从amplicon数据推断微生物共发生网络:系统评估
Dileep Kishore1,2,3, Gabriel Birzu4,5, Zhenjun Hu1
1Bioinformatics Program, Boston University , Boston, Massachusetts, USA.
mSystems
|June 20, 2023
概括
本研究分析了不同工具和参数如何影响来自16S rRNA数据的微生物共发生网络. 它确定了影响网络变异的关键步骤,并为强大的微生物关联网络推断提供了指导方针.
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.
- 计算生物学 计算生物学
背景情况:
- 微生物群落表现出复杂的相互作用,经常使用16S核糖体RNA (16SrRNA) 片序列测序进行研究.
- 从16S数据中推断微生物共发生网络涉及多个分析步骤,使用不同的工具和参数.
- 这些选择对产生的网络的稳定性和独特性的影响尚不清楚.
研究的目的:
- 从16S rRNA数据系统地分析不同工具和参数对微生物共发生网络构建的影响.
- 确定分析管道中的关键步骤,这些步骤对网络偏差有重大贡献.
- 制定指导方针和共识算法,以创建强大的微生物协会网络.
主要方法:
- 对16S数据到网络推断管道进行了细致的逐步分析.
- 评估了各种算法和参数选择对共发生网络结构的影响.
- 开发并使用模拟和合成数据集进行对比的共识网络算法.
主要成果:
- 不同的工具和参数选择大大影响了推断出的微生物共发生网络.
- 确定了对网络差异贡献最大的特定分析步骤.
- 开发并验证共识网络算法以提高稳定性.
结论:
- 系统分析提供了对影响微生物网络从16S数据推断的因素的关键见解.
- 提供了指导方针,以选择适当的工具和参数来进行可靠的网络分析.
- MiCoNE工具和共识算法有助于比较分析和了解微生物社区的组合.
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