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

Zeyu Zheng, Mingjia Zhu, Jin Zhang, Xinfeng Liu, Liqiang Hou, Wenyu Liu, Shuai Yuan, Changhong Luo, Xinhao Yao, Jianquan Liu () and Yongzhi Yang ()
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Zeyu Zheng: Lanzhou University
Mingjia Zhu: Lanzhou University
Jin Zhang: Lanzhou University
Xinfeng Liu: Lanzhou University
Liqiang Hou: Lanzhou University
Wenyu Liu: Lanzhou University
Shuai Yuan: Lanzhou University
Changhong Luo: Lanzhou University
Xinhao Yao: Lanzhou University
Jianquan Liu: Lanzhou University
Yongzhi Yang: Lanzhou University

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-45244-9

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