Genome Copy Number Variation Detection via Breakpoint Windows
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Solution Overview
Problem
Current methods for determining copy number variation in genome samples, such as those used in Pre-implantation Genetic Diagnosis and non-invasive prenatal diagnosis, face challenges in accurately analyzing single cells or trace nucleic acid samples due to limitations in sensitivity and specificity, particularly in detecting variations like aneuploidy and micro-deletions.
Innovation Solution
A method and system that involve sequencing a genome sample, aligning the reads to a reference genome, determining breakpoints based on read distribution, establishing detection windows, and calculating parameters to identify copy number variations by comparing against preset thresholds, effectively detecting aneuploidy, deletions, additions, and micro-repetitions of chromosome fragments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional copy number variation detection methods are used, then analysis can be performed on genome samples, but detection accuracy and sensitivity are insufficient for single cells or trace nucleic acid samples
Solution Approach 1:
The genome sequence is divided into multiple detection windows along the chromosome, allowing localized analysis of copy number variations. Each window independently evaluates read depth coverage, enabling precise detection even in trace samples by focusing analysis on specific regions rather than requiring whole-genome coverage.
Solution Approach 2:
The method introduces statistical significance testing as an additional dimension to the detection process. By evaluating whether read depth deviations are statistically significant rather than merely absolute, the method enhances detection sensitivity for trace samples while maintaining accuracy.
2Reliability
If conventional detection methods are applied, then copy number variations can be identified, but the ability to distinguish true variations from noise is limited
Solution Approach 1:
The method employs significance testing that compares observed read depth against expected distributions, providing a feedback mechanism to distinguish true copy number variations from random fluctuations. This statistical feedback loop enhances reliability by confirming detections through multiple layers of validation.
Solution Approach 2:
The invention transforms the detection approach by changing from absolute read depth comparison to statistically normalized parameters. By using significance levels and standardized scores, the method maintains reliability across different sequencing depths and sample types while filtering out noise.
3Adaptability or versatility
If comprehensive genome sequencing is performed, then all copy number variations can be detected, but analysis complexity and computational resources increase
Solution Approach 1:
By segmenting the genome into detection windows and analyzing each independently with standardized statistical tests, the method achieves comprehensive coverage while reducing overall complexity. Each window can be processed autonomously, allowing parallel computation and simplifying the analytical pipeline.
Solution Approach 2:
The significance testing framework serves multiple functions simultaneously: it detects copy number variations, filters false positives, normalizes for sequencing depth differences, and provides statistical confidence measures. This multi-functionality reduces the need for separate analytical steps, simplifying the overall process while maintaining comprehensive detection capability.
Data Source
AI summary
Provided are a method for determining whether a copy number variation exists in a sample genome, a system applied to implement the method, and a computer-readable medium. The method for determining whether a copy number variation exists in a sample genome comprises the steps of: sequencing the sample genome to obtain a sequencing result formed by multiple reads; comparing the sequencing result with a reference genome sequence, so as to determine the distribution of the reads on the reference genome sequence; determining, based on the distribution of the reads on the reference genome sequence, multiple breakpoints on the reference genome sequence, wherein the number of the reads on either side of each breakpoint are significantly different; determining, based on the plurality of breakpoints, a detection window on the reference genome; determining, based on the reads falling in the detection window, a parameter; and determining, based on the difference between the first parameter and a preset threshold, whether a copy number variation exists in the sample genome with respect to the detection window.