State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Plant J. 2024 Oct;120(2):687-698. doi: 10.1111/tpj.17011. Epub 2024 Sep 6.
Structural variations (SVs) pervade plant genomes and contribute substantially to the phenotypic diversity. However, most SVs were ineffectively assayed due to their complex nature and the limitations of early genomic technologies. By applying the PacBio high-fidelity (HiFi) sequencing for wheat genomes, we performed a comprehensive evaluation of mainstream long-read aligners and SV callers in SV detection. The results indicated that the accuracy of deletion discovery is markedly influenced by callers, accounting for 87.73% of the variance, whereas both aligners (38.25%) and callers (49.32%) contributed substantially to the accuracy variance for insertions. Among the aligners, Winnowmap2 and NGMLR excelled in detecting deletions and insertions, respectively. For SV callers, SVIM achieved the best performance. We demonstrated that combining the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the effect of sequencing depth on the accuracy of SV detection, revealing that low-coverage HiFi sequencing is sufficiently robust for high-quality SV discovery. This study thoroughly evaluated SV discovery approaches and established optimal workflows for investigating structural variations using low-coverage HiFi sequencing in the wheat genome, which will advance SV discovery and decipher the biological functions of SVs in wheat and many other plants.
结构变异(SVs)普遍存在于植物基因组中,对表型多样性有重要贡献。然而,由于其复杂性和早期基因组技术的限制,大多数 SVs 都无法有效地检测到。通过应用 PacBio 高保真(HiFi)测序技术对小麦基因组进行研究,我们对主流长读长比对工具和 SV 调用工具在 SV 检测中的性能进行了全面评估。结果表明,删除发现的准确性显著受调用工具的影响,占方差的 87.73%,而对齐器(38.25%)和调用工具(49.32%)都对插入的准确性方差有很大贡献。在对齐工具中,Winnowmap2 和 NGMLR 分别擅长检测缺失和插入。对于 SV 调用工具,SVIM 表现最佳。我们证明了结合上述对齐工具和调用工具是进行 SV 检测的最佳选择。此外,我们评估了测序深度对 SV 检测准确性的影响,结果表明,低覆盖度的 HiFi 测序足以进行高质量的 SV 发现。本研究全面评估了 SV 发现方法,并建立了使用低覆盖度 HiFi 测序在小麦基因组中研究结构变异的最佳工作流程,这将推进 SV 发现,并阐明 SV 在小麦和许多其他植物中的生物学功能。