Somatic Variant Classification via Methylation Segmentation
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Solution Overview
Problem
Current methods for analyzing nucleic acid sequencing data face challenges in accurately determining cancer-associated biomarkers, particularly in identifying somatic or germline variants from circulating cell-free DNA (cfDNA) due to the presence of nucleic acid molecules from healthy tissues and the absence of matched normal controls.
Innovation Solution
The method combines methylation data with whole genome or targeted genome sequencing data, using a binary classifier to identify variant alleles as somatic or germline by analyzing methylation states and sequence data from nucleic acid fragments, and includes features such as CpG site counts and p-value distributions to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If methylation sequencing data is used to identify somatic variants in cfDNA, then the accuracy of variant identification is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The method segments the complex task of somatic variant identification into distinct components: (1) obtaining methylation sequencing data, (2) identifying candidate variants, (3) determining methylation states for each candidate, and (4) classifying as somatic or germline based on methylation patterns. This segmentation allows each step to be processed independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces methylation state determination as an intermediary step between variant detection and classification. By adding this intermediate layer that analyzes methylation patterns at candidate variant positions, the system can distinguish somatic from germline variants more accurately without requiring matched normal controls, thereby improving precision while managing computational burden through focused analysis.
2Reliability
If methylation state analysis is performed for each candidate variant, then the ability to distinguish somatic from germline variants is improved, but the time required for data processing increases
Solution Approach 1:
The method applies local quality analysis by focusing methylation state determination specifically at candidate variant positions rather than analyzing the entire genome uniformly. This localized approach concentrates computational resources on regions of interest, improving classification reliability for somatic vs. germline distinction while reducing overall processing time by ignoring regions without candidate variants.
3Measurement precision
If deep learning models are used to analyze large sequencing datasets, then the ability to detect complex biological patterns is improved, but the susceptibility to overfitting on noise increases
Solution Approach 1:
The patent introduces methylation state analysis as an intermediary that provides biologically grounded features for deep learning models. By deriving methylation patterns as intermediate representations before feeding data to deep learning algorithms, the system reduces noise and overfitting susceptibility while maintaining the ability to detect complex biological patterns, thereby improving model robustness.
Data Source
AI summary
Systems and methods for identifying variant alleles as somatic or germline are provided. Reference and variant alleles for a genomic position are identified. Methylation states and sequences of nucleic acid fragment sequences that map to the genomic position are obtained from a sample of a subject. Using the sequences of nucleic acid fragment sequences, each nucleic acid fragment sequence that has the reference allele is assigned to a reference subset, and each nucleic acid fragment sequence that has the variant allele is assigned to a variant subset. One or more indications of the methylation states across the nucleic acid fragment sequences in the variant subset and an indication of the number of nucleic acid fragment sequences in the reference subset versus the variant subset are applied to a trained binary classifier. An identification of the variant allele at the genomic position as somatic or germline is obtained from the classifier.