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

DOI

10.2991/icitmi-15.2015.187How to use a DOI?

Keywords

differentially expressed genes, correlation network, Grape gene, degree number, regulatory relationship.

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Longlong Liu
AU  - Jie Zhou
AU  - Zichen Lu
AU  - Meng Ma
PY  - 2015/10
DA  - 2015/10
TI  - Mining of light-related genes in grapes based on a time-frequency analysis
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 1114
EP  - 1118
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.187
DO  - 10.2991/icitmi-15.2015.187
ID  - Liu2015/10
ER  -