The Gene Expression Omnibus (GEO) serves as a large public repository for functional genomics data. It is maintained by the National Center for Biotechnology Information (NCBI), part of the U.S. National Library of Medicine. GEO’s purpose is to collect and freely distribute high-throughput gene expression and other functional genomics datasets submitted by researchers globally. It provides a centralized location for scientists to deposit and access experimental results, fostering data sharing.
Types of Data in the Omnibus
While its name suggests a focus on gene expression, GEO has expanded to encompass a broad spectrum of high-throughput functional genomics data. This includes measurements from thousands of genes or molecular elements simultaneously. A significant portion of the data originates from microarray experiments, which measure the activity levels of genes using small chips.
Another major data type found in GEO is Next-Generation Sequencing (NGS) data, such as RNA-Seq. RNA-Seq captures all RNA molecules in a cell or tissue, offering insights into gene activity. The repository also stores epigenomic data, including DNA methylation analysis, which examines chemical modifications to DNA, and ChIP-Seq data, used to study interactions between proteins and DNA.
Understanding GEO’s Data Structure
GEO organizes its collection of data into a hierarchical structure, which helps in managing and navigating the diverse information. This organization is built around three core record types, each with a unique accession number. These distinct identifiers allow researchers to pinpoint specific components of an experiment.
The Platform record, identified by a GPL accession number (e.g., GPLxxx), describes the technology used for the experiment. This details the specific microarray chip, sequencing machine, or other assay components used. A single Platform can be referenced by many experiments submitted by various researchers.
The Sample record, marked by a GSM accession number (e.g., GSMxxx), contains the molecular abundance data from a single biological specimen. This represents the raw or processed measurements derived from one tissue sample, cell line, or patient sample. Each Sample record refers to a single Platform on which its data was generated.
Finally, the Series record, identified by a GSE accession number (e.g., GSExxx), represents a complete experiment or study. This record groups related Sample (GSM) records and the Platform (GPL) used, providing an overview of the research project. One can think of a Series as a cookbook, where each recipe is a Sample, and the list of required kitchen appliances is the Platform.
Navigating and Analyzing GEO Datasets
Finding and utilizing data within GEO involves a straightforward process, beginning with searching the database. Users can search the GEO website using keywords, such as a specific disease name, a gene symbol, or a researcher’s name, to locate relevant studies. The search results typically display a list of Series (GSE records) that match the query, allowing users to identify experiments of interest.
Once a relevant Series is identified, users can access its detailed page to view associated samples and platforms. For basic data analysis, GEO offers a built-in web-based tool called GEO2R. This tool allows users to perform differential expression analysis directly on the website, comparing gene activity levels between two or more groups of samples within a Series.
GEO2R uses R packages like `GEOquery`, `limma` (for microarray), and `DESeq2` (for RNA-seq) to identify genes with significant expression differences between groups. This functionality allows researchers to conduct preliminary analyses without needing to download large datasets or possess advanced programming skills. The results are presented as a table of genes ranked by statistical significance, along with graphical plots for visualization.
Impact on Biomedical Research
GEO contributes to biomedical research by making functional genomics data publicly available. This open access facilitates meta-analysis, where scientists combine data from multiple independent studies to increase statistical power and uncover more robust patterns or biomarkers. Such aggregated analysis can reveal insights not apparent from individual studies.
GEO also promotes reproducibility and transparency in scientific research. By providing access to the original raw and processed data underlying published findings, it allows other researchers to verify results or conduct new analyses. This ability to re-examine data supports scientific conclusions and encourages validation.
The repository further aids in hypothesis generation, enabling researchers to explore existing datasets to formulate new research questions or identify potential therapeutic targets. This exploration of previously collected data can lead to novel discoveries and inform the design of future experiments, often at a lower cost than generating entirely new data. The information within GEO accelerates discovery across various fields of biology and medicine.