An atlas of human viruses provides new insights into diversity and tissue tropism of human viruses.

MOTIVATION: Viruses continue to threaten human health. Yet, the complete viral species carried by humans and their infection characteristics have not been fully revealed. RESULTS: This study curated an atlas of human viruses from public databases and literature, and built the Human Virus Database (HVD). The HVD contains 1131 virus species of 54 viral families which were more than twice the number of the human-infecting virus species reported in previous studies. These viruses were identified in human samples including 68 human tissues, the excreta and body fluid. The viral diversity in humans was age-dependent with a peak in the infant and a valley in the teenager. The tissue tropism of viruses was found to be associated with several factors including the viral group (DNA, RNA or reverse-transcribing viruses), enveloped or not, viral genome length and GC content, viral receptors and the virus-interacting proteins. Finally, the tissue tropism of DNA viruses was predicted using a random-forest algorithm with a middle performance. Overall, the study not only provides a valuable resource for further studies of human viruses but also deepens our understanding toward the diversity and tissue tropism of human viruses. AVAILABILITY AND IMPLEMENTATION: The HVD is available at http://computationalbiology.cn/humanVirusBase/#/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of the Receptorome for the Human-Infecting Virome.

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the enterovirus, norovirus and West Nile virus. Finally, the candidate alternative receptors of the SARS-CoV-2 were also predicted in this study. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.