SeqMaker:下一代测序模拟器与变异,测序误差和扩增偏差集成

Shifu Chen, Yue Han, Lanting Guo, Jing-Shan Hu, Jia Gu
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引用次数: 8

摘要

生物信息学管道的调整和软件参数的训练需要已知地真值的测序数据,而这很难从真实的测序数据中得到。特别是,对于那些检测低频变异的应用(如ctDNA测序),很难判断所谓的变异是真阳性,还是由测序或其他过程错误引起的假阳性。在这些情况下,具有配置变化的模拟数据可用于排除故障并验证生物信息学程序。虽然新一代测序模拟器已经被开发出来,但它们大多缺乏模拟靶标捕获测序、拷贝数变异、基因融合、扩增偏倚和测序误差等许多实际功能的能力。在本文中,我们将介绍SeqMaker,一个现代的NGS模拟器,能够模拟不同种类的变异,并集成了扩增偏差和测序误差。通过使用捕获面板描述文件简单地支持目标捕获测序,其他特征,如测序错误率,平均重复水平,DNA模板长度分布和质量分布可以通过简单的JSON格式配置文件轻松配置。通过整合测序误差和扩增偏差,SeqMaker能够模拟更真实的下一代测序数据。可配置的变体和捕获区域使SeqMaker在为体细胞突变呼叫等应用程序生成训练生物信息学管道的数据方面非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SeqMaker: A next generation sequencing simulator with variations, sequencing errors and amplification bias integrated
Tuning bioinformatics pipelines and training software parameters require sequencing data with known ground truth, which are actually difficult to get from real sequencing data. Particularly, for those applications of detecting low frequency variations (like ctDNA sequencing), it is hard to tell whether a called variation is a true positive, or a false positive caused by errors from sequencing or other processes. In these cases, simulated data with configured variations can be used to troubleshoot and validate bioinformatics programs. Although lots of next generation sequencing simulators have already been developed, most of them lack of capability to simulate lots of practical features, such like target capturing sequencing, copy number variations, gene fusions, amplification bias and sequencing errors. In this paper, we will present SeqMaker, a modern NGS simulator with capability to simulate different kinds of variations, with amplification bias and sequencing errors integrated. Target capturing sequencing is simply supported by using a capturing panel description file, other characteristics like sequencing error rate, average duplication level, DNA template length distribution and quality distribution can be easily configured with a simple JSON format profile file. With the integration sequencing errors and amplification bias, SeqMaker is able to simulate more real next generation sequencing data. The configurable variants and capturing regions make SeqMaker very useful to generate data for training bioinformatics pipelines for applications like somatic mutation calling.
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