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PyOrthoANI,PyFastANI和Pyskani:一个用于计算平均核酸标识的Python库套件
Martin Larralde1,2, Georg Zeller1,2,3, Laura M Carroll4,5,6,7
1Structural and Computational Biology Unit, EMBL, 69117 Heidelberg, Germany.
新的Python库简化了使用平均核酸标识 (ANI) 的 prokaryotic 种类划分. 这些工具提供更快的计算和无集成到生物信息学工作流程,增强基因组分析.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 平均核酸标识 (ANI) 在基因组学中对 prokaryotic 种类划分至关重要.
- 现有的ANI算法往往缺乏方便的整合到基于Python的生物信息学工作流程中.
研究的目的:
- 介绍PyOrthoANI,PyFastANI和Pyskani,这是用于流行的ANI计算方法的Python库.
- 为了方便将ANI计算集成到Python生物信息学管道中.
主要方法:
- 开发了三个Python库:PyOrthoANI,PyFastANI和Pyskani. 这三种库的开发.
- 根据已建立的工具 (OrthoANI,FastANI,skani) 验证 ANI 值.
- 计算速度和多线程能力的基准测试.
主要成果:
- PyOrthoANI,PyFastANI和Pyskani产生的ANI值与它们的命令行对应值几乎相同 (调整后R2>0.999).
- 根据基因组,PyOrthoANI的速度比OrthoANI提高了3倍.
- 对于单个查询,PyFastANI提供多线程支持.
- 图书馆可以与BioPython无集成.
结论:
- 开发的Python库为ANI计算提供了高效和方便的工具.
- 这些库增强了在Python生物信息学工作流程中流行的ANI算法的可用性.
- 开源的可用性促进了研究中的更广泛的采用和整合.
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