BACKGROUND: Genomic variations, including single-nucleotide polymorphisms, small insertions and deletions, and structural variations, are crucial for understanding evolution and disease. However, comprehensive simulation tools for benchmarking genomic analysis methods are lacking. Existing simulators do not accurately represent the nonuniform distribution and length patterns of structural variations in human genomes, and simulating complex structural variations remains challenging. RESULTS: We present BVSim, a flexible tool that provides probabilistic simulations of genomic variations, primarily focusing on human patterns while accommodating diverse species. BVSim effectively simulates both simple and complex structural variations and small variants by mimicking real-life variation distributions, which often exhibit higher frequencies near telomeres and within tandem repeat regions. Notably, BVSim allows users to input single or multiple benchmark samples from any reference genome, enabling the tool to summarize and represent the unique distribution patterns of structural variation positions and lengths specific to those species. Its compatibility with standard file formats facilitates seamless integration into various genomic research workflows, making it a very useful resource for benchmarking downstream tools such as variant callers. With numerical experiments, we show that BVSim generated more realistic sequences significantly different from other simulators' outputs. CONCLUSIONS: BVSim is written in Python and freely available to noncommercial users under the GPL3 license. Source code, application guide, and toy examples are provided on the GitHub page at https://github.com/YongyiLuo98/BVSim. The tool is registered in SciCrunch (RRID:SCR_026926), bio.tools (biotools:BVSim), and WorkflowHub (doi:10.48546/WORKFLOWHUB.WORKFLOW.1361.1).
BVSim: A benchmarking variation simulator mimicking human variation spectrum.
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作者:Luo Yongyi, Zhang Zhen, Wang Shu, Shi Jiandong, Hao Jingyu, Lian Sheng, Hu Taobo, Ishibashi Toyotaka, Wang Depeng, Yu Weichuan, Fan Xiaodan
| 期刊: | Gigascience | 影响因子: | 3.900 |
| 时间: | 2025 | 起止号: | 2025 Jan 6; 14:giaf095 |
| doi: | 10.1093/gigascience/giaf095 | ||
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