title: In Silico Proficiency Testing for Clinical Next-Generation Sequencing.
abstract: Quality assurance for clinical next-generation sequencing (NGS)-based assays is difficult given the complex methods and the range of sequence variants such assays can detect. As the number and range of mutations detected by clinical NGS assays has increased, it is difficult to apply standard analyte-specific proficiency testing (PT). Most current proficiency testing challenges for NGS are methods-based PT surveys that use DNA from reference samples engineered to harbor specific mutations that test both sequence generation and bioinformatics analysis. These methods-based PTs are limited by the number and types of mutations that can be physically introduced into a single DNA sample. In silico proficiency testing, which evaluates only the bioinformatics component of NGS assays, is a recently introduced PT method that allows for evaluation of numerous mutations spanning a range of variant classes. In silico PT data sets can be generated from simulated or actual sequencing data and are used to test alignment through variant detection and annotation steps. In silico PT has several advantages over the use of physical samples, including greater flexibility in tested variants, the ability to design laboratory-specific challenges, and lower costs. Herein, we review the use of in silico PT as an alternative to traditional methods-based PT as it is evolving in oncology applications and discuss how the approach is applicable more broadly.
authors.affiliation: Department of Pathology, Washington University School of Medicine, St. Louis, Missouri.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Duncavage
authors.affiliation: Department of Genetics, Washington University School of Medicine, St. Louis, Missouri.
authors.forename: Haley J
authors.initials: HJ
authors.lastname: Abel
authors.affiliation: Department of Pathology, Washington University School of Medicine, St. Louis, Missouri. Electronic address: pfeifer@path.wusltl.edu.
authors.forename: John D
authors.initials: JD
authors.lastname: Pfeifer
mesh_terms: D001483:Base Sequence; D019295:Computational Biology; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000592:standards*; D005787:Gene Frequency; D059014:High-Throughput Nucleotide Sequencing / Q000592:standards*; D006801:Humans; D059021:Laboratory Proficiency Testing / Q000379:methods*; D012015:Reference Standards