Multi-Omic Data Exploration Using Pathway and Correlation Analysis in Agilent’s Genespring

Scientists today face serious challenges as they try to analyze increasingly large and more complex sets of data, such as those generated by genomics, transcriptomics, proteomics, and metabolomics experiments. GeneSpring provides a platform for data analysis and integration enabling comparisons across a variety of data types allowing for identification of co-regulated genes, proteins and metabolites in an intuitive way. An integrated approach for analyzing multi-omic data can significantly alleviate bottlenecks and provide valuable information that is often missed in individual experiments. One successful method for translating diverse analytical data into biological understanding can be achieved by projecting and visualizing experimental data onto the curated biological pathways (WikiPathways/BioCyc/KEGG) or literature-derived networks. GeneSpring makes it easy to resolve nomenclature inconsistencies by providing a powerful built-in tool, Agilent BridgeDB. This resource automatically links an annotated metabolite, protein, or gene identifier in the experiment to the corresponding identifier used by the pathway database, resulting in better annotation coverage for downstream analysis.  Complementing more traditional bioinformatics techniques, correlation tools and specialized visualizations allow investigators to identify co-regulated genes, metabolites, and proteins in an intuitive and easy-to-use manner.