gSV: a general structural variant detector using the third-generation sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Structural variants (SVs) significantly impact genome diversity and disease susceptibility, especially in cancer.
• Third-generation sequencing enhances SV detection but struggles with complex SVs due to alignment challenges and reliance on predefined models.

Purpose of the Study:

• To introduce gSV, a general structural variant detector designed to overcome limitations in complex SV detection.
• To provide a unified framework for comprehensive SV discovery in research and clinical settings.

Main Methods:

• gSV integrates alignment-based and assembly-based approaches with a maximum exact match strategy.
• It resolves SVs with complex or atypical alignment signatures without predefined assumptions on SV types.
• Benchmarking involved simulated datasets and real long-read sequencing data.

Main Results:

• gSV demonstrated improved sensitivity and overall performance compared to state-of-the-art SV callers, particularly for complex SV events.
• Unique SVs were discovered in cancer-associated genes within breast cancer cell lines, indicating biological relevance.
• gSV proved useful for population-scale genomic studies, as shown in a Chinese breast cancer cohort.

Conclusions:

• gSV offers a robust and generalizable approach for comprehensive structural variant detection.
• The tool enhances the ability to identify SVs, including complex ones often missed by conventional methods.
• gSV has significant potential for both research and clinical genomics applications.