Mining Abiotic Stress-related Genes In Rice Based On Multi-source Data

Abiotic stresses such as drought,high salinity,high temperature and low temperature seriously threaten the growth of rice.The mining of rice stress-related genes is of great significance to the cultivation of new rice varieties with excellent stress resistance.However,rice abiotic stress resistance is affected by multiple genes and environment,and it is not stable and accurate to mine rice related genes only based on sequencing data of a single experimental group.In the post-genome era,mining plant stress response genes based on the integration analysis and molecular biological network analysis with multi-source data has become one of the research hotspots in bioinformatics.This study focused on the mining and functional analysis of abiotic stress-related genes in rice,and the main results were as follows:(1)Integration analysis of multi-source gene expression data.Gene microarray data and transcriptome data related to the same rice abiotic stress from multiple experimental groups were collected from the NCBI library.Two fusion datasets of the same stress can be generated by fusing the gene microarray data or RNA-seq data of several experimental groups under a certain stress by data conversion method.In this paper,four fusion datasets were obtained by taking rice drought and salt stress as examples.(2)Construction of a weighted gene co-expression network based on MIC.The weighted gene co-expression network was constructed based on the similarity of gene expression,and the coexpressed gene modules were obtained by clustering,and then the key genes were identified by the correlation between gene modules and phenotypes.In this paper,in order to capture the nonlinear correlation between genes,the maximum information coefficient MIC was proposed as the similarity measure to replace the Pearson linear correlation coefficient in the original WGCNA algorithm(denoized as WGCNA-P)to construct the gene co-expression network(denoized as WGCNA-MIC).(3)Mining rice stress-related Hub gene based on WGCNA-P and WGCNA-MIC algorithms.In this paper,two methods of WGCNA-P and WGCNA-MIC were used to construct a gene co-expression network on the same data set,and the Hub gene set obtained by each was integrated and analyzed.A total of 1,936 Hub genes related to drought stress and 1,504 Hub genes related to salt stress were obtained in the experiment.Furthermore,the function enrichment analysis and literature report analysis of Hub genes showed that most of the Hub genes were enriched in pathways related to drought/salt stress,and included 31 drought stress response genes and 22 salt stress response genes reported in the literature.(4)Prediction of stress resistance candidate genes based on STRING+Cytoscape interaction network analysis.For the integrated set of Hub genes,the interaction of Hub genes was visually analyzed by using the STRING+Cytoscape tool.According to the strong correlation between the interaction network and the reported stress-related genes,11 candidate genes for drought stress and 5 candidate genes for salt stress were predicted.