Diabetes Data and Hypothesis Hub (D2H2)

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The advent of omics technologies has made it easy to survey the transcriptome, proteome, metabolome, lipidome, epigenome, and microbiome, enabling the generation of large datasets at modest cost. However, these datasets remain largely under-interrogated and thus represent a major loss of return on investment. This is mainly because most wet laboratory investigators are unaware of these data, and lack the bioinformatics skills needed to efficiently download, clean, and analyze these data. To bridge this gap, our project will establish the Diabetes Data and Hypothesis Hub (D2H2), a platform that will facilitate data-driven hypothesis generation for the diabetes and related metabolic disorder research community. D2H2 will contain: 1) thousands of high-quality assembled and curated data sets relevant to diabetes, 2) a user-friendly web-based portal to efficiently interrogate these data and link them with a suite of bioinformatics tools, and 3) a hypothesis generation interface to enable the diabetes research community to identify promising actionable clues that will emerge from the data that will be processed and integrated with the D2H2 platform. In addition, we will experimentally test tens of promising compounds, brought forward by the computational analyses, in a cell-based assays that will measure insulin secretion. Leading compounds from such a screen will be tested in animal models of diabetes to demonstrate the proof-of-concept of starting with data-driven hypotheses to facilitate early-stage therapeutics development.