High-fidelity rare structural variant detection with HiFiRE3 reduced representation via restriction enzyme ends

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Detecting rare structural variants (SVs) is difficult due to sequencing artifacts.
• Existing methods struggle to distinguish true single-molecule events from artifacts.

Purpose of the Study:

• Introduce HiFiRe3, a novel sequencing framework for high-fidelity rare SV detection.
• Enable accurate assessment of SV frequencies across diverse sequencing platforms.

Main Methods:

• Systematic characterization of SV artifacts (chimeric PCR, ligation, platform-specific, mapping errors).
• Developed artifact-aware library design with error suppression and correction strategies.
• Implemented computational filtering using forced restriction enzyme end (FREE) and <1N size logics.

Main Results:

• Enabled targeted detection of single-molecule SVs in human cells and mouse brains using nanopore sequencing.
• Significantly reduced singleton translocation artifacts.
• Extended applicability to PacBio for joint SV/SNV correction and short-read platforms for nonhomologous SV analysis.

Conclusions:

• HiFiRe3 provides a flexible framework for accurate rare genomic structural variation detection.
• Broad applicability for targeted and genome-wide studies.
• Enhances SV analysis by minimizing library preparation and sequencing-induced artifacts.