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Interoperation of Genome Databases and Tools

基因组数据库和工具的互操作

基本信息

批准号：
6946756
负责人：
KEI-HOI CHEUNG
金额：
$ 14.62万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-09-01 至 2006-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6946756
关键词：
Internet computer data analysis computer program /software computer system design /evaluation data collection methodology /evaluation data management gene expression gene frequency genome human population genetics information retrieval microarray technology molecular biology information system parallel processing

项目摘要

DESCRIPTION: (provided by applicant) This application for an NIH Mentored Quantitative Research Career Award requests support for Dr. Kei-Hoi Cheung as he embarks on a faculty career focused on genome-related bioinformatics. This application presents a research career development plan in the field of bioinformatics, bridging computer science and biology. The plan includes two partially overlapping phases: (1) a didactic phase that emphasizes training, including coursework and laboratory work in the area of genetics and genomics to complement Dr. Cheung's doctoral training in Computer Science and (ii) a development phase that focuses on intense development of the proposed research. These two phases will be closely supervised by a steering committee of senior scientists, who will serve as mentors or advisors, in the area of biology and bioinformatics. The human genome project and the rapid advance in genomic technology (e.g., microarrays) have produced numerous local, national, and international genome databases, many of which are Web-accessible. To answer questions that arise in advanced genome research projects, researchers often need to analyze a large amount of data that are collected from multiple related databases. Therefore, it is important to explore (1) how to integrate the databases involved in a flexible and useful fashion and (2) how to perform large-scale data analyses as easily and rapidly as possible. To this end, we propose two complimentary approaches. 1. The problem of data integration or interoperation is difficult because of the syntactic and semantic heterogeneities involved. To address this problem, we propose a metadata-driven approach using eXtensible Markup Language (XML), which incorporates standardized vocabulary to map heterogeneous Web-accessible data sets into a common format that facilitates interoperability. 2. To facilitate and speed up analysis of a large quantity of data, we will also explore a range of computational techniques including the use of Turbogenomics, which represents collaboration with the high performance computing group within the Yale department of Computer Science. These techniques allow (i) integration of heterogeneous software components (analysis tools) to be done easily and (ii) exploitation of the power of parallel computing. We will design, develop, test, and evaluate the approach in the context of current database projects including: 1) TRIPLES that manages data for large-scale yeast genome analysis (with Prof Snyder) and 2) ALFRED that stores gene frequency data on different human populations (with Prof Kidd). We have identified a number of related external Web-accessible databases as well as tools that users would like to access from TRIPLES and ALFRED in an integrated fashion. We will initially develop and apply our approach to integrate these databases and tools. We will extend our approach to other types of genomic data such as microarray data, which both laboratories and others will soon be generating in large quantities.

描述：（由申请人提供）此NIH辅导申请定量研究职业奖要求支持张启海博士，他开始了专注于基因组相关生物信息学的教师生涯。这申请提出了该领域的研究职业发展计划生物信息学，连接计算机科学和生物学。该计划包括两个部分重叠阶段：（1）强调培训的教学阶段，包括遗传学和基因组学领域的课程作业和实验室工作以补充张博士在计算机科学方面的博士培训;及（ii）发展阶段，重点是密集发展的拟议研究。这两个阶段将由一个高级指导委员会密切监督。科学家，谁将担任导师或顾问，在生物学领域，生物信息学人类基因组计划和基因组技术的快速发展（例如，微阵列）已经产生了许多本地、国家和国际基因组数据库，其中许多是网络访问。为了回答出现在先进的基因组研究项目，研究人员往往需要分析一个大的从多个相关数据库中收集的数据量。因此，我们认为，重要的是要探索（1）如何整合数据库中涉及的一个灵活和有用的方式和（2）如何执行大规模的数据分析，尽可能简单和快速。为此，我们提出两个免费的接近。 1.数据集成或互操作的问题是困难的，所涉及的句法和语义的异质性。为了解决这个问题，我们提出了一种使用可扩展标记语言（XML）的元数据驱动的方法，它结合了标准化的词汇表，数据集转换为通用格式，便于互操作。 2.为方便及加快分析大量数据，我们会还探讨了一系列的计算技术，包括使用涡轮基因组学，这代表了与高性能耶鲁大学计算机科学系的计算小组。这些技术允许（i）异构软件组件的集成（分析工具）容易完成和（ii）利用并行的力量计算的我们将在以下背景下设计、开发、测试和评估该方法：目前的数据库项目包括：1）TRIPLES，管理数据，大规模酵母基因组分析（与斯奈德教授）和2）ALFRED，不同人群的基因频率数据（与基德教授一起）。我们有确定了一些相关的外部网络访问数据库，以及用户希望从TRIPLES和ALFRED访问的集成工具时尚.我们将首先开发和应用我们的方法来整合这些数据库和工具。我们将把我们的方法扩展到其他类型的基因组数据例如微阵列数据，实验室和其他机构很快就会大量产生。