I/UCRC: Phase I: Computing and Genomics-An Essential Partnership for Biology Breakthroughs (CCBGM)

基因组学在生命科学行业中的应用目前面临着快速准确地生成、解释和应用基因组数据的能力不足的挑战。需要在计算基因组方法的适用性、及时性、效率和准确性方面进行重大创新，当来自学术界和工业界的科学家、工程师和医生组成跨学科团队，跨越计算机系统、医疗保健/制药和生命科学，共同努力时，这些创新将得到最好的发展。梅奥诊所和伊利诺伊大学厄巴纳-香槟分校（UIUC）正在建立长期合作，成立计算生物技术和基因组医学中心（CCBGM），这将汇集他们在计算、基因组生物学和针对患者的个性化医学方面的卓越成就。CCBGM的多学科团队将与工业界密切合作，利用计算基因组学的力量推进紧迫的社会问题，例如实现针对患者的癌症治疗，了解和修改与人类健康和农业相关的不同环境中的微生物群落，以及通过提高动植物农业的效率来支持人类对食物迅速扩大的需求。CCBGM将利用梅奥诊所几乎所有医学学科的一流医学研究——包括基础研究和转化研究。梅奥诊所从患者、生物实验和动物模型中产生了大量的大数据集。目标是将这些数据集与疾病和疗法联系起来，以帮助理解疾病过程和治疗耐药性所涉及的机制。通过该中心，将建立最先进的计算分析工具，以帮助通过精准医疗以高效和节省成本的方式改善医疗保健。这些工具还将扩展到生命科学的许多其他领域，以提高产品质量和安全性。与此同时，将学生、研究员和初级教师纳入中心活动的教育努力也将有助于培养美国下一代科学家和企业家，特别是STEM（科学、技术、工程和数学）领域的少数族裔和女性。CCBGM将汇集一个跨学科团队来应对巨大的基因组数据挑战。学术界/工业界的伙伴关系将加强研究、教育和创业，同时进行重要的技术转让。该中心将在三个方面实现变革性计算创新。(1)它将创新计算和数据管理，以应对不断增长的基因组数据量、速度和种类的扩展问题。它最初将集中于扩展全基因组关联研究数据中上位相互作用（两个或多个基因或DNA变体之间的相互作用）的计算，生成最大程度地预测表型的基因组特征列表，以及用于基因组数据存储和传输的信息压缩算法。(2)它将彻底改变从多模态结构化和非结构化数据中生成可操作情报的方式，从大数据中生成知识。重点将放在基因组和多基因组数据的处理和整合，以及将非结构化表型数据与来自精心策划的数据源（如电子病历、注释数据库）的信息合并。这些不同数据类型的整合将改善发现研究、预测基因组学、诊断、预后和治疗。应用领域包括靶向癌症治疗、药物基因组学、作物改良和预测微生物组分析。(3)它将通过设计特别适合计算基因组学的计算机系统实现系统创新，在保持分析准确性的同时提供前所未有的速度和能源效率。该系统将用于量化和提高检测基因组变异的准确性，更一般地说，用于优化基因组分析工作流程执行的计算架构。

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.