Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks

Drought stress-related gene identification is vital in revealing the drought resistance mechanisms underlying rice and for cultivating rice-resistant varieties. Traditional methods, such as Genome-Wide Association Studies (GWAS), usually identify hundreds of candidate stress genes, and further validation by biological experiements is then time-consuming and laborious. However, computational and prioritization methods can effectively reduce the number of candidate stress genes. This study introduces a random walk with restart algorithm (RWR), a state-of-the-art guilt-by-association method, to operate on rice multiplex biological networks. It explores the physical and functional interactions between biological molecules at different levels and prioritizes a set of potential genes. Firstly, we integrated a Protein–Protein Interaction (PPI) network, constructed by multiple protein interaction data, with a gene coexpression network into a multiplex network. Then, we implemented the RWR on multiplex networks (RWR-M) with known drought stress genes as seed nodes to identify potential drought stress-related genes. Finally, we conducted association analysis between the potential genes and the known drought stress genes. Thirteen genes were identified as rice drought stress-related genes, five of which have been reported in the recent literature to be involved in drought stress resistance mechanisms.