%A LIU Ya-Wen, ZHANG Hong-Yan, CAO Dan, LI Lan-Zhi %T Prediction of drought and salt stress-related genes in rice based on multi-platform gene expression data %0 Journal Article %D 2021 %J Acta Agronomica Sinica %R 10.3724/SP.J.1006.2021.02084 %P 2423-2439 %V 47 %N 12 %U {https://zwxb.chinacrops.org/CN/abstract/article_7230.shtml} %8 2021-12-12 %X

Mining stress-related genes based on multi-platform gene expression data in rice can increase the reliability of key genes prediction and obtain more universally meaningful results. In this study, 94 affymetrix microarray data and 42 RNA-seq transcriptome data related to rice abiotic stress were collected from NCBI databases. First, multiple datasets related to the same stress on the same type were fused by data conversion method to obtain the affymetrix data set D_affy and RNA-seq data set D_rnaseq related to drought stress, and the affymetrix data set S_affy and the RNA-seq data set S_rnaseq related to salt stress. Then, the four datasets were analyzed by the classical WGCNA method based on Pearson's linear correlation coefficient and the improved WGCNA method based on the MIC nonlinear correlation coefficient respectively, and the eight Hub gene sets related to stress were obtained. Further, the integration analysis of stress-related Hub genes yielded the final 1936 drought stress-related Hub genes and 1504 salt stress-related Hub genes. Finally, the biological significance of Hub gene was analyzed from multiple perspectives, including prediction performance, enrichment analysis, literature report, STRING online interaction analysis, and Cytoscape visualization analysis. The results revealed that the overall prediction performance of Hub genes was better, and most of them were enriched in the pathways related to drought/salt stress. Among them, there were 31 drought stress response genes and 22 salt stress response genes reported in the literatures. In addition, 11 drought stress candidate genes and 5 salt stress candidate genes were predicted using the interaction analysis of Hub genes. In conclusion, This study provides a new idea for the effective analysis of “high-dimensional, small-sample” crop gene sequencing data, and the experimental results provide a reference for the study of stress-resistant rice varieties.