Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data

Abstract

Reconstruction of gene co-expression networks is a powerful tool for better understanding of gene function, biological processes, and complex disease mechanisms. In essence, co-expression network analysis has been widely used for understanding which genes are highly co-expressed through special biological processes or differentially expressed in various conditions. Development of high-throughput experiments has provided a large amount of genomic and transcriptomic data for model and non-model organisms. The availability of genome-wide expression data has led to the development of in silico procedures for reconstruction of gene co-expression networks. Gene co-expression networks predict unknown genes’ functions; moreover, it has been successfully applied to understand important biological processes of living organisms such as plants. In this survey, we have reviewed the algorithms, databases, and tools of gene co-expression network reconstruction, which can lead to new landscapes for further research activities. Furthermore, we explain an application of some algorithms, databases, and tools that can significantly boost our current understanding of co-expression networks in Arabidopsis thaliana as a model plant using publicly available data. The presented example shows that using co-expression networks is an efficient way to detect genes, which may involve in various critical biological processes such as defense response.