Scope

The Gene Expression Omnibus (GEO) is an international public repository that archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomic data sets (1). Approximately 90% of the data in GEO are gene expression studies that investigate a broad range of biological themes including disease, development, evolution, immunity, ecology, toxicology, metabolism, and more. The non-expression data in GEO represent other categories of functional genomic and epigenomic studies including those that examine genome methylation, chromatin structure, genome copy number variations, and genome–protein interactions. A breakdown of GEO data types and technologies is provided on the repository Summary page.

Data in GEO represent original research submitted by the scientific community in compliance with grant or journal provisos that require data to be made available in a public repository, the objective being to facilitate independent evaluation of results, reanalysis, and full access to all parts of the study. The resource supports archiving of all parts of a study including raw data files, processed data, and descriptive metadata, which are indexed, cross-linked, and searchable. While the principal role of GEO is to serve as a primary data archive, the resource also offers several tools and features that allow users to explore, analyze, and visualize expression data from both gene-centric and study-centric perspectives.

To summarize, the main goals of GEO are to:

• Provide a robust, versatile primary data archive database in which to efficiently store a wide variety of high-throughput functional genomic data sets.
• Offer simple submission procedures and formats that support complete and well-annotated data deposits from the research community.
• Provide user-friendly mechanisms that allow users to locate, review, and download studies and gene expression profiles of interest.

History

The post-genomic era has led to a multitude of high-throughput methodologies that generate massive volumes of gene expression and other types of functional genomic and epigenomic data. The GEO database was established in 2000 to archive the burgeoning volumes of microarray gene expression data beginning to be produced by the research community at that time (2). Furthermore, as microarrays became used routinely in almost every area of biological research, many journals adopted the requirement that the microarray data discussed in manuscripts should be deposited in a public repository so that anyone could freely access and critically evaluate the data. Today, GEO archives data for approximately 40,000 studies comprising a million samples, for over 2200 organisms, submitted by 15,000 laboratories from around the world. Users can track database growth on the GEO History page.

Since its inception, many aspects of the GEO database and operating procedures have undergone major revisions and development, including: increasingly stringent submission requirements, concomitant with developing community standards like MIAME (Minimum Information About a Microarray Experiment) (3); enhanced submission formats that ease the burden on submitters and promote well-annotated MIAME-compliant data; improved indexing and analysis tools that help users more easily locate information relevant to their interests; and database modifications to support evolving data types, including next-generation sequence data.

Data Model

The GEO database archives a wide variety of rapidly evolving, large-scale functional genomic experiment types. These studies generate data of many different file types, formats, and content that consequently present considerable challenges in terms of data handling and querying. The core GEO database has built-in flexibility to accommodate diverse data types. Notably, tabular data are not fully granulated in the core database. Rather, they are stored as plain text, tab-delimited tables that have no restrictions on the number of rows or columns allowed. However, some columns reserve special meaning, and data from these are extracted to secondary resources, including the GEO Profiles database, and used in downstream query and analysis applications such as GEO2R. Accompanying raw data files are stored on an FTP server and linked from each record. Contextual biological descriptions, protocols, references, and other metadata are stored in designated fields within a relational MSSQL database.

An outline of the GEO data structure is presented in Figure 1. The data are organized into the following entities:

Figure 1.

Sketch of GEO data organization. Platform, Sample, and Series records are created from the data supplied by submitters. These records contain: (A) a description of the array or sequencer; (B) a tab-delimited table of the array template definition; (C) (more...)

Dataflow

Researchers typically initiate a data deposit to GEO before a manuscript describing the study has been submitted to a journal for review. Researchers use their MyNCBI account to login and register submissions. Several submission formats are supported including spreadsheets and XML, see the full submission instructions.

All deposits undergo syntactic validation as well as review by a GEO curator to ensure that data are organized correctly and contain sufficient information to interpret the study. If content or structural problems are identified, the curator works with the submitter until the issue is resolved. Once the data pass review, stable GEO accession numbers are assigned and can be cited in the manuscript. Researchers usually keep their GEO records private until the corresponding manuscript is published. During this period, researchers have the option to generate a reviewer URL that grants anonymous, confidential access to their private data, which can be provided to journal editors for review purposes.

Upon release, Platform, Sample, and Series records are indexed in the Entrez GEO DataSets database where users can query and download the data, or perform a gene expression analysis using the GEO2R comparison tool. Some components of GEO submissions are brokered to other NCBI databases, including original next generation sequence reads to SRA and study descriptions to BioProject, with reciprocal links back to GEO as appropriate.

At approximately monthly intervals, selected Series undergo further processing by curators to create GEO DataSet and GEO Profile records. GEO DataSet records represent curated collections of biologically and statistically comparable GEO Samples, and are indexed in the Entrez GEO DataSets database. GEO Profiles are derived from GEO DataSets and depict expression measurements for an individual gene across all Samples in that DataSet. Profiles are indexed in the Entrez GEO Profiles database.

Access

GEO has a suite of tools that allow users to browse, download, query, analyze, and visualize data relevant to their specific interests.

Query

NCBI has a powerful search and retrieval system called Entrez that can be used to search the content of its network of integrated databases. GEO data are available in two separate Entrez databases referred to as Entrez GEO DataSets and Entrez GEO Profiles. A typical workflow is for the user to first identify studies of interest by querying Entrez GEO DataSets, and then use either GEO2R or GEO Profiles to identify specific genes or gene expression patterns within that study. Alternatively, the user can query the Entrez GEO Profiles database directly to retrieve the expression patterns of a specific gene across all curated GEO DataSets. A rich complement of Entrez links is generated to connect data to related information: inter-database links reciprocally connect GEO to other NCBI resources such as PubMed, GenBank, and Gene; intra-database links connect genes related by expression pattern, chromosomal position, or sequence. Both databases are extensively indexed under many separate fields, meaning that users can refine their searches by constructing fielded queries. The Faceted search tool, located on the left side of retrievals, can help users filter and refine their Entrez results, and the Advanced search tools enable generation of complex multipart queries or combinations of multiple queries to find common intersections in search results.

Query Entrez GEO DataSets

This database stores descriptions of the original submitter-supplied Platform, Sample, and Series records as well as curated DataSets. The Entrez GEO DataSets database can be searched using many different attributes including keywords, organism, study type, and authors. More information about how the results are displayed and supported query fields is provided at About GEO DataSets and Querying GEO DataSets pages. Example queries include:

View in own window

Retrieve studies that investigate the effect of smoking or diet on non-human mammals

(smok* OR diet) AND (mammals[organism] NOT human[organism])

Search for studies that examine gene expression using next-generation sequencing

"expression profiling by high throughput sequencing"[DataSet Type]

Retrieve submissions that have Affymetrix CEL files

cel[Supplementary Files]

Search for curated DataSets that have 'age' as an experimental variable

age[Subset Variable Type]

Retrieve studies that consist of between 100 and 500 samples

100:500[Number of Samples]

Retrieve studies that include ‘Smith, A.’ as an author

smith a[Author]

Query Entrez GEO Profiles

This database stores gene expression profiles derived from curated GEO DataSets. Each Profile is presented as a chart that displays the expression level of one gene across all Samples within a DataSet. Experimental context is provided in the bars along the bottom of the charts making it possible to see at a glance whether a gene is differentially expressed across different experimental conditions. The Entrez GEO Profiles database can be searched using many different attributes including keywords, gene symbols, gene names, GenBank accession numbers, or Profiles flagged as being differentially expressed. More information about how the results are displayed and supprted query fields is provided at About GEO Profiles and Querying GEO Profiles pages. Example queries include:

View in own window

Retrieve all gene expression profiles for CYP1A1

CYP1A1[Gene Symbol]

Retrieve gene expression profiles of CYP1A1 or ME1 in DataSets that investigate the effects of smoking or diet

(CYP1A1[Gene Symbol] OR ME1[Gene Symbol]) AND (smok* OR diet)

Retrieve gene expression profiles for all kinases in the DataSet with accession number GDS182

kinase[Gene Description] AND GDS182

Retrieve all gene expression profiles for genes that have the Gene Ontology(GO) term ‘apoptosis’ in the DataSet with accession number GDS182

apoptosis[Gene Ontology] AND GDS182

Retrieve gene expression profiles for genes that lie within base range 10000:3000000 on chromosome 8 in mouse

(8[Chromosome] AND 10000:3000000[Base Position]) AND mouse[organism]

Retrieve genes that exhibit differential expression in DataSets that examine the effect of an agent

agent[Flag Information] AND "value subset effect"[Flag Type]

GEO BLAST

Another way to query the GEO Profiles database is by nucleotide sequence similarity using GEO BLAST. The GEO BLAST database contains all GenBank sequences represented on microarray Platforms that participate in curated DataSets. This BLAST interface performs a standard BLAST sequence search and retrievals point to corresponding gene expression profiles in the GEO Profiles database. This interface is helpful in identifying gene expression information for sequence homologs of interest, e.g., related gene family members or for cross-species comparisons.

Figure 2.

A selection of GEO DataSet and Profile screenshots. The DataSet Browser (A) enables simple keyword searches for curated GEO DataSets. When a DataSet is selected, a window appears (B) that contains detailed information about that DataSet, download options, and links to analysis features including gene expression profiles in Entrez GEO Profiles (C). Each expression profile chart can be viewed in more detail to see the activity of that gene across all Samples in that DataSet (D).

References

1.

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013Jan 41Database issueD991–5. [PMC free article: PMC3531084] [PubMed: 23193258]

2.

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002Jan 1 30(1):207–10. [PMC free article: PMC99122] [PubMed: 11752295]

3.

Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001Dec 29(4):365–71. [PubMed: 11726920]

4.

Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007Jul 15 23(14):1846–7. [PubMed: 17496320]

5.

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5(10):R80. [PMC free article: PMC545600] [PubMed: 15461798]