• Microbiology and Biotechnology Letters
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• v.31 no.2
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• pp.182-190
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• 2003

As DNA microarrays are widely used recently, the amount of microarray data is exponentially increasing. Until now, however, no domestic system is available for the efficient management of such data. Because the number of experimental data in a specific laboratory is limited, it is necessary to avoid redundant experiments and to accumulate the results using a shared data management system for microarrays. In this paper, a system named WEMA (WEb management of Micro Arrays) was designed and implemented to manage and process the microarray data. WEMA system was designed to include the basic feature of MIAME (Minimal Information About a Microarray Experiment), and general data units were also defined in the system in order to systematically manage the data. The WEMA system has three main features: efficient management of microarray data, integration of input/ouput data, and metafile processing. The system was tested with actual microarray data produced by a molecular biology laboratory, and we found that the biologists could systematically manage and easily analyze the microarray data. As a consequence, the researchers could reduce the cost of data exchange and communication.

• Genomics & Informatics
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• v.9 no.1
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• pp.19-27
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• 2011

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/.

• Genomics & Informatics
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• v.3 no.1
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• pp.39-42
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• 2005

DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.230-235
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• 2008

생명정보 대량 획득기술의 하나인 마이크로어레이(microarray)는 DNA와 각종 유적자 연구에 사용되는 도구로서 확립되면서, 생명정보학(bioinformatics)분야의 발전에 크게 기여하였다. 그러나 마이크로어레이는 생명정보학분야의 핵심기술 중 하나로 발전하였음에도 불구하고 마이크로어레이 실험으로 생성되는 데이터는 형태가 다양하고 매우 복잡한 형태를 갖기 때문에 데이터의 공유나 저장에서 많은 어려움을 겪는 것이 사실이다. 따라서 마이크로어레이 실험결과 분석을 위한 최소한의 컨텐츠가 정의되고 표준화 되었다. MIAME 데이터, MAGE-OM/ML과 같은 표준화된 공개 저장소는 전문 생물학 연구단체에게 과거부터 지금까지 주요 관심사가 되어왔다. 하지만 많은 공개저장소의 설립되었지만 마이크로어레이 데이터의 구조적 특징을 고려하여 효과적인 설계를 하지 않은 것이 사실이다. 본 논문은 표준을 따르는 동시에 마이크로어레이 데이터의 구조적 빈발 패턴이 반복되는 계층적 특징을 반영하는 전략을 제안한다. 이를 통하여 복잡한 데이터의 구조를 객체들의 빈발 패턴을 파악하여 그 계층을 줄임으로서 복잡도를 줄일 수 있었다. 이 과정에서 관계형 데이터베이스 기반의 공개저장소의 성능에 영향을 주는 관계 테이블(join-table)의 숫자는 줄어든다. 이에 따라, 성능은 개선된다. 이 전략을 통하여 생성된 테이블의 숫자는 원본 데이터를 단순 매핑시켜 저장하는 방법에 비하여 약 31%줄어든다. 결국 MAGE-ML 데이터의 저장과 로딩 시간은 이 논문에서 제시하는 전략을 적용하지 않은 방법에 비해 60%에서 65%를 줄일 수 있었다.