deepchopper 1.3.2

Genomic language model mitigates chimera artifacts in nanopore direct RNA sequencing

DeepChopper

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

🧬 DeepChopper leverages a language model to accurately detect and chop artificial sequences that 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 bioinformaticians working with Nanopore direct-RNA sequencing data.

✨ What's New in v1.3.0

• 🚀 Direct FASTQ Processing: No more encoding step! DeepChopper now works directly with FASTQ files
• ⚡ Simplified Workflow: Go from raw data to results in just 2 commands (predict → chop)
• 📦 Auto-format Detection: Automatically handles .fastq, .fq, .fastq.gz, and .fq.gz files
• ⚠️ Breaking Change: The encode command has been removed - update your pipelines accordingly

See full changelog →

📘 FEATURED: We provide a comprehensive tutorial that includes an example dataset in our full documentation.

🚀 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
   

Compatibility and Support

DeepChopper is designed to work across various platforms and Python versions. Below are the compatibility matrices for PyPI installations:

PyPI Support

Python Version	Linux x86_64	macOS Intel	macOS Apple Silicon	Windows x86_64
3.10	✅	✅	✅	✅
3.11	✅	✅	✅	✅
3.12	✅	✅	✅	✅

🆘 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

🎉 New in v1.3.0: DeepChopper now works directly with FASTQ files! No encoding step required.

DeepChopper offers two main commands: predict and chop.

1. Predict chimera artifacts directly from FASTQ:

   deepchopper predict input.fastq --output predictions
   

   Using GPUs? Add the --gpus flag:

   deepchopper predict input.fastq --output predictions --gpus 2
   

   Supports all FASTQ formats: .fastq, .fq, .fastq.gz, .fq.gz

2. Chop chimera artifacts:

   deepchopper chop predictions/0 input.fastq
   

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:

@article{li2026genomic,
  title = {Genomic Language Model Mitigates Chimera Artifacts in Nanopore Direct {{RNA}} Sequencing},
  author = {Li, Yangyang and Wang, Ting-You and Guo, Qingxiang and Ren, Yanan and Lu, Xiaotong and Cao, Qi and Yang, Rendong},
  date = {2026-01-19},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-026-68571-5},
  url = {https://www.nature.com/articles/s41467-026-68571-5},
  urldate = {2026-01-20}
}

🤝 Contribution

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

Build Environment

Install UV and Rust

git clone https://github.com/ylab-hi/DeepChopper.git
cd DeepChopper

# Install dependencies
uv sync

# Run DeepChopper
uv run deepchopper --help

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

🔗 Related Projects

• ChimeraLM - Identify artificial chimeric reads from whole genome amplification (WGA) processes

📬 Support

Need help? Have questions?

• 📖 Check our Documentation
• 🐛 Report issues

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