识别高维基因组因子调节生物网络跨多维基数据的多维基因组因子
Samuel Anyaso-Samuel1, Shilan Li2, Giovanny Herrera Ossa1
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, Maryland 20892, USA.
bioRxiv : the preprint server for biology
|January 7, 2026
概括
GFBioNet是一种新的计算方法,可以识别影响复杂网络中的生物特征相互作用的基因组因素. 这种工具有助于揭示跨多omics研究的生物网络的基因组架构.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 生物特征在复杂的网络中相互作用,但基因组对这些相互作用的影响尚不清楚.
- 了解这些相互作用对于破译生物系统至关重要.
研究的目的:
- 引入GFBioNet,这是一种有效的计算方法,用于识别调节生物网络中特征关联的基因组因素.
- 为了实现可扩展的分析高维的多omics数据,同时控制错误发现率 (FDR).
主要方法:
- 一个两阶段的战略:1. 使用高斯图形模型进行基线网络估计. 2. 2. 2. 这是一个很棒的节目. 测试网络边缘的基因组因子调制 (特征-特征关系).
- 显式控制错误发现率 (FDR) 进行可靠的发现.
主要成果:
- 模拟证实了可靠的FDR控制和高统计能力.
- GFBioNet确定了影响口腔微生物组网络的宿主遗传变异.
- 发现肠道微生物可以调节结直肠癌中的代谢物网络.
- 实体突变和副本数量的改变被证明在肺腺癌中重塑基因表达网络.
结论:
- GFBioNet是一个多功能工具,用于揭示生物网络的基因组架构.
- 该方法扩展了网络分析,以评估多omics研究中的特征-特征关系的修饰者.
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