MetaCore: Getting the most from your "omics" analysis  

The ability to generate massive amounts of data with "omics" analysis begs the need for a tool to analyze and prioritize the biological relevance of this information. GeneGo provides a solution for using "omics" gene lists to generate and prioritize hypotheses with MetaCore. This tutorial highlights how to work with different types of data (genomics, proteomics, metabolomics and interaction data) beginning with how to upload gene lists and expression data (if available). Here we demonstrate data manager capabilities including how to upload, batch upload, store, share and check data properties and signal distribution. We then focus on how MetaCore uses your gene list to extract functional relevance by determining the most enriched processes across several ontologies. This entails a detailed lesson on how to prioritize your hypothesis using the statistically significance enrichment histograms and associate highly interactive GeneGo Maps and pre-built networks. We further emphasize the role of expression data in your analysis and the ability to visually predict experimental results, associated disease and possible drug targets. Lastly we highlight the benefits of using MetaCore workflows to compare data sets and work with experiment intersections.