# <img src="./documentation/logo.webp" alt="logo" height="100"/> **DeepChopper** [](https://github.com/ylab-hi/DeepChopper/stargazers)
[](https://pypi.python.org/pypi/deepchopper)
[](https://pypi.org/project/deepchopper/#files)
[](https://github.com/ylab-hi/DeepChopper/blob/main/LICENSE)
[](https://pypi.python.org/pypi/deepbiop)
[](https://pypi.org/project/deepchopper/#files)
[](https://github.com/ylab-hi/DeepChopper/actions)
[](https://huggingface.co/spaces/yangliz5/deepchopper)
<!--toc:start-->
- [ **DeepChopper** ](#-deepchopper-)
- [🚀 Quick Start: Try DeepChopper Online](#-quick-start-try-deepchopper-online)
- [📦 Installation](#-installation)
- [Compatibility and Support](#compatibility-and-support)
- [PyPI Support](#pypi-support)
- [🛠️ Usage](#️-usage)
- [Command-Line Interface](#command-line-interface)
- [Python Library](#python-library)
- [📚 Cite](#-cite)
- [🤝 Contribution](#-contribution)
- [Build Environment](#build-environment)
- [Install Dependencies](#install-dependencies)
- [📬 Support](#-support)
<!--toc:end-->
🧬 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:
[](https://huggingface.co/spaces/yangliz5/deepchopper)
**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):
```bash
python -m venv deepchopper_env
source deepchopper_env/bin/activate # On Windows use `deepchopper_env\Scripts\activate`
```
3. Install DeepChopper:
```bash
pip install deepchopper
```
4. Verify the installation:
```bash
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][pypi]
| 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](./docs/troubleshooting.md) or [open an issue](https://github.com/ylab-hi/DeepChopper/issues).
## 🛠️ Usage
For a comprehensive guide, check out our [full tutorial](./documentation/tutorial.md).
Here's a quick overview:
### Command-Line Interface
DeepChopper offers three main commands: `encode`, `predict`, and `chop`.
1. **Encode** your input data:
```bash
deepchopper encode <input.fq>
```
2. **Predict** chimeric reads:
```bash
deepchopper predict <input.parquet> --output predictions
```
Using GPUs? Add the `--gpus` flag:
```bash
deepchopper predict <input.parquet> --output predictions --gpus 2
```
3. **Chop** the chimeric reads:
```bash
deepchopper chop <predictions> raw.fq
```
Want a GUI? Launch the web interface (note: limited to one FASTQ record at a time):
```bash
deepchopper web
```
### Python Library
Integrate DeepChopper into your Python scripts:
```python
import deepchopper
model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")
# Your analysis code here
```
## 📚 Cite
If DeepChopper aids your research, please cite our paper:
```bibtex
```
## 🤝 Contribution
We welcome contributions! Here's how to set up your development environment:
### Build Environment
```bash
git clone https://github.com/ylab-hi/DeepChopper.git
cd DeepChopper
conda env create -n environment.yaml
conda activate deepchopper
```
### Install Dependencies
```bash
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](./CONTRIBUTING.md) to get started!
## 📬 Support
Need help? Have questions?
- 📖 Check our [Documentation](./documentation/tutorial.md)
- 🐛 [Report issues](https://github.com/ylab-hi/DeepChopper/issues)
---
DeepChopper is developed with ❤️ by the YLab team.
Happy sequencing! 🧬🔬
[pypi]: https://pypi.python.org/pypi/deepchopper
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No installation required!\nSimply click the button below to launch the web application and start exploring DeepChopper's capabilities:\n\n[](https://huggingface.co/spaces/yangliz5/deepchopper)\n\n**What you can do online:**\n\n- \ud83d\udce4 Upload your sequencing data\n- \ud83d\udd2c Run DeepChopper's analysis\n- \ud83d\udcca Visualize results\n- \ud83c\udf9b\ufe0f Experiment with different parameters\n\nPerfect for quick tests or demonstrations! However, for extensive analyses or custom workflows, we recommend installing DeepChopper locally.\n\n> \u26a0\ufe0f Note: The online version is limited to one FASTQ record at a time and may not be suitable for large-scale projects.\n\n## \ud83d\udce6 Installation\n\nDeepChopper can be installed using pip, the Python package installer.\nFollow these steps to install:\n\n1. Ensure you have Python 3.10 or later installed on your system.\n\n2. Create a virtual environment (recommended):\n\n ```bash\n python -m venv deepchopper_env\n source deepchopper_env/bin/activate # On Windows use `deepchopper_env\\Scripts\\activate`\n ```\n\n3. Install DeepChopper:\n\n ```bash\n pip install deepchopper\n ```\n\n4. Verify the installation:\n\n ```bash\n deepchopper --help\n ```\n\n### Compatibility and Support\n\nDeepChopper is designed to work across various platforms and Python versions.\nBelow are the compatibility matrices for PyPI installations:\n\n#### [PyPI Support][pypi]\n\n| Python Version | Linux x86_64 | macOS Intel | macOS Apple Silicon | Windows x86_64 |\n| :------------: | :----------: | :---------: | :-----------------: | -------------- |\n| 3.10 | \u2705 | \u2705 | \u2705 | \u2705 |\n| 3.11 | \u2705 | \u2705 | \u2705 | \u2705 |\n| 3.12 | \u2705 | \u2705 | \u2705 | \u2705 |\n\n\ud83c\udd98 Trouble installing? Check our [Troubleshooting Guide](./docs/troubleshooting.md) or [open an issue](https://github.com/ylab-hi/DeepChopper/issues).\n\n## \ud83d\udee0\ufe0f Usage\n\nFor a comprehensive guide, check out our [full tutorial](./documentation/tutorial.md).\nHere's a quick overview:\n\n### Command-Line Interface\n\nDeepChopper offers three main commands: `encode`, `predict`, and `chop`.\n\n1. **Encode** your input data:\n\n ```bash\n deepchopper encode <input.fq>\n ```\n\n2. **Predict** chimeric reads:\n\n ```bash\n deepchopper predict <input.parquet> --output predictions\n ```\n\n Using GPUs? Add the `--gpus` flag:\n\n ```bash\n deepchopper predict <input.parquet> --output predictions --gpus 2\n ```\n\n3. **Chop** the chimeric reads:\n\n ```bash\n deepchopper chop <predictions> raw.fq\n ```\n\nWant a GUI? Launch the web interface (note: limited to one FASTQ record at a time):\n\n```bash\ndeepchopper web\n```\n\n### Python Library\n\nIntegrate DeepChopper into your Python scripts:\n\n```python\nimport deepchopper\n\nmodel = deepchopper.DeepChopper.from_pretrained(\"yangliz5/deepchopper\")\n# Your analysis code here\n```\n\n## \ud83d\udcda Cite\n\nIf DeepChopper aids your research, please cite our paper:\n\n```bibtex\n\n```\n\n## \ud83e\udd1d Contribution\n\nWe welcome contributions! Here's how to set up your development environment:\n\n### Build Environment\n\n```bash\ngit clone https://github.com/ylab-hi/DeepChopper.git\ncd DeepChopper\nconda env create -n environment.yaml\nconda activate deepchopper\n```\n\n### Install Dependencies\n\n```bash\npip install pipx\npipx install --suffix @master git+https://github.com/python-poetry/poetry.git@master\npoetry@master install\n```\n\n\ud83c\udf89 Ready to contribute? 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