A sequence-aware merger of genomic structural variations at population scale

Merging structural variations (SVs) at the population level presents a significant challenge, yet it is essential for conducting comprehensive genotypic analyses, especially in the era of pangenomics. Here, we introduce PanPop, a tool that utilizes an advanced sequence-aware SV merging algorithm to efficiently merge SVs of various types. We demonstrate that PanPop can merge and optimize the majority of multiallelic SVs into informative biallelic variants. We show its superior precision and lower rates of missing data compared to alternative software solutions. Our approach not only enables the filtering of SVs by leveraging multiple SV callers for enhanced accuracy but also facilitates the accurate merging of large-scale population SVs. These capabilities of PanPop will help to accelerate future SV-related studies. Existing tools for structural variations (SVs) calling and merging often lead to fragmented SVs and the potential of introducing unnecessary errors. Here, the authors report the PanPop pipeline to address these issues by implementing sequence-aware SV merging algorithm to efficiently merge SVs of various types.