Gene regulatory network inference resources: A practical overview

• Published in: Biochimica et biophysica acta. Gene regulatory mechanisms Vol. 1863; no. 6; p. 194430
• Main Authors: Mercatelli, Daniele, Scalambra, Laura, Triboli, Luca, Ray, Forest, Giorgi, Federico M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2020
• Subjects: ODE; GRN; TF; TG; TSS; PPI; NGS; ChIP; MI; TEP
• ISSN: 1874-9399; 1876-4320; 1876-4320
• DOI: 10.1016/j.bbagrm.2019.194430

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. •Gene Regulatory Networks (GRNs) control all aspects of cellular behavior.•Several approaches exist to infer GRNs. These can be broadly categorized based on the input data.•GRN inference can stem from: coexpression, sequence motifs, ChIP-Seq, orthology, literature and Protein-Protein Interaction.•We provide an extensive and commented list of >90 current GRN inference tools.•Best Practices and Examples of GRN inference using multiple methods are described.