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

ABSTRACT

Despite extensive studies of the error profiles of SNVs, those of insertions/deletions (indels)/structural variants (SVs) remain elusive. Using ultra-deep sequencing, we show that the error rates of indel/SVs are >100-fold lower than those of SNVs, although repeat indels have high error rates of 1%. We validated this pattern in a cohort of 103 patients with relapsed B cell acute lymphoblastic leukemia (B-ALL). We analyzed repeat indels in 339 cancer driver genes and demonstrated that the number of repeat units is highly predictive of the error rate. We then analyzed minimal residual disease samples from 72 patients with relapsed B-ALL and demonstrated that our approach had positive detections in 61% of cases, outperforming clinical flow cytometry (51% detection). Overall, we established indel and SV error profiles in deep next-generation sequencing (NGS) data, enabling superior tumor detection at very low burdens, which has a significant impact on the clinical diagnosis and monitoring of human cancers and other diseases.