Importance

Loading Importance Indicators...

Abstract

This disclosure describes embodiments of methods, non-transitory computer readable media, and systems that can utilize one or more machine learning models to predict insert lengths of a sample genomic sequence from which nucleotide read pairs are sequenced. For example, the disclosed systems can generate predictions for insert lengths based on cluster metrics from primary analysis on a sequencing device, such as signal intensity. By applying a machine-learning-based insert length prediction model to process the cluster metrics, the disclosed systems generate a predicted insert length (e.g., a distribution or a mean). To determine cluster metrics, the disclosed systems can analyze data from oligonucleotide clusters and/or from a sample genomic sequence used to sequence nucleotide read pairs during primary analysis. Based on predicted insert lengths from cluster metrics, the disclosed systems can determine improved genotype calls for genomic samples, such as calls in genomic regions comprising tandem repeats or structural variants.

Loading the Abstract Image...

First Claim