BACKGROUND The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. METHODS We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. RESULTS We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. CONCLUSIONS Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.

中文翻译：

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使用离子激流靶向的深度测序数据比较体细胞变异检测算法。

背景技术下一代测序在癌症中的应用已经揭示了许多肿瘤类型的基因组格局，并且如今在研究和临床环境中常规使用。已经开发出多种算法来使用配对的肿瘤血液或仅肿瘤样本从测序数据中检测体细胞变异。这些方法中的大多数已经开发出来，并使用中等覆盖率的Illumina测序数据集进行了鉴定，以鉴定体细胞变异。但是，尚未进行针对离子洪流靶向的深度测序数据的体细胞变异检测算法的全面评估。方法我们应用了三种体细胞检测算法：Torrent Variant Caller，MuTect2和VarScan2，在一组大型卵巢癌患者中，包括208个配对的肿瘤血液样本和253个仅肿瘤的样本，这些样本在Ion Torrent Proton平台上跨330个扩增子进行了深度测序。随后，评估了三个体细胞变异呼叫者的一致性和表现。结果我们发现，在所有三种方法中，只有0.5％的SNV和0.02％的INDEL调用在算法上的一致性较低。当使用与已知突变特征的相关性，与COSMIC变异重叠并通过检查变异特征进行评估时，所有方法的交叉点都显示出更好的性能。Torrent Variant Caller也表现出色，其优点是不会消除可能导致高II型错误的大量变体。结论我们的结果表明，将最新的体细胞变异算法应用于离子激流靶向的深度测序数据时应格外小心。更好的质量控制程序和策略结合了多种方法的结果，应确保获得更高的准确性。这对于确保使用Ion Torrent深度测序的生物信息学管道的结果能够在癌症研究和临床中得到可靠应用至关重要。