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

Genomic structural variants (SVs) are pervasive and can impose major phenotypic impacts. However, it is difficult to appreciate the individual significance of SVs when they are heterogeneously positioned across a genomic neighborhood. Further, ubiquitous variance in SV calling accuracy complicates SV counting and downstream analysis. Tools exist to simplify SV datasets but they are not suited for all applications. Here, we present a new SV merger, SVCROWS: Structural Variation Consensus with Reciprocal Overlap and Weighted Sizes. This option-rich merger summarizes SV regions using a size-weighted reciprocal overlap framework, accounting for skewed impacts of variable-length SVs. User input directs stringency, enabling various levels of resolution in complex genome regions that harbor a spectrum of SV sizes. Further, by optimizing SVCROWS parameters, the user can tailor results to their study system. When compared to other SV merging programs, SVCROWS maintained accuracy and conserved rare genotypes from both simulated and real-world datasets. Visualization of merger output was critical for identifying how some algorithms derived erroneous conclusions while SVCROWS remained reliable, especially in complex regions. Overall, the novel SVCROWS algorithm presents an improved framework for SV interpretation; its intuitive nature and generalizability facilitates its application to virtually any workflow. Graphical Abstract SVCROWS (Structural Variation Consensus with Reciprocal Overlap and Weighted Sizes) is a structural variant merger that leverages option-rich, size-weighted comparisons to better resolve complex inputs and separate out regions of meaningful biological differences.