HoPhage: an ab initio tool for identifying hosts of phage fragments from metaviromes




INTRODUCTION        
HoPhage(Host of Phage) is a computational tool that integrates two modules respectively using the deep learning and the Markov chain model to identify the host of a given phage fragment from metagenome or metavirome data at the genus level. HoP demonstrates a superior performance on short fragments within a wide candidate host range at every taxonomic level when testing on the artificial benchmark dataset of artificial phage contigs and the real virome data.

Please direct your questions or comments to jie_tan@pku.edu.cn or hqzhu@pku.edu.cn
DOWNLOAD        
DATA        
All data used to train and test HoPhage, related results and scripts are stored below.

HoPhage_S_Plog_all.zip
info_phage_host_interaction.csv
info_refseq_archaea.csv
info_refseq_bacteria.csv
genus_distribution.csv
HoPhage-G_train_data.zip
HoPhage-G_train_scripts.zip
HoPhage-G_data_preprocessing_scripts.zip
test_data.zip
HoPhage-S_cmm.py
mock_community_data.zip
data_to_explore_markergene.zip
HoPhage_predict.py
HoPhage_v_1_1.zip
HoPhage_v_1_0.zip
torch-1.3.0+cu100-cp36-cp36m-linux_x86_64.whl
torchvision-0.4.1+cu100-cp36-cp36m-linux_x86_64.whl
CITATION        
Tan J, Fang ZC, Wu SF, Guo Q, Jiang XQ, Zhu HQ*. HoPhage: an ab initio tool for identifying hosts of phage fragments from metaviromes. Bioinformatics. 2021, btab585.
REFERENCES        

  • Fang, Z., Tan, J., Wu, S., Li, M., Xu, C., Xie, Z., and Zhu, H. (2019). PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. GigaScience, 8(6), giz066.

  • Ahlgren, N.A. et al. (2017) Alignment-free d_2^* oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res.,45,39-53.

  • Galiez, C. et al. (2017) WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics,33,3113-3114.

  • Wang, W. et al. (2020) A network-based integrated framework for predicting virus-prokaryote interactions. NAR: Genomics Bioinf.,2,lqaa044.

  • Mihara, T. et al. (2016) Linking Virus Genomes with Host Taxonomy. Viruses,8,66.



Department of Biomedical Engineering and Center for Quantitative Biology, PKU