Importance

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Abstract

This disclosure describes methods, non-transitory computer readable media, and systems that can generate genotype calls from a combined pipeline for processing nucleotide reads from multiple read types/sources for robust, accurate genotype calls. For example, the disclosed systems can train and/or utilize a genotype-call-integration machine-learning model to generate predictions for genotype calls based on data associated with a first type of nucleotide reads (e.g., short reads) and a second type of nucleotide reads (e.g., long reads). As disclosed, the disclosed systems can determine sequencing metrics and can utilize a genotype-call-integration machine-learning model to generate predictions (e.g., genotype probabilities, variant call classifications) for generating output genotype calls based on the sequencing metrics. The disclosed system can utilize multiple such genotype-call-integration machine-learning models to generate genotype calls for different variant types, such as SNPs and indels, where the genotype-call-integration machine-learning models generate different predictions for each variant type.

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First Claim