Abstract: Detecting analytes using proximity-induced tagmentation, strand invasion, restriction, or ligation is provided herein. In some examples, detecting an analyte includes coupling a donor recognition probe to a first portion of the analyte. The donor recognition probe includes a first recognition element specific to the first portion of the analyte, a first oligonucleotide corresponding to the first portion, and a transposase coupled to the first recognition element and the first oligonucleotide. An acceptor recognition probe is coupled to a second portion of the analyte. The acceptor recognition probe includes a second recognition element specific to the second portion of the analyte and a second oligonucleotide coupled to the second recognition element and corresponding to the second portion. The transposase is used to generate a reporter polynucleotide including the first and second oligonucleotides. The analyte is detected based on the reporter including comprising the first and second oligonucleotides.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning model to classify or predict a probability of an oligonucleotide probe yielding an accurate genotype call or hybridizing with a target oligonucleotide—based on the oligonucleotide probe's nucleotide-sequence composition. To intelligently identify oligonucleotide probes that are more likely to yield accurate downstream genotyping—or more likely to successfully hybridize with target oligonucleotides—some embodiments of the disclosed machine-learning model include customized layers trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning to determine factors or scores indicating an error level with which a given methylation assay detects methylation of cytosine bases. For instance, the disclosed systems use a machine-learning model to generate a bias score indicating a degree to which a given methylation assay errs in detecting cytosine methylation when specific sequence contexts surround such cytosines compared to other sequence contexts. The machine-learning model may take various forms of models, including a decision-tree model, a neural network, or a combination of a decision-tree model and a neural network. In some cases, the disclosed system combines or uses bias scores from multiple machine-learning models to generate a consensus bias score.