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

Yale University, New Haven CT

Investigators

Abstract

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.