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.

中文翻译：

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基因调控网络推论资源：实用概述。

转录调节是生活中几乎各个方面的基本分子机制，从稳态到发育，从新陈代谢到行为，从反应到刺激再到疾病进展。近年来，由于其易于理解的特征，基因调控网络（GRN）的概念已成为一种流行的有效的应用生物学方法，用于描述复杂而高度动态的转录相互作用。由于对所有物种和细胞环境中的每个GRN连接进行分类，预测和理解仍然是生物学面临的巨大挑战，因此研究人员开发了许多工具和方法来推断调控过程。在这篇综述中，我们根据可用来推断GRN的主要基础信息在六个主要领域中对这些方法进行了分类：共表达，序列基序，染色质免疫沉淀（ChIP），正畸，文献和蛋白质-蛋白质相互作用（PPI）特别着重于转录复合物。此处描述的方法涵盖了广泛的用户友好性：从不需要先验计算专业知识的Web工具到用于大规模GRN推断的命令行程序和算法。本文所述的用于GRN推断的每种方法均有效地说明了一种转录关系，其中许多方法相互补充。虽然一种真正的整体方法来推断和显示GRN仍然是系统生物学领域的最大挑战之一，我们相信本文所述多种方法的整合提供了一种有效的手段，实验生物学和计算生物学家都可以通过这种手段获得最完整的转录关系图。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。