Genome Copy Number Variation Detection via Sequencing Read Depth
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Solution Overview
Problem
Current methods for detecting copy number variation (CNV) in genomes, such as FISH and Array CGH, are inefficient for whole-genome scans and fail to detect unknown CNVs, particularly micro-deletions and micro-duplications.
Innovation Solution
A method and system that utilize high-throughput sequencing to obtain reads from nucleic acid samples, align them to a genomic reference sequence, divide the sequence into windows, correct for GC content, and calculate significance values to identify CNV breakpoints, allowing for accurate detection of micro-deletions and micro-duplications through iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods like FISH and Array CGH are used for CNV detection, then detection accuracy for known chromosomal deletions or repeats is improved, but productivity and efficiency for whole-genome scans deteriorates due to high resource consumption and inability to detect unknown CNVs
Solution Approach 1:
The patent uses next-generation sequencing to generate millions of short DNA reads that are digitally copied and aligned to a reference genome. This digital copying approach allows parallel processing of entire genomes, dramatically improving productivity while maintaining detection accuracy through statistical analysis of read depth variations across the genome.
Solution Approach 2:
The patent transforms the detection approach by changing from direct visual/physical observation (FISH) or hybridization patterns (Array CGH) to quantitative analysis of sequencing read depth parameters. By monitoring changes in read depth across genomic windows and applying statistical corrections for GC content and other biases, the method achieves both high accuracy and whole-genome scalability.
2Productivity
If high-throughput sequencing is used to scan the whole genome, then productivity and ability to detect unknown CNVs are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the genome into many small windows or bins, allowing parallel processing of sequencing reads across different genomic regions. This segmentation enables efficient distribution of computational workload and facilitates the use of simple statistical tests (like read depth counting) in each window, reducing overall computational complexity while maintaining whole-genome coverage.
Solution Approach 2:
The patent introduces a reference genome as an intermediary to which all sequencing reads are aligned. This reference serves as a coordinate system that simplifies the analysis by providing expected read depth values for each genomic position, allowing detection of CNVs through comparison rather than requiring complex de novo assembly or pattern recognition algorithms.
3Measurement precision
If traditional CNV detection methods are used, then resource consumption is high, but if next-generation sequencing is used, then detection resolution for micro-deletions and micro-duplications is improved
Solution Approach 1:
The patent replaces mechanical/physical methods (FISH probes, Array CGH hybridization) with a digital information-based system using next-generation sequencing. This substitution allows detection of much smaller CNVs (micro-deletions/duplications) by counting individual sequencing reads, achieving higher resolution while actually reducing consumable resource usage compared to traditional methods that require physical probes and extensive sample preparation.
Data Source
AI summary
Disclosed are a method and a system for determining genome copy number variation, which relates to the technical field of bioinformatics. The method and the system have clinical feasibility, and can precisely detect a micro-deletion/micro-duplication area of 0.5 M under the situation of using data of about 50 M.