Merging Data for Correlation and Network-Based Analysis Using GeneSpring 13

Designed specifically for the needs of biologists, GeneSpring offers an interactive visualization and computing environment that promotes investigation and enables understanding of transcriptomics, genomics, metabolomics, and proteomics data within a biological context. New in GeneSpring 13, metadata analysis and visualization tools will allow researchers to analyze phenotypic parameters such as clinical or physiological attributes of the subjects alongside their gene or metabolite expression profiles.

In this analysis, we demonstrate how GeneSpring can be used to study correlation of expression of microRNA and their targeted mature RNAs in a joint transcriptomic analysis via clustering across omic data types to identify curated signaling pathways that might be deregulated specifically in sugar metabolism as published in GSE35963 (Liu et al., 2012) and GSE25404 (Nodine et al., 2010). Homologene and BridgeDB are implemented in GeneSpring to facilitate integrative analysis through translation functions, linking probes across data types, array platforms, and organisms that map to the same biological entity. Since GeneSpring can create literature-derived networks, we then extended our investigation to identify NLP derived networks of genes consisting largely of interesting genes from the multi-omic experiment that allow cross-talk between curated pathways were differentially expressed in the datasets.