Confidence-based Somatic Mutation Evaluation and Prioritization
Confidence-based Somatic Mutation Evaluation and Prioritization
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DOI：
10.1371/journal.pcbi.1002714
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发表时间：
2012-09-01
影响因子：
4.3
通讯作者：
Sahin, Ugur
中科院分区：
文献类型：
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作者：
Loewer, Martin;Renard, Bernhard Y.;Sahin, Ugur
下一代测序 (NGS) 实现了体细胞突变的高通量发现。检测取决于实验设计、实验室平台、参数和分析算法。然而，基于 NGS 的体细胞突变检测很容易出现错误判断，报告的验证率接近 54%，算法之间的一致性低于 50%。在这里，我们开发了一种算法，为 NGS 识别的每个体细胞突变分配一个统计数据，即错误发现率 (FDR)。该 FDR 置信值可以准确区分真正的突变和错误的识别。利用 C57BL/6 小鼠和 B16-F10 黑色素瘤细胞的三次外显子组分析生成的测序数据，我们使用现有算法 GATK、SAMtools 和 SomaticSNiPer 来识别体细胞突变。对于每个识别出的突变，我们的算法都会分配一个 FDR。我们选择了 139 个突变进行验证，其中 50 个体细胞突变分配了低 FDR（高置信度），44 个突变分配了高 FDR（低置信度）。所有高置信度体细胞突变（50 个中的 50 个）均经过验证，44 个低置信度体细胞突变均未经过验证，45 个具有中间 FDR 的突变中有 15 个经过验证。此外，将单个 FDR 分配给各个突变可以实现实验室和计算方法的统计比较，包括 ROC 曲线和 AUC 指标。使用 HiSeq 2000，重复的单端 50 nt 读数可生成最高置信度的体细胞突变调用集。
Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.