四种肿瘤体细胞单核苷酸突变检测方法的比较

随着高通量测序成本的不断降低,基于DNA测序技术的肿瘤基因组研究已经成为揭示肿瘤分子机制的主流方法,并在临床诊断和治疗中逐渐得到应用。肿瘤体细胞单核苷酸突变变异 (single nucleotide variant, SNV) 作为最简单的一种基因变异类型,其检测会受到家系多态性、肿瘤异质性、测序和分析误差等多个因素的影响,从而导致一些假阳性的结果。目前,已有一些基于肿瘤基因组测序数据的体细胞 SNV 检测软件,如 Varscan2,Mutect2,Strelka,SomaticSniper 等。本文选取四种典型的检测方法,对每种方法的检测原理进行研究,并使用 ICGC-TCGA 提供的全基因组数据,对上述四种变异检测软件进行测试。参照每种方法的分析流程,获得每种方法识别的候选变异位点集,并与真实的变异位点集合进行比较,分析每种算法的优缺点,从而为研究人员使用这些方法提供指导。

Comparison of Four Methods for Detecting Somatic Single Nucleotide Variant in Tumor Cells

With the cost of high-throughput sequencing continuous reducing, the research of tumor genome based on DNA sequencing technology has become the mainstream method to reveal the molecular mechanism of tumor, and it has gradually been applied in clinical diagnosis and treatment. The single nucleotide variation (SNV) of tumor somatic cells is the most common kind of genetic variations. Its detection will be affected by many factors such as family polymorphism, tumor heterogeneity, sequencing and analysis errors which will lead to some false positive results. There are some somatic cell SNV detection software based on tumor genome sequencing data now, such as Varscan2, Mutect2, Strelka, SomaticSniper and so on. In this paper, we selected four typical detection methods are selected, and studied the detection principle of each method. We also used the whole genome data provided by ICGC-TCGA to test the four mutation detection software. With reference to the analysis flow of each method, we obtained four sets of candidate mutation sites identified by each method. Then we compared each set with the set of true mutation sites, analyzed the advantages and disadvantages of each algorithm. After our work, we concluded some suggestions that can provide to researcher when they want to use these methods.