Cost-effective hybrid long- and short-read sequencing enables accurate somatic structural variant detection
Gao, R.; Jiang, T.; Jiang, Z.; Cao, S.; Zhou, M.; Zhao, Y.; Wang, G.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- OctopusV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and analysis 96%
- RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data 94%
- GCfix: A Fast and Accurate Fragment Length-SpecificMethod for Correcting GC Bias in Cell-Free DNA 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.