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July 18th, 2025
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UC San Diego
bioinformatics
biorxiv

Improved Mutation Detection in Duplex Sequencing Data with Sample-Specific Error Profiles

Cheng, Y.Open in Google Scholar•Nandi, S.Open in Google Scholar•Culibrk, L.Open in Google Scholar•Kristin, A.Open in Google Scholar•Stuewe, I.Open in Google Scholar•Al-Azzam, S.Open in Google Scholar•Petljak, M.Open in Google Scholar•Alexandrov, L. B.Open in Google Scholar

Duplex sequencing enables highly accurate detection of rare somatic mutations, but existing variant callers often rely on protocol-specific heuristics that limit sensitivity, reproducibility, and cross-study comparability. We present DupCaller, a probabilistic variant caller that builds sample-specific error profiles and applies a strand-aware statistical model for mutation detection. Across 50 synthetic datasets, DupCaller identified 1.25-fold more single-base substitutions (SBSs) and 1.41-fold more indels than a state-of-the-art method, while exhibiting equal or better precision. In three duplex-sequenced cell lines treated with aristolochic acid, it recovered expected mutational signatures while detecting 3.5-fold more SBSs and 2.8-fold more indels. In 93 tissue samples-including neurons, cord blood, sperm, saliva, and blood-DupCaller showed consistent gains, detecting 1.21- to 2.7-fold more mutations. Sensitivity scaled with sample duplication rate, yielding approximately 1.5-fold more mutations under optimal conditions and over 3-fold more in low-duplication samples where other tools falter. These results establish DupCaller as a robust and scalable solution for somatic mutation profiling in duplex sequencing across diverse biological and technical contexts.

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