Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Centre for Integrative Biology (CIBIO), University of Trento, Italy.
Biochim Biophys Acta Gene Regul Mech. 2020 Jun;1863(6):194430. doi: 10.1016/j.bbagrm.2019.194430. Epub 2019 Oct 31.
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
转录调控是一种基本的分子机制，几乎涉及生命的各个方面，从内稳态到发育，从代谢到行为，从对刺激的反应到疾病进展。近年来，基因调控网络（GRN）的概念作为一种描述转录相互作用复杂而高度动态的有效应用生物学方法变得流行起来，这是由于其易于解释的特点。由于对所有物种和细胞环境中的每个 GRN 连接进行编目、预测和理解仍然是生物学的一大挑战，研究人员已经开发了许多工具和方法来推断调控过程。在这篇综述中，我们根据推断 GRN 所利用的主要潜在信息将这些方法分为六个主要领域：共表达、序列基序、染色质免疫沉淀（ChIP）、同源物、文献和蛋白质-蛋白质相互作用（PPI），特别是针对转录复合物。这里描述的方法涵盖了广泛的用户友好性：从不需要事先计算专业知识的网络工具到用于大规模 GRN 推断的命令行程序和算法。这里描述的每个 GRN 推断方法都有效地说明了一种转录关系类型，许多方法是互补的。虽然推断和显示 GRN 的真正整体方法仍然是系统生物学领域的最大挑战之一，但我们相信，这里描述的多种方法的整合为实验和计算生物学家提供了一种有效的手段，可以获得转录关系的最完整图景。本文是由 Federico Manuel Giorgi 博士和 Shaun Mahony 博士编辑的题为“转录谱和调控基因网络”的特刊的一部分。
