The proposed ?I/UCRC for Computing and Genomics - An Essential Partnership for Biology Breakthroughs? proposed by the U. of Illinois at Urbana-Champaign, the U. of Chicago, and Mayo Clinic will enhance the research, education, and entrepreneurship while performing the important technology transfer by bringing together an interdisciplinary team of industry partners from computer systems, health care/pharmaceuticals, and life sciences working in collaboration with genomic experts to address the colossal big-data challenge. The application of genomics across the life sciences industry is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. One challenge has been that of integrating thought and market leaders across what had historically been orthogonal industries: those involved with computer sciences, and those involved with biological sciences. With the advent of Next Generation Sequencing technology, those industries are now interdependent and have a critical need to synthesize and coordinate activities at the interface of computing and genomics. The participating sites propose to establish a collaborative environment that improves the applicability, timeliness, efficiency, and accuracy of the computational infrastructure to address the pressing genome-based challenges. The CompGen consortium?s vision is to engineer and optimize computing systems needed by industry for genome analysis.
The CompGen Center will address the experimental process for genomic data. A variety of questions on health and social problems will be addressable, enabling much needed biological and healthcare breakthroughs. Outcomes will enrich research infrastructure, develop next generation of leaders in engineering and science, improve the quality of workforce, and involve international partners. Collaborations will produce artifacts such as new algorithms, optimizations, and statistical models, in turn driving the design of the computing enterprise. The goal is to generalize those artifacts to drive the design and evaluation of computational models and hardware/software co-designed architectures, tightly coupled with new memory and computing technologies for scalability and accuracy.
