PhaGCN is a GCN based model, which can learn the species masking feature via deep learning classifier, for new Phage taxonomy classification. To use PhaGCN, you only need to input your contigs to the program.

ATTNETION!!!

The program has been updated and move to (PhaBOX)[https://github.com/KennthShang/PhaBOX], which is more user-friendly. Hope you will enjoy it. Also, an idenpendent PhaGCN with latest ICTV can be found in PhaGCN_newICTV. But we still encourage you to use PhaBOX for your convenience. This folder will be no longer maintained.
Our web server for phage-related tasks (including phage identification, taxonomy classification, lifestyle prediction, and host prediction) is available! You can visit [https://phage.ee.cityu.edu.hk/] to use the GUI. We also provided more detailed intermediate files and visualization for further analyzation.

Required Dependencies

An easiler way to install

We recommend you to install all the package with Anaconda

After cloning this respository, you can use anaconda to install the environment.yaml. This will install all packages you need with gpu mode (make sure you have installed cuda on your system).

  conda env create -f environment.yaml -n phagcn
  conda activate phagcn

If you want to use the gpu to accelerate the program:

cuda
Pytorch-gpu

Note: please install the pytorch with correct cuda version corresponding to your system

Usage (example)

Here we present an example to show how to run PhaGCN. We support a file named "contigs.fa" in the Github folder and it contain contigs simulated from E. coli phage. The only command that you need to run is python run_Speed_up.py --contigs contigs.fa --len 8000.

There are two parameters for the program: 1. --contigs is the path of your contigs file. 2. --len is the length of the contigs you want to predict. As shown in our paper, with the length of contigs increases, the recall and precision increase. We recommend you to choose a proper length according to your needs. The default length is 8000bp and the minimum length is 2000bp.

The output file is final_prediction.csv. There are three column in this csv file: "contig_name, median_file_name, prediction".

PhaGCN will only trained on the current given database. But you can update the database if required.

Notice

If you want to use PhaGCN, you need to take care of two things:

Make sure all your contigs are virus contigs. You can separate bacteria contigs by using PhaMer
The script will pass contigs with non-ACGT characters, which means those non-ACGT contigs will be remained unpredict.
if the program output an error (which is caused by your machine): Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library. You can type in the command export MKL_SERVICE_FORCE_INTEL=1 before runing run_Speed_up.py

References

how to cite this tool:

Jiayu Shang, Jingzhe Jiang, Yanni Sun, Bacteriophage classification for assembled contigs using graph convolutional network, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i25–i33, https://doi.org/10.1093/bioinformatics/btab293

Supplementary information

The supplementary file of the paper can be found in the supplementary folder.

Contact

If you have any questions, please email us: jyshang2-c@my.cityu.edu.hk

KennthShang/PhaGCN