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

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Gene fusions are key drivers of cancer development and potential therapeutic targets.
• Long-read RNA sequencing (RNA-seq) can reveal full-length fusion structures but suffers from high error rates.
• Accurate identification of true gene fusions from noisy long-read RNA-seq data remains a challenge.

Purpose of the Study:

• To develop an accurate computational framework for detecting gene fusions from long-read RNA-seq data.
• To overcome the challenges posed by high error rates in long-read RNA-seq.
• To improve the sensitivity and reliability of gene fusion discovery.

Main Methods:

• Developed GFSeeker, a splicing-graph-based computational framework.
• Utilized a splicing graph reference and dual re-alignment validation pipeline.
• Benchmarked GFSeeker on simulated, non-tumor, and cancer cell line datasets.

Main Results:

• GFSeeker demonstrated state-of-the-art performance, achieving 6%-15% higher F1 scores than existing methods.
• Successfully identified the MATN2-POP1 fusion in MCF-7 cells, a known event missed by other tools.
• Showcased superior sensitivity in resolving complex gene fusion events.

Conclusions:

• GFSeeker is a powerful and reliable tool for gene fusion discovery using long-read RNA-seq.
• The framework effectively mitigates noise from high error rates.
• GFSeeker has significant potential to advance cancer research and precision diagnostics.