Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes

作者：Yang Zhang, Vinay Viswanadham, Michail Andreopoulos, Dominik Głodzik, Ruolin Liu, Lovelace J. Luquette, Se‐Young Jo, Azeet Narayan, Muchun Niu, Lee Anderson, Joseph Brew, Hsu Chao, Carrie Cibulskis, Guanlan Dong, Uday S. Evani, William C. Feng, Marta Grońska-Pęski, Adrienne Helland, Nazia Hilal, Nisrine Jabara, Hu Jin, Ning Li, Monica Manam, Shayna L. Mallett, Alexi Runnels, Constantijn Scharlee, Carter S. Smith, SMaHT Duplex Sequencing Focus Group, Diane D. Shao, Christopher A. Walsh, Viktor A. Adalsteinsson, Eunjung Alice Lee, Peter J. Park, Kristin G. Ardlie, Søren Germer, Richard A. Gibbs, Sangita Choudhury, HarshaVardhan Doddapaneni, Gilad D. Evrony, Chenghang Zong, Tim Coorens · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.64898/2025.12.12.692823 · 被引用次数：1 · 研究领域：Cancer Genomics and Diagnostics、Genomics and Rare Diseases、Genomic variations and chromosomal abnormalities

Detecting somatic mutations in normal tissues is challenging due to sequencing errors and the low allele fractions of post-zygotic variants. Duplex sequencing greatly reduces errors and can detect mutations at any allele fraction, but systematic, cross-platform comparisons are lacking. We present a comprehensive benchmarking of six duplex sequencing technologies used by the SMaHT Network: CODEC, CompDuplex-seq, HiDEF-seq, NanoSeq, ppmSeq, and VISTA-seq. We evaluated their performance using cord blood DNA, a tumor-normal cell line mixture, and homogenates from six human tissues. Each method shows distinct profiles in genomic footprint, sensitivity, and cost. Despite differences in library construction and sequencing platforms, estimates of mutation rates and mutational signatures are highly concordant. Integration with ultra-deep whole-genome sequencing shows that duplex approaches sensitively capture mutations and signatures beyond embryonic or clonally expanded variants. These results provide a foundation for selecting duplex methods and interpreting their data, enabling scalable single-molecule analyses of somatic mutation landscapes.