ILLUMINA, INC. (20250111899). PREDICTING INSERT LENGTHS USING PRIMARY ANALYSIS METRICS
PREDICTING INSERT LENGTHS USING PRIMARY ANALYSIS METRICS
Organization Name
Inventor(s)
Gavin Derek Parnaby of Laguna Niguel CA US
Michael Ruehle of Fort Worth TX US
Jeffrey Fun-Shen Gau of San Mateo CA US
Jeffrey Yuan of Long Island City NY US
PREDICTING INSERT LENGTHS USING PRIMARY ANALYSIS METRICS
This abstract first appeared for US patent application 20250111899 titled 'PREDICTING INSERT LENGTHS USING PRIMARY ANALYSIS METRICS
Original Abstract Submitted
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.
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