通过不同的测序技术进行全面的基准测试
Giuseppe Giovanni Nardone1, Valentina Andrioletti2, Aurora Santin1,3
1Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy.
Biomedicines
|August 28, 2025
概括
选择正确的对齐软件显著影响全基因组测序 (WGS) 中的结构变异 (SV) 检测准确性. 短读 (srWGS) 和长读 (lrWGS) 技术之间的性能不同,突出了对标准化方法的需求.
科学领域:
- 基因组学
- 生物信息学
- 分子生物学
背景情况:
- 结构变异对基因功能至关重要,并与人类疾病有关.
- 全基因组测序 (WGS) 对于识别SV至关重要,但工具的可变性阻碍了准确性.
- 没有标准化的SV检测方法.
研究的目的:
- 在不同测序技术中评估结构变异检测的准确性.
- 评估变异调用算法,参考基因组,对齐策略和测序覆盖范围对SV检测的影响.
- 为了比较短读 (srWGS) 和长读 (lrWGS) 测序性能来识别 SV.
主要方法:
- 使用HG002基准数据集的删除调用评估SV检测的准确性.
- 测试了Illumina短读,PacBio长读,以及牛津纳米孔技术 (ONT) 长读.
- 检查了变种调用者的影响,参考基因组,对齐策略和测序覆盖范围.
主要成果:
- DRAGEN v4.2显示了srWGS的最高精度;基于图表的引用改善了复杂区域的调用.
- 将Minimap2与Manta相结合,实现了与DRAGEN可比的srWGS性能.
- 在PacBio lrWGS中,Sniffles2表现优异;在ONT lrWGS中,minimap2对齐表现最好;在不同的覆盖范围中,Duet和Dysgu表现最好.
结论:
- 调整软件的选择显著影响srWGS结构变量调用精度.
- 长读数测序 (lrWGS) 检测 SV 的性能取决于特定的技术和测序覆盖范围.
- 这些发现为优化跨多种测序平台的SV检测提供了见解.
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