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GEE: An Informatics Tool for Gene Expression Data Explore / 대한의료정보학회지
Article in En | WPRIM | ID: wpr-168209
Responsible library: WPRO
ABSTRACT
OBJECTIVES: Major public high-throughput functional genomic data repositories, including the Gene Expression Omnibus (GEO) and ArrayExpress have rapidly expanded. As a result, a large number of diverse high-throughput functional genomic data retrieval systems have been developed. However, high-throughput functional genomic data retrieval remains challenging. METHODS: We developed Gene Expression data Explore (GEE), the first powerful, flexible web and mobile search application for searching whole-genome epigenetic data and microarray data in public databases, such as GEO and ArrayExpress. RESULTS: GEE provides an elaborate, convenient interface of query generation competences not available via various high-throughput functional genomic data retrieval systems, including GEO, ArrayExpress, and Atlas. In particular, GEE provides a suitable query generator using eVOC, the Experimental Factor Ontology (EFO), which is well represented with a variety of high-throughput functional genomic data experimental conditions. In addition, GEE provides an experimental design query constructor (EDQC), which provides elaborate retrieval filter conditions when the user designs real experiments. CONCLUSIONS: The web version of GEE is available at http://www.snubi.org/software/gee, and its app version is available from the Apple App Store.
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Full text: 1 Index: WPRIM Main subject: Research Design / Base Sequence / Gene Expression / Information Storage and Retrieval / Microarray Analysis / Informatics / Search Engine / Epigenomics / Mobile Applications Language: En Journal: Healthcare Informatics Research Year: 2016 Type: Article
Full text: 1 Index: WPRIM Main subject: Research Design / Base Sequence / Gene Expression / Information Storage and Retrieval / Microarray Analysis / Informatics / Search Engine / Epigenomics / Mobile Applications Language: En Journal: Healthcare Informatics Research Year: 2016 Type: Article