Somatic structural variants (SVs), which are variants that typically impact >50 nucleotides, play a significant role in cancer development and evolution but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of seven commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the seven SV callers examined in this paper. As the importance of large SVs become increasingly recognized in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection that should be considered when choosing SV callers.

中文翻译：

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从短读长下一代测序数据中检测体细胞结构变异。


体细胞结构变异 (SV) 是通常影响 >50 核苷酸的变异，在癌症的发展和进化中发挥着重要作用，但众所周知，它比短读长下一代测序 (NGS) 数据中的小变异更难检测。这是由于肿瘤样本的纯度、肿瘤异质性、NGS 短读信息的局限性以及序列比对模糊性等一系列挑战造成的。尽管过去几年SV检测工具（调用者）得到了积极的开发，但每种方法都有固有的优点和局限性。在这篇综述中，我们重点介绍了影响体细胞 SV 检测的一些重要因素，并比较了七种常用 SV 调用程序的性能。我们特别关注从具有不同变异等位基因频率和测序覆盖深度的样本中检测不同 SV 类型和大小范围的灵敏度和精度的变化程度。我们强调了为什么某些 SV 调用者在某些设置中表现良好但在其他设置中表现不佳的原因，从而使我们的评估结果能够扩展到本文检查的七个 SV 调用者之外。随着大 SV 的重要性在癌症基因组学中日益得到认识，本文对影响体细胞 SV 检测的一些最有影响力的因素进行了及时的回顾，这些因素在选择 SV 调用者时应考虑。