AMB Express. 2021 Oct 20;11(1):138. doi: 10.1186/s13568-021-01296-4.
ABSTRACT
Robust statistical tools such as the Skellam model and Bayesian networks can capture the count properties of transcriptome sequencing data and clusters of genes among treatments, thereby improving our knowledge of gene functions and networks. In this study, we successfully implemented a model to analyze a transcriptome dataset of Cucumis sativus and Botrytis cinerea before and after their interaction. First, 4200 differentially expressed genes (DEGs) from C. sativus were clustered into 17 distinct groups, and 670 DEGs from B. cinerea were clustered into 12 groups. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied on these DEGs to assess the interactions between C. sativus and B. cinerea. In C. sativus, more DEGs were divided into terms in the molecular function and biological process domains than into cellular components, and 277 DEGs were allocated to 19 KEGG pathways. In B. cinerea, more DEGs were divided into terms in the biological process and cellular component domains than into molecular functions, and 150 DEGs were allocated to 26 KEGG pathways. In this study, we constructed networks of genes that interact with each other to screen hub genes based on a directed graphical model known as Bayesian networks. Through a detailed GO analysis, we excavated hub genes which were biologically meaningful. These results verify that availability of Skellam model and Bayesian networks in clustering gene expression data and sorting out hub genes. These models are instrumental in increasing our knowledge of gene functions and networks in plant-pathogen interaction.
PMID:34669064 | DOI:10.1186/s13568-021-01296-4
