结构变异（SVs）是基因组重排的一种形式，可以采取许多不同的形式，如拷贝数改变、倒位和易位。在细胞发育和衰老过程中，体细胞SVs在基因组中积累，可能产生中性、有害或病理效应。体细胞SVs的产生是癌症发展和进展的关键突变过程。尽管它们的重要性，检测体细胞SVs具有挑战性，使其比体细胞单核苷酸变异研究得少。在本综述中，我们总结了基于全基因组测序（WGS）的方法在组织和单细胞水平上检测体细胞SVs的最新进展，并讨论了它们的优点和局限性。首先，我们描述了用于使用bulk WGS数据调用体细胞SV的先进计算算法，并比较了在存在或不存在匹配正常对照时体细胞SV检测器的性能。然后，我们讨论了用于分析体细胞SVs的尖端单细胞技术的独特特征。突出了bulk和单细胞方法的优缺点，并讨论了它们对于拷贝中性SV的敏感性、对于功能推断的有用性以及实验和计算成本。最后，我们展示了将体细胞SV与其功能读数（如来自单细胞转录组和表观基因组分析的读数）链接的计算方法，并讨论了这些方法在健康和疾病中的前景。© The Author(s) 2023. Published by Oxford University Press.

Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.© The Author(s) 2023. Published by Oxford University Press.