Literature DB >> 14645736

A comparison of oligonucleotide and cDNA-based microarray systems.

Nancy Mah1, Anders Thelin, Tim Lu, Susanna Nikolaus, Tanja Kühbacher, Yesim Gurbuz, Holger Eickhoff, Günther Klöppel, Hans Lehrach, Björn Mellgård, Christine M Costello, Stefan Schreiber.   

Abstract

Large-scale public data mining will become more common as public release of microarray data sets becomes a corequisite for publication. Therefore, there is an urgent need to clarify whether data from different microarray platforms are comparable. To assess the compatibility of microarray data, results were compared from the two main types of high-throughput microarray expression technologies, namely, an oligonucleotide-based and a cDNA-based platform, using RNA obtained from complex tissue (human colonic mucosa) of five individuals. From 715 sequence-verified genes represented on both platforms, 64% of the genes matched in "present" or "absent" calls made by both platforms. Calls were influenced by spurious signals caused by Alu repeats in cDNA clones, clone annotation errors, or matched probes that were designed to different regions of the gene; however, these factors could not completely account for the level of call discordance observed. Expression levels in sequence-verified, platform-overlapping genes were not related, as demonstrated by weakly positive rank order correlation. This study demonstrates that there is only moderate overlap in the results from the two array systems. This fact should be carefully considered when performing large-scale analyses on data originating from different microarray platforms.

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Year:  2004        PMID: 14645736     DOI: 10.1152/physiolgenomics.00080.2003

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


  47 in total

1.  An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies.

Authors:  Lingyan Ruan; Ming Yuan
Journal:  Biometrics       Date:  2011-04-22       Impact factor: 2.571

2.  Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome.

Authors:  Olof Emanuelsson; Ugrappa Nagalakshmi; Deyou Zheng; Joel S Rozowsky; Alexander E Urban; Jiang Du; Zheng Lian; Viktor Stolc; Sherman Weissman; Michael Snyder; Mark B Gerstein
Journal:  Genome Res       Date:  2006-11-21       Impact factor: 9.043

3.  A Bayesian mixture model for metaanalysis of microarray studies.

Authors:  Erin M Conlon
Journal:  Funct Integr Genomics       Date:  2007-09-19       Impact factor: 3.410

4.  Members of the glutathione and ABC-transporter families are associated with clinical outcome in patients with diffuse large B-cell lymphoma.

Authors:  Charalambos Andreadis; Phyllis A Gimotty; Peter Wahl; Rachel Hammond; Jane Houldsworth; Stephen J Schuster; Timothy R Rebbeck
Journal:  Blood       Date:  2006-12-19       Impact factor: 22.113

5.  An integrated cross-platform prognosis study on neuroblastoma patients.

Authors:  Qing-Rong Chen; Young K Song; Jun S Wei; Sven Bilke; Shahab Asgharzadeh; Robert C Seeger; Javed Khan
Journal:  Genomics       Date:  2008-07-30       Impact factor: 5.736

Review 6.  Detection call algorithms for high-throughput gene expression microarray data.

Authors:  Kellie J Archer; Sarah E Reese
Journal:  Brief Bioinform       Date:  2009-11-25       Impact factor: 11.622

7.  Comprehensive comparison of six microarray technologies.

Authors:  Carole L Yauk; M Lynn Berndt; Andrew Williams; George R Douglas
Journal:  Nucleic Acids Res       Date:  2004-08-27       Impact factor: 16.971

Review 8.  Review of the literature examining the correlation among DNA microarray technologies.

Authors:  Carole L Yauk; M Lynn Berndt
Journal:  Environ Mol Mutagen       Date:  2007-06       Impact factor: 3.216

9.  Comparative Membranome expression analysis in primary tumors and derived cell lines.

Authors:  Paolo Uva; Armin Lahm; Andrea Sbardellati; Anita Grigoriadis; Andrew Tutt; Emanuele de Rinaldis
Journal:  PLoS One       Date:  2010-07-23       Impact factor: 3.240

10.  Statistical methods for gene set co-expression analysis.

Authors:  YounJeong Choi; Christina Kendziorski
Journal:  Bioinformatics       Date:  2009-08-18       Impact factor: 6.937

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