Confidence-based Somatic Mutation Evaluation and Prioritization

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. Author Summary: Next generation sequencing (NGS) has enabled unbiased, high throughput discovery of genetic variations and somatic mutations. However, the NGS platform is still prone to errors resulting in inaccurate mutation calls. A statistical measure of the confidence of putative mutation calls would enable researchers to prioritize and select mutations in a robust manner. Here we present our development of a confidence score for mutations calls and apply the method to the identification of somatic mutations in B16 melanoma. We use NGS exome resequencing to profile triplicates of both the reference C57BL/6 mice and the B16-F10 melanoma cells. These replicate data allow us to formulate the false discovery rate of somatic mutations as a statistical quantity. Using this method, we show that 50 of 50 high confidence mutation calls are correct while 0 of 44 low confidence mutations are correct, demonstrating that the method is able to correctly rank mutation calls.