Machine-learning model for recalibrating nucleotide-base calls

Abstract

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. Indeed, the disclosed systems can generate an initial nucleotide-base call based on sequencing metrics for nucleotide reads of a sample sequence utilizing a call-generation model and further utilize a call-recalibration-machine-learning model to generate classification predictions for updating or recalibrating the initial nucleotide-base call from a subset of the same sequencing metrics or other sequencing metrics.