Comparison of the Latest Commercial Short and Long Oligonucleotide Microarray Technologies

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2006 Mar 17

PMID 16539734

Citations 18

Authors

Aurelien De Reynies

Daniela Geromin

Jean-Michel Cayuela

Fabien Petel

Philippe Dessen

Francois Sigaux

David S Rickman

Affiliations

Soon will be listed here.

Abstract

Background: We compared the relative precision and accuracy of expression measurements obtained from three different state-of-the-art commercial short and long-oligonucleotide microarray platforms (Affymetrix GeneChip, GE Healthcare CodeLink and Agilent Technologies). The design of the comparison was chosen to judge each platform in the context of a multi-project program.

Results: All wet-lab experiments and raw data acquisitions were performed independently by each commercial platform. Intra-platform reproducibility was assessed using measurements from all available targets. Inter-platform comparisons of relative signal intensities were based on a common and non-redundant set of roughly 3,400 targets chosen for their unique correspondence toward a single transcript. Despite many examples of strong similarities we found several areas of discrepancy between the different platforms.

Conclusion: We found a higher level of reproducibility from one-color based microarrays (Affymetrix and CodeLink) compared to the two-color arrays from Agilent. Overall, Affymetrix data had a slightly higher level of concordance with sample-matched real-time quantitative reverse-transcriptase polymerase chain reaction (QRT-PCR) data particularly for detecting small changes in gene expression levels.

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References

Barlund M, Forozan F, Kononen J, Bubendorf L, Chen Y, Bittner M . Detecting activation of ribosomal protein S6 kinase by complementary DNA and tissue microarray analysis. J Natl Cancer Inst. 2000; 92(15):1252-9. DOI: 10.1093/jnci/92.15.1252. View

Dallas P, Gottardo N, Firth M, Beesley A, Hoffmann K, Terry P . Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR -- how well do they correlate?. BMC Genomics. 2005; 6:59. PMC: 1142514. DOI: 10.1186/1471-2164-6-59. View

Ramdas L, Coombes K, Baggerly K, Abruzzo L, Highsmith W, Krogmann T . Sources of nonlinearity in cDNA microarray expression measurements. Genome Biol. 2001; 2(11):RESEARCH0047. PMC: 60308. DOI: 10.1186/gb-2001-2-11-research0047. View

Yang Y, Dudoit S, Luu P, Lin D, Peng V, Ngai J . Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002; 30(4):e15. PMC: 100354. DOI: 10.1093/nar/30.4.e15. View

Kuo W, Jenssen T, Butte A, Ohno-Machado L, Kohane I . Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002; 18(3):405-12. DOI: 10.1093/bioinformatics/18.3.405. View

Maass N, Rosel F, Schem C, Hitomi J, Jonat W, Nagasaki K . Amplification of the BCAS2 gene at chromosome 1p13.3-21 in human primary breast cancer. Cancer Lett. 2002; 185(2):219-23. DOI: 10.1016/s0304-3835(02)00286-0. View

Li J, Pankratz M, Johnson J . Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays. Toxicol Sci. 2002; 69(2):383-90. DOI: 10.1093/toxsci/69.2.383. View

Ball C, Sherlock G, Parkinson H, Rocca-Sera P, Brooksbank C, Causton H . Standards for microarray data. Science. 2002; 298(5593):539. DOI: 10.1126/science.298.5593.539b. View

Churchill G . Fundamentals of experimental design for cDNA microarrays. Nat Genet. 2002; 32 Suppl:490-5. DOI: 10.1038/ng1031. View

10.

Bolstad B, Irizarry R, Astrand M, Speed T . A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003; 19(2):185-93. DOI: 10.1093/bioinformatics/19.2.185. View

11.

Wright G, Tan B, Rosenwald A, Hurt E, Wiestner A, Staudt L . A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A. 2003; 100(17):9991-6. PMC: 187912. DOI: 10.1073/pnas.1732008100. View

12.

Tan P, Downey T, Spitznagel Jr E, Xu P, Fu D, Dimitrov D . Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 2003; 31(19):5676-84. PMC: 206463. DOI: 10.1093/nar/gkg763. View

13.

Bakay M, Chen Y, Borup R, Zhao P, Nagaraju K, Hoffman E . Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics. 2002; 3:4. PMC: 65691. DOI: 10.1186/1471-2105-3-4. View

14.

Kothapalli R, Yoder S, Mane S, Loughran Jr T . Microarray results: how accurate are they?. BMC Bioinformatics. 2002; 3:22. PMC: 126254. DOI: 10.1186/1471-2105-3-22. View

15.

Lee J, Bussey K, Gwadry F, Reinhold W, Riddick G, Pelletier S . Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells. Genome Biol. 2003; 4(12):R82. PMC: 329421. DOI: 10.1186/gb-2003-4-12-r82. View

16.

Wright G, Simon R . A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics. 2003; 19(18):2448-55. DOI: 10.1093/bioinformatics/btg345. View

17.

Beillard E, Pallisgaard N, van der Velden V, Bi W, Dee R, van der Schoot E . Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using 'real-time' quantitative reverse-transcriptase polymerase chain reaction (RQ-PCR) - a Europe against cancer program. Leukemia. 2003; 17(12):2474-86. DOI: 10.1038/sj.leu.2403136. View

18.

Gabert J, Beillard E, van der Velden V, Bi W, Grimwade D, Pallisgaard N . Standardization and quality control studies of 'real-time' quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - a Europe Against Cancer program. Leukemia. 2003; 17(12):2318-57. DOI: 10.1038/sj.leu.2403135. View

19.

Bloom G, Yang I, Boulware D, Kwong K, Coppola D, Eschrich S . Multi-platform, multi-site, microarray-based human tumor classification. Am J Pathol. 2003; 164(1):9-16. PMC: 1602228. DOI: 10.1016/S0002-9440(10)63090-8. View

20.

Culhane A, Perriere G, Higgins D . Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003; 4:59. PMC: 317282. DOI: 10.1186/1471-2105-4-59. View