Genome Analysis via Targeted Polymorphism Sequencing
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Solution Overview
Problem
Current nucleic acid sequencing and analysis techniques are laborious, expensive, and time-consuming, making it inefficient to screen for genetic variations across hundreds or thousands of subjects, particularly due to high reagent costs, CPU-intensive analysis, and large storage requirements.
Innovation Solution
A method and system for genome analysis that generates single-end sequence reads representing less than 5% of the genome with an average distance of at least 4000 bp, providing sequence information for over 400,000 common polymorphisms, and uses statistical models like Hidden Markov Models or maximum likelihood regression to determine heterozygosity and copy number variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional next-generation sequencing techniques are used to screen for genetic variations, then comprehensive genomic coverage is achieved, but the process becomes laborious, expensive, and time-consuming
Solution Approach 1:
The patent extracts and analyzes only the most informative portions of the genome by selecting reads that contain polymorphism information. Instead of sequencing the entire genome, the method identifies and sequences specific regions containing known polymorphisms, thereby reducing the sequencing burden while maintaining detection accuracy for genetic variations.
Solution Approach 2:
The genome is segmented into discrete readable portions, with the patent specifically targeting regions containing polymorphisms. By dividing the genomic analysis into targeted segments rather than comprehensive sequencing, the method improves productivity while preserving the ability to detect genetic variations through statistical analysis of allele frequencies.
2Loss of information
If comprehensive genome sequencing is performed, then complete genetic information is obtained, but reagent costs and storage requirements increase significantly
Solution Approach 1:
The method extracts only the essential genetic information needed for variation detection by focusing sequencing efforts on regions containing polymorphisms. This selective extraction reduces reagent consumption and data storage requirements while maintaining the ability to identify genetic variations through analysis of allele frequency distributions in the sampled regions.
Solution Approach 2:
The patent employs partial sequencing action by analyzing a representative subset of genomic regions rather than the complete genome. By sequencing a carefully selected fraction of the genome that contains polymorphism information, the method achieves sufficient genetic variation detection with reduced reagent and storage costs.
3Measurement precision
If full genome analysis is conducted, then all genetic variations are detected, but processing time and computational resources increase
Solution Approach 1:
The patent extracts copy number variation signals from a reduced set of polymorphism-containing reads. By focusing analysis on specific genomic regions with known polymorphisms and using statistical models to infer copy number changes from allele frequency data, the method achieves accurate CNV detection with significantly reduced processing time compared to comprehensive genome analysis.
Solution Approach 2:
The method uses partial genome sampling to detect copy number variations. By analyzing a representative subset of genomic regions containing polymorphisms and applying statistical inference, the patent achieves timely detection of CNVs without requiring complete genome sequencing and analysis.
Data Source
AI summary
Provided herein are novel methods, systems and processes for generating and analyzing sequence data for the determination of the presence or absence of one or more genetic variations within a genome of a subject.