Gene fusion detection in long-read transcriptome sequencing data with GFvoter.

Gene fusion is a prevalent occurrence in cancer patients, and fusions are significant both as diagnostic biomarkers and as therapeutic targets for cancer. Long-read transcriptome sequencing technology provides new opportunities for gene fusion detection. In this research, we have developed GFvoter, a novel method that employs a multivoting strategy to identify gene fusions from long-read transcriptome sequencing data. GFvoter calls two RNA-seq aligners, two fusion detection tools, and a newly designed scoring mechanism to conduct the so-called voting process in turn, which enables the accurate detection of potential fusions. We validated GFvoter using both simulated and real cell line datasets from PacBio and Nanopore and found that GFvoter significantly outperforms alternative methods. Moreover, GFvoter successfully reported the RPS6KB1:VMP1 gene fusion in the MCF-7 cell line, while none of the other tested tools detected this fusion. Overall, our findings show that GFvoter can accurately identify gene fusions from long-read RNA-seq data, which has the potential to improve cancer diagnosis and treatment. GFvoter is available at https://github.com/xiaolan-z/GFvoter .