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Nucleic Acids Res. 2014 Jan;42(Database issue):D938-43. doi: 10.1093/nar/gkt1204. Epub 2013 Nov 22.

The Gene Expression Barcode 3.0: improved data processing and mining tools.

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Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA, Informatics and Systems Development, Loyola University Stritch School of Medicine, Maywood, IL 60153, USA, Nutrition, Metabolism & Genomics Group, Division of Human Nutrition, Wageningen University, The Netherlands, Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA and Center for Biomedical Informatics, Department of Pharmacology and Molecular Therapeutics, Loyola University Stritch School of Medicine, Maywood, IL 60153, USA.


The Gene Expression Barcode project,, seeks to determine the genes expressed for every tissue and cell type in humans and mice. Understanding the absolute expression of genes across tissues and cell types has applications in basic cell biology, hypothesis generation for gene function and clinical predictions using gene expression signatures. In its current version, this project uses the abundant publicly available microarray data sets combined with a suite of single-array preprocessing, quality control and analysis methods. In this article, we present the improvements that have been made since the previous version of the Gene Expression Barcode in 2011. These include a variety of new data mining tools and summaries, estimated transcriptomes and curated annotations.

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