Durable Common Fund Data Interfaces and Tutorials with Bioconductor

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Project Summary The NIH Common Fund Data Ecosystem collects together a dozen data-rich projects producing high- throughput data with cutting-edge assays. The overall ecosystem design includes plans for cross-project data harvesting and analysis on a collaborative cloud computing platform. While a long-range plan may culminate in highly structured analytic workbenches, we propose to use approaches established in the Bioconductor project to design community-driven modular approaches to data structure and interactive analysis of selected Common Fund assets. Our first aim is to produce standard, easy to use interfaces to resources provided in the 4D Nucleome, Illuminating the Druggable Genome, and Genotype-Tissue Expression projects. These Common Fund projects provide data of considerable interest by the general research community, but data discovery and use is hampered by intrinsic complexity as well as differing access and delivery methods adopted by the various Common Fund projects. We will interface well-established data containers and query methods to allow familiar R/Bioconductor programming idioms to work smoothly with resources from the selected Common Fund projects. The two-decade history of Bioconductor's approach to modular software design, documentation, integration, and distribution will sharply increase the likelihood of durable improvement in access to and utilization of Common Fund Assets. Our second aim is to leverage the new interfaces and containers to carry out four analytical projects in the investigation of origins of treatment persistent tumor cells in organoid cultures, with the objective of identifying compounds that can attenuate and provide insights into epigenetic mechanisms of treatment persistence. The operational and substantive outcomes of this project will form the basis of a comprehensive, highly community-driven approach to building strong bridges between consortia innovating at the cutting edge of biotechnology, and scientists innovating in integrative translational inference based on genome biology.