Ghent University, Department of Plant Biotechnology and Bioinformatics, Technologiepark 71, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, 9052 Ghent, Belgium.
Ghent University, Department of Plant Biotechnology and Bioinformatics, Technologiepark 71, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, 9052 Ghent, Belgium.
Biochim Biophys Acta Gene Regul Mech. 2020 Jun;1863(6):194447. doi: 10.1016/j.bbagrm.2019.194447. Epub 2019 Oct 31.
Transcriptional regulation is a complex and dynamic process that plays a vital role in plant growth and development. A key component in the regulation of genes is transcription factors (TFs), which coordinate the transcriptional control of gene activity. A gene regulatory network (GRN) is a collection of regulatory interactions between TFs and their target genes. The accurate delineation of GRNs offers a significant contribution to our understanding about how plant cells are organized and function, and how individual genes are regulated in various conditions, organs or cell types. During the past decade, important progress has been made in the identification of GRNs using experimental and computational approaches. However, a detailed overview of available platforms supporting the analysis of GRNs in plants is missing. Here, we review current databases, platforms and tools that perform data-driven analyses of gene regulation in Arabidopsis. The platforms are categorized into two sections, 1) promoter motif analysis tools that use motif mapping approaches to find TF motifs in the regulatory sequences of genes of interest and 2) network analysis tools that identify potential regulators for a set of input genes using a range of data types in order to generate GRNs. We discuss the diverse datasets integrated and highlight the strengths and caveats of different platforms. Finally, we shed light on the limitations of the above approaches and discuss future perspectives, including the need for integrative approaches to unravel complex GRNs in plants.
转录调控是一个复杂而动态的过程，在植物生长和发育中起着至关重要的作用。基因调控的一个关键组成部分是转录因子（TFs），它协调基因活性的转录控制。基因调控网络（GRN）是 TF 和它们的靶基因之间的调控相互作用的集合。准确描绘 GRN 对我们理解植物细胞如何组织和功能，以及单个基因在不同条件、器官或细胞类型下如何被调控，具有重要贡献。在过去的十年中，使用实验和计算方法鉴定 GRN 方面取得了重要进展。然而，缺乏对支持植物中 GRN 分析的可用平台的详细概述。在这里，我们回顾了当前用于分析拟南芥基因调控的数据库、平台和工具。这些平台分为两部分，1）启动子基序分析工具，这些工具使用基序映射方法在感兴趣基因的调控序列中寻找 TF 基序，2）网络分析工具，这些工具使用一系列数据类型来识别一组输入基因的潜在调节剂，从而生成 GRN。我们讨论了集成的各种数据集，并强调了不同平台的优势和局限性。最后，我们探讨了上述方法的局限性，并讨论了未来的展望，包括需要整合方法来揭示植物中复杂的 GRN。
