NCBI GEO: archive for high-throughput functional genomic data

Tanya Barrett(National Center for Biotechnology Information), D. B. Troup(National Center for Biotechnology Information), S. E. Wilhite(National Institutes of Health), Pierre Ledoux(National Institutes of Health), Dmitry Rudnev(National Center for Biotechnology Information), C. Evangelista(National Institutes of Health), Irene F. Kim(National Center for Biotechnology Information), А. Г. Соболева(National Institutes of Health), Maxim Tomashevsky(National Center for Biotechnology Information), Kimberly A. Marshall(National Institutes of Health), Katherine Phillippy(National Center for Biotechnology Information), Philip M. Sherman(National Center for Biotechnology Information), R. N. Muertter(National Center for Biotechnology Information), Ron Edgar(National Center for Biotechnology Information)
Nucleic Acids Research
October 22, 2008
Cited by 1,048Open Access
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Abstract

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest public repository for high-throughput gene expression data. Additionally, GEO hosts other categories of high-throughput functional genomic data, including those that examine genome copy number variations, chromatin structure, methylation status and transcription factor binding. These data are generated by the research community using high-throughput technologies like microarrays and, more recently, next-generation sequencing. The database has a flexible infrastructure that can capture fully annotated raw and processed data, enabling compliance with major community-derived scientific reporting standards such as 'Minimum Information About a Microarray Experiment' (MIAME). In addition to serving as a centralized data storage hub, GEO offers many tools and features that allow users to effectively explore, analyze and download expression data from both gene-centric and experiment-centric perspectives. This article summarizes the GEO repository structure, content and operating procedures, as well as recently introduced data mining features. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.


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