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Shiny-SoSV：一个基于网络的体细胞结构变异检测性能计算器。

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.

机构信息

Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.

Central Clinical School, University of Sydney, Camperdown, New South Wales, Australia.

出版信息

PLoS One. 2020 Aug 27;15(8):e0238108. doi: 10.1371/journal.pone.0238108. eCollection 2020.

DOI:10.1371/journal.pone.0238108
PMID:32853264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7451576/
Abstract

Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user's guide and example use-cases.

摘要

体细胞结构变异是癌症发生和进化的一个重要因素。从全基因组测序数据中准确检测这些复杂的变异受到多种参数的影响。然而，目前还没有用于指导研究设计的工具，也没有可以预测体细胞结构变异检测性能的应用程序。为了解决这一差距，我们开发了 Shiny-SoSV，这是一个用户友好的基于网络的计算器，用于确定常见变量对体细胞结构变异检测的灵敏度、精度和 F1 分数的影响，包括变异检测工具的选择、测序深度覆盖、变异等位基因分数和变异断点分辨率。通过模拟研究，我们确定了这些变量的单一和组合效应，使用广义加性模型对结果进行建模，允许针对任何预测因子组合预测结构变异检测性能。Shiny-SoSV 为用户提供了一个交互和可视化的平台，方便用户轻松比较不同参数的单独和综合影响。它可以在进行实验工作之前预测拟议的体细胞结构变异检测研究设计的性能。Shiny-SoSV 可在 https://hcpcg.shinyapps.io/Shiny-SoSV 上免费获取，附带用户指南和示例用例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/522e/7451576/271601d2310b/pone.0238108.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/522e/7451576/2e3b25ab54e7/pone.0238108.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/522e/7451576/271601d2310b/pone.0238108.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/522e/7451576/2e3b25ab54e7/pone.0238108.g001.jpg
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本文引用的文献

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Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.结合精确的肿瘤基因组模拟和众包基准测试体细胞结构变异检测。
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Whole-Genome Sequencing Reveals Elevated Tumor Mutational Burden and Initiating Driver Mutations in African Men with Treatment-Naïve, High-Risk Prostate Cancer.全基因组测序揭示了未经治疗的高危前列腺癌非洲男性中肿瘤突变负担增加和起始驱动突变。
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