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Updated: Oct 21, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
Technical Advance Efficient and ultrasensitive identification of clinical somatic mutations by modified single duplex
Kelsi Peneweit1, Adam Waalkes1, Megan Nguyen1
1From the Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA.
Abstract:
Conventional next-generation sequencing (NGS) implementations have poor sensitivity for low prevalence mutations due to their inherent error rate. Error suppression that reconciles individual strands of a DNA duplex can improve sensitivity. Concatenating Original Duplex for Error Correction (CODEC) is an emerging technology that physically links duplex strands to enable their comparison from a single read pair, circumventing high sequencing depths normally required to recover strands independently. However, use in clinical practice is constrained by technical shortcomings. We modified CODEC (mCODEC) to improve performance, workflow, scalability, and informatic analyses for clinical use. Redesigned adapters improve compatibility with hybridization capture procedures, increase multiplexing capacity and molecular identifier length per molecule, and enable enzymatic removal of uninformative ligation byproducts. We benchmarked mCODEC using bacteriophage reference material and clinical specimens. Relative to the original protocol, mCODEC improved on-target reads from a targeted gene enrichment panel by over an order of magnitude and reduced malformed ligation byproducts. mCODEC achieved equivalent error correction to CODEC (<5.4×10-5 errors per nucleotide), making it two orders of magnitude more robust than conventional sequencing (∼3×10-3 per nucleotide) while consuming only three-fold more reads. We explored clinical utility of mCODEC using targeted gene enrichment to examine patient neoplasms previously interrogated through a conventional NGS oncology assay, finding mCODEC able to identify low prevalence, clinically reportable mutations not detectable by standard-of-care testing.
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