In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 10 ( 2022-10-27), p. e1010636-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010636
DOI:
10.1371/journal.pcbi.1010636.g001
DOI:
10.1371/journal.pcbi.1010636.g002
DOI:
10.1371/journal.pcbi.1010636.g003
DOI:
10.1371/journal.pcbi.1010636.g004
DOI:
10.1371/journal.pcbi.1010636.g005
DOI:
10.1371/journal.pcbi.1010636.g006
DOI:
10.1371/journal.pcbi.1010636.t001
DOI:
10.1371/journal.pcbi.1010636.t002
DOI:
10.1371/journal.pcbi.1010636.t003
DOI:
10.1371/journal.pcbi.1010636.s001
DOI:
10.1371/journal.pcbi.1010636.s002
DOI:
10.1371/journal.pcbi.1010636.s003
DOI:
10.1371/journal.pcbi.1010636.s004
DOI:
10.1371/journal.pcbi.1010636.s005
DOI:
10.1371/journal.pcbi.1010636.s006
DOI:
10.1371/journal.pcbi.1010636.s007
DOI:
10.1371/journal.pcbi.1010636.s008
DOI:
10.1371/journal.pcbi.1010636.s009
DOI:
10.1371/journal.pcbi.1010636.s010
DOI:
10.1371/journal.pcbi.1010636.s011
DOI:
10.1371/journal.pcbi.1010636.s012
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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