deepchopper 1.0.1

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

DeepChopper

• DeepChopper
  • Install
  • Usage
    • Command-Line Interface
    • Library
  • Cite
  • 🤜 Contribution
    • Build Environment
    • Install Dependencies

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.

Install

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. It's recommended to create a virtual environment:

   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
   

5. DeepChopper include a Rust command line tool for faster performance.

cargo install deepchopper-chop

For GPU support, ensure you have CUDA installed on your system, then install the GPU version:

pip install deepchopper[gpu]

Note: If you encounter any issues, please check our GitHub repository for troubleshooting guides or to report a problem.

Usage

We provide a complete guide on how to use DeepChopper for NanoPore direct-RNA sequencing data. Below is a brief overview of the command-line interface and library usage.

Command-Line Interface

DeepChopper provides a command-line interface (CLI) for easy access to its features. In total, there are three commands: encode, predict, and chop. DeepChopper can be used to encode, predict, and chop chimeric reads in direct-RNA sequencing data.

Firstly, we need to encode the input data using the encode command, which will generate a .parquet file.

deepchopper endcode <input.fq>

Next, we can use the predict command to predict chimeric reads in the encoded data.

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

If you have GPUs, you can use the --gpus flag to specify the GPU device.

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

Finally, we can use the chop command to chop the chimeric reads in the input data.

deepchopper chop <predictions> raw.fq

Besides, DeepChopper provides a web-based user interface for users to interact with the tool. However, the web-based application can only take one FASTQ record at a time.

deepchopper web

Library

import deepchopper

model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")

Cite

🤜 Contribution

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