Diabetes and Obesity Integrative Genomics

The past 10 years have led to a variety of measurement tools in molecular biology that are near comprehensive in nature. For example, RNA expression detection microarrays can provide systematic quantitative information on the expression of over 40,000 unique RNAs within cells. Yet microarrays are just one of at least 30 large-scale measurement or experimental modalities available to investigators in molecular biology. We see scientific value in being able to integrate multiple large-scale data sets from all biological modalities to address biomedical questions that could otherwise not be answered. However, we recognize that the full agenda of working out the details for all possible inferential processes between all near-comprehensive modalities is too large.

The goal of this project is to serve as a model automated system for gathering data related to particular experimental characteristic and performing inferential operators on these data. For this application, we are focusing on a pragmatic subset. Specifically, we propose intersecting near comprehensive data sets by phenotype, and intersecting lists of significant and related genes within these data sets in an automated manner. The central hypothesis for this application is that integrating large-scale data sets across measurement modalities is a synergistic process to create new knowledge and testable hypotheses in the areas of diabetes and obesity, and that inferential processes involving intersection across genes can be automated. Funded by the National Library of Medicine (K22 LM008261).