Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA.
Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA; Department of Computer Science, University of Vermont, Burlington, VT 05405, USA; Neuroscience, Behavior, and Health Initiative, University of Vermont, Burlington, VT 05405, USA.
Genomics. 2020 Jan;112(1):207-211. doi: 10.1016/j.ygeno.2019.01.015. Epub 2019 Jan 30.
Viral sequence integrations in the human genome have been implicated in various human diseases. Viral integrations remain among the most challenging-to-detect structural changes of the human genome. No studies have systematically analyzed how molecular and bioinformatics factors affect the power (sensitivity) to detect viral integrations using high-throughput sequencing (HTS). We selected a wide-range of molecular and bioinformatics factors covering genome sequence characteristics, HTS features, and viral integration detection. We designed a fast simulation-based framework to model the process of detecting variable viral integration events in the human genome. We then examined the associations of selected factors with viral integration detection power. We identified six factors that significantly affected viral integration detection power (P < 2 × 10). The strongest factors associated with detection power included proportion of sample cells with clonal viral integrations (Pearson's ρ = 0.64), sequencing depth (ρ = 0.37), length of viral integration (ρ = 0.37), paired-end read insert size (ρ = 0.23), user-defined threshold (number of supporting reads) to claim successful identification of integrations (ρ = -0.19), and read length (when sequence volume was fixed) (ρ = -0.09). As the first tool of its kind, VIpower incorporates all these factors, which can be manipulated in concert with each other to optimize the detection power. This tool may be used to estimate viral integration detection power for various combinations of sequencing or analytic parameters. It may also be used to estimate the parameters required to achieve a specific power when designing new sequencing experiments.
病毒序列整合已被认为与多种人类疾病有关。病毒整合仍然是人类基因组中最具挑战性的结构变化之一。目前还没有研究系统地分析分子和生物信息学因素如何影响使用高通量测序（HTS）检测病毒整合的能力（敏感性）。我们选择了广泛的分子和生物信息学因素，涵盖基因组序列特征、HTS 特征和病毒整合检测。我们设计了一个快速的基于模拟的框架来模拟在人类基因组中检测可变病毒整合事件的过程。然后，我们检查了选定因素与病毒整合检测能力的关联。我们确定了六个因素对病毒整合检测能力有显著影响（P<2×10）。与检测能力关联最强的因素包括具有克隆病毒整合的样本细胞比例（Pearson's ρ=0.64）、测序深度（ρ=0.37）、病毒整合长度（ρ=0.37）、配对末端读取插入大小（ρ=0.23）、用户定义的阈值（成功识别整合的支持读取数）（ρ=-0.19）和读取长度（当序列量固定时）（ρ=-0.09）。作为同类中的第一个工具，VIpower 包含了所有这些因素，可以相互协调操作，以优化检测能力。该工具可用于估计各种测序或分析参数组合下的病毒整合检测能力。当设计新的测序实验时，它还可以用于估计达到特定能力所需的参数。
