Benchmarking major somatic structural variant callers on the HG008 genome

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Somatic structural variants (SVs) are key drivers of cancer.
• Accurate detection of somatic SVs is vital for understanding tumorigenesis and finding biomarkers.
• Existing tools often struggle with somatic mutation heterogeneity.

Purpose of the Study:

• To benchmark the performance of four leading somatic SV detection tools: Sniffles2, Nanomonsv, Savana, and Severus.
• To evaluate their accuracy against a known benchmark dataset.
• To establish an ensemble strategy for improved somatic SV detection.

Main Methods:

• Benchmarking of four somatic SV detection tools (Sniffles2, Nanomonsv, Savana, Severus).
• Evaluation using the HG008 genome from the Genome in a Bottle Consortium.
• Comparison against the HG008 clonal somatic SV draft benchmark.
• Integration of multiple tool callsets to form an ensemble strategy.

Main Results:

• Performance evaluation of Sniffles2, Nanomonsv, Savana, and Severus on the HG008 genome.
• Identification of strengths and weaknesses of individual tools for somatic SV detection.
• Demonstration that an ensemble strategy improves accuracy and comprehensiveness.

Conclusions:

• Rigorous validation of somatic SV detection tools is needed.
• An ensemble approach combining multiple tools offers enhanced accuracy for somatic SV identification.
• This work provides a foundation for more reliable somatic SV detection in cancer research.