A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal
Fig 1
The SGZ pipeline is overviewed in panel A. Key components include fitting an optimal copy number model to the genome‐wide log‐ratio and minor allele frequency profiles (B), and modeling the expected allele frequencies of germline, somatic, and subclonal somatic mutations (C). In panel B, the dots in the top panel correspond to log ratios at each exon sequenced, segmented and fitted to discrete copy number levels, while the dots on the bottom panel are germline SNP minor allele frequencies. In panel C, examples of expected variant allele frequencies are shown for various scenarios of copy number and tumor purity. The expected allele frequencies are shown for germline (blue), somatic (red), and subclonal somatic (yellow).