Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
Present address: Genome Institute of Singapore (GIS), Agency of Science Research and Technology (A*STAR), Singapore, 138672, Singapore.
BMC Med Genomics. 2019 Aug 2;12(1):115. doi: 10.1186/s12920-019-0557-9.
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).
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.
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.
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 .
靶向深度测序是一种非常有效的技术，可用于识别已知和新型单核苷酸变异（SNV），在转化医学、疾病监测和癌症分析中有广泛的应用。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，因为不同的测序伪影限制了 SNV 检测的分析灵敏度，尤其是在低变异等位基因频率（VAF）下。
为了解决基于扩增子的深度测序数据（例如，使用 Ion AmpliSeq 技术）中 SNV 调用时相对较高噪声水平的问题，我们开发了一种名为 AmpliSolve 的新生物信息学工具。AmpliSolve 使用一组正常样本来构建位置特异性、链特异性和核苷酸特异性背景伪影（噪声）模型，并采用基于泊松模型的统计框架进行 SNV 检测。
我们在合成和真实数据上的测试表明，AmpliSolve 在精度和灵敏度之间实现了良好的平衡，即使在 VAF 低于 5%且低至 1%的情况下也是如此。我们进一步通过将其应用于三个临床相关基因组位置的 96 个循环肿瘤 DNA 样本中的 SNV 检测来验证 AmpliSolve，并将结果与数字液滴 PCR 实验进行比较。
AmpliSolve 是一种用于靶向深度测序数据中背景噪声的计算估计和低频 SNV 检测的新工具。尽管 AmpliSolve 是专门为基于扩增子的文库设计并在 Ion Torrent 平台上测序进行测试的，但它原则上也可以应用于其他测序平台。AmpliSolve 可在 https://github.com/dkleftogi/AmpliSolve 上免费获得。
