Mining of light-related genes in grapes based on a time-frequency analysis

Abstract

In order to better research the Grape gene expression data of temporal series, first of all, we built an undirected network by data preprocessing, screening for differential expressed genes, wavelet transform, correlation network analysis, and then carried out a correlation degree number analysis. Finally, inputting the differential degree number into Mapman software to annotate gene function, we could find out those differentially expressed genes playing a role in cell function, metabolism and transcription etc; differentially expressed genes of both genotypes show an overall upward trend under the long photoperiod than short photoperiod; the long light causes cell function of SV genotype grapes to be enhanced and may accelerate the grape's growth; Genes of SV grapes are more active than that of VR grapes in long and short light conditions. The model is very effective for mining the differential expression of genes comparatively related to the light and genotype.