基准导向的染色体对染色体的 de novo 组装在使用低覆盖率高保真度长度读取与HiFiCCL的尺度上
Zhongjun Jiang1,2, Weihua Pan3, Runtian Gao1,2
1College of Life Science, Northeast Forestry University, Harbin, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 26, 2025
概括
HiFiCCL是一个新的框架,用于组装低覆盖率,高可靠性测序数据,改善在人口基因组学中的结构变异检测. 这促进了大种群和多种物种的基因组组装.
科学领域:
- 基因组学和生物信息学
- 人口遗传学 人口遗传学
- 结构变异检测 结构变异检测
背景情况:
- 在人口基因组学中,短读测序错过了结构变异 (SV),影响了全基因组关联研究中的遗传性.
- 长读序列改进了基因组的构造,但高保真性数据是昂贵的,限制了大规模的人口研究.
- 目前的组装器在低覆盖率的测序数据上表现不佳,需要改进的组装方法.
研究的目的:
- 为了引入HiFiCCL,首个为低覆盖,高保真 (HiFi) 设计的组装框架读.
- 解决现有组装器在低覆盖度测序场景中的局限性.
- 提高在人口基因组学中的结构变异的检测.
主要方法:
- 开发了HiFiCCL,这是一个以参考为导向的染色体对染色体组装框架.
- 评估了HiFiCCL在低覆盖率HiFi读取的人类和植物数据集上的性能.
- 与最先进的装配器相比,HiFiCCL与hifiasm相结合.
主要成果:
- HiFiCCL提高了现有的低覆盖组装器的性能.
- 在人类和植物数据集上,HiFiCCL的性能优于最先进的汇编器.
- 在人类数据集 (~5×覆盖范围) 上,HiFiCCL与hifiasm的错误组装连接长度平均减少了21.19%.
结论:
- HiFiCCL能够从低覆盖率的HiFi数据中进行强大的基因组组装,这对于大规模的人口基因组学至关重要.
- 改进的组件可以更好地检测出大型生殖系结构变异,并最大限度地减少错误的支架.
- HiFiCCL增强了使用泛基因组图的生殖线和瘤体质结构变异的发现.
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