Generation of high-volume sequence data has become a routine part of genome research, genetics and the nascent field of precision medicine. With increasing interest in exRNA biology, the ability of researchers to make discoveries and extract clinically relevant knowledge from these data requires access to new types of tools and analytical approaches. However, the specialized tools and the expertise required for to develop and implement effective solutions are beyond reach of most researchers. To address this problem, we have assembled a team comprised of accomplished investigators to further host exRNA Portal resources and tools for exRNA biology. Building on the success of ERCC Stage 1, we will explore novel mechanisms to collect, identify, and characterize exRNA biotypes. Our team, in collaboration with Stage 2 investigators, will explore the use of computational deconvolution to complement UG3/UH3 physical separation and isolation methodologies to better characterize exRNA carriers and their cargo. By further extending our metadata standards for Stage 1/Stage 2, exRNA Atlas will become a unique data repository utilizing the emerging federated Data Commons based upon the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data. ERCC data will be linked with geographically distributed resources, thereby contributing to a continuous cycle of expanding knowledge and free flow of information. An agile DMRR governance structure will ensure responsiveness to the input from all ERCC Stage 2 stakeholders and to the needs of the community. Outreach and training will be targeted to various levels of expertise to ensure a lasting engagement with a diverse community of users and contributors. By accomplishing these goals, DMRR will empower researchers to make discoveries using exRNA datasets, and to extract from these datasets actionable information and apply new knowledge to improve human health.
Genome sequencing has become a routine part of genome research and is becoming used in the clinic to diagnose disease from blood samples. Building on the established Genboree platform, this project will support new technologies that will detect RNA molecules in human blood and other human biofluids for the purpose of diagnosing and monitoring diseases such as cancer.
