🧬 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.
📘 FEATURED: We provide a comprehensive tutorial that includes an example dataset in our full documentation.
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.
DeepChopper can be installed using pip, the Python package installer. Follow these steps to install:
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Ensure you have Python 3.10 or later installed on your system.
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Create a virtual environment (recommended):
python -m venv deepchopper_env source deepchopper_env/bin/activate # On Windows use `deepchopper_env\Scripts\activate`
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Install DeepChopper:
pip install deepchopper
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Verify the installation:
deepchopper --help
DeepChopper is designed to work across various platforms and Python versions. Below are the compatibility matrices for PyPI installations:
| 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.
For a comprehensive guide, check out our full tutorial. Here's a quick overview:
DeepChopper offers three main commands: encode, predict, and chop.
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Encode your input data:
deepchopper encode <input.fq>
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Predict chimera artifacts:
deepchopper predict <input.parquet> --output predictions
Using GPUs? Add the
--gpusflag:deepchopper predict <input.parquet> --output predictions --gpus 2
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Chop chimera artifacts:
deepchopper chop <predictions> raw.fq
Want a GUI? Launch the web interface (note: limited to one FASTQ record at a time):
deepchopper webIntegrate DeepChopper into your Python scripts:
import deepchopper
model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")
# Your analysis code hereIf DeepChopper aids your research, please cite our paper:
@article {Li2024.10.23.619929,
author = {Li, Yangyang and Wang, Ting-You and Guo, Qingxiang and Ren, Yanan and Lu, Xiaotong and Cao, Qi and Yang, Rendong},
title = {A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing},
elocation-id = {2024.10.23.619929},
year = {2024},
doi = {10.1101/2024.10.23.619929},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) data can confound transcriptome analyses, yet no existing tools are capable of detecting and removing them due to limitations in basecalling models. We present DeepChopper, a genomic language model that accurately identifies and eliminates adapter sequences within base-called dRNA-seq reads, effectively removing chimeric read artifacts. DeepChopper significantly improves critical downstream analyses, including transcript annotation and gene fusion detection, enhancing the reliability and utility of nanopore dRNA-seq for transcriptomics research. Competing Interests: The authors have declared no competing interests.},
URL = {https://www.biorxiv.org/content/early/2024/10/25/2024.10.23.619929},
eprint = {https://www.biorxiv.org/content/early/2024/10/25/2024.10.23.619929.full.pdf},
journal = {bioRxiv}
}We welcome contributions! Here's how to set up your development environment:
git clone https://github.com/ylab-hi/DeepChopper.git
cd DeepChopper
conda env create -n environment.yaml
conda activate deepchopperpip 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!
Need help? Have questions?
- 📖 Check our Documentation
- 🐛 Report issues
DeepChopper is developed with ❤️ by the YLab team. Happy sequencing! 🧬🔬