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在深度测序数据中分析indels和结构变体的错误概况

Ying Shao¹, Quang Tran¹, Yuan Feng¹

¹Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

|December 3, 2025

概括

这项研究表明,在深度测序中,插入/删除 (indels) 和结构变体 (SVs) 的错误率明显低于单核酸变体 (SNVs). 这一突破改善了早期癌症检测,特别是在最小残留疾病中.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
癌症研究癌症研究

背景情况:

单核酸变体 (SNVs) 的错误概况已得到充分研究,但插入/删除 (indels) 和结构变体 (SVs) 的错误概况在很大程度上仍未知.
了解indel/SV错误率对于准确的基因组分析和临床应用至关重要.

研究的目的:

在超深下一代测序 (NGS) 数据中确定indels和SVs的错误概况.
评估indel/SV分析在B细胞急性淋巴细胞白血病 (B-ALL) 中检测最小残留疾病 (MRD) 的临床实用性.

主要方法:

使用超深度测序来分析indel和sv错误率.
在103名患有复发性B-ALL.患者的队列中验证了这些发现.
分析了339个癌症驱动基因中的重复印记,并将重复单元数与错误率相关联.
应用了开发的方法来分析72名B-ALL患者的MRD样本.

主要成果:

发现INDEL/SV的错误率比SNV的错误率低100倍,重复INDEL显示的错误率为1%.
在indels中重复单元的数量强烈预测了他们的错误率.
开发的方法在61%的MRD样本中取得了积极检测,超过了临床流细胞计的51%检测率.

结论:

为深度NGS数据建立了全面的indel和SV错误配置文件.
通过使用indel/SV分析,在非常低的负载下显示出优异的瘤检测能力.
强调了这种方法对人类癌症和其他疾病的临床诊断和监测的重大影响.

关键词:

癌症早期检测癌症早期检测深度测序是一种深度测序.错误配置文件中的错误配置文件.重复印入的字符可以重复印入.敏感的检测敏感的检测结构变体和内部部分.

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