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DELFMUT:以双重测序为导向的深度估计模型,用于稳定检测低频突变.

Guiying Wu1, Mengmeng Song1, Ke Wang1,2

  • 1Geneplus-Beijing Institute, Beijing 102206, P. R. China.

Briefings in bioinformatics
|August 4, 2023
PubMed
概括

确定双重测序的最佳参数对于检测低频突变至关重要. 一个新的模型,DELFMUT,有助于设置DNA输入和测序深度,以稳定检测突变.

关键词:
双重测序是指双重的测序.低频基因突变是一种低频基因突变.测序深度估计测序深度估计零截断的负二项式分布是零截断的.

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科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 双重测序对于检测循环瘤DNA中的低频突变至关重要.
  • 优化测序深度等实验参数仍然是稳定突变检测的挑战.

研究的目的:

  • 开发一个模型来确定双重测序中的最佳实验参数.
  • 确保使用双重测序技术稳定检测低频突变.

主要方法:

  • 提出了用于稳定检测低频突变的深度估计模型 (DELFMUT).
  • 模拟模板读取关系使用零截断负二项式分布.
  • 用真实双重序列数据验证模型.

主要成果:

  • DELFMUT有效地模拟了模板和读数之间的定量关系.
  • 该模型使用实际的双重测序数据进行了验证.
  • DELFMUT可以推DNA输入和测序深度组合,用于稳定突变检测.

结论:

  • 在双重测序中,DELFMUT为指导实验参数设置提供了一个有价值的工具.
  • 该模型提高了循环瘤DNA中低频突变检测的可靠性.
  • 这种方法对癌症研究和诊断有重大影响.