SpeSpeNet:一个交互式和用户友好的工具,用于创建和探索微生物相关性网络
Abraham L van Eijnatten1, Luc van Zon1, Eleni Manousou1
1Theoretical Biology and Bioinformatics, Science4Life, Utrecht University (UU), Padualaan 8, 3584 CH Utrecht, The Netherlands.
ISME communications
|August 13, 2025
概括
SpeSpeNet是一个用户友好的R-shiny工具,用于创建和可视化微生物组相关联网络. 它帮助研究人员了解微生物群落及其环境驱动因素,而不需要编码技能.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 生态生态学 生态生态学
背景情况:
- 相关性网络对于探索微生物群数据至关重要,节点代表种类和边缘表示丰富性相关性.
- 这些网络中的共同丰富集群通常意味着对环境因素的共同反应,有助于系统理解.
- 现有的微生物组网络分析工具往往不易于使用,需要编码专业知识,或缺乏定制,并专注于环境协会.
研究的目的:
- 介绍SpeSpeNet,一个实用且用户友好的R-shiny工具,用于构建和可视化微生物组相关联网络.
- 为了自动化数据预处理,网络构建和可视化,使其无需编程技能的研究人员可以访问.
- 为了使环境数据能够纳入微生物组结构和驱动因素的更全面分析.
主要方法:
- 开发了SpeSpeNet,这是一个R-shiny应用程序,用于生成物种-物种相关性网络.
- 自动化关键步骤,包括数据预处理,网络构建和可视化.
- 集成的可定制选项,用于整合用户提供的环境数据,以探索分类-环境关联.
主要成果:
- SpeSpeNet提供了一个自动化和用户友好的平台,用于微生物组相关性网络分析.
- 该工具促进了网络的可视化,并允许整合环境驱动因素.
- 通过三个不同的案例研究证明了SpeSpeNet的实用性.
结论:
- SpeSpeNet为微生物组研究人员提供了一种有价值,易于使用的工具,用于构建和可视化相关性网络.
- 该工具增强了对微生物社区结构及其与环境因素的关系的理解.
- SpeSpeNet的用户友好性和定制选项使其成为微生物组数据分析的重大进步.
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