VISTA: An integrated framework for structural variant discovery

Structural variation SV refers to insertions deletions inversions and duplications in human genomes. With advances in whole genome sequencing WGS technologies a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge with the majority of SV detection methods prone to a high falsepositive rate and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here we report an integrated structural variant calling framework VISTA Variant Identification and Structural Variant Analysis that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensusbased tools which ignore the length and coverage VISTA overcomes this limitation by executing various combinations of topperforming callers based on variant length and genomic coverage to generate SV events with high accuracy We evaluated the performance of VISTA on using comprehensive goldstandard datasets across varying organisms and coverage. We benchmarked VISTA using the GenomeinaBottle GIAB gold standard SV set haplotyperesolved de novo assemblies from The Human Pangenome Reference Consortium HPRC12 along with an inhouse PCRvalidated mouse gold standard set VISTA maintained the highest F1 score among top consensusbased tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode where the calls can be optimized for precision or recall VISTAoptimized is able to attain 100 precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling offering a robust and accurate framework that outperforms existing consensusbased tools and sets a new standard for SV detection in genomic research ### Competing Interest Statement The authors have declared no competing interest.