Diabetes Data and Hypothesis Hub (D2H2)

组学技术的出现使得研究转录组、蛋白质组、代谢组、脂组、表观基因组和微生物组变得容易，从而能够以较低的成本生成大型数据集。然而，这些数据集在很大程度上仍然没有得到充分的询问，因此代表着投资回报的重大损失。这主要是因为大多数潮湿的实验室调查人员不知道这些数据，并且缺乏有效下载、清理和分析这些数据所需的生物信息学技能。为了弥补这一差距，我们的项目将建立糖尿病数据和假设中心(D2H2)，这是一个为糖尿病和相关代谢紊乱研究社区促进数据驱动假设生成的平台。D2H2将包含：1)数千个与糖尿病相关的高质量汇编和整理的数据集；2)一个用户友好的网络门户网站，用于有效地询问这些数据并将它们与一套生物信息学工具联系起来；3)一个假设生成界面，使糖尿病研究界能够确定从将与D2H2平台处理和集成的数据中出现的有希望的可操作线索。此外，我们将在基于细胞的分析中对数十种由计算分析提出的有希望的化合物进行实验测试，以测量胰岛素的分泌。来自这样一个筛选的领先化合物将在糖尿病的动物模型中进行测试，以证明从数据驱动的假设开始以促进早期治疗开发的概念验证。

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.