NCBI GEO: mining tens of millions of expression profiles—database and tools update

By Tanya Barrett, Dennis B. Troup, Stephen E. Wilhite, Pierre Ledoux, D. Rudnev, Carlos Evangelista, Irene F. Kim, Alexandra Soboleva, Maxim Tomashevsky, Ron Edgar. 2006

1,574 citations recorded by the source corpus.

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely disseminates microarray and other forms of high-throughput data generated by the scientific community. The database has a minimum information about a microarray experiment (MIAME)-compliant infrastructure that captures fully annotated raw and processed data. Several data deposit options and formats are supported, including web forms, spreadsheets, XML and Simple Omnibus Format in Text (SOFT). In addition to data storage, a collection of user-friendly web-based interfaces and applications are available to help users effectively explore, visualize and download the thousands of experiments and tens of millions of gene expression patterns stored in GEO. This paper provides a summary of the GEO database structure and user facilities, and describes recent enhancements to database design, performance, submission format options, data query and retrieval utilities. GEO is accessible at

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Fused Lasso Additive Model (2014, methodology): “Autism [Alter et al., 2011]: 1498 gene expression measurements from peripheral blood lymphocytes sampled from 82 children with autism and 60 controls, publicly available from GEO at accession number GDS4431 [Barrett et al., 2007].”
Clustering cancer gene expression data: a comparative study (2008, methodology): “Hierarchical clustering is also available in standard gene expression databases, such as the Gene Expression Omnibus [23].”
g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments (2007, methodology): “Motivated by user requests for gene-expression-similarity-based analysis, we developed the g:Sorter tool, which allows quick search for similarly or oppositely expressed genes from public data sets downloaded from the GEO database (17).”
Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets (2010, methodology): “We then collected the mRNA expression arrays associated with each disease from the Gene Expression Omnibus (GEO) [11].”
RBPDB: a database of RNA-binding specificities (2010, methodology): “When we were able to recover bound sequences, we included a short README ﬁle to describe how the sequences were extracted from supplementary data or GEO (Gene Expression Omnibus) (32).”
Machine learning applications in cancer prognosis and prediction (2014, background): “This study reveals that SSL can be an alternative to supervised approaches which usually suffers from small labeled samples.”
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies (2010, methodology): “The yeast expression dataset [2] (GEO [23] accession GSE1990 with genotypes provided by authors) contained 7084 expression measurements and 2925 genotyped loci in 112 crosses of segregating yeast strains.”
Selected reaction monitoring for quantitative proteomics: a tutorial (2008, background): “Gene expression GEO http://www.ncbi.nlm.nih.gov/geo/ Barrett et al (2007) Protein expression ProteinAtlas http://www.proteinatlas.org/ Uhlen and Ponten (2005) Gene ontology groups GO http://www.geneontology.org/ Ashburner et al (2000) Functional groups KEGG http://www.genome.jp/kegg/ Kanehisa and ”
Database resources of the National Center for Biotechnology Information (2006, background): “In addition to gene expression data, GEO accepts elements from other categories of experiments including studies of genome copy number variation [with optional joint submission to the Database of Genomic Structural Variation (dbVar)], genome-protein interaction surveys and methylation profiling stud”
NetPath: a public resource of curated signal transduction pathways (2010, methodology): “As a proof of principle, we selected publicly available mRNA expression datasets from Gene Expression Omnibus (GEO), a repository for gene expression data [49].”
Characterization of the CLEAR network reveals an integrated control of cellular clearance pathways. (2011, background): “To investigate the transcriptional behavior of TFEB candidate target genes, we analyzed a vast set of published microarray experiments available at the Gene Expression Omnibus (GEO) database (22).”
Human Disease-Drug Network Based on Genomic Expression Profiles (2009, methodology): “A GEO DataSet (GDS) represents a collection of biologically- and statistically-comparable samples processed using the same platform [17].”
ENCODE whole-genome data in the UCSC Genome Browser: update 2012 (2011, background): “ENCODE GEO submissions are listed on the GEO ENCODE summary page, http://www.ncbi.nlm.nih.gov/ geo/info/ENCODE.html.”