Kel Alexander E
Institute of Chemical Biology and Fundamental Medicine, SBRAN, Novosibirsk, Russia.
Biosoft.ru, Ltd., Novosibirsk, Russia.
Methods Mol Biol. 2017;1613:161-191. doi: 10.1007/978-1-4939-7027-8_8.
In this chapter, we present an approach that allows a causal analysis of multiple "-omics" data with the help of an "upstream analysis" strategy. The goal of this approach is to identify master regulators in gene regulatory networks as potential drug targets for a pathological process. The data analysis strategy includes a state-of-the-art promoter analysis for potential transcription factor (TF)-binding sites using the TRANSFAC database combined with an analysis of the upstream signal transduction pathways that control the activity of these TFs. When applied to genes that are associated with a switch to a pathological process, the approach identifies potential key molecules (master regulators) that may exert major control over and maintenance of transient stability of the pathological state. We demonstrate this approach on examples of analysis of multi-omics data sets that contain transcriptomics and epigenomics data in cancer. The results of this analysis helped us to better understand the molecular mechanisms of cancer development and cancer drug resistance. Such an approach promises to be very effective for rapid and accurate identification of cancer drug targets with true potential. The upstream analysis approach is implemented as an automatic workflow in the geneXplain platform ( www.genexplain.com ) using the open-source BioUML framework ( www.biouml.org ).
在本章中，我们提出了一种方法，借助“上游分析”策略对多个“组学”数据进行因果分析。该方法的目标是在基因调控网络中识别主调控因子，将其作为病理过程的潜在药物靶点。数据分析策略包括使用TRANSFAC数据库对潜在转录因子（TF）结合位点进行最先进的启动子分析，并结合对控制这些TF活性的上游信号转导通路的分析。当应用于与病理过程转变相关的基因时，该方法可识别出可能对病理状态的短暂稳定性起主要控制和维持作用的潜在关键分子（主调控因子）。我们以包含癌症转录组学和表观基因组学数据的多组学数据集分析为例展示了这种方法。该分析结果有助于我们更好地理解癌症发展和癌症耐药性的分子机制。这种方法有望非常有效地快速准确识别具有真正潜力的癌症药物靶点。上游分析方法在geneXplain平台（www.genexplain.com）中使用开源BioUML框架（www.biouml.org）作为自动工作流程来实施。
