Trancriptome data mining in combination with co-expression network analysis identifies the functional modules and critical regulators in

Cold stress, as an abiotic stress, is one of the most limiting factors which pose a great threat to the plant's productivity. To understand the transcriptional regulation and connectivity pattern of genes involved in barley cold stress responses, co-expression network analysis was performed based on the global transcriptome profiling. The microarray datasets related to cold stress treatments were retrieved from the Gene Expression Omnibus (GEO) and Array express databases. Four microarray datasets related to cold stress-responsive transcriptome in barley were included in our study. Gene co-expression analysis was constructed using WGCNA method. Module-Trait Relationships (MTR) analysis and hub genes determination and validation were carried out. Finally, transcription factor and kinase regulatory networks were Inferred using machine learning algorithm. The co-expression modules were determined using beta index = 10. In total 13 co-expressed modules were identified with an average size of 153 genes. Functional enrichment based on gene ontology (GO) showed that each of the stress related significant modules were enriched in different biological processes. Annotation of significant modules identifies some TFs and Kinases such as ethylene-responsive transcription factor 1-like, transcription factor PCL1-like, transcription factor MYC2, WRKY, serine/threonine-protein kinase PBL7, and receptor-like protein kinase At2g42960 were contributed in barley cold stress response. Our analysis highlighted the functional importance of ABA signaling pathway, ROS signaling, defensive and protective proteins, degrading protein, Ca2