rlooper-sim-python 1.1.2

Rlooper with R-loop Peak Simulation - Python Implementation

rlooper_sim_python v1.0.5

Rlooper with R-loop Peak Simulation - Python Implementation

Installation

Option 1: Install from PyPI (Recommended)

# Install the package
pip install rlooper-sim-python

# Run single simulation
rlooper-sim example.fasta

# Initialize and run workflow
rlooper-workflow --init-project my_project
cd my_project
rlooper-workflow all

Option 2: Install from Source

# Clone repository
git clone https://github.com/srhartono/rlooper_sim_python.git
cd rlooper_sim_python

# Install in development mode
pip install -e .

# Or install for production
pip install .

Quick Start

After installation, you can use rlooper in two ways:

1. Single File Simulation

# Run simulation on a single FASTA file
rlooper-sim my_sequence.fasta

# Copy example files to current directory
rlooper-sim --copy-examples .
rlooper-sim example.fasta

2. Snakemake Workflow (Multiple Files)

# Initialize a new project
rlooper-workflow --init-project my_rlooper_project
cd my_rlooper_project

# Run the complete workflow
rlooper-workflow all

# Generate DAG visualization
rlooper-workflow dag

# Generate summary report
rlooper-workflow summary

Synopsis

Original Usage (standalone)

python main.py example.fasta

Snakemake Workflow Usage (recommended)

This project now includes a Snakemake workflow that automates the execution of rlooper simulations on multiple FASTA files.

Prerequisites

• Python 3.7 or higher

Setup (One-time)

Choose one of the following setup methods:

Method 1: Automatic setup (recommended)

python setup_venv.py

Method 2: Manual setup

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix/macOS:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Method 3: Platform-specific scripts

• Windows: setup_venv.bat
• Unix/Linux: bash setup_venv.sh

Quick Start

1. Setup (first time only): python setup_venv.py
2. Configure your input files in config.yaml
3. Run the workflow:

python run_workflow.py all

Alternative methods:

• Direct Snakemake: snakemake --cores 1 (if activated manually)
• With venv activation:

  # Windows
  .venv\Scripts\activate
  python run_workflow.py all
  
  # Unix/macOS  
  source .venv/bin/activate
  python run_workflow.py all
  

Adding New Samples

1. Place your FASTA files in the input/ folder
2. Add entries to config.yaml:

   samples:
     my_sample: "my_sample.fasta"    # References input/my_sample.fasta
   

3. Run the workflow: python run_workflow.py all

Project Structure

rlooper_sim_python/
├── input/                    # FASTA input files
│   ├── example.fasta
│   └── example_short.fasta
├── bin/                      # Python source code and energy data
│   ├── main.py
│   ├── simulation.py
│   ├── model.py
│   ├── gene.py
│   ├── structure.py
│   └── energy.csv
├── results/                  # Output directory (created by workflow)
├── config.yaml              # Workflow configuration
├── Snakefile                # Workflow definition
└── run_workflow.py          # Convenience wrapper script

Configuration

Edit config.yaml to specify your FASTA files (relative to the input/ folder):

samples:
  sample1: "sample1.fasta"
  sample2: "sample2.fasta" 
  example: "example.fasta"
  example_short: "example_short.fasta"

Available Commands

Using wrapper script:

• python run_workflow.py all - Run all simulations
• python run_workflow.py summary - Generate summary report
• python run_workflow.py clean - Remove all output files
• python run_workflow.py dry-run - Show what would be executed
• python run_workflow.py dag - Generate workflow DAG visualization

Direct Snakemake commands:

• snakemake --cores 1 - Run all simulations
• snakemake create_summary --cores 1 - Generate summary report
• snakemake clean - Remove all output files

Output Structure

results/
├── <sample_name>/
│   ├── rlooper_output.csv    # Main simulation results
│   ├── rlooper_peaks.csv     # Identified peaks
│   └── energy.csv            # Copy of energy parameters
└── summary_report.txt        # Summary of all samples

DAG Visualization (Optional)

To generate workflow diagrams as PDFs, you need to install Graphviz:

Windows:

# Option 1: Using Chocolatey
choco install graphviz

# Option 2: Using winget
winget install graphviz

# Option 3: Manual download from https://graphviz.org/download/

macOS:

brew install graphviz

Ubuntu/Debian:

sudo apt-get install graphviz

After installing Graphviz:

python run_workflow.py dag  # Creates dag.dot and dag.pdf

Without Graphviz installed:

• python run_workflow.py dag will create dag.dot (text format)
• View online at: http://magjac.com/graphviz-visual-editor/