OmNI:一个模块化的开源框架,用于交互式的多omics数据集成和可视化
Grace Potter1, Jacob A Beierle2, Camron Bryant3
1Program in Quantitative Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, OR 97201, United States.
NAR genomics and bioinformatics
|January 12, 2026
概括
Omics Notebook Interactive (OmNI) 是一个基于R的框架,用于多omics数据集成和分析. 它确定了在小鼠大脑中对芬太尼的保存和菌株特定的分子反应.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 多学科数据集成对于理解复杂的生物系统至关重要.
- 现有的工具往往缺乏全面的集成功能和交互式可视化.
- 分析多种omics数据类型需要灵活和可扩展的计算框架.
研究的目的:
- 推出Omics Notebook Interactive (OmNI),这是一个基于R的框架,用于多omics数据集成和分析.
- 在小鼠模型中证明OmNI在识别对芬太尼暴露的分子反应中的实用性.
- 为复杂的生物数据探索提供模块化和交互式平台.
主要方法:
- OmNI使用可定制的线性模型进行微分表达式分析.
- 修改后的S-score统计数据使敏感的跨原子层集成成为可能.
- 网络和代谢学数据集成是通过路径丰富分析进行的.
- 基于TMT18的深度蛋白质组和蛋白质组分析在小鼠大脑样本上进行.
主要成果:
- OmNI成功地整合了从暴露于芬太尼的基因多样性小鼠菌株的多omics数据.
- 综合性S-score识别了所有菌株的保留差异信号和相互作用枢纽.
- 揭示了对芬太尼的菌株特异性分子神经反应.
- 创建了交互式HTML报告和Cytoscape兼容的对象.
结论:
- OmNI提供了一个强大,可扩展和交互的框架,用于多omics数据集成和分析.
- 该框架有助于发现生物反应背后的保存和特定的分子机制.
- 对于研究复杂的生物学问题,包括神经生物学和毒理学的研究人员来说,OmNI是一个有价值的工具.
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