CAREER: Combinatorial Algorithms for High-Throughput Collection and Analysis of Genomic Diversity Data

职业：基因组多样性数据高通量收集和分析的组合算法

• 批准号: 0546457
• 负责人: Ion Mandoiu
• 金额: $ 55.46万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-15 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546457&HistoricalAwards=false
• 关键词: CAREER; Combinatorial; Algorithms; Throughput; Collection

Genomic diversity analyses of large-scale case/control and population studies promise to provide answers to fundamental problems ranging from determining the genetic basis of disease susceptibility to uncovering the pattern of historical population migrations. However, the feasibility of such studies critically depends on addressing a number of technological and computational challenges. On the technological front, despite the huge advances made in recent years, there is still a need for a flexible high-throughput platform capable of typing hundreds of thousands of SNPs at a very low-cost per experiment.Computationally, there is a need for integrating recently developed statistical models of the structure of genomic variability in human populations with efficient combinatorial methods delivering predictable solution quality.The proposed research and education activities will address the above challenges at several levels, including modeling and formalizing the underlying biological and technological problems, finding efficient algorithms for the identified problems, engineering these algorithms into high-quality open-source bioinformatics tools, and collaborating closely with industry researchers and molecular geneticists in validating the proposed methods and applying them to population-scale genomic data. Major project outcomes will include (1) development of an innovative high-throughput SNP genotyping assay realizing a yet unrealized potential of k-mer arrays by combination with solution-phase single-base extension, (2) optimization of two proven technologies that are in common use in SNP genotyping - DNA tag arrays and multiplex-PCR, (3) novel likelihood maximization algorithms with predictable solution quality for challenging computational problems arising in two-stage sampling design association studies, including haplotype tagging SNP selection and haplotype reconstruction from genotype data, (4) robust open-source software implementations and principled methodologies for the empirical evaluation of proposed algorithms, and (5) innovative curriculum and educational materials, including the creation of a new textbook on computational genomics. The successful completion of the project will lead to decreased data collection costs in large-scale association studies, thus enabling more studies to be completed within the same budget. The proposed assay architecture based on k-mer arrays is expected to enable additional applications of genomic technologies, such as genomics-based point-of-care medical diagnosis and large-scale species identification. Broader impacts of proposed educational and outreach activities include increasing participation of under-represented groups in research and training of future researchers with unique interdisciplinary skills.

大规模案例/控制和人口研究的基因组多样性分析有望为基本问题提供答案，从确定疾病易感性的遗传基础到揭露历史人口迁移的模式。但是，此类研究的可行性在很大程度上取决于解决许多技术和计算挑战。 在技​​术方面，尽管近年来取得了巨大进步，但仍需要一个灵活的高通量平台，能够以每次实验的成本非常低的成本输入数十万个SNP，从而在实验中输入非常低的SNP.com.com.com.com上，需要将最近开发的统计学模型整合在整合人口中的统计学模型，以挑战人类的多种范围，以挑战有效的研究，以挑战有效的质量，并挑战质量，以挑战质量，以挑战质量，并将其质量挑战。并正式化基础的生物学和技术问题，为确定的问题找到有效的算法，将这些算法设计为高质量的开源生物信息学工具，并与行业研究人员和分子遗传学家紧密合作，以验证所提出的方法并将其应用于人群刻板基因组数据。主要的项目结果将包括（1）开发创新的高通量SNP基因分型测定法，通过结合解决方案相单基量扩展来实现K-MER阵列的尚未实现的潜力，（2）两种已验证的技术的优化，这些技术在SNP基因分类阵列中常用于SNP基因分类和多样化的质量 - DNA Tag阵列和多重型号（3）Nembistim（3）Nembistim（3）Maximimimimimimimimimimimimimimimimimimimimimim（3）在两阶段抽样设计关联研究中引起的计算问题，包括基因型数据的单倍型标记和单倍型重建，（（4）强大的开源软件实现以及对拟议算法的经验评估的主要方法论，以及（5）创新的课程和教育材料，包括新的基因培训，包括创建基因概述的文章。该项目的成功完成将导致大规模协会研究中的数据收集成本降低，从而使更多的研究能够在同一预算内完成。 提出的基于K-MER阵列的测定结构有望实现基因组技术的其他应用，例如基于基因组学的医学诊断和大规模物种鉴定。拟议的教育和外展活动的更广泛影响包括增加代表性不足的群体参与具有独特的跨学科技能的未来研究人员的研究和培训。