Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants. Gene networks are valuable for gene function discovery and candidate gene prioritization. Statistical methods for gene network reconstruction using transcriptome data have been vastly improved. Methods to evaluate the quality of a constructed network are available and improve the ability of the user to use the networks to inform research. Meta-prediction methods that combine multiple statistical models are generally more robust and accurate for gene network reconstruction. The use of gene networks to functionally annotate genes is a boon for genomes for which little functional information exists.