BACKGROUND Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .

中文翻译：

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使用深度测序数据中的位置特异性误差估计来识别单核苷酸变异。

背景靶向深度测序是一种识别已知和新型单核苷酸变异（SNV）的高效技术，在转化医学、疾病监测和癌症分析中有许多应用。然而，使用深度测序数据识别 SNV 是一个具有挑战性的计算问题，因为不同的测序工件限制了 SNV 检测的分析灵敏度，特别是在低变异等位基因频率 (VAF) 下。方法 为了解决 SNV 调用背景下基于扩增子的深度测序数据（例如使用 Ion AmpliSeq 技术）中相对较高的噪声水平问题，我们开发了一种名为 AmpliSolve 的新生物信息学工具。AmpliSolve 使用一组正常样本对位置特异性、链特异性和核苷酸特异性背景伪影（噪声）进行建模，并部署基于泊松模型的统计框架进行 SNV 检测。结果我们对合成数据和真实数据的测试表明，AmpliSolve 在精度和灵敏度之间实现了良好的权衡，即使 VAF 低于 5% 甚至低至 1%。我们进一步验证了 AmpliSolve，将其应用于 96 个循环肿瘤 DNA 样本中三个临床相关基因组位置的 SNV 检测，并将结果与​​数字液滴 PCR 实验进行比较。结论 AmpliSolve 是一种新工具，用于计算机模拟估计背景噪声和检测目标深度测序数据中的低频 SNV。尽管 AmpliSolve 专门针对使用 Ion Torrent 平台测序的基于扩增子的文库进行了设计和测试，但原则上它也可以应用于其他测序平台。AmpliSolve 可在 https://github.com/dkleftogi/AmpliSolve 免费获取。