Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes

Zhang, Y.; Viswanadham, V.; Andreopoulos, M.; Glodzik, D.; Liu, R.; Luquette, L.; Jo, S.-Y.; Narayan, A.; Niu, M.; Anderson, L.; Brew, J.; Chao, H.; Cibulskis, C.; Dong, G.; Evani, U.; Feng, W.; Gronska-Peski, M.; Helland, A.; Hilal, N.; Jabara, N.; Jin, H.; Li, N.; Manam, M.; Mallett, S.; Runnels, A.; Scharlee, C.; Smith, C.; The SMaHT Duplex Sequencing Working Group,; Shao, D.; Walsh, C.; Adalsteinsson, V.; Germer, S.; Gibbs, R.; Choudhury, S.; Doddapaneni, H.; Evrony, G.; Zong, C.; Coorens, T.

doi:10.64898/2025.12.12.692823

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bioRxiv · 10.64898/2025.12.12.692823

Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes

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Abstract

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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/692823v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@123b221org.highwire.dtl.DTLVardef@83ba0aorg.highwire.dtl.DTLVardef@2b0ab0org.highwire.dtl.DTLVardef@1cae35e_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Zhang, Y., Viswanadham, V., Andreopoulos, M., Glodzik, D., Liu, R., Luquette, L., Jo, S.-Y., Narayan, A., Niu, M., Anderson, L., Brew, J., Chao, H., Cibulskis, C., Dong, G., Evani, U., Feng, W., Gronska-Peski, M., Helland, A., Hilal, N., Jabara, N., Jin, H., Li, N., Manam, M., Mallett, S., Runnels, A., Scharlee, C., Smith, C., The SMaHT Duplex Sequencing Working Group,, Shao, D., Walsh, C., Adalsteinsson, V., Germer, S., Gibbs, R., Choudhury, S., Doddapaneni, H., Evrony, G., Zong, C., Coorens, T.. 2025-12-15. Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes. https://doi.org/10.64898/2025.12.12.692823

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