Related Experiment Video
Updated: Mar 18, 2026
Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
Published on: April 11, 2016
A Model Study of In Silico Proficiency Testing for Clinical Next-Generation Sequencing
Eric J Duncavage1, Haley J Abel, Jason D Merker
1From the Departments of Pathology (Drs Duncavage and Pfeifer) and Genetics (Dr Abel), Washington University School of Medicine, St Louis, Missouri; the Department of Pathology (Dr Merker), Stanford University School of Medicine, Stanford, California; Product Development, Laboratory Improvement Program (Mr Bodner), and the Surveys Department (Dr Zhao), College of American Pathologists, Northfield, Illinois; and the Department of Pathology and ARUP Laboratories, University of Utah, Salt Lake City (Dr Voelkerding).
In silico proficiency testing using next-generation sequencing data is a feasible method for evaluating clinical laboratories. This approach demonstrated high sensitivity and specificity in identifying genetic variants.
Area of Science:
- Genomics
- Clinical Diagnostics
- Bioinformatics
Background:
- Current proficiency testing for next-generation sequencing (NGS) relies on physical DNA samples, limiting the types and number of detectable mutations.
- Existing methods-based proficiency testing surveys face challenges due to the inherent limitations of single-sample mutation diversity.
Purpose of the Study:
- To explore an in silico proficiency testing model for NGS assays.
- To overcome limitations of traditional methods-based proficiency testing by electronically manipulating sequence data.
Main Methods:
- Utilized reference genome DNA enriched with Illumina TruSeq and Life Technologies AmpliSeq panels.
- Sequenced data on MiSeq and Ion Torrent platforms, then introduced 26 variants (SNVs, deletions, dinucleotide substitutions) in silico at 10-50% variant allele fractions (VAFs).
- Participating labs analyzed the manipulated data using their clinical bioinformatics pipelines.
Main Results:
- Laboratories achieved high variant identification rates, averaging 95% overall and 97% for variants with VAFs >15%.
- No false-positive variant calls were reported.
- Excellent variant allele fraction (VAF) concordance was observed, with a median absolute difference <1%.
Conclusions:
- In silico proficiency testing is a viable and effective approach for methods-based proficiency testing in NGS.
- Current NGS bioinformatics pipelines in clinical laboratories demonstrate high sensitivity and specificity for variant detection.
