作者：Wong, D. C. J.,Sweetman, C.,Ford, C. M.
作者单位：University of Adelaide
摘要：Background: The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world\'s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a \"guilt-by-association\" principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. Results: We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. Conclusions: Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx webcite), for the gene co-expression analysis in citrus.
文献注录：Wong, D. C. J.,Sweetman, C.,Ford, C. M.. Annotation of gene function in citrus using gene expression information and co-expression networks [J]. BMC Plant Biology. 2014, 186: (15 July 2014)-.
报/刊名：《BMC Plant Biology》，发表于2014 / 第 186 期。
文献类型： [J]
页码：第 (15 July 2014)- 页 / 共17 页