Minority Genotype Detection via Deep Sequencing and Statistical Clustering
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Solution Overview
Problem
Current methods are inadequate for efficiently detecting minority genotypes, which are crucial for understanding biological responses and treatment outcomes in diseases like hepatitis C and cancer, due to their genetic heterogeneity and rapid mutation rates.
Innovation Solution
A method involving deep sequencing, statistical analysis, and clustering techniques to identify minority genotypes by detecting variant nucleobases and determining their significance, using techniques such as SMRT sequencing, Ion Torrent semiconductor sequencing, and Bayesian models to identify co-occurring variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep sequencing is used to detect minority genotypes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex sequencing problem into distinct components: (1) performing deep sequencing to generate raw data, (2) detecting variant nucleobases at reference positions, (3) performing statistical analysis to determine significance, and (4) analyzing co-occurrence patterns. This segmentation allows each component to be optimized independently while maintaining overall system manageability.
Solution Approach 2:
The patent applies preliminary action by performing statistical analysis on variant significance and co-occurrence patterns before final genotype classification. This preliminary processing of data filters out noise and identifies meaningful patterns, making the subsequent detection more accurate and reducing the complexity of the final interpretation step.
2Measurement precision
If statistical analysis is performed to identify significant variants, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent implements partial action by focusing statistical analysis only on variant positions that meet predetermined significance thresholds, rather than analyzing all possible variants. This selective approach maintains high detection accuracy for meaningful variants while reducing computational time and resources spent on irrelevant data.
Solution Approach 2:
The patent utilizes parameter changes by adjusting significance thresholds and co-occurrence criteria to optimize the balance between detection accuracy and analysis time. By dynamically setting these parameters based on the specific application needs, the system can achieve high precision when necessary while reducing analysis time when speed is prioritized.
3Measurement precision
If co-occurrence analysis is performed to identify minority genotypes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the co-occurrence analysis into distinct steps: (1) identifying significant variants from statistical analysis, (2) grouping variants that co-occur in the same sequences, and (3) classifying sequences into minority genotype groups based on shared variant patterns. This segmentation simplifies the complex analysis while maintaining high identification accuracy.
Solution Approach 2:
The patent uses copying by creating simplified representations of genotype patterns from the complex sequencing data. Once minority genotype patterns are identified through co-occurrence analysis, these patterns are copied and used as reference profiles for rapid identification in future samples, reducing the complexity of repeated full analyses.
Data Source
AI summary
Disclosed are methods for detecting a minority genotype of a target nucleic acid. The disclosed method generally includes the steps of (a) deep sequencing at least a portion of the target nucleic acid; (b) using the deep sequencing results of (a) to detect the presence of variant nucleobases at one or more nucleotide reference positions within the target nucleic acid; (c) using the variant detection results generated in step (b) to perform a statistical analysis of whether the variants are significant; and (d) using the variant detection and variant significance results generated in steps (b) and (c) to perform a statistical analysis of whether a subset of sequences together exhibit a common set of significant variants.