Abstract
Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.
Highlights
Parallel sequencing (MPS) of cancer exomes is becoming a commonplace technique, and has led to the identification of genes underlying the pathogenesis of a number of cancer types [1,2,3,4,5,6]
There appeared to be an association between tumor grade and the number of predicted somatic mutations (Figure 1A), with invasive mucinous tumors harboring a higher number of predicted point mutations than benign or borderline tumors
No association was found between estimates of tumor ploidy and normal contamination (as measured by Allele-specific copy number analysis of tumors (ASCAT) (Allele-Specific Copy number Analysis of Tumors [13])) or number of predicted somatic single nucleotide variant (SNV)
Summary
Parallel sequencing (MPS) of cancer exomes is becoming a commonplace technique, and has led to the identification of genes underlying the pathogenesis of a number of cancer types [1,2,3,4,5,6]. In response to the volume of data generated by these genome-scale studies, a host of software tools has been developed to aid in distinguishing genuine somatic mutations from germline variation, alignment artifacts, and inherent MPS errors [7,8,9,10,11]. The rarity and diversity of somatic events that occur on a background of tumor heterogeneity, normal contamination, technical artifacts, and genomic complexity makes this task challenging [1,12]. The germline sample is usually assumed to be free of genetic material from the tumor, this assumption can be tested and
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