Abstract
Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current “gold standard” is the cycle-threshold (Ct) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the Ct method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.
Highlights
The real-time polymerase chain reaction has become a routine technique in microbiology, bioengineering, and molecular biology for detecting and quantifying nucleic acids.[1−3] This is predominantly due to its large dynamic range (7−8 magnitudes), desirable sensitivity (5−10 molecules per reaction), and reproducible quantification results.[4−6] New methods to improve the analysis of qPCR data are invaluable to a number of application fields, including environmental monitoring and clinical diagnostics.[7−10]
The current “gold standard” for absolute quantification of DNA using standard curves is the cycle-threshold (Ct) method.[11−13] The Ct value is a feature of the amplification curve defined as the cycle number in the exponential region from which there is a detectable increase in fluorescence
We provide a novel framework that combines existing standard curve methods into a multidimensional standard curve (MSC). This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space
Summary
The real-time polymerase chain reaction (qPCR) has become a routine technique in microbiology, bioengineering, and molecular biology for detecting and quantifying nucleic acids.[1−3] This is predominantly due to its large dynamic range (7−8 magnitudes), desirable sensitivity (5−10 molecules per reaction), and reproducible quantification results.[4−6] New methods to improve the analysis of qPCR data are invaluable to a number of application fields, including environmental monitoring and clinical diagnostics.[7−10]. The current “gold standard” for absolute quantification of DNA (or RNA if preceded by a reverse transcription step) using standard curves is the cycle-threshold (Ct) method.[11−13] The Ct value is a feature of the amplification curve defined as the cycle number in the exponential region from which there is a detectable increase in fluorescence. This method is known to provide inaccurate quantification under inconsistent reaction efficiencies.[14]. We provide a novel framework that combines existing standard curve methods into a multidimensional standard curve (MSC)
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