OctopuSV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Structural variants (SVs) are crucial for gene regulation and disease but are challenging to integrate across platforms due to inconsistent annotations and fragmented workflows.
• Ambiguous breakend (BND) annotations are often lost, hindering variant characterization, while existing tools lack essential merging capabilities for precise variant identification.
• Current SV analysis pipelines demand manual intervention and complex tuning, impacting reproducibility and scalability, necessitating improved methods for clinical utility.

Purpose of the Study:

• To develop novel computational tools, OctopuSV and TentacleSV, for enhanced structural variant (SV) analysis.
• To address limitations in SV call integration, annotation ambiguity, and workflow fragmentation.
• To enable sophisticated SV analyses for researchers without specialized programming skills.

Main Methods:

• OctopuSV incorporates a BND correction module to standardize ambiguous annotations into canonical SV types.
• OctopuSV provides advanced set operations for flexible variant filtering and identification of sample-specific SVs.
• TentacleSV automates the SV analysis pipeline from raw sequencing data to high-confidence callsets, ensuring reproducibility.

Main Results:

• OctopuSV recovers overlooked variants by correcting ambiguous BND annotations.
• Advanced set operations in OctopuSV facilitate sophisticated variant filtering for identifying disease-specific or group-specific SVs.
• Benchmarking demonstrated OctopuSV and TentacleSV achieve superior performance (F1 score, SV type consistency) across short-read and long-read platforms compared to existing tools.
• The integrated framework enables complex comparative SV studies, such as cancer subtype analysis, without requiring programming expertise.

Conclusions:

• OctopuSV and TentacleSV significantly improve the accuracy, interpretability, and clinical utility of structural variant analysis.
• These tools democratize advanced SV analysis, empowering researchers to conduct complex studies with enhanced reproducibility and scalability.
• The developed framework addresses critical gaps in SV integration and characterization, paving the way for more robust genomic research and diagnostics.