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用长读序列数据检测体质结构变异的基准和评估

Ziting Feng¹, Xuyan Liu¹, Yahui Liu¹

¹Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.

Genomics, proteomics & bioinformatics

|December 31, 2025

概括

使用长读序列 (LRS) 检测瘤特异性结构变异 (体性SV) 是具有挑战性的. 这项研究对51种LRS策略进行了基准评估,发现没有任何一种方法是最佳的,并突出了体质SV检测工具需要改进的领域.

科学领域:

基因组学就是基因组学.
癌症基因组学癌症基因组学
生物信息学是一种生物信息学.

背景情况:

身体结构变异 (SVs) 是瘤的关键特征,但很难全面检测.
长读测序 (LRS) 提供了改善体质SV检测的潜力,因为它能够跨越大型基因组区域.
目前基于LRS的体质SV检测算法缺乏系统性性能特征.

研究的目的:

严格评估各种基于LRS的体质SV检测策略的性能.
为了确定当前体质VS检测方法的技术瓶和局限性.
为优化基于LRS的体质VS检测和工具开发提供建议.

主要方法:

使用LRS数据评估了51种体质VS检测策略.
集成了3个参考基因组,2个对齐器,5个SV调用器和5种处理方法.
利用来自HCC1395/HCC1395BL细胞系 (ONT和PacBio平台) 的模拟数据集和经验数据.

主要成果:

在所有评估的场景中,没有一个基于LRS的策略在所有评估场景中始终超过其他策略.
使用生殖系SV调用者的工作流显示出高假阳性率,不受测序深度或瘤纯度的影响.
在检测插入,并联重复区域和超长SVs方面仍然存在挑战.

结论:

目前基于LRS的体质SV检测工具需要进一步改进以保持一致的性能.
基于 Germline SV 调用者的工作流程对于体质 SV 检测是不理想的,因为错误阳性结果很高.
该基准为选择工具和推进未来基于LRS的体质VS检测方法提供了洞察力.

关键词:

算法的性能算法的性能.基准策略评估基准策略评估长读序列的测序方式测序的深度测序的深度.检测体质结构变异检测体质结构变异检测

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