svCapture: efficient and specific detection of very low frequency structural variant junctions by error-minimized...

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Error-corrected sequencing is standard for detecting single-nucleotide variants (SNVs) and small insertion/deletions (indels) at low frequencies.
• Strategies for detecting rare structural variant (SV) junctions with similar accuracy are less developed.
• SV detection requires addressing specific error mechanisms distinct from SNV/indel detection.

Purpose of the Study:

• To develop and validate a method for accurate and efficient detection of rare structural variant (SV) junctions.
• To compare the efficacy of duplex sequencing (DuplexSeq) and tagmentation-based library preparation for SV detection.
• To establish a pipeline for routine SV detection in capture sequencing data.

Main Methods:

• Utilized duplex sequencing (DuplexSeq) to eliminate chimeric PCR artifacts in SV junction detection.
• Employed tagmentation libraries and strand family size filtering to reduce intermolecular ligation artifacts.
• Developed and applied the open-source svCapture pipeline for SV analysis.

Main Results:

• DuplexSeq effectively eliminated false SV junctions from chimeric PCR.
• Tagmentation libraries with data filtering significantly reduced ligation artifacts, enabling single-molecule SV junction detection.
• The svCapture pipeline provided high throughput and base-level accuracy, revealing microhomology profiles and de novo SNVs near SV junctions.

Conclusions:

• Tagmentation-based library preparation combined with duplex sequencing and data filtering is highly effective for rare SV junction detection.
• The svCapture pipeline facilitates the routine integration of SV detection into standard capture sequencing workflows.
• Findings suggest end joining as a potential mechanism for SV formation near junctions.