GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data.
GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data.
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DOI：
10.1186/s12859-017-1537-8
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发表时间：
2017-03-23
影响因子：
3
通讯作者：
Tommasi S
中科院分区：
文献类型：
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作者：
De Summa S;Malerba G;Pinto R;Mori A;Mijatovic V;Tommasi S
NGS技术代表了在临床背景下标准桑格测序的一种强有力的替代方案。通常用于变异识别的专有软件通常依赖于预设参数，这些参数可能无法以令人满意的方式适合不同的基因。在学术界广泛使用的GATK中有丰富的变异识别参数。然而，GATK的自调节参数校准需要来自大量外显子组的数据。当这些参数不可用时（这是诊断实验室的标准条件），必须由操作员设置参数（硬过滤）。本论文的目的是建立一个程序来评估最佳参数用于硬过滤的GATK。这是通过对来自真实的数据集数据的模拟序列的真和假变体使用分类树来实现的。我们模拟了两个数据集，具有不同的覆盖率，包括根据观察到的频率在真实的数据集中识别的所有序列改变。将模拟序列与标准方案进行比对，然后建立回归树以鉴定最可靠的参数和截止值以区分真和假变体调用。此外，我们分析了呈现高假阳性检出率的区域的侧翼序列，观察到此类序列呈现低复杂性组成。我们的研究结果表明，GATK硬过滤参数值可以通过基于感兴趣的DNA区域的模拟研究来定制，以改善变异识别的准确性。本文的在线版本（doi：10.1186/s12859-017-1537-8）包含补充材料，可供授权用户使用。
NGS technology represents a powerful alternative to the standard Sanger sequencing in the context of clinical setting. The proprietary software that are generally used for variant calling often depend on preset parameters that may not fit in a satisfactory manner for different genes. GATK, which is widely used in the academic world, is rich in parameters for variant calling. However the self-adjusting parameter calibration of GATK requires data from a large number of exomes. When these are not available, which is the standard condition of a diagnostic laboratory, the parameters must be set by the operator (hard filtering). The aim of the present paper was to set up a procedure to assess the best parameters to be used in the hard filtering of GATK. This was pursued by using classification trees on true and false variants from simulated sequences of a real dataset data. We simulated two datasets, with different coverages, including all the sequence alterations identified in a real dataset according to their observed frequencies. Simulated sequences were aligned with standard protocols and then regression trees were built up to identify the most reliable parameters and cutoff values to discriminate true and false variant calls. Moreover, we analyzed flanking sequences of region presenting a high rate of false positive calls observing that such sequences present a low complexity make up. Our results showed that GATK hard filtering parameter values can be tailored through a simulation study based-on the DNA region of interest to ameliorate the accuracy of the variant calling. The online version of this article (doi:10.1186/s12859-017-1537-8) contains supplementary material, which is available to authorized users.