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Agilent GeneSpring Software for Multi-omic data analysis

Scientists today face serious challenges as they try to analyze increasingly larger and more complex sets of data, such as those generated by genomics, transcriptomics, proteomics, and metabolomics experiments. These rapidly growing multi-omic experiments have disease or treatment outcome prediction and toxicogenomic analysis in support of drug development. Interpretation and analysis of the results typically require expertise from different disciplines across an organization. Agilent’s GeneSpring software tool is specifically designed for biologists to apply powerful statistical methods needed to analyze multi-omic data, without requiring expertise in statistics.  One successful method for translating diverse analytical data into biological understanding can be achieved by projecting and visualizing processed experimental data onto the curated biological pathways (WikiPathways/BioCyc/KEGG) or literature-derived networks. 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. Clustering across omic data types in the correlation space opens new ways for elucidating novel biological, pathological or toxicological pathways. GeneSpring users can apply metadata tools to a broad range of tasks such as bioinformatics data analyses, classifier development or quality control. Complementing more traditional bioinformatics techniques, correlation tools and visualizations will allows investigators to identify co-regulated genes, metabolites, and proteins in an intuitive and easy-to-use manner.

Presenters:
Vanessa Lordi, GeneSpring Product Manager
Shweta Shukradas, Application Scientist
 
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