Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers
Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers
复制标题
DOI：
10.1038/s41598-019-45835-3
复制
发表时间：
2019-06-27
影响因子：
4.6
通讯作者：
Du, Hongli
中科院分区：
文献类型：
--
作者：
Chen, Jiayun;Li, Xingsong;Du, Hongli
下一代测序技术及其后续分析工具的发展和创新在科学研究和临床诊断应用中越来越受欢迎。因此，测序平台和变异识别管道的系统比较可以为基于NGS的科学和临床基因组学提供重要指导。在这项研究中，我们比较了27种测序平台和变体调用管道组合的性能，一致性和运行效率，测试了三种变体调用管道-基因组分析工具试剂盒HaplotypeCaller，Strelka 2和Samtools-Varscan 2，用于不同平台（包括BGISEQ 500，MGISEQ 2000，HiSeq 4000，NovaSeq和HiSeq Xten）测序的NA 12878基因组的9个数据集。对于WES数据集中12个组合的变异识别性能，所有组合在识别SNP方面都表现出良好的性能，其F-得分均高于0.96，其识别INDEL的性能在0.75至0.91之间变化。WGS数据集中的所有15种组合也表现出良好的性能，在调用SNP时的F分数完全高于0.975，在调用INDEL时的性能从0.71到0.93不等。所有组合在变异识别上都表现出很高的一致性，但WGS数据集的变异识别差异大于WES数据集。我们还在多个平台上以一系列梯度覆盖率对原始WES和WGS数据集进行下采样，然后分别计算每个覆盖率下三个管道消耗的变体调用周期。对于BGI和Illumina平台上的GIAB数据集，Strelka 2在检测准确性和处理效率方面表现出了各自测序平台上其他两条管道的超强性能，在下一代测序技术的进一步推广和应用中得到了推荐。我们的研究结果将为个人或组织的研究人员在可靠和一致的变异识别提供有用和全面的指导方针。
The development and innovation of next generation sequencing (NGS) and the subsequent analysis tools have gain popularity in scientific researches and clinical diagnostic applications. Hence, a systematic comparison of the sequencing platforms and variant calling pipelines could provide significant guidance to NGS-based scientific and clinical genomics. In this study, we compared the performance, concordance and operating efficiency of 27 combinations of sequencing platforms and variant calling pipelines, testing three variant calling pipelines-Genome Analysis Tool Kit HaplotypeCaller, Strelka2 and Samtools-Varscan2 for nine data sets for the NA12878 genome sequenced by different platforms including BGISEQ500, MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten. For the variants calling performance of 12 combinations in WES datasets, all combinations displayed good performance in calling SNPs, with their F-scores entirely higher than 0.96, and their performance in calling INDELs varies from 0.75 to 0.91. And all 15 combinations in WGS datasets also manifested good performance, with F-scores in calling SNPs were entirely higher than 0.975 and their performance in calling INDELs varies from 0.71 to 0.93. All of these combinations manifested high concordance in variant identification, while the divergence of variants identification in WGS datasets were larger than that in WES datasets. We also down-sampled the original WES and WGS datasets at a series of gradient coverage across multiple platforms, then the variants calling period consumed by the three pipelines at each coverage were counted, respectively. For the GIAB datasets on both BGI and Illumina platforms, Strelka2 manifested its ultra-performance in detecting accuracy and processing efficiency compared with other two pipelines on each sequencing platform, which was recommended in the further promotion and application of next generation sequencing technology. The results of our researches will provide useful and comprehensive guidelines for personal or organizational researchers in reliable and consistent variants identification.