Small Variant Calling With Family-Specific Error-Rate Models
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Solution Overview
Problem
Existing methods for identifying small genetic variants, such as single-nucleotide variants (SNVs) and small insertions and deletions (indels), are inadequate for accurately and reproducibly detecting variants at low frequencies in heterogeneous DNA samples, particularly in cancer treatment settings, due to challenges in distinguishing true mutations from sequencing errors.
Innovation Solution
A method involving categorizing sequence reads into family types, determining error rates, and using a trained machine learning unit to detect genetic variants based on error rates and alignment to a reference genome, with filters to remove sequencing artifacts and enrich for true variants, including probabilistic models and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional variant calling methods are used to identify small genetic variants in heterogeneous DNA samples, then the detection process can be completed with standard computational resources, but the accuracy of detecting low-frequency variants is insufficient and false positives cannot be effectively reduced
Solution Approach 1:
The patent segments sequence reads into different family types based on their error patterns and characteristics. By categorizing reads into families with similar error profiles, the method can apply targeted error rate models to each family, improving detection accuracy for low-frequency variants while managing computational complexity through structured organization of the data.
Solution Approach 2:
The patent dynamically adjusts error rate thresholds and detection parameters based on the specific family type and observed error patterns. By changing parameters adaptively rather than using fixed thresholds, the method achieves higher precision in variant detection while maintaining computational efficiency through parameter optimization.
2Measurement precision
If advanced computational techniques are applied to improve variant detection accuracy, then the precision of small variant calling can be enhanced, but the computational time and resource consumption increase
Solution Approach 1:
The patent performs preliminary categorization of sequence reads into family types before conducting detailed variant analysis. By pre-organizing reads based on error patterns and assigning appropriate error rate models in advance, the method reduces the computational burden during the actual variant calling process, achieving high precision without excessive time consumption.
Solution Approach 2:
The patent applies different error rate models and analysis strategies tailored to specific family types rather than using a uniform approach for all reads. This localized optimization allows the method to achieve high precision for each category while minimizing overall computational time by avoiding unnecessary complex analysis for reads that can be processed more simply.
3Reliability
If error rate modeling is performed for each family type to reduce false positives, then the reliability of variant detection can be improved, but the complexity of the analysis pipeline increases
Solution Approach 1:
The patent develops a unified error rate modeling framework that can handle multiple family types through a common computational structure. By creating a versatile model that adapts to different read categories rather than requiring separate independent models for each type, the method improves reliability across all family types while keeping the pipeline complexity manageable through code reusability and standardized processing steps.
4Measurement precision
If stringent filtering criteria are applied to remove sequencing artifacts, then the false positive rate can be reduced, but the sensitivity for detecting true low-frequency variants decreases
Solution Approach 1:
The patent implements dynamic filtering criteria that adapt based on the observed error patterns and family type characteristics. Rather than applying fixed stringent thresholds that may remove true variants, the method adjusts filtering stringency dynamically according to the specific context, maintaining high false positive rejection while preserving sensitivity for detecting authentic low-frequency variants.
Data Source
AI summary
Described herein are methods and compositions related to small variant calling Characterizing rare variants implicated in common diseases remains a challenge. Towards these aims, computational efficiency of variant calling have leveraged more advanced computational techniques, including to improve variation detection across more samples or and meet quality control standards for variant calls. Nevertheless, there remains a great need in the art for faster, more effective and accurate variant detection. Here, a small variant calling model based on an error-rate is provided.