GFHunter enables accurate and efficient gene fusion detection in long-read cancer transcriptomes

Abstract

The precise identification of gene fusions is crucial for cancer diagnosis and therapeutic decision-making. Long-read transcriptome sequencing provides distinct advantages over short-read technologies by capturing full-length fusion gene structures. However, fully harnessing long-read data for cancer research necessitates advanced computational approaches. In this study, we present GFHunter, a novel computational framework designed for efficient and accurate gene fusion detection. Benchmarking on both simulated and real long-read transcriptome datasets from non-tumor and cancer cell lines demonstrates that GFHunter accurately detects gene fusions with high sensitivity and significantly reduces false positives. Additionally, GFHunter runs 2-3 times faster and requires only 16%-50% of the memory compared to state-of-the-art tools. Notably, GFHunter uniquely identifies two known cancer-related fusions in HCT-116 and SKBR-3 cancer cell lines. These results highlight GFHunters potential as a powerful tool for advancing precision oncology and molecular diagnostics.