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

Cheng, Y.; Nandi, S.; Culibrk, L.; Kristin, A.; Stuewe, I.; Al-Azzam, S.; Petljak, M.; Alexandrov, L. B.

doi:10.1101/2025.07.13.664565

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bioRxiv · 10.1101/2025.07.13.664565

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

Cheng, Y.·Nandi, S.·Culibrk, L.·Kristin, A.·Stuewe, I.·Al-Azzam, S.·Petljak, M.·Alexandrov, L. B.

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Abstract

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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Cheng, Y., Nandi, S., Culibrk, L., Kristin, A., Stuewe, I., Al-Azzam, S., Petljak, M., Alexandrov, L. B.. 2025-07-18. Improved Mutation Detection in Duplex Sequencing Data with Sample-Specific Error Profiles. https://doi.org/10.1101/2025.07.13.664565

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