Exploration and Integration of Multi-Omic Data Using Tools in Agilent GeneSpring

Scientists today are often using multiple omics technologies to better understand the biological problem. This has created a serious challenge to analyze increasingly large and complex data sets generated by genomics, transcriptomics, proteomics, and metabolomics experiments. 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. 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. Since, GeneSpring allows import of data from public domain, we can demonstrate workflows that help users compare their data with the data provided by other researchers. In this analysis, we show use of GeneSpring software to study correlation of expression of microRNA and their targeted mature RNAs in a joint transcriptomic analysis to identify curated signaling pathways.