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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.

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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.

关键词:
一个基准最好的做法长时间阅读结构变体变种调用全基因组测序

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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.