Standard-based Integration of Heterogeneous Large-scale DNA Microarray Data for Improving Reusability.

Jung,  Yong; Seo,  Hwa Jeong; Park,  Yu Rang; Kim,  Jihun; Bien,  Sang Jay; Kim,  Ju Han

doi:2011.9.1.19

Genomics Inform > Volume 9(1); 2011 > Article

Original Article

Genomics & Informatics 2011;9(1): 19-27.

DOI: https://doi.org/10.5808/gi.2011.9.1.019

Standard-based Integration of Heterogeneous Large-scale DNA Microarray Data for Improving Reusability.

Yong Jung, Hwa Jeong Seo, Yu Rang Park, Jihun Kim, Sang Jay Bien, Ju Han Kim

¹Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Korea. juhan@snu.ac.kr
²Systems Biomedical Informatics Research Center, Seoul National University College of Medicine, Seoul 110-799, Korea.
³Institute of Endemic Diseases, Seoul National University College of Medicine, Seoul 110-799, Korea.
⁴Medical Informatics, Graduate School of Public Health, Gachon University of Medicine and Science, Incheon 405-760, Korea.

Abstract

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/

Keywords: gene expression data; data integration; classification