Attempting to understand the gene-environment interactions that lead to disease is the basis of modern molecular medicine. DNA microarray technology has provided vast quantities of new data for building up these models. Unfortunately the vast majority of studies using this technology consist of samples from populations of European ancestry. To understand the gene-environment interactions that contribute to racial and ethnic health disparities data from studies with samples from diverse populations must be analyzed.

Currently, this data is limited and hard to access. The few studies that use samples from diverse populations are lost among the millions of published microarray datasets. To address this issue we have developed Disparitome-Miner, a microarray database and web-based data-mining platform that will serve as a tool for facilitating discovery from published transcriptomic datasets. To date, Disparitome-Miner contains 18 microarray datasets consisting of samples from diverse patient populations. Differential expression analyses comparing tissue samples from healthy and disease states in multiple ancestry backgrounds are provided. Transcriptomic microarray data is analyzed and visualized interactively through Shiny Server, an open-source package for web-based R applications. Our hope is that Disparitome-Miner allows life scientists without advanced statistical training to evaluate their gene-of-interest for a role in the gene-environment interactions that contribute to racial and ethnic health disparities. This web-app can provide help in hypothesis-generation and facilitate further investigation into health disparities.

Citation Format: Aasakiran Madamanchi, David Cappel, Chaitanya Madamanchi, Roman Covarrubias, John Micheal Thomson. Disparitome-Miner: A data visualization web-application for ‘gene-of-interest’ in health disparities. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr C03. doi:10.1158/1538-7755.DISP13-C03