Research Resource Discovery

The overarching goal of this exploratory project is to accelerate biomedical innovation and enable personalized medicine in a cost-effective manner. We will achieve that by (1) identifying sweet-spot genomics architectures that are based on existing electronic components and are accessible via conventional programming models, which achieve performance/density/cost that is >10√ó superior to that of commodity architecture, and which can be produced in high volume116, (2) accelerating adoption of these architectures by removing the barriers formed by current software infrastructure117, and (3) developing key algorithms118 and their mappings to the sweet-spot architecture for workflows to be accelerated, including disease-associated variant calling, metagenomics, and de novo assembly.

An NSF Industry/University Cooperative Research Center. The multidisciplinary group will comprise companies that address biology, computers, and big data, initially addressing four challenges in genomic biology: Genotypic Determinants of Human Disease,Understanding How Microbiomes Affect Health, Agriculture, and the Environment,Detection of Genomic Variation,Gene Network Analysis.

The goal of this project is to generate actionable intelligence using smart analytics to integrate big-data in the form of omics (genomics, transcriptomics, metabolomics etc.), clinical data and longitudinal data from electronic health records (EHR). The actionable intelligence is a descriptive piece of information with high-confidence and accuracy that can be used to tailor and individualize diagnosis and therapeutics for a given patient or inform potential candidates for biomarker discovery. The analytics and tools will be developed using engineering expertise at the Univ. of Illinois in close collaboration and partnership with leading clinicians, biologists and bioinformatics specialists at Mayo Clinic. This project will explore societally relevant, prevalent and yet less-understood diseases such as triple-negative breast cancer, major depressive disorder and diabetes.

Data compression is crucial for enabling timely exchange and long-term storage of heterogeneous biological and clinical data. To facilitate efficient organization and maintenance of genomic databases and allow for fast random access, query, and search, specialized software solutions for compression and computing in the compressive domain will be developed.

We will develop and translate the use of nutritional interventions for improving human health and for management practices for improved agricultural production. Data-analysis approaches from our study will provide the industry and broader scientific community with a valuable resource to address many questions about host-microbe interactions in the health sector, resulting in development of new diagnostic tools, therapeutic strategies, and targeted probiotic interventions.

This project will improve the robustness of error analysis and correction by developing new root-cause error analysis methods and hybrid error-correction algorithms accelerated via FPGA or GPU implementations. Variant calling will be improved by both suppressing errors and building new machine-learning based techniques that would work with clinical workflows.

Calculating epistatic interactions between genomic variants in studies incorporating complex endophenotypes is a computationally challenging problem that requires emphasis on accelerating and parallelizing the code and on workload distribution efficiency. Accelerating and scaling this process will enable the detection of epistasis in many existing GWAS datasets, from both the biomedical and agricultural areas.

Our goal is to provide an unprecedented capability for metabolic and physiological imaging for biomedical applications, including drug discovery, brain mapping, and early detection of cancers. Our initial focus will be on implementing and validating a novel technology to enable the acquisition, processing, and analysis of high-dimensional MR spatiospectral data. Specialized algorithms and software will be developed for signal processing, image reconstruction and spectral estimation to facilitate biomedical applications of the new technology.