London Calling 2023: SAVANA: a computational method to characterise structural variation in human cancer genomes using nanopore sequencing

To date, the study of cancer genomes has relied on the analysis of short-read whole-genome sequencing, which generates short, highly accurate 100–300 bp reads. However, the detection of structural variants (SVs) using short reads is limited. As a result, our understanding of the patterns and mechanisms underpinning structural variation in cancer genomes remains incomplete.

SAVANA is a novel structural variant caller for long-read sequencing data specifically designed to detect somatic SVs. Extensively validated against a multi-platform truth set, we show that SAVANA identifies a range of somatic rearrangements with high recall and precision, outperforming existing tools while maintaining a low execution time. In tumour samples, SAVANA can identify clinically relevant SVs with high accuracy. Additionally, SAVANA permits the reconstruction of double minutes, multi-chromosomal  chromothripsis events, and SVs mapping to highly repetitive regions. In summary, SAVANA permits the characterization of complex structural variants and can uncover clinically relevant mutations across diverse cancer types with high accuracy.