Chasman Deborah, Roy Sushmita
Wisconsin Institute for Discovery University of Wisconsin-Madison, Madison, WI 53715.
Department of Biostatistics and Medical Informatics University of Wisconsin-Madison, Madison, WI 53792.
Curr Opin Syst Biol. 2017 Apr;2:130-139. doi: 10.1016/j.coisb.2017.04.001. Epub 2017 Apr 17.
Transcriptional regulatory networks are at the core of establishing cell type specific gene expression programs. In mammalian systems, such regulatory networks are determined by multiple levels of regulation, including by transcription factors, chromatin environment, and three-dimensional organization of the genome. Recent efforts to measure diverse regulatory genomic datasets across multiple cell types and tissues offer unprecedented opportunities to examine the context-specificity and dynamics of regulatory networks at a greater resolution and scale than before. In parallel, numerous computational approaches to analyze these data have emerged that serve as important tools for understanding mammalian cell type specific regulation. In this article, we review recent computational approaches to predict the expression and sequence-based regulators of a gene's expression level and examine long-range gene regulation. We highlight promising approaches, insights gained, and open challenges that need to be overcome to build a comprehensive picture of cell type specific transcriptional regulatory networks.
转录调控网络是建立细胞类型特异性基因表达程序的核心。在哺乳动物系统中，此类调控网络由多种调控水平决定，包括转录因子、染色质环境和基因组的三维组织。最近，在多种细胞类型和组织中测量不同调控基因组数据集的努力，提供了前所未有的机会，以比以往更高的分辨率和规模来研究调控网络的上下文特异性和动态变化。与此同时，涌现出了许多分析这些数据的计算方法，这些方法是理解哺乳动物细胞类型特异性调控的重要工具。在本文中，我们回顾了最近用于预测基因表达水平的表达和基于序列的调控因子以及研究远程基因调控的计算方法。我们强调了有前景的方法、所获得的见解以及构建细胞类型特异性转录调控网络全貌需要克服的开放挑战。
