Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Ministry of Education, Guangzhou, China.
Genome Biol. 2024 Jul 15;25(1):188. doi: 10.1186/s13059-024-03324-5.
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.
使用第三代测序数据的结构变异（SV）检测方法被广泛应用，但准确检测 SV 仍然具有挑战性。不同的方法通常对某些 SV 类型产生不一致的结果，这使得工具选择变得复杂，并揭示了检测中的偏差。
本研究使用 PacBio（CLR：连续长读，CCS：圆形一致测序）和 Nanopore（ONT）平台的模拟和真实数据，全面评估了 53 个 SV 检测管道。我们评估了它们在检测各种大小和类型的 SV、断点偏差以及具有不同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，使用相同的比对器（如 pbmm2 或 winnowmap）结合多个管道可以显著提高性能。可以在动态表中查看各个管道的详细排名和性能指标：http://pmglab.top/SVPipelinesRanking。
本研究全面描述了众多管道的优缺点，为提高第三代测序数据中的 SV 检测提供了有价值的见解，并为 SV 注释和功能预测提供了信息。
