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. 2013 Jan;41(Database issue):D991-5.
doi: 10.1093/nar/gks1193. Epub 2012 Nov 27.

NCBI GEO: archive for functional genomics data sets--update

Tanya Barrett  1 , Stephen E Wilhite, Pierre Ledoux, Carlos Evangelista, Irene F Kim, Maxim Tomashevsky, Kimberly A Marshall, Katherine H Phillippy, Patti M Sherman, Michelle Holko, Andrey Yefanov, Hyeseung Lee, Naigong Zhang, Cynthia L Robertson, Nadezhda Serova, Sean Davis, Alexandra Soboleva
Affiliations

NCBI GEO: archive for functional genomics data sets--update

Tanya Barrett et al. Nucleic Acids Res. 2013 Jan.

Abstract

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.

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Figures

Figure 1.
Figure 1.
Distribution of the number and types of selected studies released by GEO each year since inception. Users can explore and download historical submission numbers using the ‘history’ page at http://www.ncbi.nlm.nih.gov/geo/summary/?type=history, as well as constructing GEO DataSet database queries for specific data types and date ranges using the ‘DataSet type’ and ‘publication date’ fields as described at http://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html.
Figure 2.
Figure 2.
GEO2R screenshots. After selecting ‘analyse with GEO2R’ on series record GSE18388 (19), the user is presented with a table of the samples in that study and their descriptions (Panel 1). In this case, two sample groups are defined, and four samples are assigned to each group. The user can view the distribution of the sample values using the boxplot feature (Panel 2) and click the ‘Top250’ button to retrieve a table of the top 250 differentially expressed genes with statistics and gene annotation (Panel 3). The top hit is clicked to reveal the expression profile chart for that gene.
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