Melting Curve Fitting for Copy Number Variation Detection
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Solution Overview
Problem
Existing methods for analyzing nucleic acids, particularly in identifying copy number variations (CNVs), face challenges in accurately normalizing DNA melting curves and distinguishing between background and DNA-specific signals, which complicates genotyping and mutation detection, especially when the difference in melting temperatures between wild-type and mutant nucleic acids is small.
Innovation Solution
A method and system that utilize a temperature control system and optical system to measure DNA melting curves, with a processor fitting a mathematical model to the data to determine copy number variations by calculating mixture coefficients, and employing the Van't Hoff mixture model to align and rescale melting curves, allowing for automated identification of CNVs and genotyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual inspection of thermal melt profiles is used to determine melting temperature, then the method is simple and quick, but the measurement precision is insufficient when the difference in melting temperature between wild-type and mutant nucleic acids is small (less than 0.25°C)
Solution Approach 1:
The patent replaces manual visual inspection with automated computational analysis. The system automatically calculates melting temperatures, determines copy number variations, and identifies genotypes using algorithms that process melting curve data mathematically, eliminating the need for human eyes to detect subtle temperature differences.
Solution Approach 2:
The patent transforms the melting curve analysis from direct temperature measurement to derivative-based parameter extraction. By computing the first and second derivatives of the melting curve, the system identifies inflection points and peak positions that correspond to melting temperatures, enabling precise differentiation of small temperature changes that are invisible to the naked eye.
2Device complexity
If manual genotyping classification is performed by visually comparing melting profiles with reference samples, then the process requires minimal equipment, but the productivity is low and reliability is inconsistent
Solution Approach 1:
The system performs self-service analysis by automatically comparing test sample melting profiles against reference profiles stored in a database. The algorithm independently identifies genotypes without requiring manual intervention, enabling high-throughput processing of multiple samples simultaneously while maintaining consistent accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its analysis by comparing automated results against known reference data and adjusting its algorithms. This feedback loop ensures consistent and reliable genotyping classification across multiple samples and operators.
3Productivity
If copy number variation analysis is performed without mathematical modeling, then the analysis is faster and simpler, but the measurement precision and reliability of CNV determination is insufficient
Solution Approach 1:
The patent applies preliminary mathematical modeling before final CNV determination. The system first fits melting curve data to theoretical models (such as the Van't Hoff equation) to extract thermodynamic parameters, then uses these parameters to calculate copy numbers. This preliminary processing ensures accurate CNV detection even when melting temperature differences are subtle.
Solution Approach 2:
The system transforms raw melting curve data into derived parameters including first and second derivatives, inflection point positions, and thermodynamic constants. These transformed parameters provide enhanced sensitivity for detecting copy number variations, enabling precise CNV determination that maintains high productivity through automated processing.
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 precise determination of CNVs and genotyping by effectively separating DNA-specific signals from background information, improving the accuracy of nucleic acid analysis and reducing manual intervention, thereby enhancing the reliability of genetic testing.
Implementation Method 1
a fluorescent dye that indicates whether the two DNA strands are bound or not is used. Examples of such indicator dyes include non-specific binding dyes such as SYBR® Green I, whose fluorescence efficiency depends strongly on whether the DNA is double stranded or single stranded.
Implementation Method 2
The process of causing DNA to transition from dsDNA to ssDNA with increasing temperature is sometimes referred to as a 'high-resolution temperature (thermal) melt (HRTm)' process, or simply a 'high-resolution melt' process.
Data Source
AI summary
The present invention relates to a method and system for determining Copy Number Variations (CNVs) in a genomic test sample including target amplicons and a reference amplicons. Specifically, nucleic acid melting curves are generated for the test sample. A mathematical model is fitted to each of the nucleic acid melting curves to separate target and reference melting reactions within the measured nucleic acid melting curve. The fitting parameters of the mathematical model are calculated. A CNV of the test sample is determined based on the fitting parameters of the mathematical model corresponding to the target and reference melting reactions.