Cost-effective hybrid long- and short-read sequencing enables accurate somatic structural variant detection

Abstract

Somatic structural variant (SSV) calling typically requires matched normal data. Incorporating relatively inexpensive short-read sequencing not only provides essential germline information but can also replace a substantial portion of long-read sequencing, thereby enabling more cost-effective somatic SV detection. Here, we present SomaSV, a hybrid sequencing framework that integrates 30x tumor long-read data with matched normal data comprising 10x long-read and 30x short-read sequencing. This design achieves high-accuracy somatic SV detection while remaining cost-competitive. Comprehensive benchmarking demonstrates that SomaSV outperforms current state-of-the-art methods by more than 13% in F1 score while reducing sequencing costs by approximately 19%. Moreover, SomaSV identifies clinically relevant cancer-associated genes, including CLDN4 and ROBO2, highlighting its potential for discovering valuable biomarkers to support early cancer screening and diagnosis. The source code for SomaSV can be accessed at https://github.com/eioyuou/SomaSV.