STTR Phase I: Development of a high-performance clinical genomics analysis platform to support precision medicine

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The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project is to investigate the feasibility of developing a novel clinical genomics software analysis platform that may help reduce or eliminate some of the most significant challenges associated with genetic testing. In the US, genomics plays a role in 9 of the 10 leading causes of death including cancer, heart disease, stroke, diabetes, and Alzheimer's disease. As such, genetic testing is growing very rapidly, as are some of the associated challenges of scale, performance, cost and quality. Genomic medicine and genetic testing are key underpinnings of precision medicine, which may help lead to improved diagnosis, treatment, and even prevention of complex diseases and disorders. This feasibility study targets the development of an advanced analysis software platform to address analysis bottlenecks and ultimately improve time-to-treatment. The team will determine feasibility based on prototyping key technologies and methods to address current state limitations and point towards an improved future-state approach. The study will be done in close collaboration with actual clinical genomics users to deliver clear and compelling benefits to the target market and end-users and to provide a high-value solution to the rapidly growing market segment.This STTR Phase I project proposes to design and prototype a novel genomics software analysis platform. It will include access to a large work bench of bioinformatics applications and performance optimized clinical analysis workflows running on scalable public cloud-based, HIPAA-compliant computing infrastructure. The platform will be developed following the software-as-a-service model, designed to optimize performance and costs for next generation sequencing (NGS) analysis. The project will entail development of advanced proprietary computational algorithms to parallelize execution of analysis tasks, and the creation of highly optimized genomics analysis workflows. These workflows will result in dramatic time-savings as well as reduced costs compared to current state approaches. Project efforts also will focus on the development of sophisticated resource provisioning logic to exploit scale and cost optimization running on public cloud infrastructure. In addition, the project will include the feasibility of developing a dual-purpose platform for R&D and clinical usage for faster testing and adoption of newer and advanced tools and procedures. The technology will help to improve patient care by delivering results substantially faster, with higher quality and at lower cost. Additionally, users will be able to construct and validate custom analysis workflows that meet HIPAA, CLIA-CAP, and other clinical requirements.