Jung Gun Tae, Kim Kwang-Pyo, Kim Kwoneel
Department of Biomedical Science and Technology, Kyung Hee University, Seoul, Republic of Korea.
Department of Applied Chemistry, Kyung Hee University, Yongin, Republic of Korea.
Anim Cells Syst (Seoul). 2020 Jan 30;24(1):1-7. doi: 10.1080/19768354.2020.1721321. eCollection 2020.
Current parallel sequencing technologies generate biological sequence data explosively and enable omics studies that analyze collective biological features. The more omics data that is accumulated, the more they show the regulatory complexity of biological phenotypes. This high order regulatory complexity needs systems-level approaches, including network analysis, to understand it. There are a series of layers in the omics field that are closely connected to each other as described in 'central dogma.' We, therefore, have to not only interpret each single omics layer but also to integrate multi-omics layers systematically to get a full picture of the regulatory landscape of the biological phenotype. Especially, individual omics data has their own adequate biological network to apply systematic analysis appropriately. A full regulatory landscape can only be obtained when multi-omics data are incorporated within adequate networks. In this review, we discuss how to interpret and integrate multi-omics data systematically using recent studies. We also propose an analysis framework for systematic multi-omics interpretation by centering on the transcriptional core regulator, which can be incorporated in all omics networks.
当前的并行测序技术能迅速生成生物序列数据，并推动了对集体生物学特征进行分析的组学研究。积累的组学数据越多，就越能显示出生物表型的调控复杂性。这种高阶调控复杂性需要系统层面的方法，包括网络分析，来加以理解。如“中心法则”所述，组学领域存在一系列相互紧密关联的层面。因此，我们不仅要解读每个单一的组学层面，还必须系统地整合多个组学层面，以全面了解生物表型的调控格局。特别是，个体组学数据都有其适用系统分析的适当生物网络。只有将多组学数据纳入适当的网络中，才能获得完整的调控格局。在本综述中，我们将利用近期研究探讨如何系统地解读和整合多组学数据。我们还提出了一个以转录核心调节因子为中心的系统多组学解读分析框架，该调节因子可纳入所有组学网络。
