Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data

• Published in: PLoS computational biology Vol. 18; no. 10; p. e1010636
• Main Authors: Lee, Che Yu, Chen, Yuhang, Duan, Ziheng, Xu, Min, Girgenti, Matthew J, Xu, Ke, Gerstein, Mark, Zhang, Jing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27-10-2022; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Computer simulation; Computer-generated environments; HIV Infections; Humans; Liver Neoplasms; Medicine and Health Sciences; Methods; RNA sequencing; RNA-Seq; Sequence Analysis, RNA; Software; Viruses; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010636

Early and accurate detection of viruses in clinical and environmental samples is essential for effective public healthcare, treatment, and therapeutics. While PCR detects potential pathogens with high sensitivity, it is difficult to scale and requires knowledge of the exact sequence of the pathogen. With the advent of next-gen single-cell sequencing, it is now possible to scrutinize viral transcriptomics at the finest possible resolution-cells. This newfound ability to investigate individual cells opens new avenues to understand viral pathophysiology with unprecedented resolution. To leverage this ability, we propose an efficient and accurate computational pipeline, named Venus, for virus detection and integration site discovery in both single-cell and bulk-tissue RNA-seq data. Specifically, Venus addresses two main questions: whether a tissue/cell type is infected by viruses or a virus of interest? And if infected, whether and where has the virus inserted itself into the human genome? Our analysis can be broken into two parts-validation and discovery. Firstly, for validation, we applied Venus on well-studied viral datasets, such as HBV- hepatocellular carcinoma and HIV-infection treated with antiretroviral therapy. Secondly, for discovery, we analyzed datasets such as HIV-infected neurological patients and deeply sequenced T-cells. We detected viral transcripts in the novel target of the brain and high-confidence integration sites in immune cells. In conclusion, here we describe Venus, a publicly available software which we believe will be a valuable virus investigation tool for the scientific community at large.