SVPV: a structural variant prediction viewer for paired-end sequencing datasets

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Numerous algorithms exist for predicting structural variants (SVs) from whole genome sequencing (WGS) data.
• Effective quality control necessitates the visualization and comparison of SV predictions from diverse algorithms.

Purpose of the Study:

• To introduce the Structural Variant Prediction Viewer (SVPV) tool.
• To facilitate the visual comparison of SV calls from multiple prediction algorithms.
• To integrate population allele frequencies and gene annotations for enhanced SV analysis.

Main Methods:

• Development of a visualization tool for SV prediction data.
• Implementation of features for simultaneous visualization of SV calls from multiple algorithms.
• Inclusion of population allele frequency annotations from reference datasets.
• Support for gene annotations.
• Provision of both Graphical User Interface (GUI) and batch processing modes.

Main Results:

• The SVPV tool provides a visual summary of key features for SV prediction from WGS data.
• It enables direct comparison of SV calls generated by different prediction tools.
• The tool integrates population-level data and gene annotations to contextualize SV findings.
• SVPV supports interactive, one-by-one visualization and serial processing of numerous SVs.

Conclusions:

• SVPV is a valuable tool for the quality control and comparative analysis of structural variant predictions.
• It enhances the interpretability of SV data by integrating multiple sources of information.
• The tool's flexible interface supports diverse research workflows in genomics.