267 results about "Gene Variant" 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.

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 invention relates to a method for carrying out pathogenic microorganism identification and gene variation state joint detection on a pulmonary alveolar lavage fluid sample based on high-throughput sequencing. The method comprises the following steps: extracting nucleic acid from the pulmonary alveolar lavage fluid sample; constructing a metagenome capture library, and constructing a lung cancer polygene library; carrying out final library pooling; performing high-throughput sequencing; and analyzing data. Compared with the prior art, the scheme provided by the invention is based on a technical path of'macro 'captured by metagenome and'target' of targeted high-throughput sequencing; after the constructed final library is subjected to high-throughput sequencing and data analysis, index output (pathogenic microorganism identification, host chromosome copy number variation, gene mutation, gene fusion, gene copy number variation and methylation state) of multiple biomarkers can be met in one-time detection.

The invention provides an instruction data matching experiment method, system and device and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining each data set participating in the fine tuning of a large model, setting the parameters of a genetic algorithm, and randomly generating an initial population; each individual in the initial population is used for fine tuning training of a large model; evaluating the large model after fine tuning training to obtain a fitness value corresponding to each individual; selecting an individual with the highest fitness value for generating a next-generation population; pairing the selected individuals, and performing crossover operation according to a set crossover probability; performing gene variation on newly generated individuals according to a set variation probability to generate a new generation of population; and replacing the current population with a new generation of population generated through genetic manipulation, repeatedly executing the steps until a set termination condition is reached, and outputting an individual with the highest fitness value in the new generation of population to obtain an optimal matching scheme. The data matching efficiency is improved, and the labor cost is reduced.

The present disclosure provides methods, compositions and systems for identifying variant targets of interest, for example, in clinically actionable genes. Disclosed herein are representative assays for identifying gene variants, for example, that are implicated in one or more drug metabolism pathways. The methods, composition and systems disclosed herein enable a highly streamlined and cost-effective workflow for moving forward the emerging field of personalized medicine and related fields of study such as pharmacogenomics.

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.

The invention relates to a double-gene rare variation and disease relevance prediction model and an establishment method and application thereof, and belongs to the technical field of biological medicines.The establishment method of the double-gene rare variation and disease relevance prediction model comprises the following steps that S1, a sample library is screened; s2, performing quality control on whole exome sequencing data (WES); s3, performing phenotype screening; s4, performing grouping design; s5, carrying out PheWAS logistic regression analysis; s6, performing Firth logistic regression analysis and verification; and S7, carrying out double-gene feature analysis and double-gene pathogenicity relevance prediction. The method for analyzing the correlation between the rare double-gene variation and all disease phenotypes is designed for the first time, and a new method is provided for screening hereditary pathogenic factors of various diseases.

The invention provides a gene variation detection method and device, electronic equipment and a storage medium. The gene variation detection method comprises the following steps: acquiring first long read length sequencing data of a to-be-detected sample, and comparing the first long read length sequencing data with a reference genome to obtain first comparison data; determining a region of the target gene in the reference genome, and calculating a first sequencing depth of the target gene according to the determined region and the first comparison data; determining a first base distribution ratio of the first sub-target gene to the second sub-target gene, and calculating a first sub-sequencing depth of the first sub-target gene and a second sub-sequencing depth of the second sub-target gene according to the first base distribution ratio and the first sequencing depth; and calling a pre-trained detection model to perform copy number variation detection based on the first sub-sequencing depth and the second sub-sequencing depth to obtain a target copy number variation type of the to-be-detected sample. According to the embodiment of the invention, the accuracy of gene variation detection can be improved.

The invention relates to a pathogenic gene identification method and system based on multi-agent debate and a medium. The method comprises the following steps: acquiring gene detection data and clinical phenotype data; the data agent processes the acquired data, and calls a gene variation pathogenicity analysis operator and a molecular genetic large model to obtain first pathogenic gene information; the knowledge agent calls a molecular genetic large model to obtain second pathogenic gene information based on the first pathogenic gene information; the knowledge and data agents sequentially speak and debate pathogenicity of candidate pathogenic genes in the first or second pathogenic gene information on the basis of sorting results in the pathogenic gene information output by the knowledge and data agents; after each round of debate is finished, the debate agent judges whether the sorting results of the data and the knowledge agent on the candidate pathogenic genes are consistent or not, if the sorting results are consistent or the number of debate rounds reaches a threshold value, debate is finished, the debate agent conducts reasoning and outputs a result, and if not, the next round of debate is started. Compared with the prior art, the method has the advantages of no dependence on large-scale training data, high interpretability and the like.

The present disclosure relates to genetic copy number variation (CNV) detection. Particularly, aspects are directed to sequencing nucleic acid obtained from a biological sample obtained from a subject to generate sequencing data. The sequence reads are ordered by mapping the sequence reads to a reference genome and stored in an ordered format, A global segmentation of the target region is performed based on the stored sequence reads and a set of segments of the target region is identified and used to determine a copy number variation (CNV) metric. A first status of a genetic condition for the subject is determined based on the CNV metric, and a report of the corresponding genetic condition screening test is determined based on the CNV metric and the status.

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.

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid. Also provided herein are methods for partitioning one or more genomic regions of a reference genome into a plurality of portions according to one or more features.

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention relates to the field of gene detection, and particularly discloses a gene detection device and method applying Beidou satellite positioning, and the device comprises a sample collection and preprocessing module which is used for obtaining a body fluid or tissue biological sample of a user, and carrying out cell lysis, DNA / RNA extraction and purification operation on the sample; the gene sequencing analysis module is connected with the sample collecting and preprocessing module and is used for performing gene sequencing on the purified nucleic acid sample and identifying gene variation information related to tumors; the high-precision positioning capability of the Beidou system is utilized to obtain the environmental parameters of the geographic position of the user in real time, then the genetic variation data and the regional environmental carcinogenic factors are subjected to weighted fusion analysis through the risk assessment algorithm, and finally a highly personalized detection report is generated. In this way, the detection result can truly reflect the specific influence of the external environment on the individual health, and the accuracy and practicability of the report are remarkably improved.

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.

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

The application belongs to the technical field of gene detection, and discloses a method and system for judging the influence of homologous regions in NGS gene variation detection. The method comprises the following steps: obtaining a first type of gene and a plurality of second type of genes according to a public database; obtaining corresponding original sequence files of the first type of gene and the second type of gene based on a local gene database, obtaining corresponding sequence alignment files based on a local BAM database, and combining them to obtain known NGS gene data; calculating the GC content, the proportion of repeat units, and the similarity ratio in the original sequence file, and calculating the alignment quality in the corresponding sequence alignment file to construct a training sample; inputting the training sample into a classification model to obtain an optimized classification model; extracting the GC content, the proportion of repeat units, the similarity ratio, and the alignment quality from the NGS gene data to be detected and inputting them into the optimized classification model to judge whether the variation detection is affected. The application can judge the influence of homologous regions in variation detection without the aid of prior information of homologous regions.

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.

Provided are plants, cells, tissues, and germplasm thereof comprising one or more targeted alterations of the genomic sequences of the MAM1 gene that encode for methylthioalkylmalate synthase, as well as meal made from such plant materials. Also provided are methods and compositions to make altered MAM1 gene variants; breeding methods and methods of identifying and selecting plant materials having the disclosed MAM1 variants.

The application relates to the technical field of gene variation detection, in particular to a copy number variation detection method based on semi-supervised learning. The method comprises the following steps: obtaining read depth signals and mapping quality signals of each normal window of a reference genome sequence from alignment information of sequencing reads, correcting the read depth signals of all normal windows in terms of GC content bias, adopting a cyclic binary segmentation algorithm to divide all normal windows into segmented regions with uniform read depth signals, identifying copy number variation breakpoint positions in combination with a split read strategy, performing normalization processing on the mapping quality signals, performing smoothing and noise reduction processing on the read depth signals, labeling pseudo labels for corresponding segmented regions, performing clustering analysis on all segmented regions through an improved density clustering algorithm, integrating and determining the variation types of abnormal segmented regions, and outputting copy number variation detection results, so that efficient detection of copy number variation is realized, and the accuracy and reliability of the detection results are significantly improved.

The present invention relates to the field of data processing technology, and in particular to a genomic data analysis method. The method comprises the following steps: preprocessing genomic data, performing intelligent compression processing on the preprocessed genomic data using an adaptive multidimensional space compression algorithm to obtain compressed genomic data; performing gene variation detection and filtering on the compressed genomic data to obtain variant genomic data, and performing functional annotation to obtain genomic data with annotation results; performing gene expression analysis on the genomic data with annotation results to obtain gene expression data; and performing association analysis based on the genomic data with annotation results and gene expression data using a gene variation-phenotype association analysis method to screen out potential disease markers. The method solves the technical problems of inaccurate genomic data processing, low computational efficiency, and low analysis accuracy in the genomic data analysis process of traditional genomic data storage and processing technologies.

The embodiment of the present application discloses a genotype classification method for diffuse large B-cell lymphoma, a computer device, and a computer-readable storage medium. The method includes the following steps: testing a sample according to a preset specific gene set to obtain variation detection data; preprocessing the variation detection data to obtain variation information; generating an initial feature matrix based on the variation information, screening the initial feature matrix to obtain a feature matrix, and the feature matrix is ​​used to characterize the gene variation contained in the specific gene set in the corresponding sample; obtaining a first genotype label and an important feature set, constructing a data set based on the feature matrix, the first genotype label, and the important feature set, and training a classification model; obtaining a second genotype label output by the classification model, and determining a genotype classification report based on the second genotype label. Therefore, the present application can reduce the cost of testing, can effectively predict the patient's genotype, and has a high clinical application value.

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.

The invention discloses a gene detection management method and system, which comprises a complete process from biological sample collection, biological sample treatment, gene detection, data analysis to result report, realizes multi-level feature extraction, also comprises extraction and analysis of various biological information of genome DNA, RNA, protein, miRNA and lncRNA, improves the depth of gene detection, and improves the detection efficiency. According to the method, a gene-disease database and optimized feature selection are combined in reality, gene variation features with the strongest relevance with diseases are recognized, the gene-disease database is continuously updated and accurate through continuous feature selection, regression model training and verification processes, the prediction level of relevance between genes and diseases is improved, and the prediction efficiency is improved. A probability distribution diagram is generated by monitoring the difference between a database and a verification set, it is ensured that the gene-disease database is stably converged to an accurate and credible level as time goes on, a personalized site variation analysis report is automatically generated according to an analysis result, and an accurate seed selection basis is provided for breeding experts.

The present invention relates to a method for extracting the latest clinical significance of a genetic mutation, comprising the steps of: receiving unique, identifiable mutation information read by a user, a date of reading, and clinical information of the mutation read by the user; loading a database having clinical information of the mutation; searching the database for mutation information corresponding to the inputted mutation information, extracting clinical information for the searched mutation information, and comparing the extracted clinical information with the clinical information of the mutation read by the user; and summarizing whether the extracted clinical information is updated, on the basis of the date of reading.

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.