Binomial Probability CNV Determination in Mixed Genomic Samples
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Solution Overview
Problem
Current diagnostic processes face challenges in accurately determining copy number variation (CNV) in genomic regions, particularly when distinguishing between minor nucleic acid species and a background of higher levels, such as fetal DNA in maternal plasma or viral nucleic acids, which affects the sensitivity and accuracy of disease diagnosis and prognosis.
Innovation Solution
The method employs binomial probability distributions to calculate the relative contribution of different sources in a mixed sample by analyzing frequency data from informative loci, allowing for the determination of CNV in genomic regions through comparison with empirical copy numbers and source contributions, using a computer-implemented process that accesses and processes frequency data sets to estimate source contributions and calculate CNV.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic processes use standard techniques for identifying nucleic acids, then disease detection capability is improved, but sensitivity for detecting low level nucleic acid from one source against a background of much higher level nucleic acids deteriorates
Solution Approach 1:
The method segments the nucleic acid population by analyzing individual molecule characteristics (such as fragment length, sequence composition, or epigenetic markers) to distinguish fetal cfDNA from maternal cfDNA. This segmentation allows precise measurement of the minor fetal component even when it constitutes only a small fraction of total cfDNA in maternal plasma.
Solution Approach 2:
The invention changes the measurement parameters from bulk nucleic acid quantification to analyzing specific molecular parameters such as fragment size distribution, sequence-specific markers, or epigenetic modifications. This parameter change enables differentiation between nucleic acids from different sources based on their distinct molecular characteristics rather than relying on quantity alone.
2Loss of information
If diagnostic processes analyze mixed samples containing nucleic acids from multiple sources, then comprehensive diagnostic information is improved, but accuracy in determining contribution of minor nucleic acid species deteriorates
Solution Approach 1:
The method introduces intermediary markers or reference sequences that are specific to each nucleic acid source (e.g., fetal-specific sequences or maternal-specific sequences). These intermediaries serve as identifiers that allow the system to track and quantify the contribution of each source independently within the mixed sample, resolving the accuracy problem while maintaining comprehensive information.
Solution Approach 2:
The invention adds another dimension to the analysis by measuring not just the quantity of nucleic acids but also their molecular characteristics (such as epigenetic markers, sequence composition, or fragment length). This dimensional expansion allows differentiation and quantification of minor components in mixed samples that would be indistinguishable using quantity-based methods alone.
3Difficulty of detecting and measuring
If diagnostic processes focus on detecting presence of nucleic acid sequences, then ease of detection is improved, but ability to establish genetic inheritance or copy number variation deteriorates
Solution Approach 1:
The method performs preliminary characterization of the nucleic acid population by analyzing molecular markers and establishing baseline profiles before attempting to detect copy number variations or inheritance patterns. This preliminary action enables subsequent accurate interpretation of genetic information by first identifying and quantifying the contributing sources.
Solution Approach 2:
The invention replaces simple presence/absence detection mechanisms with more sophisticated analytical methods that measure molecular characteristics and statistical distributions. This substitution transforms the detection system from a binary yes/no approach to a quantitative analysis that can establish genetic inheritance and copy number variations with high precision.
Data Source
AI summary
This invention relates to a binomial calculation of copy number of data obtained from a mixed sample having a first source and a second source.