deepchopper 1.2.1

A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing

DeepChopper

• DeepChopper
  • 🚀 Quick Start: Try DeepChopper Online
  • 📦 Installation
  • 🛠️ Usage
    • Command-Line Interface
    • Python Library
  • 📚 Cite
  • 🤝 Contribution
    • Build Environment
    • Install Dependencies
  • 📬 Support

🧬 DeepChopper leverages language model to accurately detect and chop artificial sequences which may cause chimeric reads, ensuring higher quality and more reliable sequencing results. By integrating seamlessly with existing workflows, DeepChopper provides a robust solution for researchers and bioinformatics working with NanoPore direct-RNA sequencing data.

🚀 Quick Start: Try DeepChopper Online

Experience DeepChopper instantly through our user-friendly web interface. No installation required! Simply click the button below to launch the web application and start exploring DeepChopper's capabilities:

What you can do online:

• 📤 Upload your sequencing data
• 🔬 Run DeepChopper's analysis
• 📊 Visualize results
• 🎛️ Experiment with different parameters

Perfect for quick tests or demonstrations! However, for extensive analyses or custom workflows, we recommend installing DeepChopper locally.

⚠️ Note: The online version is limited to one FASTQ record at a time and may not be suitable for large-scale projects.

📦 Installation

DeepChopper can be installed using pip, the Python package installer. Follow these steps to install:

1. Ensure you have Python 3.10 or later installed on your system.

2. Create a virtual environment (recommended):

   python -m venv deepchopper_env
   source deepchopper_env/bin/activate  # On Windows use `deepchopper_env\Scripts\activate`
   

3. Install DeepChopper:

   pip install deepchopper
   

4. Verify the installation:

   deepchopper --help
   

🆘 Trouble installing? Check our Troubleshooting Guide or open an issue.

🛠️ Usage

For a comprehensive guide, check out our full tutorial. Here's a quick overview:

Command-Line Interface

DeepChopper offers three main commands: encode, predict, and chop.

1. Encode your input data:

   deepchopper encode <input.fq>
   

2. Predict chimeric reads:

   deepchopper predict <input.parquet> --output-path predictions
   

   Using GPUs? Add the --gpus flag:

   deepchopper predict <input.parquet> --output-path predictions --gpus 2
   

3. Chop the chimeric reads:

   deepchopper chop <predictions> raw.fq
   

Want a GUI? Launch the web interface (note: limited to one FASTQ record at a time):

deepchopper web

Python Library

Integrate DeepChopper into your Python scripts:

import deepchopper

model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")
# Your analysis code here

📚 Cite

If DeepChopper aids your research, please cite our paper:

🤝 Contribution

We welcome contributions! Here's how to set up your development environment:

Build Environment

git clone https://github.com/ylab-hi/DeepChopper.git
cd DeepChopper
conda env create -n environment.yaml
conda activate deepchopper

Install Dependencies

pip install pipx
pipx install --suffix @master git+https://github.com/python-poetry/poetry.git@master
poetry@master install

🎉 Ready to contribute? Check out our Contribution Guidelines to get started!

📬 Support

Need help? Have questions?

• 📖 Check our Documentation
• 💬 Join our Community Forum
• 🐛 Report issues

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DeepChopper is developed with ❤️ by the YLab team. Happy sequencing! 🧬🔬