SEGF: A Novel Method for Gene Fusion Detection from Single-End Next-Generation Sequencing Data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) and target capture technologies generate vast amounts of data.
• Accurate detection of gene fusions from high-throughput sequencing data is increasingly important for research and clinical applications.

Purpose of the Study:

• To develop and evaluate a novel method for detecting gene fusions using single-end DNA sequencing data.
• To assess the accuracy and sensitivity of the new method compared to existing approaches.

Main Methods:

• Developed the single-end gene fusion (SEGF) analysis method.
• Integrated Basic Local Alignment Search Tool (BLAST) and Short Oligonucleotide Analysis Package (SOAP) with stringent filters.
• Compared SEGF with four other methods using standard and lung cancer FFPE samples.

Main Results:

• SEGF successfully detected gene fusions in both standard and clinical samples.
• SEGF exhibited the highest accuracy and sensitivity among the five methods evaluated.
• SEGF identified complex gene fusion types from single-end NGS data.

Conclusions:

• SEGF provides a timely, cost-effective, and straightforward approach for precise gene fusion detection.
• Acquiring gene fusion information at the DNA level using SEGF can enhance clinical diagnosis and treatment guidance without RNA sequencing.
• The method offers valuable insights from DNA panels and sequencing for research and diagnostics.