A gene expression/molecular abundance repository and a curated, online resource for gene expression data
Tutorial and training materials by OpenHelix
|Learn to use the Gene Expression Omnibus, or GEO, which is a valuable resource designed to store high-throughput gene expression and molecular abundance data. GEO acts as a repository for the data, and provides interfaces to search, retrieve, and display a wealth of information about genes in many species. This includes microarray data and many other high-throughput techniques. GEO is one of the many useful resources supported by the National Center for Biotechnology Information, or NCBI.|
- efficient ways to query GEO for specific genes or experimental designs
- how to navigate through GEO output displays to find the specific information you want
- how to navigate GEO
Recent BioMed Central research articles citing this resource
Silverman M Ian et al., RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome. Genome Biology (2014) doi:10.1186/gb-2014-15-1-r3
Wada Yusaku et al., Development of detection method for novel fusion gene using GeneChip exon array -No section-. Journal of Clinical Bioinformatics (2014) doi:10.1186/2043-9113-4-3
Barøy Tale et al., Reexpression of LSAMP inhibits tumor growth in a preclinical osteosarcoma model. Molecular Cancer (2014) doi:10.1186/1476-4598-13-93
Kupfer Peter et al., Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients Bioinformatic and algorithmical studies. BMC Medical Genomics (2014) doi:10.1186/1755-8794-7-40
Pawar Shrikant et al., KIFCI, a novel putative prognostic biomarker for ovarian adenocarcinomas: delineating protein interaction networks and signaling circuitries. Journal of Ovarian Research (2014) doi:10.1186/1757-2215-7-53
More about the resource:
GEO can be browsed or queried in several ways, including basic searches, advanced searches, and using nucleotide sequences to begin a search. GEO contains information about platforms, data series, samples, and more. Analysis tools including clustering features are available. Learning to mine the GEO data will provide the researcher with copious amounts of information about their species, tissues, or genes of interest.
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