DNA Methylation Analysis via Fragment-Level MFR and UFR
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Solution Overview
Problem
Existing DNA methylation analysis methods suffer from biases due to incomplete conversion and sequencing errors, particularly in low ctDNA fractions, leading to inaccurate beta-value measurements and distorted predictions in cancer diagnosis and monitoring.
Innovation Solution
A method involving paired-end next-generation sequencing (NGS) to identify methylation status by merging read-pairs, calculating Methylated Fragment Ratios (MFR) and Unmethylated Fragment Ratios (UFR) for Methylation-Correlated Blocks (MCBs), and using p-values and likelihood equations to determine methylation scores and ctDNA Fraction (CTDF) values, reducing noise and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bisulfite or enzymatic conversion is performed to distinguish methylation status, then methylation analysis can be performed, but beta-values become biased due to incomplete conversion and sequencing errors
Solution Approach 1:
The patent segments the DNA analysis into fragment-level methylation status determination rather than relying on aggregate beta-values. By analyzing individual DNA fragments and their methylation patterns, the method avoids the bias introduced by incomplete conversion and sequencing errors that affect beta-value calculations.
Solution Approach 2:
The patent introduces an intermediary approach by using fragment-level methylation patterns as a mediator between the raw sequencing data and the final methylation status determination. This intermediary layer allows for noise filtering and bias reduction before reaching the final conclusion about methylation status.
2Loss of information
If beta-values are used for cancer detection analysis, then methylation information can be obtained, but predictions are distorted due to bias in low ctDNA fraction samples
Solution Approach 1:
The patent inverts the conventional approach by not starting with beta-values and working downward to methylation status, but rather starting with raw sequencing data, determining fragment-level methylation patterns, and only then deriving methylation status. This inversion allows for bias reduction at the source rather than correcting biases after they have distorted the data.
Solution Approach 2:
The patent changes the fundamental parameter used for methylation analysis from beta-values to fragment-level methylation patterns. This parameter change fundamentally alters the analysis approach, allowing for more accurate detection in low ctDNA fraction samples by avoiding the bias inherent in beta-value calculations.
3Productivity
If conventional methylation analysis methods are used, then analysis can be performed, but noise significantly interferes with detection in low ctDNA fraction samples
Solution Approach 1:
The patent merges multiple pieces of information at the fragment level, including read pair sequences, methylation patterns, and positional data, to create a comprehensive view of DNA fragment characteristics. This merging allows for more robust detection by aggregating information that reduces the impact of noise from individual reads or fragments.
Solution Approach 2:
The patent performs preliminary actions by determining methylation status at the fragment level before performing cancer detection analysis. This preliminary classification of fragments into methylated or unmethylated categories allows for subsequent analysis to focus only on relevant fragments, reducing noise and improving detection accuracy in low ctDNA fraction samples.
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
The method enhances the performance of epigenetic models by suppressing noise and accurately determining methylation patterns, thereby improving cancer diagnosis and monitoring through reduced bias and increased sensitivity and specificity.
Implementation Method 1
treating isolated DNA of (b) with bisulfite or enzyme to perform conversion of unmethylated cytosines in the DNA
Data Source
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AI summary
Provided are methods for determining a methylation score of DNA and determining a ctDNA Fraction (CTDF) value.