Variant Call Recalibration for Multiallelic and Haploid Genomes
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Solution Overview
Problem
Existing nucleotide base sequencing platforms and sequencing data analysis software inaccurately determine nucleotide base calls, particularly for multiallelic and haploid genomic coordinates, and often produce false negative variant calls, while being computationally inefficient and lacking interpretability.
Innovation Solution
A call recalibration machine learning model is employed to update sequencing metrics, generating variant call classifications that improve accuracy and efficiency by adapting to specific genomic coordinates, such as multiallelic, haploid, and falsely identified homozygous reference genotypes, using a combination of internal and external sequencing metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing variant callers are used to determine nucleotide base calls, then variant calls can be generated, but accuracy is poor particularly for multiallelic and haploid genomic coordinates
Solution Approach 1:
The patent introduces an intermediary processing step between raw sequencing data and final variant calls. A machine learning model acts as a mediator to recalibrate base calls by learning from both biallelic and multiallelic training data, then applying this knowledge to improve accuracy for challenging genomic coordinates including haploid and multiallelic regions.
Solution Approach 2:
The patent changes the parameters used for variant calling by training machine learning models with expanded datasets that include multiallelic coordinates. The model learns to adjust prediction parameters based on sequencing metrics and contextual information, improving accuracy for previously challenging genomic regions.
2Adaptability or versatility
If machine learning based sequencing systems are used, then nucleotide base calls can be determined, but training data is largely biallelic leading to poor performance on multiallelic coordinates
Solution Approach 1:
The patent makes the machine learning model universal by training it on multiple types of genomic coordinates simultaneously. The model is designed to handle both biallelic and multiallelic coordinates, as well as haploid and diploid regions, using a unified architecture that adapts to different coordinate types through learned patterns from diverse training data.
Solution Approach 2:
The patent performs preliminary action by pre-training the machine learning model on comprehensive datasets that include multiallelic coordinates before deployment. This pre-training ensures the model has learned appropriate patterns for handling diverse genomic coordinate types, improving its performance on multiallelic regions from the outset.
3Reliability
If existing sequencing systems are used, then variant calls can be generated, but excessive false negative variant calls are identified
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from sequencing metrics and recalibrates base calls based on predicted classifications. The system uses feedback from training data and performance metrics to improve its ability to identify true variants while reducing false negatives in production sequencing.
4Productivity
If conventional sequencing data analysis software is used, then nucleotide base calls can be determined, but computational efficiency is low and interpretability is lacking
Solution Approach 1:
The patent segments the variant calling process into distinct stages: initial base call determination, sequencing metric extraction, machine learning classification, and final variant call generation. This segmentation allows each component to be optimized independently, improving overall computational efficiency while maintaining interpretability through clear process boundaries.
Data Source
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AI summary
This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide base calls (e.g., variant calls) of a call generation model. For instance, the disclosed systems can train and utilize a call recalibration machine learning model to generate a set of predicted variant call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant call classifications, the disclosed systems can further update or modify nucleotide base calls (e.g., variant calls) corresponding to genomic coordinates, such as multiallelic genomic coordinates, haploid genomic coordinates, and genomic coordinates indicated (by the call generation model) to exhibit homozygous reference genotypes.