Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.
Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.
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基因组规模基因调控网络推理的综合方法。
DOI：
10.1007/978-1-4939-8882-2_7
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Roy,Sushmita
中科院分区：
文献类型：
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作者：
Siahpirani,AlirezaFotuhi;Chasman,Deborah;Roy,Sushmita
转录调控网络指定靶基因的调控蛋白，其控制基因的上下文特异性表达水平。由于我们能够在不同条件下分析细胞的不同类型的分子组分，我们现在处于独特的地位，可以推断不同生物背景下的调控网络，例如不同的细胞类型，组织和时间点。在这一章中，我们将介绍两类主要的计算方法，以整合不同类型的信息来推断基因组规模的转录调控网络。第一类方法集中于通过结合基因表达数据和调控边缘特异性知识来特异性地推断转录因子和靶基因之间的连接的整合方法。第二类方法通过将基因表达数据与蛋白质-蛋白质相互作用网络和蛋白质组学数据集相结合来整合上游信号网络与转录调控网络。最后，我们用一节介绍网络推理算法在推断基因组规模调控网络中的实际应用。
Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.