Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs
Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs
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DOI：
10.1093/bioinformatics/bts271
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发表时间：
2012-07-15
期刊：
影响因子：
5.8
通讯作者：
Cheetham, R. Keira
中科院分区：
文献类型：
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作者：
Saunders, Christopher T.;Wong, Wendy S. W.;Cheetham, R. Keira
动机：肿瘤-正常样本配对的全基因组和外显子组测序正在成为癌症研究的常规方法。因此，对配对样本的体细胞变异分析的需求增加，需要专门的方法来模拟这个问题，以便在任何实际水平的肿瘤杂质中敏感地调用变异。结果：我们描述了一种从匹配肿瘤-正常样本的测序数据中检测体细胞SNV和小indel的方法Strelka。该方法使用了一种新的贝叶斯方法，该方法表示肿瘤和正常样本的连续等位基因频率，同时利用正常样本的预期基因型结构。这是通过将正常样本表示为种系变异与噪声的混合物，并将肿瘤样本表示为正常样本与体细胞变异的混合物来实现的。模型结构的一个自然结果是，灵敏度可以保持在高肿瘤杂质而不需要纯度估计。我们证明，与基于二倍体基因型可能性或一般等位基因频率测试的方法相比，该方法在不纯样品上具有优越的准确性和灵敏度。
Motivation: Whole genome and exome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. The consequent increased demand for somatic variant analysis of paired samples requires methods specialized to model this problem so as to sensitively call variants at any practical level of tumor impurity.Results: We describe Strelka, a method for somatic SNV and small indel detection from sequencing data of matched tumor-normal samples. The method uses a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, while leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. We demonstrate that the method has superior accuracy and sensitivity on impure samples compared with approaches based on either diploid genotype likelihoods or general allele-frequency tests.