ABSTRACT
Differential co-expression is a cutting-edge approach to analyze gene expression data and identify both shared and divergent expression patterns. The availability of high-throughput gene expression datasets and efficient computational approaches have unfolded the opportunity to a systems level understanding of functional genomics of different stresses with respect to plants. We performed the meta-analysis of the available microarray data for reoviridae and sequiviridae infection in rice with the aim to identify the shared gene co-expression profile. The microarray data were downloaded from ArrayExpress and analyzed through a modified Weighted Gene Co-expression Network Analysis (WGCNA) protocol. WGCNA clustered the genes based on the expression intensities across the samples followed by identification of modules, eigengenes, principal components, topology overlap, module membership and module preservation. The module preservation analysis identified 4 modules; salmon (638 genes), midnightblue (584 genes), lightcyan (686 genes) and red (562 genes), which are highly preserved in both the cases. The networks in case of reoviridae infection showed neatly packed clusters whereas, in sequiviridae, the clusters were loosely connected which is due to the differences in the correlation values. We also identified 83 common transcription factors targeting the hub genes from all the identified modules. This study provides a coherent view of the comparative aspect of the expression of common genes involved in different virus infections which may aid in the identification of novel targets and development of new intervention strategy against the virus.
