DeepHoF: A bioinformatics tool to predict the potential hosts of viruses using deep learning techniques
DeepHoF is designed to prediction the potential host types (plant, germ, invertebrate, vertebrate, human) of a given virus, which is represented by its nucleotide sequences. The tool will provide five scores and the corresponding p-values which reflect the propobilities of the virus infecting each host type. In addition, the score pattern and the p-value pattern reflect the infectivity pattern of the given virus.
- DeepHoF 1.0 (Tested on Ubuntu 16.04)
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MATLAB Component Runtime (MCR) R2018a or MATLAB R2018a
Note: (1) DeepHoF should be run under Linux operating system. (2) For compatibility, we recommend installing the tools with the similar version as described above. (3) If GPU is available in your machine, we recommend installing a GPU version of the TensorFlow to speed up the program.
First, please install numpy, h5py, pandas, TensorFlow and Keras according to their manuals. All of these are python packages, which can be installed with pip. If “pip” is not already installed in your machine, use the command “sudo apt-get install python-pip python-dev” to install “pip”. Here are example commands of installing the above python packages using “pip”.
pip install numpy
pip install h5py
pip install pandas
pip install tensorflow==1.4.0 #CPU version
pip install tensorflow-gpu==1.4.0 #GPU version
pip install keras==2.1.3
If you are going to install a GPU version of the TensorFlow, specified NVIDIA software should be installed. See https://www.tensorflow.org/install/install_linux to know whether your machine can install TensorFlow with GPU support.
To run DeepHoF, please see https://www.mathworks.com/support/ to install the MATLAB.
Clone DeepHoF package
git clone https://github.com/qianguopku/DeepHoF.git
Change directory to DeepHoF:
cd DeepHoF
All scripts are under the folder.
Nucleotide sequence
python DeepHoF.py <input_file_folder>/input_file.fna <output_file_folder>/output_file.tsv
The output of DeepHoF consists of 11 columns:
| Header | plant_score | germ_score | invertebrate_score | vertebrate_score | human_score | plant_pvalue | germ_pvalue | invertebrate_pvalue | vertebrate_pvalue | human_pvalue |
|---|
The content in Header column is the same with the header of corresponding sequence in the input file. With the input of viral nucleotide sequence, DeepHoF will output five scores for each host type, reflecting the infectivity within each host type respectively. Furthermore, DeepHoF provides five p-values, statistical measures of how distinct the infections are compared with non-infection events.
- Guo Q, Li M, Wang C, et al. Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic. Sci Rep. 2021;11(1):17422.
- Guo Q, Li M, Wang C, et al. Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm. Biorxiv. 2020.01.21.914044.
Any question, please do not hesitate to contact me: qianguo@pku.edu.cn.