Zhao Na, Quicksall Zachary, Asmann Yan W, Ren Yingxue
Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States.
Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States.
Front Genet. 2022 Sep 16;13:984338. doi: 10.3389/fgene.2022.984338. eCollection 2022.
The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating "big data" which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
包括基因组学、转录组学、代谢组学、脂质组学和蛋白质组学在内的多组学方法，近年来在方法学上取得了进展，通过生成“大数据”彻底改变了研究领域，极大地增进了我们对大脑分子复杂性和疾病状态的理解。网络方法已常规应用于单组学数据，以提供对疾病生物学的关键见解。此外，多组学整合已成为连接疾病机制背后不同信息层的迫切需求和新方向。在这篇综述文章中，我们总结了用于单组学数据和多组学整合的常用网络分析方法，并讨论了这些方法如何被用于研究神经退行性疾病。
