Earticle

To promote molecular biology studies, public repositories for microarray data need to be constructed; the minimum contents for analysis of microarray experiment have been defined and standardized. Public repositories have been constructed by some researches which follow the standards such as MIAME-compliant data and MAGE-OM/ML. However, enough consideration has not been taken into the design of storage structure for the hierarchy of microarray data. In this paper, we propose alternative mapping strategy to mine the structural similarity and an advanced mapping rule from the algorithm. Object-relational mapping technique is used for extracting advanced storage design schema for microarray data and structural similarity of elements is evaluated for efficient storage construction. The mapping strategy reduced the number of relational tables remarkably. The strategy will contribute to design of the storage structure of microarray data and performance enhancement of a public repository.

Public repositories for microarray data have been constructed by some researches which follow the standards such as MIAME-compliant data and MAGE-OM/ML. However, enough consideration has not been taken into the design of storage structure for the hierarchy of microarray data. In this paper, we propose alternative mapping strategy to mine the structural similarity and an advanced mapping rule from the algorithm. Object-relational mapping technique is used for extracting advanced storage design schema for microarray data and structural similarity of elements is evaluated for efficient storage construction. The mapping strategy reduced the number of relational tables remarkably.

There are lots of studies attempting to identify the expression changes in oral squamous cell carcinoma. Most studies include insufficient samples to apply statistical methods for detecting significant gene sets. This study combined two small microarray datasets from a public database and identified significant genes associated with the progress of oral squamous cell carcinoma. There were different expression scales between the two datasets, even though these datasets were generated under the same platforms - Affymetrix U133A gene chips. We discretized gene expressions of the two datasets by adjusting the differences between the datasets for detecting the more reliable information. From the combination of the two datasets, we detected 51 significant genes that were upregulated in oral squamous cell carcinoma. Most of them were published in previous studies as cancer-related genes. From these selected genes, significant genetic pathways associated with expression changes were identified. By combining several datasets from the public database, sufficient samples can be obtained for detecting reliable information. Most of the selected genes were known as cancer-related genes, including oral squamous cell carcinoma. Several unknown genes can be biologically evaluated in further studies.

Objective:Gene expression microarrays become a widely used tool in biomedicine. With growing needs of microarray data sharing, there are efforts for the development of microarray standards. MAGE-OM(Microarray Gene Expression Object Model) is a data exchange model and MAGE-ML is an XML-based data exchange format. Most database, however, do not have a suitable structure for MAGE-ML storage and maximum use of the data. Therefore, we have created relational database implementing MAGE-OM for the storage of MAGE-ML with importing and exporting capabilities. Methods:A relational schema is derived from MAGE-OM with simple object-relational mapping strategy to reduce complexity of MAGE-OM. Data transfer between database and MAGE-ML document is performed via MAGE-OM using the MAGE Software Toolkit(MAGEstk). Results:Our database accepts microarray data as MAGE-ML files through web-based interface, classifying into two types of submission, array or experiment. MAGE-ML import-export function is flexible to accommodate changing data model by separating model definition and implementation layers. Conclusion:Standard-based implementation of gene expression database enhances the collection and the structured storage of large-scale gene expression data from heterogeneous data sources.

: 유전자칩 표준 모델을 구현한 데이터베이스 설계--------------------------------------------------------------------------------------------------------------------------------------------This study was supported by a grant from Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (03-PJ1-PG3-21000-0009).

With growing needs of microarray data sharing, there are efforts for the development of microarray standards. The standard data exchange model, MAGE-OM (Microarray Gene Expression Object Model) is an object-oriented conceptual model for microarray expression data. MAGE-OM database system is applicable for storage of the associated XML data exchange format MAGE-ML (Microarray Gene Expression Markup Language) and for higher level analysis and integration with biomedical resources. We have implemented MAGE-OM in both frame-based ontology and relational database to exploit the great modeling power of MAGE-OM and compared them in terms of consistency, efficiency and flexibility to the data model. Two implementations showed considerable difference in representing relationships among classes. The ontology in the frame-based system nearly matched the object-oriented model, but performance may become problematic as the database grows. The relational database schema was preferable for performance but it is difficult to guarantee the consistency to the conceptual object level. Our relational schema is also shown to be simplified and provide improved efficiency in comparison with recently published database ArrayExpress at the European Bioinformatics Institute. These design approaches would be helpful to understand the suitability and limitations of each implementation in the context of building standard-compliant database for microarray.