Exploring Approaches for Microarray Databases That Enable Flexible Design and Integrative Analysis

本提案将探讨用于表示、存储和处理微阵列数据的一些技术，并将其应用于耶鲁微阵列数据库（YMD）的开发。微阵列数据集，同时提供有关基因表达或蛋白质相互作用的信息，提出了数据管理的重大挑战之一。 涉及三项具体技术。总的来说，这些方法将使我们能够解决微阵列数据库设计中的一些重要问题：（i）处理大规模和多样化的信息;（ii）允许数据传播;（iii）促进异构信息资源之间的数据互操作和交换;以及（iv）支持综合数据分析。使用灵活的EAV数据建模方法。在YMD中，将利用实体属性值（EAV）方法以灵活的方式对各种稀疏分布的属性进行建模，以表示包括实验条件和多水平分析结果在内的异构数据。2.基于XML的互操作性。XML可以方便地将来自不同信息源的异构（但相关）数据集转换和合并为一种通用格式，从而方便综合数据访问。将开发MAGEML（一种基于XML的用于描述基因表达数据的标记语言）作为YMD和其他MIAME-compliant微阵列数据库之间的通用数据交换机制。并行计算。将使用分布式并行计算方法来帮助加速对大量基因表达数据的复杂查询和分析，为用户提供对各种工具的综合访问。

This proposal will explore a number of technologies for representing, storing, and processing microarray data, applied to development of the Yale Microarray Database (YMD). Microarray data sets, while providing information about gene expression or protein interactions, present one of the significant challenges in data management. Three specific technologies are involved. Collectively these approaches will allow us to address a number of the important issues in microarray database design: (i) dealing with large-scale and diverse information; (ii) allowing data dissemination; (iii) facilitating interoperation and exchange of data among heterogeneous information resources; and (iv) supporting integrative data analysis.1. The use of the flexible EAV data modeling approach. The entity-attribute-value (EAV) approach in modeling a wide variety of sparsely populated attributes in a flexible fashion will be utilized in YMD to represent heterogeneous data including experimental conditions and multi-level analysis results. 2. XML-based interoperability. XML can facilitate the conversion and merging of heterogeneous (but related) data sets from different information sources into a common format so that integrated data access can be facilitated. MAGEML (an XML-based markup language for describing gene expression data) as a common data exchange mechanism between YMD and other MIAME-compliant microarray data repositories will be developed.3. Parallel computing. A distributed, parallel computing approach to help speed up complex queries and analyses of large amounts of gene expression data will be used, providing users with integrated access to a variety of tools.