Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection. This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines’ detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking . This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

中文翻译：

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利用第三代测序数据全面深入评估结构变异检测流程


使用第三代测序数据的结构变异（SV）检测方法已被广泛采用，但准确检测 SV 仍然具有挑战性。对于某些 SV 类型，不同的方法通常会产生不一致的结果，从而使工具选择变得复杂并揭示检测中的偏差。本研究使用 PacBio（CLR：连续长读、CCS：循环一致性测序）和 Nanopore (ONT) 平台的模拟和真实数据全面评估了 53 个 SV 检测流程。我们评估了它们在检测各种大小和类型的 SV、断点偏差以及各种测序深度的基因分型准确性方面的性能。值得注意的是，Minimap2-cuteSV2、NGMLR-SVIM、PBMM2-pbsv、Winnowmap-Sniffles2 和 Winnowmap-SVision 等管道表现出相对较高的召回率和精度。我们的研究结果还表明，将多个管道与同一个对齐器（如 pbmm2 或 winnowmap）相结合，可以显着提高性能。各个管道的详细排名和性能指标可以在动态表中查看：http://pmglab.top/SVPipelinesRanking。这项研究全面描述了众多流程的优缺点，提供了宝贵的见解，可以改善第三代测序数据中的 SV 检测，并为 SV 注释和功能预测提供信息。