Bridging Sustainable Distribution of TRBD Bioinformatics Resources

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"Inflammation and the Host Response to Injury" is one of the Large-Scale Collaborative Project Awards of the National Institute of General Medical Sciences (NIGMS). Commonly known as the Glue Grants program, the stated purpose of these awards was "to enable the solution of major problems in biomedical research and to facilitate the next evolutionary stage of integrative biomedical science". Our Glue Grant investigators agreed that the most important biological problem to tackle in the area of injury research centered on the innate inflammatory response, and specifically, to improve our systems level understanding of the key regulatory elements, and their relative roles and importance that drive the host's response to serious injury and its accompanying severe systemic inflammation. The problem could only be addressed by first acquiring large amounts of new data in a discovery-driven approach. The start-up costs associated with our introduction of high throughput genomics in clinical medicine in terms of time, effort, and money have been substantial. An important strategy for our investigators is to sustain the infrastructure, resources, tools, and methodologies developed in this Glue Grant through funding collaborations with other federal funding agencies, industry, and/or other nonprofit organizations. Funding opportunity PAR-10-261 allows us to seek financial support to preserve the availability of our unique datasets as resources "not yet self-sustaining" and to improve the usability of the visualization tools we have created to store, organize, archive, analyze, and visualize the data generated by our program. There are three specific aims that define the activities for maintaining and enhancing our existing bioinformatics databases and tools. (1) Optimize the TRDB information system deployed and in production during the Glue Grant program. (2) Enhance the user-friendly web interface environment by improving the linkage between databases, models, algorithms, and visualization tools. (3) Optimize the display of the heterogeneous datasets, to allow the first-of-its-kind comparative analyses between differing injury types, cell/tissue types, time point sampling, and time course.