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).

Getting Started with MetaCore: Getting the Most from your “omics” Analysis

Thursday, January 21, 2011  |  10:00 AM- 12:00 PM
Dominion Tower, Room 1003A
1400 NW 10th Avenue, Miami, FL 33136
(click here for map/directions)

Space is limited. Please RSVP attendance via email to bioinformatics@med.miami.edu.
For additional information, please contact the Bioinformatics Group at 305-243-4962.

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.

How to choose the right network building or interactome algorithm to test and expand your hypothesis

Thursday, February 18, 2011  |  10:00 AM-12:00 PM
Dominion Tower, Room 1003A
1400 NW 10th Avenue, Miami, FL 33136
(click here for map/directions)

One aspect of systems biology is to integrate complex interactions of biological systems and address connectivity. GeneGo provides a highly annotated and dense interaction database with over ten different network building algorithms and 6 different interactome tools for this purpose. Here we demonstrate the strength of these tools in the ability to visualize signaling interaction networks and expand on your hypotheses outside of the realm of your core research areas. This tutorial describes each network building and interactome algorithm and highlights how to optimize the visualization of your interactions of interest on a network we will build or to determine central hugs of regulation. From the network view, we will show tools such as how to add/ hide/show objects and how to manipulate visualizations of pathways using post-filters such as disease, tissue, orthologs or gene otology processes. The interactome options will include learning how to obtain inter- and intra-connectivity information to confirm the relevance of your networks hubs according to topology or protein class. We then highlight how to determine if central network hubs are truly significant for the entire data set, avoiding literature bias to drive hypotheses further.

How to identify microRNA targets, work with microRNA data and use predicted information

Wednesday, June 9, 2011  |  10:00 AM- 12:00 PM
Dominion Tower, Room 1003A
1400 NW 10th Avenue, Miami, FL 33136
(click here for map/directions)

MicroRNAs (miRNAs) regulate gene expression by directly binding to messenger RNA sequences leading to the repression of protein translation. It is has been hypothesized that identifying patterns of miRNA expression in disease states and elucidating the processes dictated by their targets will be key defining mechanisms of disease progression or classification. Here we use MetaCore in combination with MetaLink to visualize and characterize the biological functions of miRNA (from breast cancer subtypes) and their novel, known or predicted targets. Learn how to create interaction file types to be used in conjunction with expression analysis to first identify sets of miRNA for each cancer subtype then exploit our network building algorithms to build expanded networks for each set of microRNA. In this session you will also learn how to expand beyond the direct interactions of miRNA and their targets to export gene lists of associated biological functions for further enrichment analysis and comparison of function between cancer subtypes defined by miRNA trends. Also exploit how to use your miRNA target network as your own ontology for enrichment of a list of new targets. Lastly, learn how to overlay gene expression data from aggressive tumor samples to demonstrate a causative relationship with changes miRNA expression and target expression.