SNP Characterization System for Off-Target Variant Detection
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Solution Overview
Problem
Existing genotyping methods face challenges in accurately identifying off-target variants (OTVs) which can lead to incorrect genotype calls due to uncharacterized genomic DNA variations, resulting in additional genotype clusters that are often miscalled as heterozygote clusters, and require human expert intervention for probe set selection and quality assessment.
Innovation Solution
The implementation of a system and method that uses an OTV score and expectation maximization algorithm to separate OTV clusters from other genotype clusters, employing posterior information from initial cluster algorithms like AxiomGT1, and automatically selects the best probeset for genotyping based on quality metrics such as PolyHighResolution, MonoHighResolution, OTV, Call Rate Below Threshold, No Minor Homozygote, and Other classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing genotyping methods are used to identify genotype clusters, then genotype calls can be obtained, but off-target variants (OTVs) cause additional clusters to be miscalled as heterozygote clusters, reducing genotyping accuracy
Solution Approach 1:
The patent segments the genotype clustering process into multiple stages: initial clustering to identify candidate clusters, OTV detection to identify clusters containing off-target variants, and separate handling of OTV clusters versus non-OTV clusters. This segmentation allows the system to distinguish between true heterozygote clusters and OTV-induced clusters, preventing miscalls and improving genotyping accuracy.
Solution Approach 2:
The patent introduces an intermediary OTV detection step between initial clustering and final genotype calling. This intermediary process analyzes cluster characteristics to identify OTV-containing clusters and applies appropriate correction methods, serving as a mediator that prevents erroneous genotype calls while preserving accurate heterozygote identification.
2Reliability
If human experts manually select probe sets and assess quality, then genotype calls can be reviewed, but the process requires significant time and human intervention
Solution Approach 1:
The patent implements self-service automation where the system automatically performs probe set selection, quality assessment, and OTV detection without requiring human expert intervention. The automated pipeline includes built-in quality metrics and decision algorithms that independently evaluate and process genotype data, dramatically reducing time loss while maintaining or improving reliability through consistent automated criteria.
Solution Approach 2:
The patent replaces the mechanical system of manual human expert review with an automated computational system. The automated system uses algorithms to perform probe set selection, quality assessment, and genotype calling, substituting human mechanical processes with electronic computation that is both faster and more consistent, eliminating time loss associated with manual intervention.
3Device complexity
If all genotype clusters are treated uniformly, then the analysis process is simple, but OTV clusters are miscalled as heterozygotes, reducing measurement precision
Solution Approach 1:
The patent applies local quality differentiation by treating OTV clusters and non-OTV clusters differently based on their specific characteristics. Instead of uniform processing, the system identifies clusters with OTV signatures and applies specialized analysis methods to those specific clusters, while using standard methods for others. This localized approach improves measurement precision without requiring complete restructuring of the entire analysis process.
Data Source
AI summary
Methods for processing data using information gained from examining biological materials identifies and characterized probes for Single Nucleotide Polymorphisms and identifies Off Target Variants.