Glia Open Access Database (GOAD): A comprehensive gene expression encyclopedia of glia cells in health and disease. Issue 9 (25th March 2015)

• Record Type: Journal Article
• Authors: Holtman, Inge R.; Noback, Michiel; Bijlsma, Marieke; Duong, Kim N.; van der Geest, Marije A.; Ketelaars, Peer T.; Brouwer, Nieske; Vainchtein, Ilia D.; Eggen, Bart J. L.; Boddeke, Hendrikus W. G. M.
• Is Part Of: Glia. Volume 63:Issue 9(2015:Sep.)
• Page Start: 1495
• Page End: 1506
• Publication Date: 2015-03-25
• Subjects: Neuroglia -- Periodicals; Neurology -- Periodicals; 611.0188
• DOI: 10.1002/glia.22810
• Languages: English
• ISSNs: 0894-1491

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Recently, the number of genome‐wide transcriptome profiles of pure populations of glia cells has drastically increased, resulting in an unprecedented amount of data that offer opportunities to study glia phenotypes and functions in health and disease. To make genome‐wide transcriptome data easily accessible, we developed the Glia Open Access Database (GOAD), available via <ext-link ext-link-type="uri" xlink:href="http://www.goad.edu" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">www.goad.edu</ext-link><italic>cation</italic>. GOAD contains a collection of previously published and unpublished transcriptome data, including datasets from isolated microglia, astrocytes and oligodendrocytes both at homeostatic and pathological conditions. It contains an intuitive web‐based interface that consists of three features that enable searching, browsing, analyzing, and downloading of the data. The first feature is differential gene expression (DE) analysis that provides genes that are significantly up and down‐regulated with the associated fold changes and <italic>p</italic>‐values between two conditions of interest. In addition, an interactive Venn diagram is generated to illustrate the overlap and differences between several DE gene lists. The second feature is quantitative gene expression (QE) analysis, to investigate which genes are expressed in a particular glial cell type and to what degree. The third feature is a search utility, which can be used to find a gene of interest and depict its expression in all available expression data sets by generating a gene card. In addition, quality guidelines and relevant concepts for transcriptome analysis are discussed. Finally, GOAD is discussed in relation to several online transcriptome tools developed in neuroscience and immunology. In conclusion, GOAD is a unique platform to facilitate integration of bioinformatics in glia biology. GLIA 2015;63:1495–1506</p> </abstract>