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从NGS图书馆的准备中获得的工件的表征和减轻,这是由于人类基因组中的结构特异序列
HuiJuan Chen1,2,3, YiRan Zhang1, Bing Wang1
1Beijing ChosenMed Clinical Laboratory Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing, 100176, China.
BMC genomics
|March 1, 2024
概括
使用超声波或酶分解的下一代测序 (NGS) 库准备可以引入文物读取. 一个新的PDSM模型解释了这些错误,而ArtifactsFinder算法有助于过它们以提高准确性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 基于混合捕获的下一代向测序 (NGS) 对癌症临床实践至关重要.
- DNA 库的准备质量直接影响测序数据.
- 使用超声波和酶断片方法观察到意想不到的低变异元基因频率调用.
研究的目的:
- 调查从超声波和酶分裂中读取的文物.
- 开发一个生物信息算法来过测序错误.
主要方法:
- 来自瘤DNA的体质单核酸变体 (SNVs) 和indels的比较分析.
- 采用超声波和酶分裂协议制备的样品.
- 开发一种生物信息算法 (ArtifactsFinder) 来创建一个突变黑名单.
主要成果:
- 酶分裂产生了明显更多的工艺品变体,而不是超声波.
- 超声波工件是虚构的读数与反转的重复.
- 酶的工件是嵌合式的,读取有着palindromic序列和不匹配的基础.
- 提出了PDSM (来自相似分子的部分单一链的配对) 模型用于错误生成.
结论:
- 提出了PDSM模型,解释了基因组重复结构的测序错误.
- PDSM模型解释了以前无法解释的虚构读取.
- 文物Finder算法有效地减少了NGS库中的序列错误.
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