Analysis of error profiles of indels and structural variants in deep sequencing data

Accurate detection of low frequency mutations is of critical importance in the study of genetic heterogeneity, such as detection of minimal residual diseases for cancer prognosis. Prior works have resulted in successful computational error suppression for substitutions (SNV). However, the error profiles of small insertion/deletion (Indel) and structural variants (SV) remain elusive.We established Indel and SV error profiles in deep next generation sequencing data that enabled superior tumor detection performance at very low burdens, which has a significant impact on the clinical diagnosis and monitoring of human cancers and beyond. Our data also suggests future research directions to improve recovery of mutant reads in ultra-deep sequencing applications.