Integration of Clinical, Genomic and Proteomic Data using a Bioinformatic Approac

使用生物信息学方法整合临床、基因组和蛋白质组数据

• 批准号: 7897734
• 负责人: STANLEY A SCHWARTZ
• 金额: $ 39万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-22 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7897734
• 关键词: Algorithms; Amino Acids; Area; Arts; Bioinformatics; Biological; Biological Markers; Biological Sciences; Biology; Biomedical Computing; Biomedical Research; Buffaloes; Cell Culture Techniques; Clinical; Clinical Data; Clinical Management; Complex; Computational Science; Computing Methodologies; Coupled; Data; Data Set; Databases; Decision Making; Development; Discipline; Disease; Disease Outcome; Disease Progression; Engineering; Gene Expression; Gene Proteins; Genetic; Genetic Polymorphism; Genetic Variation; Genomics; Goals; Growth; HIV; HIV Infections; HIV-1; HLA-B27 Antigen; Haplotypes; Health; Human; Infection; Informatics; Information Technology; Integration Host Factors; Interleukin-10; Interleukin-4; Internet; Isotopically-Coded Affinity Tagging; Knowledge; Label; Liquid Chromatography; Medical; Medical Informatics; Methods; Metric; Mining; Molecular; Molecular Biology; Molecular Target; NIH Program Announcements; Online Systems; Outcome; Pathogenesis; Patients; Pattern; Peripheral Blood Mononuclear Cell; Phenotype; Polymorphism Analysis; Predisposition; Process; Proteins; Proteomics; Public Health; RANTES; Relative (related person); Research; Research Project Grants; Resources; Role; Science; Scientist; Single Nucleotide Polymorphism; Stable Isotope Labeling; Technology; Therapeutic; Time; Translational Research; United States National Institutes of Health; Virus; antiretroviral therapy; biomedical informatics; cDNA Arrays; cohort; computer science; computerized tools; data mining; design; evidence base; flexibility; gel electrophoresis; innovation; interest; member; novel; novel marker; programs; protein expression; receptor; relational database; response; tandem mass spectrometry; tool; transmission process; two-dimensional

DESCRIPTION (provided by applicant): Major developments in molecular biology, coupled with strides in genomics and proteomics, have led to an explosive growth in biological data. To obtain usable, relevant information from these vast databases (e.g. through data mining) it is mandatory to use advanced computational methods and hardware. The rapidly expanding field of bioinformatics, where biology, computer science, and information technology merge to form a single discipline, has the potential to serve as a bridge between medical informatics and experimental science. In this proposal we focus on a unique cohort of HIV-1 infected subjects, long term non-progressors (LTNP), who can remain asymptomatic for >15 yr without antiretroviral therapy. Many factors underlie the LTNP state. This project, which expands our current bioinformatics studies in a more translational direction, will develop a complex clinical and experimental database that will be analyzed by powerful informatics tools to identify key genes and proteins that contribute to the LTNP phenotype. We hypothesize that using innovative informatics technology that we specifically develop, we shall prepare a publicly available, large, interactive database containing medical, genomic and proteomic information on HIV-1 infected patients that can be queried to guide rational, evidence-based, decision making at both the clinical and public health levels. Our proposal represents a productive partnership between 3 specialized research groups, with extensive expertise in: a) clinical management of HIV-1 disease, b) molecular and cellular immunovirology, and c) bioinformatics and computational sciences. The goal of this biomedical informatics proposal is the characterization and efficient utilization of data obtained from basic biomedical research and integrate it with clinical outcome. The following specific aims are proposed. Specific Aim 1: To identify functional genes and proteins that are unique to our specific HIV-1 infected patient cohorts using state of the art genomic and proteomic technologies. Specific Aim 2: To evaluate the role of human allelic variants in influencing the rate of HIV-1 disease progression using SNP analysis and real time, quantitative PCR. Specific Aim 3: To develop new computational tools to analyze and integrate genomic, proteomic, and clinical data from our HIV-1 patient cohorts and convert them into clinically useful information relating to the pathogenesis, transmission and therapeutic response of HIV-1 infected patients. This will involve the design of: data mining-oriented schemas; advanced algorithms for integrating genomic, proteomic, and clinical data in a data warehouse; and metrics and methods to explore the association between host genetic variations and HIV disease. Ultimately this database will be provided on the web as a publicly accessible resource. This study will contribute to our fundamental understanding of the pathogenesis of HIV infections and identify new biomarkers of disease progression and potential molecular targets for therapy.

描述（由申请人提供）： 分子生物学的重大发展，加上基因组学和蛋白质组学的长足发展，导致生物数据爆炸式增长。为了从这些庞大的数据库中获得可用的相关信息（例如通过数据挖掘），必须使用先进的计算方法和硬件。生物信息学的迅速扩展领域，生物学，计算机科学和信息技术融合形成一个单一的学科，有可能成为医学信息学和实验科学之间的桥梁。在本提案中，我们关注一组独特的HIV-1感染受试者，长期无进展者（LTNP），他们在没有抗逆转录病毒治疗的情况下可以保持无症状超过15年。许多因素构成了LTNP状态的基础。该项目将我们目前的生物信息学研究扩展到更多的翻译方向，将开发一个复杂的临床和实验数据库，通过强大的信息学工具进行分析，以确定有助于LTNP表型的关键基因和蛋白质。我们假设，使用我们专门开发的创新信息学技术，我们将准备一个公开的，大型的，互动的数据库，包含HIV-1感染患者的医学，基因组和蛋白质组信息，可以查询，以指导合理的，循证的，在临床和公共卫生层面的决策。我们的提议代表了3个专业研究小组之间富有成效的伙伴关系，这些研究小组在以下方面拥有广泛的专业知识：a）HIV-1疾病的临床管理，B）分子和细胞免疫病毒学，以及c）生物信息学和计算科学。该生物医学信息学提案的目标是表征和有效利用从基础生物医学研究中获得的数据，并将其与临床结果相结合。提出了以下具体目标。具体目标1：使用最先进的基因组学和蛋白质组学技术鉴定我们特定的HIV-1感染患者队列所特有的功能基因和蛋白质。具体目标二：采用SNP分析和真实的时间定量PCR评价人类等位基因变异体在影响HIV-1疾病进展率中的作用。具体目标3：开发新的计算工具来分析和整合来自HIV-1患者队列的基因组、蛋白质组和临床数据，并将其转化为与HIV-1感染患者的发病机制、传播和治疗反应相关的临床有用信息。这将涉及设计：面向数据挖掘的模式;在数据仓库中整合基因组，蛋白质组和临床数据的高级算法;以及探索宿主遗传变异与HIV疾病之间关联的指标和方法。最终，该数据库将作为一个可供公众查阅的资源在网上提供。这项研究将有助于我们对HIV感染发病机制的基本理解，并确定疾病进展的新生物标志物和潜在的治疗分子靶点。