Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers.
Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers.
复制标题
作者：
Wang Q;Jia P;Li F;Chen H;Ji H;Hucks D;Dahlman KB;Pao W;Zhao Z
在高通量下一代测序技术和破译癌症基因组的迫切需要的推动下，检测体细胞单核苷酸变异（ssnv）的计算方法在过去两年中有了显着的改进。最近开发的工具通常会在每个变异位点直接将肿瘤样本与匹配的正常样本进行比较，以提高sSNV呼叫的准确性。这些程序还涉及低等位基因频率的ssnv检测，允许研究肿瘤异质性，癌症亚克隆和癌症发展中的突变进化。我们使用黑色素瘤样本和匹配血液的全基因组测序（Illumina genome Analyzer IIx平台），18对肺肿瘤-正常对和7个肺癌细胞系的全外显子组测序（Illumina HiSeq 2000平台）来评估6种sSNV检测工具：EBCall、JointSNVMix、MuTect、SomaticSniper、Strelka和VarScan 2，重点关注MuTect和VarScan 2这两种广泛使用的公开软件工具。默认/建议参数用于运行这些工具。通过PCR和基因组DNA直接测序对这些样本中检测到的错义ssnv进行验证。我们还模拟了10对肿瘤-正常对，以探索这些程序检测低等位基因频率ssnv的能力。在我们的癌症样本中成功验证的237个ssnv中，VarScan 2和MuTect检测到的ssnv最多（分别为204和192）。MuTect比VarScan 2多发现了11个低覆盖率的ssnv，但在正常样本中比VarScan 2多遗漏了11个具有替代等位基因的ssnv。当使用169个无效的ssnv检查每个工具的假呼叫时，我们观察到在正常样本中肺癌细胞系中检测到的bb0 63%的假呼叫具有替代等位基因。此外，从我们的模拟数据来看，VarScan 2比其他工具鉴定出更多的ssnv，而MuTect鉴定出的ssnv等位基因分数最低。我们的研究探讨了使用ssnv调用工具产生的典型假阳性和假阴性检测。我们的研究结果表明，尽管最近取得了进展，但这些工具仍有很大的改进空间，特别是在正常样本中低覆盖率/等位基因频率的ssnv和具有备用等位基因的ssnv的区分方面。
Driven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development. We used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs. Out of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs. Our study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.
登录
查看更多内容
使用下一代靶向重新取样对稀有突变的超敏感检测。
影响因子：
14.9
作者：
Flaherty P;Natsoulis G;Muralidharan O;Winters M;Buenrostro J;Bell J;Brown S;Holodniy M;Zhang N;Ji HP
通讯作者：
Ji HP
DOI：
10.1093/bioinformatics/btt375
发表时间：
2013-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Roberts ND;Kortschak RD;Parker WT;Schreiber AW;Branford S;Scott HS;Glonek G;Adelson DL
通讯作者：
Adelson DL
DOI：
10.1093/bioinformatics/bts053
发表时间：
2012-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Roth A;Ding J;Morin R;Crisan A;Ha G;Giuliany R;Bashashati A;Hirst M;Turashvili G;Oloumi A;Marra MA;Aparicio S;Shah SP
通讯作者：
Shah SP
影响因子：
3.7
作者：
Jia P;Li F;Xia J;Chen H;Ji H;Pao W;Zhao Z
通讯作者：
Zhao Z
影响因子：
5.8
作者：
Saunders, Christopher T.;Wong, Wendy S. W.;Cheetham, R. Keira
通讯作者：
Cheetham, R. Keira