DNA Testing Internal Controls for Rare Sequence Detection
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Solution Overview
Problem
Current DNA sequencing methods face challenges in accurately detecting rare DNA sequences, such as those from fetal or tumor samples, due to systematic and random errors in sample preparation and alignment processes, leading to reduced sensitivity and accuracy in identifying conditions like aneuploidy or cancerous mutations.
Innovation Solution
A method involving the construction of internal controls using a normalization function with weights determined over real numbers to minimize variance, allowing for improved accuracy and sensitivity in DNA testing by normalizing target sequence measurements against covariate sequences uncorrelated with the condition of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment methods are used to map reads against reference sequences, then the ability to identify DNA variations is maintained, but computational time and computing power consumption increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for normalization by selecting covariate reference sequences that are uncorrelated with the condition of interest. This extraction approach allows the system to focus computational resources on calculating normalization factors rather than performing complete alignment, thereby reducing computational time while maintaining accuracy in detecting rare DNA sequences.
Solution Approach 2:
The patent performs preliminary calculations of normalization factors using covariate reference sequences before the main analysis. By pre-computing these normalization factors and storing them for later use, the system avoids repeating expensive alignment computations during actual disease detection, thus reducing overall computational time while preserving measurement precision.
2Measurement precision
If normalization is performed to correct systematic errors in sequencing, then accuracy of rare sequence detection is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality control by selecting specific covariate reference sequences that are uncorrelated with the condition of interest. Instead of normalizing all reference sequences uniformly, the method identifies and uses only those local regions (covariate sequences) that provide appropriate normalization without introducing confounding variables, thereby improving accuracy while managing computational complexity.
Solution Approach 2:
The patent changes the parameters of the normalization process by using weighted sums of covariate reference sequence counts rather than simple counts. By adjusting these parameters (weights and normalization factors) based on statistical properties of the data, the system achieves better normalization accuracy without proportionally increasing computational complexity.
3Reliability
If the fetal DNA portion in maternal blood is used for analysis, then diagnostic information can be obtained, but sensitivity is reduced due to the small proportion of target DNA
Solution Approach 1:
The patent uses covariate reference sequences as counterweights to compensate for the small proportion of fetal DNA in maternal blood samples. By normalizing the target sequence counts against these covariate sequences, the method amplifies the signal from the rare fetal DNA sequences, effectively counteracting the dilution effect and improving detection sensitivity while maintaining diagnostic reliability.
Solution Approach 2:
The patent incorporates feedback through iterative refinement of normalization factors based on the distribution of covariate reference sequence counts. By continuously adjusting normalization parameters based on observed data patterns, the system optimizes its ability to detect rare fetal DNA sequences, thereby improving sensitivity without compromising the reliability of diagnostic results.
Data Source
AI summary
Techniques for construction of internal controls for improved accuracy and sensitivity of DNA testing include obtaining first data and determining weights over real numbers for a normalization function in less than a day. The first data indicates a measured amount of reference sequences for nucleic acids from training samples. The reference sequences include a target, for which an abundance is indicative of a condition of interest, and covariates not correlated with the condition of interest. The normalization function involves a sum of abundances of the covariates, as internal controls, each multiplied by a corresponding one of the weights. The weights are determined based on minimizing variance of a Taylor expansion of a ratio of a measured amount of the target divided by a value of the normalization function evaluated with measured amounts of the covariates over a portion of the first data in which the condition is absent.