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. 2016 Nov 2;17(1):846.
doi: 10.1186/s12864-016-3176-2.

Large-scale gene co-expression network as a source of functional annotation for cattle genes

Affiliations

Large-scale gene co-expression network as a source of functional annotation for cattle genes

Hamid Beiki et al. BMC Genomics. .

Abstract

Background: Genome sequencing and subsequent gene annotation of genomes has led to the elucidation of many genes, but in vertebrates the actual number of protein coding genes are very consistent across species (~20,000). Seven years after sequencing the cattle genome, there are still genes that have limited annotation and the function of many genes are still not understood, or partly understood at best. Based on the assumption that genes with similar patterns of expression across a vast array of tissues and experimental conditions are likely to encode proteins with related functions or participate within a given pathway, we constructed a genome-wide Cattle Gene Co-expression Network (CGCN) using 72 microarray datasets that contained a total of 1470 Affymetrix Genechip Bovine Genome Arrays that were retrieved from either NCBI GEO or EBI ArrayExpress.

Results: The total of 16,607 probe sets, which represented 11,397 genes, with unique Entrez ID were consolidated into 32 co-expression modules that contained between 29 and 2569 probe sets. All of the identified modules showed strong functional enrichment for gene ontology (GO) terms and Reactome pathways. For example, modules with important biological functions such as response to virus, response to bacteria, energy metabolism, cell signaling and cell cycle have been identified. Moreover, gene co-expression networks using "guilt-by-association" principle have been used to predict the potential function of 132 genes with no functional annotation. Four unknown Hub genes were identified in modules highly enriched for GO terms related to leukocyte activation (LOC509513), RNA processing (LOC100848208), nucleic acid metabolic process (LOC100850151) and organic-acid metabolic process (MGC137211). Such highly connected genes should be investigated more closely as they likely to have key regulatory roles.

Conclusions: We have demonstrated that the CGCN and its corresponding regulons provides rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on gene function in this poorly annotated genome. The network is publicly accessible at http://www.animalgenome.org/cgi-bin/host/reecylab/d .

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Figures

Fig. 1
Fig. 1
Composition of the 1470 Affymetrix Genechip Bovine Genome Arrays used in this study. Arrays were classified according to the experimental conditions (a) and distribution (b)
Fig. 2
Fig. 2
Network topology of the CGCN. a Visualizing the CGCN (based on TOM similarity matrix) using heatmap plot. Light color represents low overlap and progressively darker red color represents higher overlap. Blocks of darker colors along the diagonal are the modules. The gene dendrogram and module assignment are also shown along the left side and the top. b Scale free topology evaluation of CGCN using Scale-Free Topology Fitting Index [18]
Fig. 3
Fig. 3
Manual category of ove-represented BP GO terms in CGCN modules
Fig. 4
Fig. 4
Heatmap visualization of module one gene interactions based on cattle protein interaction network [21]
Fig. 5
Fig. 5
Functional analysis of the white module genes. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. a Representative biological processes interactions among module genes. b Representative molecular function interactions among module genes. c Representative Ractome analysis interactions among module genes

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