Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets.

Pablo Ivan Pereira Ramos,Artur Trancoso L De Queiroz,Luis Willian Pacheco Arge,Kiyoshi F Fukutani,Nicholas Costa Barroso Lima

doi:10.3389/fgene.2019.01120

Abstract

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein–protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools’ user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Highlights

The analysis of high-throughput datasets using the framework of networks has gained widespread adoption in the biological sciences
In this Review, we focus on the various uses of network methods to the analysis of large-scale omics datasets, which are those generated using medium- and high-throughput technologies in genomics, transcriptomics, proteomics, and metabolomics experiments
Key concepts and terminology of this area are presented, followed by the introduction of biological network variants, namely correlation networks (Correlation networks allow disclosing of relevant associations in omics datasets), gene regulatory networks (GRNs) (Gene regulatory networks permit an improved understanding of the cell's transcriptional circuitry), and protein–protein interaction (PPI) networks (Protein–protein interaction networks provide an integrated view of the proteome's organization and interactions)

Summary

INTRODUCTION

The analysis of high-throughput datasets using the framework of networks has gained widespread adoption in the biological sciences. The representation of inferred GRNs can be in the form of bipartite graphs which, in contrast to the simple graphs presented in the Introduction and in the construction of co-expression networks, have nodes of two types: TFs or target genes, and edges between them indicate a regulatory interaction (Table 1, Figure 4A). This type of representation is usually employed to GRNs originated from co-expression relationships because usually no a priori information is available about the type of regulation that the TF exerts on the target genes. PathLinker, a Cytoscape app, can infer signaling networks from PPI networks by computing short paths in a PIN between receptor proteins, as source nodes, and target proteins, as TFs

A Primer on Network Analysis and Visualization

Findings

CONCLUSIONS