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A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies
Authors:Kuo Winston Patrick  Liu Fang  Trimarchi Jeff  Punzo Claudio  Lombardi Michael  Sarang Jasjit  Whipple Mark E  Maysuria Malini  Serikawa Kyle  Lee Sun Young  McCrann Donald  Kang Jason  Shearstone Jeffrey R  Burke Jocelyn  Park Daniel J  Wang Xiaowei  Rector Trent L  Ricciardi-Castagnoli Paola  Perrin Steven  Choi Sangdun  Bumgarner Roger  Kim Ju Han  Short Glenn F  Freeman Mason W  Seed Brian  Jensen Roderick  Church George M  Hovig Eivind  Cepko Connie L  Park Peter  Ohno-Machado Lucila  Jenssen Tor-Kristian
Affiliation:Department of Developmental Biology, Harvard School of Dental Medicine, 188 Longwood Ave., Boston, Massachusetts 02115, USA. wkuo@genetics.med.harvard.edu
Abstract:Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.
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