Automated Melting Curve Analysis via Deviation Function
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Solution Overview
Problem
Current melting curve analysis methods require manual operation to identify and subtract background fluorescence, which is time-consuming and requires prior knowledge of the melting curve data, limiting their automation and accuracy, especially at low temperatures or extended temperature ranges.
Innovation Solution
The implementation of deviation analysis to automatically identify background and melting regions within melting curve data, using a deviation function to quantify deviations from a model of background fluorescence, thereby facilitating automated background subtraction and region identification without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation is used to identify and subtract background fluorescence, then measurement precision can be maintained, but productivity decreases and device complexity increases
Solution Approach 1:
The system automatically identifies background fluorescence regions and performs subtraction without manual intervention. The deviation analysis algorithm autonomously processes melting curve data, selecting background regions based on predefined criteria and executing subtraction operations automatically, thereby eliminating the need for manual operation while maintaining precision.
Solution Approach 2:
Manual mechanical operation is replaced with an automated computational system. The patent implements electronic deviation analysis that substitutes human expertise with algorithmic processing, using computer-based detection and calculation to perform background fluorescence subtraction automatically.
2Measurement precision
If manual operation is used for background identification, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The system performs self-service by automatically detecting and processing background fluorescence regions. The deviation analysis algorithm independently identifies melting regions and background regions without requiring user input or manual selection, significantly reducing analysis time while maintaining accuracy through automated computational methods.
Solution Approach 2:
The system performs preliminary actions by pre-defining criteria for background fluorescence identification and preparing automated processing routines in advance. This allows rapid automatic analysis of new data without requiring manual setup or configuration each time.
3Productivity
If automated analysis is implemented, then productivity increases, but measurement precision may decrease
Solution Approach 1:
The deviation analysis algorithm incorporates feedback mechanisms that continuously monitor and adjust its processing. By comparing detected background regions against predefined criteria and melting curve characteristics, the system refines its automatic identification and subtraction processes, ensuring maintained precision despite automation.
Solution Approach 2:
The system manages precision through dynamic parameter adjustment in the deviation analysis algorithm. It modifies analysis parameters based on the specific characteristics of melting curve data, adapting the automated processing to maintain accuracy across different samples and conditions.
4Measurement precision
If manual expertise is required for analysis, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent replaces complex manual operational requirements with automated electronic systems. The deviation analysis algorithm substitutes human expertise with computational methods, using electronic processing to achieve reliable analysis without requiring manual intervention or complex user skills.
Solution Approach 2:
The system performs self-service by incorporating built-in algorithms that automatically handle background fluorescence identification and subtraction. This embedded automation reduces the need for external manual expertise while maintaining analysis reliability, simplifying the overall system operation.
Data Source
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AI summary
An experimental melting curve is modeled as a sum of a true melting curve and background fluorescence. A deviation function may be generated based upon the experimental melting curve data and a model of a background signal. The deviation function may be generated by segmenting a range of the experimental curve into a plurality of windows. Within each window, a fit between the model of the background signal and the experimental melting curve data may be calculated. The deviation function may be formed from the resulting fit parameters. The deviation function may include background signal compensation and, as such, may be used in various melting curve analysis operations, such as data visualization, clustering, genotyping, scanning, negative sample removal, and the like. The deviation function may be used to seed an automated background correction process. A background-corrected melting curve may be further processed to remove an aggregation signal.