Title |
A simple, accurate and universal method for quantification of PCR
|
---|---|
Published in |
BMC Biotechnology, March 2016
|
DOI | 10.1186/s12896-016-0256-y |
Pubmed ID | |
Authors |
Nicky Boulter, Francia Garces Suarez, Stephen Schibeci, Trevor Sunderland, Ornella Tolhurst, Tegan Hunter, George Hodge, David Handelsman, Ulla Simanainen, Edward Hendriks, Karen Duggan |
Abstract |
Research into gene expression enables scientists to decipher the complex regulatory networks that control fundamental biological processes. Quantitative real-time PCR (qPCR) is a powerful and ubiquitous method for interrogation of gene expression. Accurate quantification is essential for correct interpretation of qPCR data. However, conventional relative and absolute quantification methodologies often give erroneous results or are laborious to perform. To overcome these failings, we developed an accurate, simple to use, universal calibrator, AccuCal. Herein, we show that AccuCal quantification can be used with either dye- or probe-based detection methods and is accurate over a dynamic range of ≥10(5) copies, for amplicons up to 500 base pairs (bp). By providing absolute quantification of all genes of interest, AccuCal exposes, and circumvents, the well-known biases of qPCR, thus allowing objective experimental conclusions to be drawn. We propose that AccuCal supersedes the traditional quantification methods of PCR. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 20% |
Unknown | 4 | 80% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 60% |
Scientists | 1 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
Geographical breakdown
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Chile | 1 | <1% |
China | 1 | <1% |
Denmark | 1 | <1% |
Unknown | 185 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 31 | 16% |
Student > Bachelor | 29 | 15% |
Student > Ph. D. Student | 23 | 12% |
Student > Master | 22 | 12% |
Professor | 7 | 4% |
Other | 28 | 15% |
Unknown | 48 | 26% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 40 | 21% |
Medicine and Dentistry | 8 | 4% |
Immunology and Microbiology | 8 | 4% |
Neuroscience | 4 | 2% |
Other | 20 | 11% |
Unknown | 59 | 31% |