Cattle condition-independent Gene Co-expression Network (CGCN)

Query for genes that are associated with known expressed genes

RAW DATA: Curated gene expression datasets of original Series and Platform records from the NCBI Gene Expression Omnibus (GEO) repository.

PROCESS: Weighted correlation network analysis ( Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics 2008, 9:559) was used to identify similarity between genes expression values under different tissues and experimental conditions. Briefly, adjacency matrix was formed based on correlation between gene expression values. Subsequently, the adjacency matrix raised to power 7 based on network scale free properties and used to calculate similarity between gene expression values based on "Topological Overlap Measure (TOM)". Theoretically, TOM measure varies between 0 and 1. It should be noted that this measure calculated after raising correlations to power 7, so its values are expected to be small. For example TOM values more than 0.01 imply tight interconnections between genes.

OUTCOME: 137,887,921 connectivities were identified among 16,608 probe sets which represent 11,125 genes. The genes identified with "guilt-by-association" assumption helps to provide clues for related genes that might worth further investigation for their roles regulating certain phenotypes.

Step 1: Search by gene (symbol, name, id), probe name, or a cut-off TOM connectivity threshold (how closely genes are related).

Step 2: Review the preliminary search results summary, decide if you want to download the data

Step 3: Download data to visualize the network relationships with Cytoscape, Miru or similar software