Abstract 3538: Analysis of error profiles in deep next-generation sequencing data

Abstract

Background Sequencing errors are a key confounding factor for detecting low frequency genetic variants for cancer screening, testing, treatment and surveillance through deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, enrichment PCR, and sequencing. In this study we systematically investigated the above question by using the current NGS technology. Results We discovered that the substitution error rate can be computationally suppressed to 10-5~10-4, which is 10~100-fold lower than current reports. We then quantified substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing using multiple deep sequencing datasets from multiple sequencing centers. We show that 1) error rate differs by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes; 2) C>T/G>A errors exhibit strong sequence context dependency; 3) sample-specific effects dominate elevated C>A/G>T errors; 4) target enrichment PCR lead to ~6-fold increase of overall error rate; 5) more than 70% of hotspot variants, such as BRAF V600E, can be detected at 0.1%~0.01% frequency with the current NGS technology by applying in-silico error suppression. Conclusions We present the first comprehensive analysis of error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS accuracy both experimentally and computationally which will lead to enhanced precision for deep sequencing. Citation Format: Xiaotu Ma, Jinghui Zhang. Analysis of error profiles in deep next-generation sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3538.