pyRNAither

Python reimplementation of RNAither adapted for high-throughput CRISPR screening analysis

📖 Overview

pyRNAither is a powerful Python package for analyzing high-throughput CRISPR screening data. It provides a comprehensive suite of tools for quality control, normalization, statistical analysis, and visualization of CRISPR screening results.

✨ Features

• Data Processing: Efficient handling of large-scale CRISPR screening data
• Quality Control: Comprehensive QC metrics and visualization
• Normalization: Multiple normalization methods for CRISPR data
• Statistical Analysis: Advanced statistical tests for hit identification
• Visualization: Publication-quality plots and interactive visualizations
• Modular Design: Easy to extend and customize for specific needs

🚀 Installation

Using pip

pip install pyRNAither

From source

# Clone the repository
git clone https://github.com/Takshan/pyRNAither.git
cd pyRNAither

# Install in development mode
pip install -e .

📚 Documentation

For detailed documentation, including API reference and examples, please visit our documentation.

🎯 Quick Start

We are currently wokring on a quick start guide consisting of example analysis pipelines and a step-by-step guide to get started with pyRNAither. In the meantime, please refer to the documentation.

Basic Usage

import pyrnaither as prt

#load data

Example Analysis Pipeline

import pyrnaither as prt
from pyrnaither import stats, visualization

# Load and preprocess data

📊 Example Plots

Quality Control Metrics

Volcano Plot

Heatmap

📄 License

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

📧 Contact

For questions or support, please contact:

• Rahul Brahma: rahul.brahma[at]uni-greifswald[dot]de
• Yasas Wijesekara: yasas.wijesekara[at]uni-greifswald[dot]de

📚 References

1. Rieber N, Knapp B, Eils R, Kaderali L. RNAither, an automated pipeline for the statistical analysis of high-throughput RNAi screens. Bioinformatics. 2009 Mar 1;25(5):678-9. doi: 10.1093/bioinformatics/btp014. Epub 2009 Jan 25. PMID: 19168909.