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

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....

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Autores principales: Lee, Che Yu, Chen, Yuhang, Duan, Ziheng, Xu, Min, Girgenti, Matthew J., Xu, Ke, Gerstein, Mark, Zhang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9642901/
https://www.ncbi.nlm.nih.gov/pubmed/36301997
http://dx.doi.org/10.1371/journal.pcbi.1010636
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author Lee, Che Yu
Chen, Yuhang
Duan, Ziheng
Xu, Min
Girgenti, Matthew J.
Xu, Ke
Gerstein, Mark
Zhang, Jing
author_facet Lee, Che Yu
Chen, Yuhang
Duan, Ziheng
Xu, Min
Girgenti, Matthew J.
Xu, Ke
Gerstein, Mark
Zhang, Jing
author_sort Lee, Che Yu
collection PubMed
description 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.
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spelling pubmed-96429012022-11-15 Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data Lee, Che Yu Chen, Yuhang Duan, Ziheng Xu, Min Girgenti, Matthew J. Xu, Ke Gerstein, Mark Zhang, Jing PLoS Comput Biol Research Article 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. Public Library of Science 2022-10-27 /pmc/articles/PMC9642901/ /pubmed/36301997 http://dx.doi.org/10.1371/journal.pcbi.1010636 Text en © 2022 Lee et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lee, Che Yu
Chen, Yuhang
Duan, Ziheng
Xu, Min
Girgenti, Matthew J.
Xu, Ke
Gerstein, Mark
Zhang, Jing
Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title_full Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title_fullStr Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title_full_unstemmed Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title_short Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data
title_sort venus: an efficient virus infection detection and fusion site discovery method using single-cell and bulk rna-seq data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9642901/
https://www.ncbi.nlm.nih.gov/pubmed/36301997
http://dx.doi.org/10.1371/journal.pcbi.1010636
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