GFSeeker: a splicing-graph-based approach for accurate gene fusion detection from long-read RNA sequencing data

TL;DR

GFSeeker is a new computational tool that improves the detection of gene fusions in cancer using long-read RNA sequencing data.

Contribution

GFSeeker introduces a splicing-graph-based framework that achieves higher accuracy in detecting gene fusions compared to existing methods.

Findings

GFSeeker outperforms existing methods with 6%–15% higher F1 scores on benchmark datasets.

GFSeeker successfully identified the MATN2–POP1 fusion in MCF-7 cells, which other tools missed.

The tool's dual re-alignment validation effectively reduces noise from high error rates in long-read RNA-seq.

Abstract

Gene fusions are critical oncogenic drivers and therapeutic targets in diverse cancers. Long-read ribonucleic acid sequencing (RNA-seq) offers an unprecedented opportunity to resolve the full-length structure of fusion isoforms, but its high intrinsic error rates pose significant challenges to the precise identification of true fusion events. Here, we developed GFSeeker, an innovative splicing-graph-based computational framework for accurate gene fusion detection from long-read RNA-seq. GFSeeker employs a unique pipeline based on a splicing graph reference and a dual re-alignment validation to effectively overcome data noise from high error rates. Benchmarking across simulated, non-tumor, and cancer cell line datasets demonstrated GFSeeker’s state-of-the-art performance, achieving 6%–15% higher F1 score compared to existing methods. Notably, GFSeeker successfully identified the known fusion event, MATN2–POP1, in the MCF-7 cancer cell line, missed by other tools, highlighting its superior sensitivity in resolving complex fusion events. These results validate GFSeeker as a powerful and reliable tool for gene fusion discovery, heralding its significant potential to advance cancer research and precision diagnostics.

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