DeepHoF: Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic
DeepHoF (using deep learning to virus-host finder) is designed to predict 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 infection likelihood profile the given virus is provided.
- DeepHoF 1.0 (Tested on Ubuntu 16.04)
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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
Or you can use the command conda env create -p DeepHoF -f DeepHoF_env.yaml to automatically install all the prerequisites of DeepHoF.
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/PKUbioinfo-ZhuLab/DeepHoF.git
Change directory to DeepHoF:
cd DeepHoF/DeepHoF
All scripts are under the folder.
Nucleotide sequence
Please execute the following command directly in MATLAB command window:
DeepHoF('<input_file_folder>/input_file.fna','<output_file_folder>/output_file.tsv')
For example, if you want to identify the sequences in "example.fna", please execute:
DeepHoF('example.fna','result.tsv')
Please remember to set the working path of MATLAB to DeepHoF folder before running the programme.
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.
DeepHoF is also available at our website http://cqb.pku.edu.cn/ZhuLab/DeepHoF/ and the Dryad git repository https://datadryad.org/stash/share/Rzua1ir-vRpkiUhODvO7Swd8lxEYdVnGwvl7wiYIX9c. If you have some problems downloading DeepHoF from GitHub and if you want to use the big training and test datasets of DeepHoF, you can go to the alternatives.
Please direct your questions to us, hqzhu@pku.edu.cn or qianguo@pku.edu.cn.