Department of Computer Science, University of California, Irvine, California, United States of America.
Computational Biology & Bioinformatics Program, Yale University, New Haven, Connecticut, United States of America.
PLoS Comput Biol. 2022 Oct 27;18(10):e1010636. doi: 10.1371/journal.pcbi.1010636. eCollection 2022 Oct.
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.
早期且准确地检测临床和环境样本中的病毒对于有效的公共卫生保健、治疗和疗法至关重要。虽然 PCR 具有高灵敏度，可以检测到潜在的病原体，但它难以扩展，并且需要了解病原体的确切序列。随着下一代单细胞测序的出现，现在可以以最精细的分辨率——细胞来仔细研究病毒转录组学。这种研究单个细胞的新能力为我们理解病毒病理生理学提供了前所未有的分辨率。为了利用这种能力，我们提出了一种高效准确的计算管道 Venus，用于在单细胞和批量组织 RNA-seq 数据中检测病毒和整合位点。具体来说，Venus 解决了两个主要问题：组织/细胞类型是否被病毒感染？如果感染了，病毒是否已经插入了人类基因组？我们的分析可以分为两部分——验证和发现。首先，对于验证，我们将 Venus 应用于研究充分的病毒数据集，如 HBV-肝癌和接受抗逆转录病毒治疗的 HIV 感染。其次，对于发现，我们分析了 HIV 感染的神经科患者和深度测序的 T 细胞等数据集。我们在大脑的新靶点和免疫细胞中的高可信度整合位点检测到了病毒转录本。总之，在这里我们描述了 Venus，这是一个可公开获取的软件，我们相信它将成为广大科学界有价值的病毒研究工具。
