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

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

SUBMITTER: Ma X 

PROVIDER: S-EPMC6417284 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature