the workflow:

snakemake -n

Execute the workflow:

snakemake --use-conda --cores <number_of_cores>

For cluster execution:

snakemake --use-conda --profile <profile> --cores <number_of_cores>

Contributing

Contributions are welcome! Please ensure code follows the modular structure and includes appropriate documentation.