SpikeHunter: A Deep Learning Tool for Identifying Phage Tailspike Proteins
SpikeHunter utilizes a deep learning approach to identify phage tailspike proteins. It achieves this by using a protein language model, ESM-2 transformer, which can detect patterns in protein sequences. The PyTorch framework enables efficient processing of large datasets, making SpikeHunter a valuable tool for researchers studying phage-host interactions.
Table of Contents
Installation
To install SpikeHunter, follow these steps:
- Install the required dependencies:
conda env create --file environment.yml
# or if you have mamba installed
# mamba env create --file environment.yml
- Clone the repository:
git clone https://github.com/nlm-irp-jianglab/SpikeHunter.git
- Change into the directory:
cd SpikeHunter
Usage
usage: SpikeHunter.py [-h] [-c CONFIG] [-r RESUME] [-d DEVICE]
SpikeHunter
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
config file path (default: None)
-r RESUME, --resume RESUME
path to latest checkpoint (default: None)
-d DEVICE, --device DEVICE
indices of GPUs to enable (default: all)
Simply run SpikeHunter using the following command:
# The input and output files are in folder `test/`
python SpikeHunter.py -c config.yaml -r trained_model/model_best.pth
Features
- Identifies tailspike proteins in phage sequences using advanced algorithms.
- Supports analysis of multiple phage genomes in a single run.
- Exports results in a TSV format for easy downstream analysis.
Dependencies
SpikeHunter requires the following Python libraries:
- Pytorch
- Pandas
- Numpy
License
SpikeHunter is released under the MIT License.
Citation
If you use SpikeHunter in your research, please cite it as follows: To be added
Credits
This project uses code adapted from the pytorch-template repository by Victor Huang. We thank Victor Huang for providing this useful code as a starting point for our project.