基于WGCNA和图形自编码器的Magnaporthe oryzae-米多组数据的关系预测方法
Enshuang Zhao1, Liyan Dong1,2, Hengyi Zhao1
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Journal of fungi (Basel, Switzerland)
|October 27, 2023
概括
一种新的方法,称重基因自编码器多omics关系预测 (WGAEMRP),整合了大米和Magnaporthe oryzae Oryzae (MoO) 多omics数据. 这种方法增强了对大米和真菌相互作用以及疾病抵抗机制的理解.
科学领域:
- 植物病理学 植物病理学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 大米病原体 (MOO) 是大米的主要病原体,但其感染机制尚未完全理解.
- 单一的OMIC数据不足以阐明MOO和大米之间的复杂的跨王国相互作用.
- 现有的方法缺乏综合多主题分析的能力.
研究的目的:
- 开发一种新的计算方法,用于整合来自MoO和rice的多omics数据.
- 构建一个全面的相互作用网络,揭示参与菌病原发生的关键生物分子.
- 为了解MOO感染和开发抗病米提供基础.
主要方法:
- 拟议的权重基因自编码器多奥米克关系预测 (WGAEMRP).
- 组合加权基因共同表达网络分析 (WGCNA) 与图形自编码器.
- 将WGAEMRP应用于MoO-rice多omics数据,以构建一个异质的交互网络.
主要成果:
- 构建了一个MoO-rice多omics异质交互网络.
- 确定了关键的生物分子:18个MoO小RNA (sRNA),17个大米基因,26个大米mRNA和28个大米蛋白.
- 在感染期间发现了与基因表达,蛋白质动力学和新陈代谢相关的功能模块和途径.
结论:
- WGAEMRP显著提高了多omics数据集成效率和准确性.
- 这项研究为调查MoO病原体的研究提供了强大的数据基础.
- 这些发现为开发抗病米品种的新战略提供了新的见解.
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