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CMAPLE:在流行病时代有效的基因推理
Nhan Ly-Trong1, Chris Bielow2, Nicola De Maio3
1School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia.
Molecular biology and evolution
|June 27, 2024
概括
我们开发了CMAPLE,这是一个优化的C++工具和库,用于最大限度的节概率估计 (MAPLE),增强了基因组流行病学的遗传学推断. 这有助于改善大规模的病原体分析,以防范流行病.
科学领域:
- 基因组流行病学 基因组流行病学
- 计算生物学 计算生物学
- 人类遗传学 是一个学科.
背景情况:
- 遗传学推断对于理解流行病期间病原体进化和传播至关重要.
- 现有的方法可能会面临大型基因组数据集的可扩展性挑战.
- 在此之前,最大节概率估计 (MAPLE) 已被引入用于流行病规模分析.
研究的目的:
- 为了提高MAPLE方法的性能和可扩展性.
- 为了引入CMAPLE软件,C++重新实现了MAPLE.
- 提供一个CMAPLE库,以便将MAPLE集成到其他遗传学工具中.
主要方法:
- 使用C++开发CMAPLE软件,以优化性能.
- 创建了一个CMAPLE库与API,以实现更广泛的集成.
- 在IQ-TREE 2软件包中集成了CMAPLE.
主要成果:
- 与原来的MAPLE相比,CMAPLE提供了显著的性能和可扩展性改进.
- 在CMAPLE图书馆促进无集成到现有的生物信息工作流程.
- 成功集成到IQ-TREE 2中,使得更广泛的社区采用成为可能.
结论:
- 在基因组流行病学上,CMAPLE代表了遗传学推断的重大进步.
- 这些工具提高了大规模病原体基因组分析的能力.
- 这些进展有助于改善对未来流行病的准备.
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