qPCR Curve Analysis via Probability Density Function
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Solution Overview
Problem
Quantitative Polymerase Chain Reaction (qPCR) methods face inaccuracies and fluctuations due to baseline drift and noise, making it challenging to accurately determine the presence and starting concentration of DNA strand segments, especially in the presence of artifacts and thermal noise.
Innovation Solution
The method employs a probability density function to analyze qPCR curves, assuming Gaussian distributions for intensity values and using features like peak ratios and widths to differentiate between amplification and baseline drift, allowing for reliable identification of amplification and determination of the cycle threshold (ct) value, even in noisy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple parallel measurements are carried out to improve accuracy through averaging, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by creating a probability density function from the qPCR intensity values before making the final evaluation decision. This preprocessing step transforms the raw measurement data into a statistical representation that can be evaluated more efficiently, allowing accurate detection without requiring multiple parallel measurements to be performed and averaged.
2Measurement precision
If conventional evaluation methods are used to identify amplification, then device complexity is low, but measurement precision deteriorates due to noise and baseline drift
Solution Approach 1:
The patent introduces an intermediary element - the probability density function - that mediates between the raw qPCR intensity values and the final amplification detection decision. This intermediate statistical representation filters out noise and baseline drift effects, enabling more accurate amplification detection while maintaining manageable system complexity through a well-defined mathematical transformation.
3Reliability
If the qPCR method is used to detect DNA strand segments, then reliability of pathogen detection is improved, but object-generated harmful factors increase due to artifacts and thermal noise
Solution Approach 1:
The patent converts the harmful effect of noise and artifacts into a benefit by using the probability density function transformation. Instead of trying to eliminate the noise, the method transforms the noisy intensity values into a statistical representation where the noise effects are averaged out and the true amplification signal emerges more clearly, thereby improving detection reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate identification of DNA strand segment amplification and determination of the ct value, improving the reliability of qPCR measurements by distinguishing between sigmoidal and linear curve shapes, thus overcoming noise-induced inaccuracies.
Implementation Method 1
at least some of the nucleotides are provided with fluorescent molecules which, upon binding to the individual strand of the DNA strand segment to be detected, activate a fluorescence property
Data Source
AI summary
The disclosure relates to a method for carrying out a quantitative polymerase chain reaction (qPCR) process, comprising the following steps:—cyclically carrying out qPCR cycles; —measuring the fluorescence for each qPCR cycle to obtain a qPCR curve of intensity values (I); —creating a probability density function (PDF) from the intensity values (I); —establishing a presence or absence of the DNA strand section to be detected depending on the presence of one or more features of the probability density function (PDF); —carrying out the qPCR process depending on the presence or absence of the DNA strand section to be detected.