Patents by Inventor Rigoberto Pantoja

Rigoberto Pantoja has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240124921
    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.
    Type: Application
    Filed: December 21, 2023
    Publication date: April 18, 2024
    Inventors: Andrew KENNEDY, Sarah SHULTZABERGER, Kayla BUSBY, Colin BROWN, Andrew PRICE, Eric VERMAAS, Rigoberto PANTOJA, Matthew Feeley, Jennifer ZOU, Yong LI, Sepideh ALMASI, Anindita DUTTA, Michelle ALVAREZ
  • Publication number: 20230340571
    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.
    Type: Application
    Filed: April 26, 2023
    Publication date: October 26, 2023
    Inventors: Sepideh Almasi, Yong Li, Anindita Dutta, Eric Vermaas, Rigoberto Pantoja