Hyper-Partition Molecule Distribution for Methylation-Based CNV Detection
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Solution Overview
Problem
Current methods for detecting copy number variations (CNVs) rely predominantly on genomic data, neglecting the potential of methylation data, which can provide complementary information for accurate CNV detection and disease diagnosis.
Innovation Solution
A computational approach that infers CNVs from the distribution of hyper-methylated molecules in off-target regions of genomic/epigenomic panels, integrating methylation data to enhance CNV detection algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genomic data is used for CNV detection, then CNV detection can be performed, but methylation data potential is neglected and diagnostic accuracy is limited
Solution Approach 1:
The patent combines genomic data and methylation data into a unified CNV detection framework. The computational approach integrates read depth information from genomic sequencing with methylation status information from the same sequencing data, merging two previously separate information sources to achieve more accurate CNV detection and comprehensive diagnostic insights.
2Adaptability or versatility
If only genomic data is analyzed, then the detection process is simpler, but diagnostic comprehensiveness and treatment strategy development are limited
Solution Approach 1:
The patent creates a multi-functional analytical system that uses the same sequencing data for multiple purposes: CNV detection, methylation status determination, and integrated diagnostic analysis. This universal approach allows the system to serve multiple diagnostic functions (cancer detection, CNV detection, treatment response prediction) without requiring separate analytical pipelines, thereby increasing adaptability while managing complexity through data reusability.
3Reliability
If methylation data is incorporated into CNV detection, then diagnostic accuracy and treatment strategy development are enhanced, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary classification of DNA molecules by methylation status before conducting CNV analysis. By pre-sorting molecules into methylated and unmethylated categories using computational methods, the system prepares the data in advance for targeted analysis, reducing the complexity of subsequent CNV detection while improving diagnostic reliability through stratified analysis approaches.
Data Source
AI summary
Methods and systems are described for improving detection of copy number by distribution of molecules. In the context of applying a genomic and epigenomic panel, on target molecules are exceedingly sparse, whereas large structural genomic alterations like CNV require observations of broader regions of the genome. Bins for analyses can be generated that do not overlap with genomic and epigenomic panels, based on distribution of off-target molecules. Reference samples from a pool of samples generates reference background against which test samples are normalized. A CNV determination takes into account, the tumor fraction of a sample and noise levels.