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

ABSTRACT: 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. Mo

SUBMITTER: Zhao X 

PROVIDER: S-EPMC12269178 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature