Analysis of error profiles in deep next-generation sequencing data

Abstract

BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using 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, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10−5 to 10−4, which is 10- to 100-fold lower than generally considered achievable (10−3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ 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. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target-enrichment PCR led to ~ 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1 ~ 0.01% frequency with the current NGS technology by applying in silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1659-6) contains supplementary material, which is available to authorized users.

Rampant C->U hypermutation in the genomes of SARS-CoV-2 and other coronaviruses – causes and consequences for their short and long evolutionary trajectories

“…In seeking alternative, non-biological explanations for this observation, they cannot have arisen through misincorporation errors in the next generation sequence methods used to produce the dataset because the analysis in the current was restricted to consensus sequences. These are generally assembled from libraries that typically possess reasonable coverage and read depth; error frequencies of < 10 -4 per site (14) would therefore improbably create 170 a consensus change in a sequence library. There was furthermore, no comparable increase in G->A mutations ( Fig.…”

Probabilistic Approach to Understand Errors in Sequencing and its based Applications

“…Therefore, Substitution error rates are more significant than deletion and insertion. The substitution error rate by conventional NGS was reported to be >1% in 2011 and was found to be similar in later reports [5]. Some groups tried to systematically investigate substitution error profile by analysing multiple datasets [10].…”

Quality control of low-frequency variants in SARS-CoV-2 genomes

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