ArticleBMC genomics2006
Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.
Article in BMC genomics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 2 of them syntheses that pooled it.
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132 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds.BMC genomics · 2007Pooled it
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- Comparative transcriptional analysis of flavour-biosynthetic genes of a nativeJournal of biological research (Thessalonike, Greece) · 2019Article
- A qPCR expression assay of IFI44L gene differentiates viral from bacterial infections in febrile children.Scientific reports · 2019Article
- A Novel, 5-Transcript, Whole-blood Gene-expression Signature for Tuberculosis Screening Among People Living With Human Immunodeficiency Virus.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2019Article
- A biological function based biomarker panel optimization process.Scientific reports · 2019Article
- Selection of Reference Genes for Expression Analysis in Chinese Medicinal HerbFrontiers in pharmacology · 2019Article
- Review
72 more citing papers are in PubMed but not listed here.
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA microarrays are rapidly becoming a fundamental tool in discovery-based genomic and biomedical research. However, the reliability of the microarray results is being challenged due to the existence of different technologies and non-standard methods of data analysis and interpretation. In the absence of a "gold standard"/"reference method" for the gene expression measurements, studies evaluating and comparing the performance of various microarray platforms have often yielded subjective and conflicting conclusions. To address this issue we have conducted a large scale TaqMan Gene Expression Assay based real-time PCR experiment and used this data set as the reference to evaluate the performance of two representative commercial microarray platforms.
resultsIn this study, we analyzed the gene expression profiles of three human tissues: brain, lung, liver and one universal human reference sample (UHR) using two representative commercial long-oligonucleotide microarray platforms: (1) Applied Biosystems Human Genome Survey Microarrays (based on single-color detection); (2) Agilent Whole Human Genome Oligo Microarrays (based on two-color detection). 1,375 genes represented by both microarray platforms and spanning a wide dynamic range in gene expression levels, were selected for TaqMan Gene Expression Assay based real-time PCR validation. For each platform, four technical replicates were performed on the same total RNA samples according to each manufacturer's standard protocols. For Agilent arrays, comparative hybridization was performed using incorporation of Cy5 for brain/lung/liver RNA and Cy3 for UHR RNA (common reference). Using the TaqMan Gene Expression Assay based real-time PCR data set as the reference set, the performance of the two microarray platforms was evaluated focusing on the following criteria: (1) Sensitivity and accuracy in detection of expression; (2) Fold change correlation with real-time PCR data in pair-wise tissues as well as in gene expression profiles determined across all tissues; (3) Sensitivity and accuracy in detection of differential expression.
conclusionOur study provides one of the largest "reference" data set of gene expression measurements using TaqMan Gene Expression Assay based real-time PCR technology. This data set allowed us to use an alternative gene expression technology to evaluate the performance of different microarray platforms. We conclude that microarrays are indeed invaluable discovery tools with acceptable reliability for genome-wide gene expression screening, though validation of putative changes in gene expression remains advisable. Our study also characterizes the limitations of microarrays; understanding these limitations will enable researchers to more effectively evaluate microarray results in a more cautious and appropriate manner.
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