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Genome Biol. 2019 Mar 14;20(1):50. doi: 10.1186/s13059-019-1659-6.

Analysis of error profiles in deep next-generation sequencing data.

Author information

1
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA. Xiaotu.Ma@stjude.org.
2
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
3
Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
4
Key Laboratory of Pediatric Hematology and Oncology Ministry of Health, Department of Hematology and Oncology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
5
Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
6
HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.
7
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA. Jinghui.Zhang@stjude.org.

Abstract

BACKGROUND:

Sequencing 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.

RESULTS:

By 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.

CONCLUSIONS:

We 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.

KEYWORDS:

Deep sequencing; Detection; Error rate; Hotspot mutation; Subclonal; Substitution

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