132 results about "Genetic variants" patented technology

Provided herein are methods of identifying genetic variants in biological samples. In particular, the present disclosure uses spatial transcriptomic templated ligation methods and compositions to identify the presence or absence, and the location of genetic variants in the transcriptome or genome of a biological sample.

Provided herein are methods, compositions, and kits to spatially detect genetic variants in a target nucleic acid via templated-ligation and reversible terminator nucleotides.

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

Systems and methods for predicting a medical trait in an individual are provided. The techniques include obtaining genomic data including information indicative of genetic variants present in a genome of the individual and calculating, using an at least one processor and a trained computational model, a trait score based on the information indicative of the genetic variants, wherein the trained computational model was obtained by training a first computational model using data describing phenotypes, genotypes, and / or phenotype-genotype relationships for a population including admixed individuals of multiple ancestries. The trait score may be used to alter a course of medical treatment of the individual and / or to implement a clinical trial.

This application is directed to multiomic and biomimetic digital twin techniques for identifying disease-specific genetic variants. A computer system obtains information of subject genetic variants that are identified from a plurality of biological samples of a plurality of patients who are diagnosed with a target disease. A subset of subject genetic variants are selected based on a plurality of subject phenotypes of the target disease. The subset of subject genetic variants are ranked based on the plurality of subject phenotypes to generate subject genetic variant information. The computer system further obtains subject medical information of the plurality of patients. The computer system applies a biomimetic information model to process the subject genetic variant information, the subject medical information, and the general genetic variant information of the target disease and identify a set of target genetic variants associated with the phenotype of the disease(s) satisfying a variant selection criterion.

The present disclosure provides a method and a gene detection panel for evaluating treatment response, recurrence and survival by detecting genetic variants and their changes before and after concurrent chemoradiotherapy in tumor tissues of patients with esophageal cancer. The present disclosure develops a set of esophageal cancer NGS analysis panel. Aiming at 402 mutation sites including 35 genes that frequently occur in esophageal squamous cell carcinoma tissue cells, 62 pairs of esophageal squamous cell carcinoma tissues before and after CCRT are analyzed for specific site variation, hoping to find new predictive markers. The present disclosure combines these potential markers into an esophageal cancer detection panel, which has extremely high value for improving the prognosis of esophageal cancer.

Various embodiments relate generally to the field of nucleic acid amplification and detection, in particular loop-mediated isothermal nucleic acid amplification and the detection of amplicons using designed detection probes. Moreover, various embodiments also relate to methods and kits for determining the presence or quantity of point mutations in a target nucleic acid molecule in a sample using loop-mediated isothermal amplification, which may be used for identifying genetic variants. In one aspect, the detection probe is a single-stranded probe comprising a nucleotide sequence fully complementary to a nucleotide sequence of the probe binding site comprising the point mutation. In another aspect, the detection probe comprising a nucleotide complementary to the point mutation at the penultimate base relative to the 3′ end of the detection probe.

The present invention discloses the application of TMPRSS9 gene in the diagnosis and treatment of venous thromboembolism, and belongs to the field of medical diagnosis and treatment technology. The present invention verifies the association between missense mutations of TMPRSS9 gene and thrombophilia in human population, in vivo and in vitro, and draws the following conclusions: nonsense mutations and missense mutations at the sites of c.334C>T, c.692C>T, c.917G>A, and c.1709C>T of TMPRSS9 gene are representative in the effect of TMPRSS9 gene mutation on the occurrence of venous thrombosis. The discovery of the above-mentioned pathogenic gene mutations can prevent and treat thrombophilia at an early stage, which is of great significance for the diagnosis, treatment and management of thrombophilia. At the same time, the inclusion of the new TMPRSS9 gene enriches the genetic variation spectrum of venous thromboembolic diseases, which is of guiding significance for the screening of diseases.

The present disclosure provides systems and methods to detect somatic or germline variants by providing a predetermined genomic DNA (gDNA) to an assay mixture, and capturing a sample of a subject's genetic information using a DNA sequencer and detecting genetic variants from the genetic information. A mutation may then be classified as being from a germline source if gDNA derived molecules have lengths inconsistent with those expected from cell-free DNA (cfDNA) derived molecules.

The present invention concerns selective renal denervation treatment of drug resistant hypertensive patients by correlating the patients' genetic panel by categorization and hierarchy according to patients' genetic variants within the functional genes for heart activity, for the renin-angiotensin aldosterone system, and for renal activity.

Methods for detecting a short genetic variant in a test sample are described herein. In some exemplary methods, the short genetic variant is called using one or match scores, which are determined using one or more sequencing data sets obtained from a test nucleic acid molecule, wherein the test sequencing data sets are determined by sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to a flow-cycle order. Also described herein are methods of sequencing a test nucleic acid molecule using two or more different flow-cycle orders and / or extended flow cycle orders having five or more nucleotide flows per flow cycle.

Examples may use pathogenicity evidence data associated with genetic variants and health conditions to create input data for a causal machine learning model. Examples may apply a causal machine learning model to input data to produce a trained causal model. A graphical representation of the trained causal model may include nodes connected via acyclic directed edges. A first node of the nodes may represent a pathogenic evidence variable associated with the health condition. The at least one second node may represent a cause of the pathogenic evidence variable. The at least one third node may represent the impact of the pathogenic evidence variable. The non-cyclic directed edge may represent a relationship between two nodes. Examples may output prediction data sampled from the trained causal model.

Described herein are methods for making genetically modified cells by introducing combinations of genetic variants (designed or random) or constructs (genes or otherwise arbitrary DNA) into a population of cells, and for tracking each variant combination by sequentially building an array of barcodes at a common locus (chromosomal or plasmid), termed the barcode locus. Also described are the cells made by such methods.

The invention relates to a computer-implemented method for determining pharmacogenetic star alleles from high-throughput sequencing data, the method comprising the following steps: providing a plurality of result files which have been output by a plurality of different computer programs for genotyping and which each have a plurality of result elements, wherein each result element has at least one gene designation; shortening the allele designation as far as possible on the right for each result element of the plurality of result elements from each result file of the plurality of result files; and outputting the gene designation and a specific result diplotype. The invention also relates to a computer-implemented method for determining rare genetic variants. Finally, the invention also relates to a device having a processor and a memory in order to carry out such methods.

Systems and methods herein provide for rapid patient information to healthcare providers such that the healthcare providers can make more informed diagnoses. One method includes storing gene sequencing data and called genetic variants of a patient in a data structure. The method also includes receiving a request from a healthcare provider for results of a test that reports at least a portion of the called genetic variants in relation to a diagnosis of the patient by the healthcare provider, and delivering the results of the test to the healthcare provider if a quality control value of said at least a portion of the called genetic variants meets or exceeds a predetermined threshold of quality for assisting the healthcare provider.

Methods and apparatus for selecting genetic variants for a tumour-informed assay are provided. The method includes receiving a sample collected from a patient, the sample being associated with a cancer type, generating a mutational catalogue for the sample, the mutational catalogue indicating a proportion of genetic mutation types observed in the sample, selecting a set of signatures associated with the cancer type, the set including one or more signatures, each signature comprising a mutational profile, determining, based on the set of signatures associated with the cancer type and the mutational catalogue, a set of genetic variants most likely to be genuine somatic variants associated with the sample, and outputting the set of genetic variants for use in creating a tumour-informed assay for the patient.

Disclosed are methods, systems and computer-program products for the determination of complex genetic variants. The disclosed methods, systems and computer-program products may include obtaining a mutant scaffold nucleotide sequence that comprises a sequence that includes mutations characteristic of the complex genetic variant; obtaining a wild-type scaffold nucleotide sequence having a wild-type sequence; generating an alignment of at least one sequence from the sample to the mutant scaffold and to the wild-type scaffold; and determining that the sample contains a mutation characteristic of the complex genetic variant based on alignment to the mutant scaffold and not the wild-type scaffold.

The present disclosure relates to analyzing genetic data. In one arrangement, a method operates on input data comprising the strength of association between one or more phenotypes, including a target phenotype, and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. For each association, a set of one or more fine-mapped variants is identified. A fine-mapping prediction model is calculated based on the input data and the set of fine-mapped variants. The effect of the set of fine-mapped variants on the target phenotype is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

The types of genetic variants detected by NGS are very wide and not all genetic variants always lead to diseases, and thus it is difficult to quickly and accurately interpret the meaning of disease relevance for detected genetic variants. The present invention relates to a method of interpreting genetic variants based on nucleic acid sequencing. The method of interpreting genetic variants according to the present invention provides a logic tree for interpreting NGS variant data, which can classify the pathogenicity of genetic variants based on the ACMG guidelines and determine the level of pathogenicity of the genetic variants, and thus it is expected to be widely used in the life sciences and medical health fields.

An apparatus for generating personalized risk assessments for neurodegenerative diseases includes a computing device that receives user data containing genetic and medical information. It processes the data to create genotype identification and gene detection modules, identifying user genotypes and relevant genetic markers. The user's mitochondrial haplogroup is examined to refine the assessment. A risk calculation module employs machine learning to weigh genetic variants against population-based data, calculating a polygenic risk score (PRS). The PRS forms a personalized risk profile, displayed through a visual interface. The disclosed systems offer a comprehensive approach to accurate risk assessment, enabling targeted interventions and informed decision-making in neurodegenerative disease management.

Methods and devices are provided for selecting genetic variants for tumor awareness assays. A method includes receiving a sample collected from a patient, the sample associated with a cancer type; generating a directory of mutations for the sample, the directory of mutations indicating a proportion of genetic mutation types observed in the sample; selecting a set of features associated with the cancer type, the set comprising one or more features, each feature comprising a profile of mutations; determining a set of genetic variants most likely to be a true cell variant associated with the sample based on the set of features and the directory of mutations associated with the type of cancer; and outputting the set of genetic variants for creating a tumor awareness assay for the patient.

Disclosed herein are methods and compositions for associating a genetic variant with intraretinal fluid. Also disclosed herein are methods and compositions for associating a genetic variant with visual acuity, anatomic outcomes or treatment frequency.

Methods and compositions for preparing a library of target nucleic acid sequences are provided that can be used to assess gene mutations for tumor biomarker profiling of a sample. In particular, a target-specific primer set is provided that allows for selective amplification of a target sequence of a biomarker of tumor in a sample. In one aspect, the invention relates to target-specific primers that can be used to selectively amplify one or more target sequences associated with neoplastic biomarkers from two or more sample types. In some aspects, the amplified target sequences obtained using the disclosed methods and compositions can be used in various processes, including nucleic acid sequencing, and for detecting the presence of genetic variants of one or more targeting sequences associated with oncology.

Systems and methods for estimating tumor fraction in a biological sample are disclosed. One method may include: receiving sequencing data for each of a plurality of biological samples included on a combined targeted sequencing panel, wherein the sequencing data for each of the plurality of biological samples includes: genetic variant sequencing data covering one or more genetic variants of each of the plurality of biological samples; and background sequencing data covering all other genetic variants from all of the other biological samples; calculating, from the background sequencing data associated with each of the plurality of biological samples, a site-specific error rate for each genomic location associated with each of the one or more genetic variants; and establishing, based on the calculated site-specific error rate, a threshold for tumor fraction estimation for each of the one or more genetic variants. Other aspects are described and claimed.

The present invention refers to an in vitro method for predicting whether cattle are susceptible or resistant to paratuberculosis (PTB), or for classifying and / or selecting cattle according to whether they are susceptible or resistant to PTB; and / or for preventing susceptibility to PTB or to increase resistance to PTB.

The disclosure relates to analysing genetic data. In one arrangement, a method operates on input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. A set of one or more fine-mapped variants is identified for each association. A fine-mapping predictive model is calculated on the basis of the input data and the set of fine-mapped variants. The effect on the target phenotype of the set of fine-mapped variants is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

Provided herein are computer-implemented methods for assigning maternal or fetal origin to one or more genetic variants in cell free DNA (cfDNA) from a sample from a pregnant mammal, preferably a pregnant human, using a probabilistic model for assigning maternal or fetal origin to genetic variants in DNA from a sample obtained from a pregnant mammal, wherein the model assigns maternal or fetal origin based on a combination of fetal fraction and DNA fragment size.

The present disclosure provides, in part, a method of identifying genetic variants in an embryo. The method comprises: (a) obtaining two or more analyte sources from the embryo; (b) analyzing the two or more analyte sources to obtain genetic information for each source; (c) comparing the genetic information for each source to one or more reference genomes using at least one variant call program, wherein the variant call program identifies variants between each source and the reference genomes; and (d) combining the variants to identify the difference presented only by the source, wherein the difference is a genetic variant in the embryo.

The invention relates to a method for detecting changes of nucleotide sequences by using an isothermal nucleic acid amplification technology, a primer set for detecting selected genetic variants and components of a reaction mixture.