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. 2009 Jan;37(Database issue):D885-90.
doi: 10.1093/nar/gkn764. Epub 2008 Oct 21.

NCBI GEO: archive for high-throughput functional genomic data

Tanya Barrett  1 , Dennis B Troup, Stephen E Wilhite, Pierre Ledoux, Dmitry Rudnev, Carlos Evangelista, Irene F Kim, Alexandra Soboleva, Maxim Tomashevsky, Kimberly A Marshall, Katherine H Phillippy, Patti M Sherman, Rolf N Muertter, Ron Edgar
Affiliations

NCBI GEO: archive for high-throughput functional genomic data

Tanya Barrett et al. Nucleic Acids Res. 2009 Jan.

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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Figures

Figure 1.
Figure 1.
A selection of GEO screenshots. The DataSet Browser (A) enables simple keyword searches for DataSets. When a DataSet is selected, a window appears (B) which contains detailed information about that DataSet, download options, and links to analysis features including gene expression profiles (C). Each expression profile can be viewed in more detail to see the activity of that gene across all Samples in the DataSet (D).
Figure 2.
Figure 2.
A schematic overview of query workflow, and how various features and tools are interlinked. A description of the location and purpose of many of these features is provided in Table 2.
See this image and copyright information in PMC

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