Technical Advance Efficient and ultrasensitive identification of clinical somatic mutations by modified single duplex DNA sequencing.

Authors

Kelsi Peneweit, Adam Waalkes, Megan Nguyen, Scott R Kennedy, Eric Q Konnick, Stephen J Salipante

Published in

The Journal of molecular diagnostics : JMD. Oct 19, 2026. Epub Oct 19, 2026.

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.

PMID:
42896807
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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