Methylome Divergence Analysis for DIMP Selection
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Solution Overview
Problem
Current methods for analyzing DNA methylation data struggle to effectively correlate methylation patterns with phenotypic characteristics in plants and animals, particularly in identifying differentially informative methylated positions (DIMPs) and regions (DIMRs) that are indicative of specific traits such as yield and stress tolerance.
Innovation Solution
A computer-implemented method that calculates the divergence between methylation levels in sample and reference methylomes, selecting DIMPs or DIMRs based on an approximation of the energy required to produce these divergences, which considers the biophysical nature of DNA methylation and reduces subjective results from statistical tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical tests are used to identify differentially methylated positions, then the analysis can be performed, but the results become subjective and less reliable for correlating methylation patterns with phenotypic characteristics
Solution Approach 1:
The patent changes the fundamental parameter used for identifying differentially methylated positions from statistical significance (p-values) to information theory metrics (Shannon entropy, Kullback-Leibler divergence). This parameter transformation eliminates subjectivity in result selection and provides an objective mathematical framework for correlating methylation patterns with phenotypic characteristics, directly resolving the contradiction between measurement precision and result reliability
2Measurement precision
If many DNA methylation markers are used to ensure comprehensive coverage, then the correlation with phenotypic characteristics improves, but the computational complexity and cost increase
Solution Approach 1:
The patent extracts only the most informative methylation positions by calculating Shannon entropy and Kullback-Leibler divergence for each position, then selecting only those that meet specific information content thresholds. This extraction approach identifies a minimal set of high-value markers that maintain phenotype correlation accuracy while dramatically reducing computational complexity and marker quantity requirements
3Quantity of substance
If comprehensive methylome sequencing is performed to capture all methylation patterns, then complete data is obtained, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the comprehensive methylome data into individual cytosine positions and evaluates each position's information content independently using Shannon entropy calculations. This segmentation allows the system to process and evaluate methylation data in manageable units, identifying only the most informative positions rather than processing complete methylomes, thereby maintaining data completeness for key positions while improving computational efficiency
Data Source
AI summary
A computer-implemented method of preparing a set of differentially informative methylated positions (DIMPs) or differentially informative methylated regions (DIMRs) from a sample methylome of an animal or plant having a phenotypic characteristic different from a wild-type of the same species of animal or plant, and the characteristic is associate with differences in methylation of the genome, comprises: providing a computer with the sample methylome, and a reference methylome of the wild-type of the same species of animal or plant; calculating with the computer a divergence between a plurality of cytosine positions of the sample methylome and the reference methylome; and selecting with the computer a set of DIMPs or DIMRs. Each DIMP or DIMR is selected based on an approximation of the energy required to produce the divergence between methylation levels of the plurality of cytosine positions of the sample methylome as compared to the wild-type methylome.