SVCROWS: A User-Defined Tool for Interpreting Significant Structural Variants in Heterogeneous Datasets.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Structural variants (SVs) significantly impact genome and transcriptome function but are challenging to analyze due to heterogeneous distribution and sequencing-induced variance.
• Existing SV merging tools struggle with large, highly variable datasets, complicating SV counting and association studies.

Purpose of the Study:

• To introduce SVCROWS, a novel R package designed to merge and summarize structural variant regions.
• To provide a robust solution for handling large and complex SV datasets, improving SV interpretation and discovery.

Main Methods:

• Developed SVCROWS, an R package utilizing a size-weighted reciprocal overlap framework for SV merging.
• Implemented option-rich comparisons allowing adjustable stringency for various SV sizes and resolutions.
• Evaluated SVCROWS performance against existing SV merging programs on large and variable datasets.

Main Results:

• SVCROWS accurately merges SVs, effectively accounting for variable-length SV impacts.
• The package maintains less frequent genotypes from unmerged SV calls, crucial for comprehensive analysis.
• SVCROWS demonstrates particular utility with large, highly variable single-cell datasets, enhancing SV discovery.

Conclusions:

• SVCROWS offers a novel size-weighted comparison framework for improved interpretation of structural variant calls.
• The package's ease of use facilitates its application in diverse upstream genomic analyses.
• SVCROWS addresses limitations of existing tools, enabling more robust SV analysis in challenging datasets.