SVCROWS: a user-defined tool for interpreting significant structural variants in heterogeneous datasets.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genomic structural variants (SVs) significantly impact phenotypes but are challenging to analyze due to heterogeneous positioning and variable calling accuracy.
• Existing tools for simplifying SV datasets have limitations, necessitating new approaches for robust SV interpretation.

Purpose of the Study:

• To introduce SVCROWS (Structural Variation Consensus with Reciprocal Overlap and Weighted Sizes), a novel algorithm for merging and summarizing genomic structural variants.
• To provide a flexible and accurate tool for SV analysis that accounts for variant size and position heterogeneity.

Main Methods:

• Developed SVCROWS, a size-weighted reciprocal overlap framework for summarizing SV regions.
• Incorporated user-adjustable stringency parameters to control resolution in complex genomic areas.
• Compared SVCROWS performance against existing SV merging programs using simulated and real-world genomic datasets.

Main Results:

• SVCROWS demonstrated maintained accuracy and conserved rare genotypes compared to other SV merging tools.
• The algorithm proved reliable in complex genomic regions, outperforming alternatives where visualization revealed errors in other methods.
• SVCROWS provides an improved framework for SV interpretation with intuitive controls and broad generalizability.

Conclusions:

• SVCROWS offers a novel and effective approach to merging and interpreting genomic structural variants.
• Its flexibility and accuracy make it a valuable tool for diverse genomic analysis workflows, enhancing the understanding of SVs' phenotypic significance.