Genomic Coverage Bias Reduction via Normalization
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Solution Overview
Problem
Current methods for characterizing genomic samples face challenges in accurately determining copy number profiles due to biases caused by label density and other factors, particularly in identifying genetic abnormalities such as aneuploidy and structural variations.
Innovation Solution
The method involves labeling sample molecules, translocating them through a fluidic channel, and using normalization techniques like Global Renormalization of Optical Maps (GROM) and Single MOlecule Normalization to Detect Aberrations (SIMONIDA) to minimize biases, generating copy number profiles that accurately reflect genomic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If normalization techniques are applied to minimize bias in coverage measurements, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary normalization actions to the coverage depth data before final analysis. By pre-scaling and normalizing the coverage depths using reference data and statistical models, the system eliminates biases from label density variations and molecular length differences beforehand, ensuring accurate copy number profile generation without requiring complex real-time corrections during measurement
Solution Approach 2:
The patent introduces intermediary reference data and statistical models as mediators between the raw coverage measurements and the final copy number profiles. These intermediaries include reference genomes, control samples, and normalization algorithms that bridge the gap between biased measurements and accurate biological interpretations, enabling precise measurement without directly modifying the measurement device
2Manufacturing precision
If comprehensive normalization by multiple factors is performed, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the normalization process into distinct, independent steps: (1) scaling coverage depths to remove chromosomes, (2) normalizing by molecular length characteristics, (3) normalizing by label density, and (4) generating copy number profiles. Each segment can be processed independently and optimized separately, reducing overall processing time while maintaining comprehensive normalization accuracy
Solution Approach 2:
The patent changes parameters such as coverage depth scaling factors, molecular length distributions, and label density metrics to transform raw data into normalized profiles. By dynamically adjusting these parameters based on reference data and sample characteristics, the system achieves high manufacturing precision through automated parameter optimization rather than manual intervention
Data Source
AI summary
Methods are provided for detecting and quantitating molecules using fluidics. In some embodiments, the methods comprise minimizing or eliminating biases caused by label density, or minimizing or eliminated biases caused by factors other than label density. In some embodiments, the methods comprise automated identification of genetic structural variation. In some embodiments, the methods comprise analyzing blood to detect the presence of circulating DNA or cells from a fetus or tumor.