Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20–300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50–300 bp) SVs.

结构变体 (SV)，包括小的插入和缺失变体 (indel)，很难通过标准的基于比对的变体调用方法进行检测。序列组装为识别 SV 提供了一种强大的方法，但由于其计算复杂性和从组装重叠群中提取 SV 的难度，很难在全基因组范围内应用于 SV 检测。我们描述了 SvABA，这是一种有效且准确的方法，用于使用具有低内存和计算要求的全基因组本地组装从短读测序数据中检测 SV。我们评估了 SvABA 在 NA12878 人类基因组以及模拟和真实癌症基因组中的表现。SvABA 在大范围的 SV 中表现出卓越的灵敏度和特异性，并显着提高了 20-300 bp 范围内变体的检测性能，与现有方法相比。SvABA 还识别具有从远处基因组区域复制的短（<1000 bp）模板序列插入链的复杂体细胞重排。我们将 SvABA 应用于来自 11 种癌症类型的 344 个癌症基因组，发现短模板序列插入发生在所有体细胞重排的 ~4% 中。最后，我们证明 SvABA 可以识别包含中等大小（50-300 bp）SV 的病毒整合位点和癌症驱动改变。