Literature DB >> 27388684

A Model Study of In Silico Proficiency Testing for Clinical Next-Generation Sequencing.

Eric J Duncavage1, Haley J Abel, Jason D Merker, John B Bodner, Qin Zhao, Karl V Voelkerding, John D Pfeifer.   

Abstract

CONTEXT: -Most current proficiency testing challenges for next-generation sequencing assays are methods-based proficiency testing surveys that use DNA from characterized reference samples to test both the wet-bench and bioinformatics/dry-bench aspects of the tests. Methods-based proficiency testing surveys are limited by the number and types of mutations that either are naturally present or can be introduced into a single DNA sample.
OBJECTIVE: -To address these limitations by exploring a model of in silico proficiency testing in which sequence data from a single well-characterized specimen are manipulated electronically.
DESIGN: -DNA from the College of American Pathologists reference genome was enriched using the Illumina TruSeq and Life Technologies AmpliSeq panels and sequenced on the MiSeq and Ion Torrent platforms, respectively. The resulting data were mutagenized in silico and 26 variants, including single-nucleotide variants, deletions, and dinucleotide substitutions, were added at variant allele fractions (VAFs) from 10% to 50%. Participating clinical laboratories downloaded these files and analyzed them using their clinical bioinformatics pipelines.
RESULTS: -Laboratories using the AmpliSeq/Ion Torrent and/or the TruSeq/MiSeq participated in the 2 surveys. On average, laboratories identified 24.6 of 26 variants (95%) overall and 21.4 of 22 variants (97%) with VAFs greater than 15%. No false-positive calls were reported. The most frequently missed variants were single-nucleotide variants with VAFs less than 15%. Across both challenges, reported VAF concordance was excellent, with less than 1% median absolute difference between the simulated VAF and mean reported VAF.
CONCLUSIONS: -The results indicate that in silico proficiency testing is a feasible approach for methods-based proficiency testing, and demonstrate that the sensitivity and specificity of current next-generation sequencing bioinformatics across clinical laboratories are high.

Mesh:

Substances:

Year:  2016        PMID: 27388684     DOI: 10.5858/arpa.2016-0194-CP

Source DB:  PubMed          Journal:  Arch Pathol Lab Med        ISSN: 0003-9985            Impact factor:   5.534


  11 in total

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Authors:  Simon A Hardwick; Ira W Deveson; Tim R Mercer
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Review 3.  Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists.

Authors:  Lawrence J Jennings; Maria E Arcila; Christopher Corless; Suzanne Kamel-Reid; Ira M Lubin; John Pfeifer; Robyn L Temple-Smolkin; Karl V Voelkerding; Marina N Nikiforova
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Review 4.  Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis.

Authors:  Scott Quainoo; Jordy P M Coolen; Sacha A F T van Hijum; Martijn A Huynen; Willem J G Melchers; Willem van Schaik; Heiman F L Wertheim
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5.  Proficiency Testing of Standardized Samples Shows Very High Interlaboratory Agreement for Clinical Next-Generation Sequencing-Based Oncology Assays.

Authors:  Jason D Merker; Kelly Devereaux; A John Iafrate; Suzanne Kamel-Reid; Annette S Kim; Joel T Moncur; Stephen B Montgomery; Rakesh Nagarajan; Bryce P Portier; Mark J Routbort; Craig Smail; Lea F Surrey; Patricia Vasalos; Alexander J Lazar; Neal I Lindeman
Journal:  Arch Pathol Lab Med       Date:  2018-10-30       Impact factor: 5.534

6.  Creation of an Expert Curated Variant List for Clinical Genomic Test Development and Validation: A ClinGen and GeT-RM Collaborative Project.

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7.  Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory.

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Journal:  Sci Rep       Date:  2022-01-27       Impact factor: 4.379

Review 9.  Practical Guidance for Clinical Microbiology Laboratories: Diagnosis of Ocular Infections.

Authors:  Sixto M Leal; Kyle G Rodino; W Craig Fowler; Peter H Gilligan
Journal:  Clin Microbiol Rev       Date:  2021-06-02       Impact factor: 50.129

10.  Sequencing of animal viruses: quality data assurance for NGS bioinformatics.

Authors:  Gianpiero Zamperin; Pierrick Lucas; Irene Cano; David Ryder; Miriam Abbadi; David Stone; Argelia Cuenca; Estelle Vigouroux; Yannick Blanchard; Valentina Panzarin
Journal:  Virol J       Date:  2019-11-21       Impact factor: 4.099

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