Learning from Co-expression Networks: Possibilities and Challenges

Abstract

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

Comparative transcriptomics of hepatic differentiation of human pluripotent stem cells and adult human liver tissue

“…Proteins with node degree Ͻ10 were discarded. Nodes with clustering coefficient Ͼ0.7 were identified as major hubs (44). In addition, nodes with at least two combinations of the top 5% of the following network centralities (30), closeness centrality, betweenness centrality, and stress centrality, regardless of the clustering coefficient measurement, were considered as hubs.…”

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

“…calculated based on dataset median instead of mean (Serin et al, 2016). Recently, GCC has been shown to be a better correlation method for gene expression analysis because of its capacity to detect nonlinear relationships and its insensitivity to outliers (Ma and Wang, 2012).…”

“…Although recent work has made substantial progress toward describing genomewide expression patterns in many genotypes, environmental conditions, and tissues, relatively little is known about the function and regulation of most maize genes. Because genes with related biological functions or regulatory mechanisms often have similar expression patterns (Aoki et al, 2007), one way to enhance understanding of gene function is by construction of a gene coexpression network (GCN;D'haeseleer et al, 2000;Aoki et al, 2007;Usadel et al, 2009;Li et al, 2015c;Serin et al, 2016). GCNs are constructed using data mining tools and algorithms that describe the relatedness between the expression patterns of multiple genes in a pairwise fashion.…”

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

“…Last, the RAP method has been implemented as an R package, providing a flexible framework for aggregating gene prioritizations from different types of biological networks. Besides the functional association networks (e.g., AraNet v2 and STRING), co-expression networks capture the functional relationships between genes solely from gene expression datasets, which can also be integrated in RAP for gene functional analysis in several crop species, including maize, rice, soybean and wheat (Mutwil et al, 2011; Aoki et al, 2016; Ruprecht et al, 2016; Serin et al, 2016). …”