165 results about "Genetic variation" patented technology

Disclosed herein are methods of increasing nitrogen fixation in a non-leguminous plant. The methods can comprise exposing the plant to a plurality of bacteria. Each member of the plurality comprises one or more genetic variations introduced into one or more genes or non-coding polynucleotides of the bacteria's nitrogen fixation or assimilation genetic regulatory network, such that the bacteria are capable of fixing atmospheric nitrogen in the presence of exogenous nitrogen. The bacteria are not intergeneric microorganisms. Additionally, the bacteria, in planta, produce 1% or more of the fixed nitrogen in the plant.

The invention provides a method for constructing a rice epigenetic recombinant inbred line, and belongs to the technical field of molecular biology and genetic breeding. According to the method for constructing the rice epigenetic recombination inbred line, a fertile rice DDM1 gene function weakening mutant is obtained through a CRISPR / Cas9 gene editing technology and hybridized with wild type rice with the same background to obtain F1, then the F1 and the wild type rice are subjected to backcrossing to obtain BC1F1, single-plant offspring selfing with the DDM1 genotype being the wild type is selected, selfing is conducted for multiple generations through a single-grain transmission method, and the rice epigenetic recombination inbred line is obtained. According to the present invention, the strain population with stable inheritance is constructed, such that the rice epigenetic recombinant inbred line is obtained, and has characteristics of rich phenotype, genetic and epigenetic variation so as to provide important population resources for batch identification and the like of important agronomic shape functional sites of rice.

The present disclosure provides a systems and methods for processing genetic variations and phenotypic data. The systems and methods may be used to generate one or more databases comprising genetic variations and associations with phenotypes. The system and methods may be used to determine a pathogenicity of a genetic variation. The systems and methods may comprise human interpretation using pre-determined criteria.

The invention discloses a monogenic disease genetic variation intelligent interpretation method and device and a medium, and relates to the technical field of bioinformatics, and the method comprises the steps: generating an enhanced similarity matrix through a disease-specific phenotype template and a cosine similarity algorithm based on a standardized phenotype feature vector, and obtaining a dynamic weight vector through a dynamic adjustment function; integrating the dynamic weight vector into a normalized multi-omics data matrix, calculating a preliminary disturbance score through a path integral formalization algorithm, and obtaining a path disturbance score; and fusing the pathway disturbance score and the dynamic weight vector, calculating a pathogenicity score through a multi-level evidence fusion algorithm inspired by a quantum field theory, and generating a comprehensive report of variation pathogenicity grading and clinical suggestions according to a thermodynamic partition function model. According to the method, nonlinear and high-dimensional collaborative modeling and thermodynamic stability optimization of heterogeneous biological evidence are realized, and the interpretability of pathogenicity judgment under a complex genetic background is also improved.

The invention relates to a disease-specific quantitative trait locus identification method based on multi-omics integration. The method comprises the following steps: acquiring variation sites of whole genome sequencing data of a target object, and molecular phenotypes and molecular abundance of molecular phenotype data; determining an association significance probability value of an association pair formed by the variation point and the molecular phenotype based on the variation point and the molecular abundance, and screening a first association pair from the association pair based on the association significance probability value and condition analysis; determining a consistent second association pair in the normal association pair and the disease association pair, and determining a third association pair with a disease interaction effect in the second association pair; calculating a first effect estimation value and a second effect estimation value of each third association pair; and based on the first effect estimation value and the second effect estimation value of the third correlation pair, determining a target correlation pair related to the Parkinson's disease, and taking the target correlation pair as the identified quantitative trait site. By adopting the method, the Parkinson's specific pathogenic heritable variation can be accurately identified.

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.

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 invention discloses a comprehensive energy scheduling method and system based on genetic variation optimization, and the method comprises the steps: firstly issuing a price strategy through an energy manager based on a Stackelberg game theory, and then responding to a price signal and optimizing the own energy consumption behavior or equipment scheduling through a user aggregator, an energy supplier and an energy storage operator, therefore, a double-layer game feedback cycle taking the price as the core is formed. According to the method, a price initialization strategy inspired by improved load characteristics is designed to perform population initialization, and a more representative initial solution set is constructed, so that the convergence efficiency of the algorithm is expected to be improved from the source; meanwhile, a self-adaptive variation mechanism based on the load change rate is introduced, variation intensity is dynamically adjusted to balance exploration and utilization, and therefore the solving quality and the convergence efficiency of the algorithm are effectively improved, and the method adapts to the complex dynamic characteristics of the multi-main-body energy system.

To provide a method, process, and apparatus for non-invasive assessment of genetic variation.SOLUTION: Provided is a method for partitioning one or more genomic regions of a reference genome into a plurality of portion. The method comprises: (a) determining sequencing coverage variability across the reference genome; (b) selecting an initial portion length; (c) partitioning at least two genomic regions according to the initial portion length in (b); (d) comparing the sequencing coverage variability determined in (a) for each of the at least two genomic regions, thereby generating a comparison; (e) recalculating the number of portions for at least one of the genomic regions according to the comparison in (d), thereby determining an optimized portion length; and (f) re-partitioning at least one of the genomic regions into a plurality of portions according to the optimized portion length in (e).SELECTED DRAWING: None

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

The invention discloses a porcine epidemic diarrhea virus G2c genotype strain as well as a separation method and application thereof. Aiming at the current epidemic PEDV variant strain, the genetic variation rule of the epidemic strain is clarified by carrying out virus separation, identification and pathogenicity analysis, and the method has important significance on epidemiology, vaccine development and the like of the PEDV. Specifically, an applicant successfully separates a PEDV strain capable of realizing stable passage propagation in Vero-CCL81 cells from intestinal contents of diarrhea piglets collected from a pig farm of Jiangpermanent Province in Hunan, the PEDV strain is identified and determined as a porcine epidemic diarrhea virus G2c subtype, the PEDV strain is named PEDV CH / HNJY / 2025, the PEDV strain is preserved in China General Microbiological Culture Collection Center on December 11, 2025, and the preservation number of the PEDV strain is CGMCC NO. The preservation number is CGMCC (China General Microbiological Culture Collection Center)

The invention discloses a method for determining an equipment operation strategy. Relates to the field of artificial intelligence, and the method comprises the steps: obtaining an initial operation parameter group set, determining fitness evaluation information matched with each initial operation parameter group in the initial operation parameter group set, and enabling parameters in the initial operation parameter groups to be used for indicating configuration information of equipment; determining a target operation parameter group set from the initial operation parameter group set according to the fitness evaluation information; performing genetic variation operation on the target operation parameter group set to obtain a variation result matched with the target operation parameter group set; and under the condition of determining that the fitness evaluation information matched with the variation result meets the target expected condition, determining an operation strategy matched with the equipment according to the variation result. Through the method and the device, the problem of inaccurate determination of the equipment operation strategy in related technologies is solved.

The invention discloses an evaluation method, system and device for cell specific genetic variation disturbance and a storage medium. An integrated single cell data set is used for training a variational auto-encoder, and a high-dimensional gene expression profile is mapped into a cell state vector in a low-dimensional hidden space through an encoder of the variational auto-encoder; freezing parameters of the encoder, training a conditional disturbance module by using training data with a mononucleotide variation tag, and generating conditional variation embedding; performing anti-fact prediction for target cell and target mononucleotide variation, and generating a reference prediction expression profile and a disturbance prediction expression profile through a conditional disturbance module and a decoder of a variational auto-encoder based on the original genotype and the overturned genotype of the target cell; and calculating the difference between the two to obtain the disturbance effect score of the single nucleotide variation in the target cell, and quantitatively describing and predicting the specific disturbance mode of the SNV in different cell states.

The application discloses a molecular marker for identifying salvia miltiorrhiza cultivars and application thereof, and belongs to the technical field of plant molecular identification. The molecular marker comprises SM-M1, SM-M2, SM-M3, SM-M4, SM-M5 and SM-M6. The molecular marker can be used for effectively identifying two subgroups of salvia miltiorrhiza cultivars, and is beneficial to the research on genetic variation in salvia miltiorrhiza and evolution of cultivars.

The invention discloses a carbapenem drug resistance marker screening method and system based on a cross-species compressed Debrueine diagram and a medium. The method comprises the following steps: starting from whole genome sequencing data of gram-negative bacteria belonging to different species and carbapenem drug phenotypes of the gram-negative bacteria, constructing a compressed Debrueine graph based on cross-species joint data, and taking existence / deletion of nodes in the graph as unified genetic variation characteristics. Performing correlation analysis on the nodes and the drug resistance phenotypes by using a linear hybrid model to obtain a candidate node set related to the phenotypes; k-mer is extracted based on the candidate node sequence, and secondary statistical screening is completed in combination with chi-square test and mutual information; and finally determining a group of carbapenem drug-resistant genetic markers which can be applicable across species through a hierarchical feature selection strategy of random forest and XGBoost. Efficient dimension reduction of large-scale cross-species genome data, cross-species consistent variation representation and high-interpretability marker screening are achieved.

The invention relates to a screening method of anti-breast cancer key targets of a traditional Chinese medicine composition for treating triple-negative breast cancer pulmonary metastasis, which sequentially comprises the following steps: component-target preliminary screening, two sample Mendel randomization, SMR and co-localization, full phenotype association and side effect evaluation, pathway enrichment, clinical expression verification, molecular docking verification and the like. After Mendel randomization (MR) and SMR are placed in network pharmacology, population genetic variation is used as a natural randomization tool, evidence of causal relationship between exposure (target level) and outcome (breast cancer risk) is provided on a public GWAS level, traditional association analysis mixing and reverse causal risk are reduced, and the false positive rate of network pharmacology prediction is greatly reduced; pheWAS-MR is introduced to perform system identification on phenotypes of various diseases, so that a potential off-target effect can be identified in advance, the multiple effects and possible adverse reactions of potential treatment targets in traditional Chinese medicine compound targets can be evaluated, powerful data support is provided for subsequent clinical test design and drug alert strategies, and the risk of later failure of research and development is effectively reduced.

The application discloses a gene prdR for regulating myxobacteria predation and a protein coded by the gene, and belongs to the technical field of biotechnology. The mutant DK10 with significantly improved predation capacity is obtained by using the strategy of adaptive evolution; genetic variation sites of the mutant DK10 are analyzed by using the bacterial genome resequencing technology, and functions of the genetic variation sites are researched by using the gene knockout technology, and it is found that the gene deletion of MXAN_2902 (prdR, the nucleotide sequence is shown as SEQ ID NO. 1) significantly enhances the predation capacity of Myxococcus xanthus. The nucleic acid sequence and the amino acid sequence of the transcriptional activator PrdR for regulating the predation of Myxococcus xanthus are disclosed, and the functional strain of Myxococcus xanthus capable of efficiently predating Salmonella enteritidis can be developed by using the prdR gene, and a new strategy is provided for the prevention and control of drug-resistant HVPG pathogenic bacteria.

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 invention provides a method and system for gene expression quantitative trait locus analysis, and relates to the technical field of genomics, the method comprises the following steps: carrying out sample and locus level quality control on input genotype data, and generating a standardized genotype matrix; performing standardization processing and low expression gene filtering on the input gene expression data to generate a standardized expression quantity matrix; performing principal component analysis according to the standardized genotype matrix to obtain a group structure covariable; carrying out implicit factor analysis on the basis of the standardized expression quantity matrix to obtain a technical batch effect covariable; quantitative character site correlation analysis is carried out through a standardized genotype matrix, a standardized expression quantity matrix, a population structure covariable and a technical batch effect covariable. The method is suitable for functional genomics research, complex disease genetic mechanism analysis and precision medical related functional genetic variation mining scenes.

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.

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

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.

The invention discloses a resequencing-based space mutagenesis peanut population genotype variation map and a construction method thereof, and belongs to the technical field of plant biotechnology and plant molecular breeding, and the technical key points are as follows: a space mutagenesis peanut mutant plant is utilized, based on a whole genome resequencing technology, genetic variation sites in a genome are systematically detected, and the genotype variation map of the space mutagenesis peanut population is obtained. A variation map of a space mutagenesis peanut population genome is constructed, the map comprises no less than 500,000 mononucleotide variations (SNP), 100,000-200,000 insertion and deletion (InDel), about 3000 copy number variations (CNV) and about more than 3000 structural variations (SV), and 66 genes related to peanut grease anabolism are screened out. The construction method comprises the following steps: obtaining a mutant plant, extracting DNA, sequencing, detecting and identifying SNP, InDel, CNV and SV variation sites, and the like.

The present invention provides the use of an antigen-binding molecule that binds to HER3 for the treatment or prevention of a HER3-related cancer wherein the HER3-related cancer does not comprise a genetic variation that results in an increase in MET expression or an increase in gene product activity.

The invention relates to a genetic variation category prediction method and device based on convex hull geometric constraint, and relates to the fields of bioinformatics, artificial intelligence, applied mathematics and the like, and the method comprises the steps: obtaining multi-modal data corresponding to target genetic variation, including a target variation DNA sequence, a target reference DNA sequence and target semantic text information; performing feature extraction on the multi-modal data through the trained gene variation category prediction model, and predicting the category of target gene variation; wherein loss functions adopted in the model training process comprise a classification loss function used for indicating the difference between a prediction category and a real category, the convex hull geometric constraint loss function is used for indicating the concentration degree of the same type of gene variation corresponding to the distribution range of the feature space and the separation degree of different types of gene variation corresponding to the distribution range of the feature space. According to the invention, based on multi-modal feature fusion and convex hull geometric constraint, efficient and accurate prediction of the gene variation category is realized.

The invention belongs to the field of vaccine microorganisms, and provides a swine pasteurella multocida attenuated strain PmA4-750, which is preserved in the China Center for Type Culture Collection (CCTCC), and the preservation number is CCTCC NO: M 20251644. The invention further provides a preparation method of the swine pasteurella multocida attenuated strain PmA4-750. After alternate high-temperature continuous passage, three generations of whole-gene sequencing and pathogenicity contrast tests find that a vaccine candidate strain (PmA4-750 strain) has a section of specific insertion fragment with the length of about 43kb, and 14 genetic variation sites, including 10 InDel (insertion / deletion) and 4 SNP (single nucleotide polymorphism), exist in the vaccine candidate strain. The inserted genes may generate adaptive evolution of strains in the passage process of the strains, so that the pathogenic characteristics of the strains are changed, and the toxicity is promoted to be remarkably reduced. In addition, a piglet immune challenge protection test result shows that the PmA4-750 strain has an immune effect on swine pasteurellosis caused by type A, type D or type B pasteurella multocida. In addition, the invention also discloses a swine pasteurellosis live vaccine and a preparation method thereof.

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.

The application relates to the technical field of genetic variation interpretation systems, and provides a second-generation sequencing data driven non-phenotype genetic variation intelligent interpretation system and method, which comprises a sequencing data import terminal, a non-phenotype variation annotation terminal, a rare variation structure modeling terminal, an intelligent inference and typing terminal and a result visualization terminal; the sequencing data import terminal is used for importing original format sequencing files and generating variation file information; the non-phenotype variation annotation terminal is used for performing basic annotation and non-phenotype function inference on variations in the variation file information; the rare variation structure modeling terminal is used for extracting non-phenotype rare features and constructing a discrimination model; the intelligent inference and typing terminal is used for performing pathogenic potential scoring and classification on the variations; and the result visualization terminal is used for displaying the original format sequencing files, the variation file information, the discrimination model, the pathogenic potential scoring and the discrimination classification. The application has the effect of improving variation judgment accuracy.

The present disclosure provides a systems and methods for processing genetic variations and phenotypic data. The systems and methods may be used to generate one or more databases comprising genetic variations and associations with phenotypes. The system and methods may be used to determine a pathogenicity of a genetic variation. The systems and methods may comprise human interpretation using pre-determined criteria.

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.