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Center for Computing and Genomics (CCBGM)

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the National Science Foundation.

Center Overview

The Center for Computational Biotechnology and Genomic Medicine (CCBGM) leverages the power of data analytics, artificial intelligence, machine learning, and high-performance computation to advance health care discovery. To do this, CCBGM combines research insights in engineering and genomic biology with the world-renowned expertise in individualized medicine and clinical research and practice of the Mayo Clinic. It uses the power of computational predictive genomics to advance pressing societal issues such as enabling patient-specific cancer treatment, determining phenotype from a person's or organism's genotype, understanding and modifying microbiomes, and meeting the rapidly expanding need for food.

CCBGM offers:

An approach to Big Data problems in genomic biology that comprehensively spans all of its key elements, including analytics, computing, and generation of actionable intelligence.
Biological expertise (e.g., human genomics, crop and animal sciences) combined with technical expertise in algorithms and computing systems (e.g., high performance computing, cloud, and special-purpose acceleration).
A strong track record of working with industry in the multidisciplinary domains of computing, biotechnology, and life sciences.
Access to multidisciplinary faculty, clinicians, and students working in bioinformatics, genomic applications, security, health sciences, and computing systems and algorithms.

Universities

University of Illinois, Urbana-Champaign

View Center Website

Center Personnel

Liewei Wang
Center Director
(507) 293-0408
wang.liewei@mayo.edu

Leila Jones
Research Program Manager - Mayo
(507) 266-0991
jones.leila@mayo.edu

Ravishankar RaviIyer
Center Director
(217) 333-7774
rkiyer@illinois.edu

Kathleen Atchley
Research Program Manager - University of Illinois
(217) 244-9527
katchley@illinois.edu

Research Focus

These following topics leverage the multidisciplinary capabilities of the CCBGM team to focus on clinical knowledge in human patients. However, the methods, tools, and algorithms developed as part of these efforts (e.g., microbiome, compression, imaging, genomic security, and acceleration) also apply in the broader context of analyzing the sequence data of crops, animals, and other organisms.

Actionable intelligence

This research area looks at the translation of Big Data to clinical knowledge. The overarching goal is to enhance patient-specific understanding of disease to tailor diagnoses and individualized treatment. Projects in this thematic component develop technologies to identify and classify genomic variants, genes, and drivers for human disease. Specifically, CCBGM develops algorithms to help merge heterogeneous datasets (e.g., multiomics, clinical, and microbiome) and identify statistically significant mutations, genes, metabolites, pathways, and networks that are associated with clinical or functional outcomes.

Computing and data management

This research area focuses on innovations in security, storage, and compression technologies for patient-specific and genomic data. Such methods are required to process and understand large-scale bioinformatics problems.

Systems innovation

Systems innovation research addresses the design and implementation of specialized computer systems to efficiently and accurately execute the algorithms for mining actionable intelligence from multiomics data. CCBGM's application-specific computing systems will have the ability to:

Efficiently handle storage and retrieval of large quantities of data produced in sequencing experiments as well as a body of medical information that maintains known correlations between genomic variants, genes, pathways, and human diseases.
Efficiently compute complex statistical analyses and machine-learning algorithms on parallel-processing platforms such as graphics processing units and field programmable gate arrays, as well as scale out to utilize large warehouse-scale computers (clouds, supercomputers).

CCBGM designs will also address constant evaluation, monitoring, and quality control of algorithms, workflows, and systems, which will provide the flexibility to incorporate new data, statistical models, and algorithms as they become available.

Awards

1624790

I/UCRC: Computing and Genomics-An Essential Partnership for Biology Breakthroughs

The application of genomics across the life sciences industries is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. Major Innovations in the applicability, timeliness, efficiency, and accuracy of computational genomic methods are needed, and these innovations will develop best when an interdisciplinary team of scientists, engineers, and physicians from academia and industry, spanning computer systems, health care/pharmaceuticals, and life sciences, work together. The University of Illinois at Urbana-Champaign (UIUC) and the Mayo Clinic are building on their longstanding collaboration to form the Center for Computational Biotechnology and Genomic Medicine (CCBGM), which will bring together their excellence in computing, genomic biology, and patient-specific individualized medicine. Working closely with industry, the CCBGM's multidisciplinary teams will use the power of computational genomics to advance pressing societal issues, such as enabling patient-specific cancer treatment, understanding and modifying microbial communities in diverse environments related to human health and agriculture, and supporting humanity's rapidly expanding need for food by improving the efficiency of plant and animal agriculture. The CCBGM will leverage UIUC's long-standing prowess in large-scale parallel systems, big data analytics, and hardware and software system design, to develop new technologies that enable future genomic breakthroughs. A key element of the Center's vision is to advance breakthroughs at the interface of biology and computing to transform health-care delivery while enhancing efforts that focus on the health science needs of underrepresented minorities. The CCBGM will bring together an interdisciplinary team to address the colossal genomic data challenge. Academia/industry partnerships will enhance research, education, and entrepreneurship while performing important technology transfer. The Center will achieve transformational computing innovations on three fronts. (1) It will innovate computing and data management to deal with issues of scaling to the ever-growing volume, velocity, and variety of genomic data. It will concentrate initially on scaling the computation of epistatic interactions (interactions between two or more genes or DNA variants) in genome-wide association study data, generating lists of genomic features that are maximally predictive of phenotypes, and information-compression algorithms for genomic data storage and transfer. (2) It will revolutionize the generation of actionable intelligence from multimodal structured and unstructured data, to generate knowledge from big data. The emphasis will be on the processing and integration of genomic and multi-omic data, and on the merging of unstructured phenotypic data with information from curated data sources (e.g., electronic medical records, annotation databases). The integration of these diverse data types will improve discovery research, predictive genomics, diagnostics, prognostics, and theranostics. Application areas include targeted cancer therapy, pharmacogenomics, crop improvement, and predictive microbiome analysis. (3) It will achieve systems innovation by designing computer systems specially suited for computational genomics, providing unprecedented speed and energy efficiency while preserving the accuracy of the analytics. The systems will be used to quantify and improve the accuracy of detecting genomic variation and, more generally, to optimize computing architectures for the execution of genome analysis workflows. Read More >

1624615

I/UCRC: Computing and Genomics-An Essential Partnership for Biology Breakthroughs

The application of genomics across the life sciences industries is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. Major Innovations in the applicability, timeliness, efficiency, and accuracy of computational genomic methods are needed, and these innovations will develop best when an interdisciplinary team of scientists, engineers, and physicians from academia and industry, spanning computer systems, health care/pharmaceuticals, and life sciences, work together. The Mayo Clinic and the University of Illinois at Urbana-Champaign (UIUC) are building on their longstanding collaboration to form the Center for Computational Biotechnology and Genomic Medicine (CCBGM), which will bring together their excellence in computing, genomic biology, and patient-specific individualized medicine. Working closely with industry, the CCBGM's multidisciplinary teams will use the power of computational genomics to advance pressing societal issues, such as enabling patient-specific cancer treatment, understanding and modifying microbial communities in diverse environments related to human health and agriculture, and supporting humanity's rapidly expanding need for food by improving the efficiency of plant and animal agriculture. The CCBGM will leverage Mayo Clinic's first-rate medical research - both basic and translational - across virtually all medical disciplines. Mayo Clinic generates a large quantity of big data sets from patients, biological experiments, and animal models. The goal is for these data sets to be linked to diseases and therapies to help understand the mechanisms involved in disease processes and in treatment resistance. Through this Center, state-of-the-art computational analytical tools will be built to help improve health care in an efficient and cost-saving manner through precision medicine. These tools will also be extended into many other areas of life science to improve product quality and safety. At the same time, educational efforts to include students, fellows, and junior faculty within the Center activities will also help build the nation's next generation of scientists and entrepreneurs, especially minority and women in the area of STEM (science, technology, engineering, and mathematics). The CCBGM will bring together an interdisciplinary team to address the colossal genomic data challenge. Academia/industry partnerships will enhance research, education, and entrepreneurship while performing important technology transfer. The Center will achieve transformational computing innovations on three fronts. (1) It will innovate computing and data management to deal with issues of scaling to the ever-growing volume, velocity, and variety of genomic data. It will concentrate initially on scaling the computation of epistatic interactions (interactions between two or more genes or DNA variants) in genome-wide association study data, generating lists of genomic features that are maximally predictive of phenotypes, and information-compression algorithms for genomic data storage and transfer. (2) It will revolutionize the generation of actionable intelligence from multimodal structured and unstructured data, to generate knowledge from big data. The emphasis will be on the processing and integration of genomic and multi-omic data, and on the merging of unstructured phenotypic data with information from curated data sources (e.g., electronic medical records, annotation databases). The integration of these diverse data types will improve discovery research, predictive genomics, diagnostics, prognostics, and theranostics. Application areas include targeted cancer therapy, pharmacogenomics, crop improvement, and predictive microbiome analysis. (3) It will achieve systems innovation by designing computer systems especially suited for computational genomics, providing unprecedented speed and energy efficiency while preserving the accuracy of the analytics. The systems will be used to quantify and improve the accuracy of detecting genomic variation and, more generally, to optimize computing architectures for the execution of genome analysis workflows. Read More >

Center for Computing and Genomics (CCBGM)

Center Overview

Universities

Center Personnel

Research Focus

Awards

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