A comparative analysis of algorithms for somatic SNV detection in cancer.
A comparative analysis of algorithms for somatic SNV detection in cancer.
复制标题
DOI：
10.1093/bioinformatics/btt375
复制
发表时间：
2013-09-15
期刊：
影响因子：
--
通讯作者：
Adelson DL
中科院分区：
文献类型：
--
作者：
Roberts ND;Kortschak RD;Parker WT;Schreiber AW;Branford S;Scott HS;Glonek G;Adelson DL
动机：随着相对负担得起的高通量技术的出现，癌症的DNA测序现在是癌症研究项目中的常见做法，并将越来越多地用于临床实践，为诊断和治疗提供信息。体细胞(仅限癌症)单核苷酸变异(SNV)是最简单的一类突变，但它们在DNA测序数据中的识别受到种系多态、肿瘤异质性以及测序和分析错误的干扰。最近发表的四种用于在匹配的癌症-正常测序数据集中检测体细胞SNV位置的算法是VarScan、SomaticSniper、JointSNVMix和Strelka。在这项分析中，我们将这四种SNV调用算法应用于一名慢性粒细胞白血病(CML)患者的癌症正常Illumina外显子组测序。对每种算法返回的候选SNV站点进行过滤，去除可能的误报，然后对其进行特征描述和比较，以考察每种SNV调用算法的优缺点。结果：比较VarScan、SomaticSniper、JointSNVMix2和Strelka返回的候选SNV集合，发现它们在返回的站点的数量和特征、分配给相同站点的体细胞概率分数、它们对各种噪声源的敏感性以及它们对低等位基因比例候选的敏感性方面存在显著差异。可获得性：数据登录号SRA081939，代码：http://code.google.com/p/snv-caller-review/联系方式：david.adelson@adelaide.edu.au补充信息：补充数据可在生物信息学在线上获得。
Motivation: With the advent of relatively affordable high-throughput technologies, DNA sequencing of cancers is now common practice in cancer research projects and will be increasingly used in clinical practice to inform diagnosis and treatment. Somatic (cancer-only) single nucleotide variants (SNVs) are the simplest class of mutation, yet their identification in DNA sequencing data is confounded by germline polymorphisms, tumour heterogeneity and sequencing and analysis errors. Four recently published algorithms for the detection of somatic SNV sites in matched cancer–normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer–normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterized and compared to investigate the strengths and weaknesses of each SNV calling algorithm. Results: Comparing the candidate SNV sets returned by VarScan, SomaticSniper, JointSNVMix2 and Strelka revealed substantial differences with respect to the number and character of sites returned; the somatic probability scores assigned to the same sites; their susceptibility to various sources of noise; and their sensitivities to low-allelic-fraction candidates. Availability: Data accession number SRA081939, code at http://code.google.com/p/snv-caller-review/ Contact: david.adelson@adelaide.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.