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

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data.
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DOI:
10.1038/srep36540
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发表时间:
2016-11-22
期刊:
影响因子:
4.6
通讯作者:
Chou KC
Chou KC
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Cai L;Yuan W;Zhang Z;He L;Chou KC

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在真实的全外显子组测序(WES,深度~ 50 X)和超深度靶向测序(UDT-Seq,深度~ 370 X)数据上对四种流行的体细胞单核苷酸变异(SNV)识别方法(Varscan,SomaticSniper,Strelka和MuTect 2)进行了仔细评估。这四个工具对候选人的共识很差(只有20%的呼叫者多次命中)。对于WES和UDT-Seq,MuTect 2和Strelka获得了最大比例的COSMIC条目以及最低的dbSNP存在率和高替代等位基因对照调用,证明了其上级灵敏度和准确性。组合不同的调用者确实增加了候选者的可靠性,但将列表缩小到肿瘤读取深度和变体等位基因频率的非常有限的范围。在UDT-Seq数据上调用SNV,其读取深度要高得多,发现了额外的真阳性变异,尽管假阳性预测的增长更为巨大。我们的研究结果不仅为最先进的SNV识别方法提供了有价值的基准,而且为将来获得更准确的SNV识别提供了依据。
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.
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