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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays
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Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR.

Jeanine S Morey1, James C Ryan, Frances M Van Dolah

  • 1NOAA Marine Biotoxins Program, Center for Coastal Environmental Health and Biomolecular Research. 219 Fort Johnson Rd., Charleston, SC 29412. USA. Jeanine.Morey@noaa.gov

Biological Procedures Online
|January 24, 2007
PubMed
Summary

Quantitative real-time PCR (qPCR) validates microarray gene expression but often disagrees. Filtering microarray data by quality metrics significantly improves correlation with qPCR results.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Biotechnology

Background:

  • Quantitative real-time PCR (qPCR) is a standard method for validating gene expression data from microarray analysis.
  • Discrepancies between microarray and qPCR results are frequently observed, necessitating an investigation into contributing factors.

Purpose of the Study:

  • To assess factors influencing the correlation between microarray and qPCR gene expression data.
  • To identify critical parameters for improving data concordance between these two techniques.

Main Methods:

  • Utilized two-color 60-mer oligonucleotide microarrays and SYBR Green-based qPCR across five experiments.
  • Evaluated various factors including fold-change, spot intensity, cycle threshold (C(t)), and tissue characteristics.
  • Applied data filtering based on quality measures such as fold-change and rho-value.

Main Results:

  • A significant overall correlation (rho=0.708, p<0.0001) was found between microarray and qPCR results.
  • Filtering microarray data based on quality metrics (fold-change and rho-value) was the most critical factor for improving correlation.
  • Applying quality scores consistently yielded significant correlations (rho>0.80).

Conclusions:

  • Microarray data quality filtering is essential for achieving high concordance with qPCR validation.
  • Optimizing data quality assessment enhances the reliability of gene expression validation studies.
  • Understanding these factors improves the accuracy of gene expression profiling using complementary techniques.