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      The Gene Expression Omnibus Database.

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          Abstract

          The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.

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          Author and article information

          Journal
          Methods Mol. Biol.
          Methods in molecular biology (Clifton, N.J.)
          1940-6029
          1064-3745
          2016
          : 1418
          Affiliations
          [1 ] National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, MSC 6510, Building 45, Room AS13B, Bethesda, MD, 20892-6510, USA.
          [2 ] National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, MSC 6510, Building 45, Room AS13B, Bethesda, MD, 20892-6510, USA. barrett@ncbi.nlm.nih.gov.
          Article
          NIHMS801110
          10.1007/978-1-4939-3578-9_5
          4944384
          27008011
          c0027181-53f8-4419-ad9f-e3a771fc39a0
          History

          Data mining,Database,Functional genomics,Gene expression,High-throughput sequencing,Microarray

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