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

Noah Brown1, Charles Danis1, Vazira Ahmedjanova1

  • 1Department of Biology, University of Virginia. Charlottesville VA 22903, United States.

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|January 12, 2026
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Summary

SVCROWS is a new tool that merges structural variants (SVs) in genomes. It improves accuracy and reliability in analyzing complex genomic regions, aiding in understanding SV impacts on phenotypes.

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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.