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In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

机译:基于不同肿瘤下一代测序深度数据的体细胞点突变调用者的深入比较

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摘要

Four popular somatic single nucleotide variant (SNV) calling methods (Varscan, SomaticSniper, Strelka and MuTect2) were carefully evaluated on the real whole exome sequencing (WES, depth of ~50X) and ultra-deep targeted sequencing (UDT-Seq, depth of ~370X) data. The four tools returned poor consensus on candidates (only 20% of calls were with multiple hits by the callers). For both WES and UDT-Seq, MuTect2 and Strelka obtained the largest proportion of COSMIC entries as well as the lowest rate of dbSNP presence and high-alternative-alleles-in-control calls, demonstrating their superior sensitivity and accuracy. Combining different callers does increase reliability of candidates, but narrows the list down to very limited range of tumor read depth and variant allele frequency. Calling SNV on UDT-Seq data, which were of much higher read-depth, discovered additional true-positive variations, despite an even more tremendous growth in false positive predictions. Our findings not only provide valuable benchmark for state-of-the-art SNV calling methods, but also shed light on the access to more accurate SNV identification in the future.
机译:在真实的完整外显子组测序(WES,约50倍深度)和超深度靶向测序(UDT-Seq,深度)上仔细评估了四种流行的体细胞单核苷酸变异(SNV)调用方法(Varscan,SomaticSniper,Strelka和MuTect2) 〜370X)数据。这四个工具对候选人的反馈不佳(只有20%的呼叫被呼叫者多次点击)。无论是WES还是UDT-Seq,MuTect2和Strelka的COSMIC条目所占比例最大,而dbSNP的出现率最低,而高等位基因在对照中的呼叫率最高,这表明它们具有出色的灵敏度和准确性。组合不同的呼叫者确实可以提高候选者的可靠性,但可以将列表缩小到非常有限的肿瘤读取深度和变异等位基因频率范围。在读取深度更高的UDT-Seq数据上调用SNV时,尽管假阳性预测的增长甚至更大,但发现了其他的正阳性变异。我们的发现不仅为最新的SNV呼叫方法提供了有价值的基准,而且为将来获得更准确的SNV识别提供了启示。

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