DOI: 10.5061/dryad.zgmsbcc8v
title: Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic

description: The SARS-CoV-2 pandemic has raised the concern for reservoir hosts of the
 virus since the early-stage outbreak. To address this problem, we proposed
 a deep learning method, DeepHoF, based on extracting the viral genomic
 features, to calculate the infection likelihoods and further predict the
 probable hosts of novel viruses. Overcoming the limitation of sequence
 similarity-based methods, DeepHoF was applied to the analysis of
 SARS-CoV-2 in the 2020 pandemic. Using the isolates sequenced in the
 earliest stage of COVID-19, DeepHoF identified minks, bats, dogs and cats
 can be highly susceptible to SARS-CoV-2, while minks might be one of the
 most noteworthy reservoir hosts. Several genes of SARS-CoV-2 demonstrated
 their significance in determining the infection likelihood on human or the
 host range. With a large-scale genome analysis based on DeepHoF’s
 computation for the later world-wide pandemic, it should not be slighted
 for the probably bidirectional transmission of SARS-CoV-2 between humans
 and minks.
description: Datasets
 construction for training and test
 We downloaded 63,049
 whole viral genomes from GenBank by 9 July, 2019, and tagged them with
 five host labels (plant, germ, invertebrate, non-human vertebrate and
 human), which were integrated from the host metadata provided by GenBank
 (Supplementary Table 3). For viruses infecting multiple host types,
 multiple labels were given. Following the data collection procedure, short
 fragments were generated randomly from those tagged whole genomes because
 of the computational cost in long sequence processing. The training set
 was constructed with short fragments from 55,283 genomes released before 1
 January, 2018, and the test set was constructed with the rest (the
 Accession list and the host information of the genomes used for training
 and test are in Supplementary Data 6). Mathematical
 representation of viral whole genomes Due to the long-term
 adaptation to natural reservoirs, viruses share some evolutionary
 signatures in nucleotide sequences, such as codon pair, dinucleotide,
 codon, and amino acid biases, with their natural
 reservoirs<sup>17</sup>. Besides,
 viral proteins, especially the receptors that are effectively attached to
 the host cell membrane, are crucial factors for viruses to invade and
 infect the host cells<sup>39</sup>.
 In brief, the genome compositions of viruses can inform host-virus
 correlation. Herein, we represent a
 given viral sequence with a base one-hot matrix (BOH) and a codon one-hot
 matrix (COH), digitizing the genetic information of the virus on
 nucleotide and codon level respectively. To start with, bases and codons
 are encoded with one-hot format to work with deep learning algorithms. In
 the coding of BOH, each consecutive base of a query sequence linked by its
 complementary strand is encoded by one-hot. For COH, we do not extract
 ORFs since coding sequences make up most of the viral genome. Instead, we
 directly concatenate the six phases of the input sequence (Supplementary
 Fig. 3), and then each consecutive codon of the joined sequences is
 encoded by one-hot. Consequently, for an input sequence
 of length L, it will be transformed to a BOH matrix, with the size of
 2L×4, and a COH matrix, with the size of 2L×64.
 BiPathCNN Model
 descriptions In building the framework
 of DeepHoF, we firstly utilize a
 BiPathCNN<sup>40</sup>, containing
 two CNN paths, digging information from the BOH matrix and COH matrix
 respectively. The information is naturally corresponding to the viral
 genomic features for the viruses which infect the same kind of hosts.
 After independent convolution and pooling operations at the beginning, the
 two paths are combined by a concatenation layer. Following a normalization
 layer, five prediction scores will be provided by five sub-paths,
 corresponding to the probability of infecting plants, germs,
 invertebrates, non-human vertebrates and humans individually. The
 architecture of DeepHoF is shown in Supplementary Fig. 4 and the details
 of each layer in BiPathCNN are described in Supplementary
 Methods. Implementation of
 DeepHoF In the practical
 application for a viral whole genome sequence (or a partial genome
 sequence), a cut window moves along the long sequence without overlapping
 to separate it into suitable fragments for the pre-trained BiPathCNN
 model. DeepHoF calculates the final score by weighting and summing the
 predicted likelihoods of each fragment. For example, a 2,000 bp query
 sequence is separated into three consecutive fragments, corresponding to
 the first 800 bp, the middle 800 bp and the last 400 bp of the query
 sequence. Then DeepHoF predicts the three fragments independently and
 calculates the weighted average of the three predicted likelihood vectors
 with the weights of 800/2,000, 800/2,000, and 400/2,000 respectively. For
 each input sequence, DeepHoF outputs five scores corresponding to the
 probabilities of the virus to infect the five host types respectively.
 Besides, DeepHoF provides the <i>p</i>-values of each score,
 statistically measuring of how distinct the likelihoods are compared with
 those of non-infectious
 viruses<sup>22</sup>. For example, if
 an input virus has a probability of 0.4 to infect humans, we compare 0.4
 with the scores of non-human-infecting viruses in our dataset and provide
 the <i>p</i>-values as a judgment basis. If the
 <i>p</i>-value is less than 0.05, we conclude the input virus
 can infect humans with a significantly different infection likelihood
 compared with non- human-infecting viruses. As the infection
 likelihood profile of a virus, consisting of the five predicted scores
 given by DeepHoF, can be regarded as an infection-related feature vector
 extracted by DeepHoF, we utilize it to characterize the virus. It is
 logistical to regard the hosts that can be infected by viruses possessing
 similar profiles as the probable hosts of the given virus. To
 quantitatively compare infection likelihood profiles between viruses, we
 calculated the Euclidean distance between the profiles. In the case of
 SARS-CoV-2, we searched the detailed vertebrate host of the earliest
 detected isolates, which are closer to the most recent common ancestor of
 SARS-CoV-2. To start with, we added the host annotations provided by
 Virus-Host DB<sup>41</sup> to the
 vertebrate-infecting viruses included in GenBank. Here, the average of
 infection likelihood profiles of 17 earliest sequenced isolates was used
 as the representation of SARS-CoV-2. We calculated the Euclidean distance
 between infection likelihood profile of SARS-CoV-2 and that of each
 vertebrate-infecting virus (discovered before the outbreak of SARS-CoV-2).
 We assumed the vertebrate infected by a virus possessing profile close to
 that of SARS-CoV-2 was the probable host of
 SARS-CoV-2. Data filtering and
 trimming for SARS-CoV-2 genome sequences There were 102,804
 SARS-CoV-2 genomes released on GISAID EpiCoV Database as of 15th September
 2020. We downloaded all the sequences and filtered them with the quality
 standard given by the Chinese Academy of
 Sciences<sup>34</sup>. Because the
 untranslated regions were not taken as seriously as the protein-coding
 regions and the lengths of sequenced UTR varied a lot in different
 SARS-CoV-2 genomes, we trimmed the 5′- and 3′-untranslated regions (UTR)
 according to the annotation of NC_045512 to get rid of noises. Thus, we
 finally got 53,759 clean sequences.
description: <pre style="color:rgb(0, 0, 0);text-indent:0px;letter-spacing:normal;font-style:normal;font-weight:400;white-space:pre-wrap;"> # DeepHoF: Predicting reservoir hosts based on early
 SARS-CoV-2 samples and analyzing later world-wide pandemic *
 [Introduction](#introduction) * [Version](#version) *
 [Requirements](#requirements) * [Installation](#installation) *
 [Usage](#usage) * [Output](#output) * [Citation](#citation) *
 [Contact](#contact) ## Introduction 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. ## Version + DeepHoF 1.0 (Tested on
 Ubuntu 16.04) ## Requirements ### To run the physical host version of
 DeepHoF, you need to install: + [Python 3.6.10](https://www.python.org/) +
 [numpy 1.17.5](http://www.numpy.org/) + [h5py
 2.10.0](http://www.h5py.org/) + [pandas
 0.25.3](https://pandas.pydata.org/) + [TensorFlow
 1.4.0](https://www.tensorflow.org/) + [Keras 2.1.3](https://keras.io/) +
 [MATLAB R2018a](https://www.mathworks.com/products/matlab.html) **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. ##
 Installation ### 1. Prerequisites 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. ### 2.
 Install DeepHoF using git 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. ## Usage ###
 Input Nucleotide sequence ### Command Please execute the following command
 directly in MATLAB command window:
 DeepHoF('&lt;input_file_folder&gt;/input_file.fna','&lt;output_file_folder&gt;/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. ### Output 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. # Citation + https://doi.org/10.1101/2020.01.21.914044v2 # Note 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. # Contact Please direct your questions to us, hqzhu@pku.edu.cn or qianguo@pku.edu.cn. </pre>