VirNet

VirNet: A deep attention model for viral reads identification

This tool is able to identifiy viral sequences from a mixture of viral and bacterial sequences. Also, it can purify viral metagenomic data from bacterial contamination

Dependencies

Python 3.6, Tensorflow, Keras, Pandas and BioPython

Installation

To download and install the package

git clone https://github.com/alyosama/virnet
cd virnet
pip3 install -r requirments.txt

Usage

The input of VirNet is the fasta file, and the output is a .csv file containing scores and prediction for each read. you can have to specify the input dimention you want to work with flag --input_dim {100,500,1000 or 3000}.

python predict.py --input_dim=500 --input=data/test/data.fna --output=output.csv

For Re-Training

python train.py --input_dim=<n> --data=<data_folder> --work_dir=<work_dir>

Reference and Citation

please cite this paper, if you use our method:

@inproceedings{abdelkareem2018virnet,
  title={VirNet: Deep attention model for viral reads identification},
  author={Abdelkareem, Aly O and Khalil, Mahmoud I and Elaraby, Mostafa and Abbas, Hazem and Elbehery, Ali HA},
  booktitle={2018 13th International Conference on Computer Engineering and Systems (ICCES)},
  pages={623--626},
  year={2018},
  organization={IEEE}
}

Author:

Aly O. Abdelkareem

License

Apache-2.0