AmpSeqR:用于安普利康深度测序数据分析的R包

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3' End Sequencing Library Preparation with A-seq2

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Jiru Han^1,2, Jacob E Munro^1,2, Melanie Bahlo^1,2

¹Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia.

|November 25, 2024

概括

本研究介绍了AmpSeqR,这是一个用于处理安普利康测序数据的R包. 它提高了在传染病样本中检测低频变异的准确性.

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:

扩增序列测序 (AmpSeq) 对于针对性基因组区域的高深度测序至关重要.
现有的生物信息学管道正在努力减少噪音和对AmpSeq数据的下游分析.
应用范围包括癌症,传染病和大脑马赛克研究.

研究的目的:

为了介绍AmpSeqR,一个新的R包处理深度短读安普利康测序数据.
通过过读数和减少PCR错误和文物来提高序列数据的准确性.
为了方便在混合样本中检测非常低频率的克隆,特别是用于传染病研究.

主要方法:

开发AmpSeqR R软件包,将现有的R软件包与新功能相结合.
实施最佳的读取过以减少噪声和序列精度.
包括用于数据预处理,安普利康序列变异 (ASV) 估计和后处理的功能.
自动生成全面的Rmarkdown报告,用于结果可视化和报告.

主要成果:

AmpSeqR有效地处理深度短读安普利康测序数据.
该包通过最小化错误和工件来提高检测到的序列的准确性.
它可以在混合样本中灵敏地检测和量化低频变异和小克隆.
自动报告有助于整合到科学出版物中.

结论:

AmpSeqR提供了一个强大的,用户友好的管道,用于amplicon测序数据分析.
该套件提高了序列变异检测的可靠性,特别是对于低频元件.
AmpSeqR对于需要高灵敏度和准确度的传染病研究特别有价值.

关键词:

在R包中,R包是R包.安普利康序列的测序方法数据可视化数据可视化总结报告总结报告总结报告

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