Patent classifications
G16B20/20
METHOD FOR PROVIDING, ON BASIS OF HETEROLOGOUS ORGANISM-DERIVED GENETIC MARKER MATCHING, GENETIC TESTING SERVICE BY USING ONE OR MORE GENETIC MARKERS OF MODEL ORGANISM AND PATTERN INFORMATION THEREOF AS GENETIC MARKER INFORMATION OF TARGET ORGANISM
Provided is a method for providing, on the basis of genetic marker matching of heterologous organisms, a genetic testing service by using genetic markers of a model organism as genetic markers of a target organism, the method comprising the steps of: selecting at least one genetic marker of a pre-stored model organism; comparing genomic information of the model organism to genomic information of a target organism to be subjected to a genetic testing service, when the selected at least one genetic marker is a previously known genetic marker through pre-analyzed information; and providing a genetic mutation-based genetic report about a target organism on the basis of the results of comparing genomic information of target and model organisms.
Variant Calling For Multi-Sample Variation Graph
A method for calling variants in genetic data includes sorting nodes in a graph-based reference genome, assigning identification information to the sorted nodes, assigning depth values to respective ones of the sorted nodes, determining a reference genome path and one or more variation paths, and determining one or more variants in the graph-based reference genome based on the depth values assigned to nodes on the one or more variation paths.
Variant Calling For Multi-Sample Variation Graph
A method for calling variants in genetic data includes sorting nodes in a graph-based reference genome, assigning identification information to the sorted nodes, assigning depth values to respective ones of the sorted nodes, determining a reference genome path and one or more variation paths, and determining one or more variants in the graph-based reference genome based on the depth values assigned to nodes on the one or more variation paths.
MITIGATION OF STATISTICAL BIAS IN GENETIC SAMPLING
Provided herein are methods, systems, and storage media that may find use, e.g., in sequencing (e.g., via NGS) polymorphic alleles, such as detecting loss-of-heterozygosity (LOH) of a human leukocyte antigen (HLA) gene or another polymorphic human gene. In some embodiments, the methods and systems comprise obtaining observed allele frequencies and observed binding propensities for the alleles to one or more bait molecule(s), then applying an optimization model to determine adjusted allele frequencies that take into account these binding propensities, thereby adjusting for and/or minimizing any potential bias rooted in differential allele: bait binding propensities with regard to determination of allele frequency.
MITIGATION OF STATISTICAL BIAS IN GENETIC SAMPLING
Provided herein are methods, systems, and storage media that may find use, e.g., in sequencing (e.g., via NGS) polymorphic alleles, such as detecting loss-of-heterozygosity (LOH) of a human leukocyte antigen (HLA) gene or another polymorphic human gene. In some embodiments, the methods and systems comprise obtaining observed allele frequencies and observed binding propensities for the alleles to one or more bait molecule(s), then applying an optimization model to determine adjusted allele frequencies that take into account these binding propensities, thereby adjusting for and/or minimizing any potential bias rooted in differential allele: bait binding propensities with regard to determination of allele frequency.
SYSTEM, METHOD, AND APPARATUS FOR PREDICTING GENETIC ANCESTRY
In one embodiment, a method includes accessing a sample of genetic material associated with a first animal, wherein the sample of genetic material comprises raw genotypes, generating phased haplotypes based on the raw genotypes, generating local assignments for genetic populations for the phased haplotypes by machine learning algorithms based on comparisons between the phased haplotypes and a reference panel comprising reference haplotypes associated with reference populations, and sending instructions to a user device for presenting an output associated with the first animal to a user, wherein the output is generated based on the local assignments for the genetic populations.
SYSTEM, METHOD, AND APPARATUS FOR PREDICTING GENETIC ANCESTRY
In one embodiment, a method includes accessing a sample of genetic material associated with a first animal, wherein the sample of genetic material comprises raw genotypes, generating phased haplotypes based on the raw genotypes, generating local assignments for genetic populations for the phased haplotypes by machine learning algorithms based on comparisons between the phased haplotypes and a reference panel comprising reference haplotypes associated with reference populations, and sending instructions to a user device for presenting an output associated with the first animal to a user, wherein the output is generated based on the local assignments for the genetic populations.
BIOINFORMATIC PROCESSES FOR DETERMINATION OF PEPTIDE BINDING
This invention relates to the identification of peptide binding to ligands, and in particular to identification of epitopes expressed by microorganisms and by mammalian cells. The present invention provides polypeptides comprising the epitopes, and vaccines, antibodies and diagnostic products that utilize or are developed using the epitopes.
BIOINFORMATIC PROCESSES FOR DETERMINATION OF PEPTIDE BINDING
This invention relates to the identification of peptide binding to ligands, and in particular to identification of epitopes expressed by microorganisms and by mammalian cells. The present invention provides polypeptides comprising the epitopes, and vaccines, antibodies and diagnostic products that utilize or are developed using the epitopes.
METHOD OF AND SYSTEM FOR GENERATING A LONGEVITY ELEMENT AND AN INSTRUCTION SET FOR A LONGEVITY ELEMENT PLAN
A system for generating a longevity element and an instruction set for a longevity element plan, the system including at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least an element of user-reported data, determine, using the at least an element of user-reported data and a first machine-learning process, a longevity element, calculate, using a longevity element and at least a second element of data, a compensatory supplement, and generate, using the at least an element of user-reported data and at least a longevity element, an instruction set for a longevity element plan.