FuSViz-visualization and interpretation of structural variation using cancer genomics and transcriptomics data

Structural variation (SV) is a frequent category of genetic alterations important for understanding cancer genome evolution and revealing key cancer driver events. With the development of high-throughput sequencing technologies, the ability to detect SVs of various sizes and types has improved, at both the DNA and RNA levels. However, SV calls are still prone to a considerable fraction of false positives, which necessitates visual inspection and manual curation as part of the quality control process. Identification of reliable and recurrent SVs in larger cohorts lends strength to revealing the driving roles of SVs in cancer development and to the discovery of potential diagnostic and prognostic biomarkers. Here, we present FuSViz, an application for visualization, interpretation, and prioritization of SVs. The tool provides multiple data view approaches in a user-friendly interface, allowing the investigation of prevalence and recurrence of SVs and relevant partner genes in a sample cohort. It integrates SV calls from DNA and RNA sequencing datasets to comprehensively illustrate the biological impact of SVs on the implicated genes and associated genomic regions. The functionality of FuSViz is intended for interrogation of both recurrent and private SVs, effectively assisting with pathogenicity evaluation and biomarker discovery in cancer sequencing projects.