Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2002-06-18

AUTHORS

Jo Vandesompele, Katleen De Preter, Filip Pattyn, Bruce Poppe, Nadine Van Roy, Anne De Paepe, Frank Speleman

ABSTRACT

BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. RESULTS: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. CONCLUSIONS: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences. More... »

PAGES

research0034.1-research0034.11

Journal

TITLE

Genome Biology

ISSUE

7

VOLUME

3

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/gb-2002-3-7-research0034

    DOI

    http://dx.doi.org/10.1186/gb-2002-3-7-research0034

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039751959

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/12184808


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