Abnormal Karyotype Detection via Read Coverage and Allele Balance
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Solution Overview
Problem
Current methods lack the capability to accurately detect abnormal karyotypes from population-scale whole-exome sequencing data, due to challenges in defining copy number variants and dealing with biases in read coverage.
Innovation Solution
The method involves determining read coverage data, allele balance distributions, and identifying deviations in these metrics across multiple samples, using linear regression models and quality control metrics to normalize and validate the data, thereby identifying abnormal karyotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If DNA microarrays are used for detecting chromosomal abnormalities, then the detection capability is provided, but it is difficult to define copy number variants
Solution Approach 1:
The patent introduces read-depth information as an intermediary metric that bridges the gap between raw sequencing data and CNV detection. By analyzing the depth of sequencing reads across genomic regions, the method creates a measurable signal that indicates copy number changes, enabling both detection and precise definition of CNVs without relying solely on traditional array technologies.
Solution Approach 2:
The patent replaces the mechanical/procedural approach of traditional DNA microarray hybridization with a computational analysis of next-generation sequencing read-depth data. This substitution allows for more precise CNV detection by leveraging the quantitative information embedded in sequencing read counts, transforming a qualitative detection method into a quantitative measurement system.
2Measurement precision
If next-generation sequencing methods are used for detecting chromosomal abnormalities, then the depth of coverage is increased, but existing methods focus only on maternal plasma samples for non-invasive prenatal testing
Solution Approach 1:
The patent develops a universal read-depth analysis framework that can be applied across multiple sample types and clinical scenarios. The same computational methodology described for maternal plasma samples can be extended to detect chromosomal abnormalities in various contexts, including cancer genomics, prenatal diagnosis, and population genetics, thereby achieving multi-functionality and broad adaptability.
Solution Approach 2:
The patent segments the analysis into distinct functional components: read-depth calculation, normalization, deviation detection, and CNV calling. This segmentation allows the methodology to be independently adapted and optimized for different sample types and clinical applications while maintaining the core analytical framework, thus enhancing versatility without sacrificing the depth of coverage achieved through NGS.
3Difficulty of detecting and measuring
If read coverage data is used for detecting abnormal karyotypes, then the detection of chromosomal abnormalities is enabled, but biases in read coverage must be accounted for
Solution Approach 1:
The patent implements feedback mechanisms through iterative normalization and deviation analysis. By continuously comparing observed read-depth deviations against expected values and adjusting the analysis accordingly, the method accounts for biases in real-time, ensuring that detection accuracy is maintained despite variations in sequencing depth, GC content, or other confounding factors.
Solution Approach 2:
The patent employs parameter changes by transforming raw read-depth values into normalized coverage ratios and deviation scores. These parameter transformations adjust the data to compensate for systematic biases, converting biased raw counts into corrected metrics that accurately reflect true chromosomal copy numbers, thereby maintaining high detection reliability.
Data Source
AI summary
Methods and systems for detecting abnormal karyotypes are disclosed. An example method can comprise determining read coverage data, allele balance distributions of heterozygous SNPs, and chromosomal segments where heterozygosity is not observed. The methods and systems can then determine one or more metrics which can be indicative of abnormal karyotype(s).