Electrophoresis Data Alignment Using Virtual Units
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Solution Overview
Problem
Capillary electrophoresis data variability due to run-to-run and instrument-to-instrument differences makes manual adjustment of raw data necessary, hindering efficient analyte screening and measurement processes.
Innovation Solution
A method involving the alignment of electrophoretic separation data by combining raw data with electric current and potential data, using polynomial and cubic spline models to normalize migration times, and transforming data into standardized virtual units for improved peak detection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of raw electrophoresis data is performed to account for run-to-run and instrument-to-instrument variability, then measurement precision is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The system performs automated alignment and standardization of electrophoresis data using computer-executable instructions. The processor automatically receives raw data, applies alignment algorithms to correct migration time variations, and generates standardized output without requiring manual scientist intervention for each data set, thereby maintaining precision while dramatically improving throughput
Solution Approach 2:
The patent replaces the manual mechanical process of data adjustment with an automated computational system. Computer-executable instructions perform the alignment and standardization functions that previously required manual scientist effort, substituting automated digital processing for manual analytical work
2Productivity
If automated peak detection is implemented without data alignment, then productivity is improved, but measurement precision deteriorates due to data variability
Solution Approach 1:
The system performs data alignment and standardization as preliminary steps before automated peak detection. By pre-processing the raw electrophoresis data to correct migration time variations and align with reference data, the system ensures that subsequent automated detection operates on standardized, high-quality data, thereby maintaining both automation benefits and detection accuracy
3Ease of operation
If migration time values are used directly for analyte identification, then ease of operation is improved, but measurement precision deteriorates due to time variability between runs and instruments
Solution Approach 1:
The patent transforms the raw migration time parameter into a standardized parameter through alignment with reference data. The system receives raw migration times, applies alignment algorithms that reference external or internal standards, and outputs corrected migration times that account for run-to-run and instrument-to-instrument variability. This parameter transformation maintains operational simplicity while dramatically improving identification accuracy
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 standardizes electrophoretic data, enhancing the accuracy and efficiency of analyte detection and quantification by reducing data variability and improving automated peak detection, allowing for more reliable comparison and analysis of analyte mixtures.
Implementation Method 1
a power source for applying a potential across the channel to cause a sample to migrate there along
Implementation Method 2
a detector for measuring signal intensity associated with a sample migrating along the channel as a function of time
Data Source
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AI summary
The present teachings provide systems, and methods, for improving the analysis of analytes using electrophoresis apparatus. Exemplary methods of the present teachings can provide, for example, an increase in the yield of useful results, e.g., quantity and quality of useable data, in automated peak detection, in connection with an electrophoretic separation, e.g., capillary electrophoresis. In various embodiments, the system virtualizes the raw data, transforming the migration time into virtual units thereby allowing the visual comparison of analyte electropherograms and the reliable measurement of unknown analytes. The analytes can be, for example, any organic or inorganic molecules, including but not limited to nucleic acids (DNA, RNA), proteins, peptides, glycans, metabolites, secondary metabolites, lipids, or any combination thereof. Analyte detection can be performed by any method including but not limited to fluorescence detection or UV absorption. The present teachings provide, among other things, for consistent comparisons of analyte peaks across samples, across instruments, across runs and across migration times.