Literature DB >> 15846362

Standardizing global gene expression analysis between laboratories and across platforms.

Theodore Bammler, Richard P Beyer, Sanchita Bhattacharya, Gary A Boorman, Abee Boyles, Blair U Bradford, Roger E Bumgarner, Pierre R Bushel, Kabir Chaturvedi, Dongseok Choi, Michael L Cunningham, Shibing Deng, Holly K Dressman, Rickie D Fannin, Fredrico M Farin, Jonathan H Freedman, Rebecca C Fry, Angel Harper, Michael C Humble, Patrick Hurban, Terrance J Kavanagh, William K Kaufmann, Kathleen F Kerr, Li Jing, Jodi A Lapidus, Michael R Lasarev, Jianying Li, Yi-Ju Li, Edward K Lobenhofer, Xinfang Lu, Renae L Malek, Sean Milton, Srinivasa R Nagalla, Jean P O'malley, Valerie S Palmer, Patrick Pattee, Richard S Paules, Charles M Perou, Ken Phillips, Li-Xuan Qin, Yang Qiu, Sean D Quigley, Matthew Rodland, Ivan Rusyn, Leona D Samson, David A Schwartz, Yan Shi, Jung-Lim Shin, Stella O Sieber, Susan Slifer, Marcy C Speer, Peter S Spencer, Dean I Sproles, James A Swenberg, William A Suk, Robert C Sullivan, Ru Tian, Raymond W Tennant, Signe A Todd, Charles J Tucker, Bennett Van Houten, Brenda K Weis, Shirley Xuan, Helmut Zarbl.   

Abstract

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.

Mesh:

Year:  2005        PMID: 15846362     DOI: 10.1038/nmeth754

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  167 in total

1.  Influence of RNA labeling on expression profiling of microRNAs.

Authors:  John S Kaddis; Daniel H Wai; Jessica Bowers; Nicole Hartmann; Lukas Baeriswyl; Sheetal Bajaj; Michael J Anderson; Robert C Getts; Timothy J Triche
Journal:  J Mol Diagn       Date:  2011-11-07       Impact factor: 5.568

Review 2.  Standards affecting the consistency of gene expression arrays in clinical applications.

Authors:  Steven A Enkemann
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-03-23       Impact factor: 4.254

Review 3.  Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments.

Authors:  Yulan Liang; Arpad Kelemen
Journal:  Funct Integr Genomics       Date:  2005-11-15       Impact factor: 3.410

Review 4.  Reliability and reproducibility issues in DNA microarray measurements.

Authors:  Sorin Draghici; Purvesh Khatri; Aron C Eklund; Zoltan Szallasi
Journal:  Trends Genet       Date:  2005-12-27       Impact factor: 11.639

Review 5.  A review of statistical methods for expression quantitative trait loci mapping.

Authors:  Christina Kendziorski; Ping Wang
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

Review 6.  Bioinformatics and cancer: an essential alliance.

Authors:  Joaquín Dopazo
Journal:  Clin Transl Oncol       Date:  2006-06       Impact factor: 3.405

7.  In vitro transcription amplification and labeling methods contribute to the variability of gene expression profiling with DNA microarrays.

Authors:  Changqing Ma; Maureen Lyons-Weiler; Wenjing Liang; William LaFramboise; John R Gilbertson; Michael J Becich; Federico A Monzon
Journal:  J Mol Diagn       Date:  2006-05       Impact factor: 5.568

Review 8.  The end of the microarray Tower of Babel: will universal standards lead the way?

Authors:  Ernest S Kawasaki
Journal:  J Biomol Tech       Date:  2006-07

Review 9.  Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity.

Authors:  Alison Hege Harrill; Ivan Rusyn
Journal:  Expert Opin Drug Metab Toxicol       Date:  2008-11       Impact factor: 4.481

10.  Tumor prognostic factors and the challenge of developing predictive factors.

Authors:  Emma B Holliday; Erik P Sulman
Journal:  Curr Oncol Rep       Date:  2013-02       Impact factor: 5.075

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