Evaluation of DNA microarray results with quantitative gene expression platforms
- PMID: 16964225
- DOI: 10.1038/nbt1236
Evaluation of DNA microarray results with quantitative gene expression platforms
Abstract
We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.
Similar articles
-
Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.BMC Genomics. 2006 Mar 21;7:59. doi: 10.1186/1471-2164-7-59. BMC Genomics. 2006. PMID: 16551369 Free PMC article.
-
Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project.Nat Biotechnol. 2006 Sep;24(9):1140-50. doi: 10.1038/nbt1242. Nat Biotechnol. 2006. PMID: 16964228
-
Cross platform microarray analysis for robust identification of differentially expressed genes.BMC Bioinformatics. 2007 Mar 8;8 Suppl 1(Suppl 1):S5. doi: 10.1186/1471-2105-8-S1-S5. BMC Bioinformatics. 2007. PMID: 17430572 Free PMC article.
-
Microarray RNA transcriptional profiling: part I. Platforms, experimental design and standardization.Expert Rev Mol Diagn. 2006 Jul;6(4):535-50. doi: 10.1586/14737159.6.4.535. Expert Rev Mol Diagn. 2006. PMID: 16824028 Review.
-
Expression Profiling Using Affymetrix GeneChip Microarrays.Methods Mol Biol. 2009;509:35-46. doi: 10.1007/978-1-59745-372-1_3. Methods Mol Biol. 2009. PMID: 19212713 Review.
Cited by
-
Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments.BMC Genomics. 2011 Dec 1;12:589. doi: 10.1186/1471-2164-12-589. BMC Genomics. 2011. PMID: 22133085 Free PMC article.
-
RNA-Seq mapping and detection of gene fusions with a suffix array algorithm.PLoS Comput Biol. 2012;8(4):e1002464. doi: 10.1371/journal.pcbi.1002464. Epub 2012 Apr 5. PLoS Comput Biol. 2012. PMID: 22496636 Free PMC article.
-
Transcriptome analysis of injured human meniscus reveals a distinct phenotype of meniscus degeneration with aging.Arthritis Rheum. 2013 Aug;65(8):2090-101. doi: 10.1002/art.37984. Arthritis Rheum. 2013. PMID: 23658108 Free PMC article.
-
Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study.EBioMedicine. 2020 Nov;61:103023. doi: 10.1016/j.ebiom.2020.103023. Epub 2020 Oct 14. EBioMedicine. 2020. PMID: 33069062 Free PMC article.
-
ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.BMC Genomics. 2016 Aug 4;17:541. doi: 10.1186/s12864-016-2848-2. BMC Genomics. 2016. PMID: 27488180 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources