SnakeHichipTF 0.1.1

A Snakemake pipeline for integrating HiChIP differential interaction analysis and TF binding analysis.

SnakeHichipTF

A Comprehensive Snakemake Pipeline for integrating HiChIP / PLAC-Seq Processing and Transcription Factor Binding Analysis

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Overview

SnakeHichipTF is a modular, reproducible, and scalable Snakemake-based workflow for integrative analysis of HiChIP and PLAC-seq datasets, with optional integration of ATAC-seq–based transcription factor (TF) footprinting.

The pipeline supports:

• End-to-end processing from FASTQ → interaction maps → loop calling → TF binding inference
• Multiple mapping engines: HiC-Pro, Chromap
• Multiple loop callers: MAPS, FitHiChIP, HiChipper, HiCDCPlus
• AI-based TF binding inference via scPrinter
• Footprinting-based TF analysis using TOBIAS
• Genome setup utilities (restriction fragments, indices)
• Read downsampling and QC reporting
• Fully reproducible execution via Snakemake

SnakeHichipTF is designed for flexible experimental designs, including bulk HiChIP, PLAC-seq, and integration with matched ATAC-seq data.

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Key Features

1. Modular Architecture

Each analytical stage is implemented as an independent Snakemake rule:

• Genome preparation
• Significant interaction analysis
• Differential interaction analysis
• TF binding and footprint analysis

This allows selective execution and easy customization.

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2. Multi-Algorithm Loop Calling for HiChIP/PLAC-seq

Supported loop callers:

• MAPS
• FitHiChIP
• HiChipper
• HiCDCPlus

Users can benchmark across algorithms within a single workflow.

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3. AI-Based TF Binding Inference (scPrinter)

SnakeHichipTF integrates scPrinter, enabling:

• seq2PRINT model training
• TF binding signal inference
• Motif scanning and TFBS scoring
• Comparative TF activity visualization

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4. ATAC-Seq Footprinting (TOBIAS)

For chromatin accessibility-based TF analysis:

• Bias correction
• Footprint detection
• Motif enrichment
• TF activity scoring

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5. Reproducibility and Scalability

• Snakemake-managed dependency control
• Conda environment integration
• HPC cluster compatibility
• Parallelized execution
• Resume capability

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Installation

1. Clone Repository

git clone https://github.com/YidanSunResearchLab/SnakeHichipTF.git
cd SnakeHichipTF

2. Create Environment

conda create -n SnakeHichipTF -c conda-forge python=3.13
conda activate SnakeHichipTF
pip install .

(Advanced users may use a dedicated environment for GPU-enabled scPrinter workflows.)

3. Verify Installation

Snakehichip --help

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🖥️ Optional: Launch GUI

For a graphical interface:

snakehichiptf-ui

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Quick Start Guide

SnakeHichipTF provides several high-level commands.

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1️⃣ Genome Setup

Prepare genome indices and restriction fragments.

Genomesetup \
  --organism hg38 \
  --organism_fasta /path/to/genome.fa \
  -o ./genome \
  --restriction_enzyme mboi \
  --bin_size 5000 \
  --threads 32

Outputs:

• Indexed genome
• Restriction fragment BED
• Precomputed binning files

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2️⃣ HiChIP / PLAC-Seq Processing

Process FASTQ files into interaction matrices and loop calls.

Snakehichip \
  -i /path/to/input_fastqs/ \
  --genome_dir /path/to/genome \
  -o /path/to/output_directory/ \
  --organism hg38 \
  --bin_size 5000 \
  --threads 32 \
  --downsample_size 50000000

Steps Performed:

1. Read alignment
2. Filtering & deduplication
3. Contact matrix construction
4. Loop calling (multiple algorithms)
5. Quality control reporting

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3️⃣ Differential HiChIP Analysis

Diffhichip \
  -s /path/to/sample_sheet.tsv \
  -o /path/to/output_directory/ \
  --threads 16 \
  --bin_size 5000 \
  --fitType mean \
  --fdr 0.05

Inputs:

• Sample sheet describing sample file paths and condition labels, examples are in the data/ folder

Outputs:

• Differential interaction results
• FDR-filtered loop sets

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4️⃣ TF Binding Analysis via scPrinter

Run AI-based TF binding inference using ATAC-seq input and loop regions.

TFscprinter \
  -o /path/to/output_directory/ \
  --organism hg38 \
  --atacseq_bam "/path/to/sample.bam" \
  --enhancer_bedpe "/path/to/significant_loops.bedpe" \
  --samples "Sample1" \
  --threads 32

Outputs:

• seq2PRINT trained models
• TFBS bigWig tracks
• Motif scanning results
• TFBS score tables
• Scatter plot comparisons

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5️⃣ ATAC-Seq Footprinting with TOBIAS

TFtobias \
  -o /path/to/output_directory/ \
  --organism hg38 \
  --atacseq_bam "/path/to/sample1.bam /path/to/sample2.bam" \
  --enhancer_bedpe "/path/to/sample1.bedpe /path/to/sample2.bedpe" \
  --samples "Sample1 Sample2" \
  --threads 32 \
  --motif "/path/to/JASPAR2024_CORE_non-redundant_pfms.meme"

Outputs:

• Corrected bigWig tracks
• Footprint regions
• Motif enrichment reports
• TF activity scores

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Directory Structure

SnakeHichipTF/
│
├── snakehichiptf/
│   ├── workflows/
│   ├── rules/
│   ├── scripts/
│   ├── docs/
│
└── setup.py

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Documentation

Detailed documentation for each module:

• Genomesetup.py
• Snakehichip.py
• Diffhichip.py
• TFscprinter.py
• TFtobias.py

See the docs/ directory for command-level explanations.

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Recommended Usage Strategy

For large-scale projects:

1. Perform genome setup once.
2. Run Snakehichip for each sample.
3. Aggregate and run Diffhichip if comparing conditions.
4. Perform TFscprinter or TFtobias for mechanistic interpretation.

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Contributing

Issues and pull requests are welcome:

https://github.com/YidanSunResearchLab/SnakeHichipTF

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License

See LICENSE for details.