24 results about "Genetic distance" patented technology

The invention discloses a corn crossbreeding intelligent matching system and method based on artificial intelligence, and the method comprises the steps: collecting molecular marker genotype data and phenotype data of candidate parents, calculating the genetic distance between the parents, and constructing a genetic distance matrix; analyzing the phenotypic complementarity degree of the parent pair, and calculating a phenotypic complementarity index; constructing a heterosis prediction model based on genetic distance and phenotype complementarity, and predicting the yield heterosis value of the candidate hybrid combination; evaluating the environmental adaptability and resistance comprehensive score of the candidate combination; and adopting a Pareto multi-objective optimization strategy to screen an optimal matching scheme. According to the method, molecular marker data and phenotype data are integrated, a heterosis prediction model considering a genetic distance nonlinear effect is constructed, collaborative optimization of multiple breeding targets such as yield, resistance and environmental adaptability is achieved, the prediction accuracy is improved by about 20% compared with that of a traditional method, and the method is suitable for large-scale popularization and application. And an intelligent and precise decision support tool is provided for corn crossbreeding.

The invention relates to the technical field of virus traceability, and particularly discloses a coronavirus sample detection method which comprises the following steps: S1, acquiring high-throughput original sequencing data of a target sample; s2, inputting the original sequencing data into a dynamic learning type recognition model, and outputting each virus pedigree and the credibility of each pedigree; s3, performing noise perception variation detection on the original sequencing data; s4, aiming at each virus lineage, determining a variation point; s5, detecting a mixed infection indicator; s5, constructing a Bayesian network to determine the genetic relationship among the variation points, and generating a virus haplotype sequence; s6, calculating a genetic distance, and deducing a propagation path through a maximum likelihood method; and outputting a traceability report. The method solves the problem of frequency conflict when multiple pedigree coexist, is suitable for the situation that multiple pedigree viruses coexist to form mixed infection or co-infection, and avoids misjudgment of attribution of variation sites.

The invention discloses a fig germplasm resource diversity evaluation system and a fig germplasm resource diversity evaluation method, and relates to the field of comprehensive evaluation.The fig germplasm resource diversity evaluation method comprises the steps that after multi-climate zone fig germplasm resources are collected and samples are processed in a standardized mode, 32 phenotypic characters are obtained in combination with hyperspectral imaging, 3D morphological scanning and a physiological sensor; a UPLC-QTOF-MS technology is utilized to construct metabolite fingerprints of leaves and fruits, and an SNP / InDel molecular marker is developed through whole genome re-sequencing for genotyping. Integrating stress resistance physiological data in a stress environment, and calculating a comprehensive score by adopting an analytic hierarchy process. And screening core evaluation indexes through weighted principal component analysis, constructing a similarity matrix in combination with an improved Shannon-Wiener index and a Neii's genetic distance algorithm, and finally forming a germplasm clustering map and a genetic diversity thermodynamic diagram. The method has the advantages that germplasm representativeness is guaranteed through multi-climate-zone sampling, multi-dimensional data are integrated, core indexes are accurately analyzed and screened, a visual atlas is finally generated, and scientific support is provided for germplasm research and breeding.

This invention provides a data storage method and system for tuberculosis detection, relating to the field of tuberculosis detection technology. The data storage method for tuberculosis detection includes the following steps: S1. Collecting whole-genome sequencing data of Mycobacterium tuberculosis, host serum IgG titer, and drug sensitivity test results; S2. Calculating genetic distance D based on a reverse evolution model to generate four-dimensional spatiotemporal coordinates (t, x, y); S3. Performing data partitioning and storage based on the drug target barrier value β; S4. Generating dynamic metadata using a host-pathogen dynamics model and compressing and storing it. This invention implements a dynamic storage entropy adjustment algorithm at the hardware and software collaborative level, continuously optimizing the matching efficiency of data compression and physical storage. This results in an intelligent data hub that can perceive the evolutionary pulse of pathogens and autonomously optimize resources, providing support for clinical tuberculosis prevention and control decisions with temporal depth, spatial correlation, and risk evolution.

The application discloses a high-throughput genotype intelligent analysis method, which comprises data acquisition and preprocessing, molecular marker identification and prediction of genetic relationship and hybridization advantage; high-throughput genotype data are subjected to standardization treatment, three quality evaluation standards of coverage, transversion / transversion ratio and error rate are established, and batch effect is corrected through a position effect index; then genetic similarity matrix between samples is calculated based on the pretreated data, a label SNP with high centrality is identified as a molecular marker through linkage disequilibrium network analysis; the population genetic relationship is estimated by using the marker, and a phylogenetic tree is constructed, and the hybridization advantage is predicted based on the relationship index of heterozygosity and genetic distance. The application significantly improves the quality of genotype data, optimizes the screening of molecular markers, accurately estimates the population genetic relationship and accurately predicts the hybridization advantage, and provides an efficient bioinformatics solution for modern breeding.

This invention discloses a method, system, and device for branching the gene sequence of influenza A virus, relating to the field of bioinformatics. The method includes the following steps: reading a user-input phylogenetic tree structure file and a Fasta file; dividing the phylogenetic tree into multiple branches based on a set first custom parameter and the gene sequence data of each cotyledon node; verifying the relationship between branches based on a set second custom parameter, and merging branches whose genetic distance is within a set threshold; verifying the merged branches; and if there are unassigned cotyledon nodes, assigning them to the branch with the closest genetic distance that satisfies the custom parameter, thereby obtaining the final branching result of the influenza A virus gene sequence. The method employed in this invention enables targeted phylogenetic branching based on the characteristics of influenza A virus, improving the accuracy of the branching results.

The application discloses a breeding method of Yunnan fragrant soft rice, which comprises the following steps: step one is to excavate specific rice resources, step two is to create new germplasm backbone parents, step three is to perform 'two-way' separation expression and single recessive relative trait elimination, and step four is to perform excellent trait balanced selection. In the application, indica rice or semi-indica rice resources from 1450-1650m warm and cool rice areas in Yunnan are collected, apparent morphological identification is performed on the resources and low-heat valley rice area materials, excellent materials with complementary traits are screened, and fragrant soft rice breeding intermediate materials and backbone parent reserves are created. In addition, parent populations with far genetic distance and different ecotypes are created, excellent genes with far genetic distance are introduced into Yunnan fragrant soft rice materials, according to the physiological characteristics of similar ecotype rice, affinity is improved, blood penetration and fusion are increased, backbone parents with rich and varied gene types are created, and'sticky + waxy' and 'colored vs. non-colored' parents are selected to perform hybridization, recessive trait materials are selected or eliminated, and the breeding efficiency can be improved.

A computer-implemented method for calculating an individual's tailored Polygenic Risk Score is based on known genetic information. A dataset is provided related to a reference panel including genetically characterized individuals with known disease status and diversified global ancestry. An individual-specific genetic reference group of individuals is selected as a subset from the reference panel. Genetic distances of the individual from each of the reference panel individuals are computed; each being the individual's genetic distance from a respective reference panel individual. Individuals of the individual-specific genetic reference group based on the individual's computed genetic distances are selected. The individual's basic Polygenic Risk Score for a disease is calculated to provide the individual's disease risk prediction. An ancestry-based background PRS contribution is determined. The ancestry contribution is removed from the individual's calculated basic Polygenic Risk Score to obtain the individual's tailored Polygenic Risk Score and provide a disease risk prediction.

This invention discloses a method for recommending germplasm resources and hybrid combinations for new peach variety breeding, belonging to the field of agricultural bio-breeding technology. The method involves collecting phenotypic observation data and genotypic detection data of peach germplasm resources; determining phenotypic characteristics including fruit development duration and dynamic parameters of sugar and acid accumulation, and generating a phenotypic dataset through genetic association analysis; generating a preliminary genetic similarity matrix through genetic distance calculation and kinship analysis, and obtaining a standardized genetic similarity matrix by correcting phenotypic expression stability data; inputting the dataset into a genetic breeding digital model, adjusting the germplasm unit arrangement and genetic effect parameters to update the model; acquiring cultivation environment data, determining phenotypic segregation patterns under different breeding cycles, analyzing the correlation between phenotypic segregation patterns and cultivation environment data, and generating hybrid combination recommendation results. This invention can provide systematic guidance for parent selection in peach breeding work, improving breeding efficiency.

The invention belongs to the technical field of agricultural biology, and particularly relates to a grape stock-scion bidirectional screening and predicting system, which comprises an interaction module, a database module, a stock-scion prediction module, a stock-scion prediction module and a stock-scion prediction module, the database module is used for storing germplasm feature data, grafting feature original data and genetic distance data between germplasm; the calculation module generates grafting evaluation data based on the grafting feature original data and updates the grafting evaluation data to the database module; the screening and predicting module is used for responding to a germplasm name input by a user, screening out a first related germplasm set with similar genetic distances based on genetic distance data among germplasm, screening out a set of candidate germplasm recommended to be suitable for grafting in combination with grafting evaluation data, and predicting the first related germplasm set; and selectively screening out a second related germplasm set with the genetic distance close to that of the germplasm in the candidate germplasm set, and outputting the second related germplasm set by the output module. The scheme is used for solving the technical problems of low grape stock-scion selection efficiency, high individual experience dependency and poor precision in the prior art.

The invention relates to the technical field of virus traceability, and particularly discloses an anomaly identification traceability method based on coronavirus high-throughput detection, comprising the following steps: S1, acquiring high-throughput original sequencing data of a target sample; s2, inputting the original sequencing data into a dynamic learning type recognition model, and outputting each virus pedigree and the credibility of each pedigree; s3, performing noise perception variation detection on the original sequencing data; s4, aiming at each virus lineage, determining a variation point; s5, constructing a Bayesian network to determine a genetic relationship among the variation sites, and generating a virus haplotype sequence based on the genetic relationship; s6, calculating a genetic distance between the abnormal variation site and the reference database, and deducing a propagation path through a maximum likelihood method; and outputting a traceability report, wherein the traceability report comprises the geographic position and the propagation timeline of the abnormal variation source. A dynamic learning type recognition model is adopted, and continuous optimization of the recognition capability of new variants is ensured through incremental learning mechanism training.

The present application is suitable for the technical field of corn breeding, and particularly relates to a corn heterosis group division method based on marker dominance effect weight, which comprises the following steps: taking 3-4 copies of a representative inbred line of each corn heterosis group, and performing double cross of all the representative corn inbred lines to form a hybrid population; performing genotype identification on the parents of the representative corn inbred lines by using an SNP chip to obtain a contribution value of a single SNP dominance effect to the heterosis of the hybrid population; calculating the heterosis genetic distance and establishing a fingerprint database; obtaining SNP marker information of a to-be-tested corn inbred line, calculating the heterosis genetic distance between the to-be-tested inbred line and the representative inbred lines of the heterosis group, and dividing the class group of the to-be-tested inbred line. The present application greatly improves the breeding efficiency, saves time, manpower, material resources and financial resources, provides a decision basis for breeders, reduces the blindness of assembly and matching, and provides a reference and guidance for crop breeders.

The invention relates to the technical field of oyster cross breeding, in particular to a COI genetic distance-based crassostrea calthrop hybridization prejudgment and combined configuration method. The method comprises the following steps: collecting COI gene sequences of calthrop oysters and four cultured oyster samples, constructing an allelic point sliding window, and calculating genetic differences by adopting a Kimura-2P distance, so as to obtain a refined COI genetic distance index; establishing a COI heredity-hybridization advantage regression model by combining historical hybridization survival data, heredity variation data and disturbance factors, introducing a multi-dimensional threshold structure based on genetic distance, and performing regression pre-judgment of threshold logic regulation and control on the hybridization advantage; on the basis, a COI genetic distance matrix is constructed, intraspecific fertility analysis and character complementation inference are coupled, and optimization and combined configuration of cross parents are achieved. Quantitative prediction of the hybridization advantage can be achieved before hybridization, the breeding period is shortened, the hybridization test cost is reduced, and the accuracy of breeding hybridization auxiliary decision making is remarkably improved.

The application discloses a kind of phenotypic data visualization analysis method and system of potato germplasm resources, it is related to agricultural information technology and data visualization technical field.The multidimensional dataset containing variety identification, multi-time point phenotypic trait value, environmental factor parameter and genetic distance is obtained;Variety is mapped to the first dimension axis of three-dimensional space based on genetic distance;Multi-phenotypic trait is weighted and projected to the second dimension axis;Time and environmental factor are complexly weighted and mapped to the third dimension axis;The control point of each variety in three-dimensional space is generated, and the phenotypic development trajectory curve is generated using spline interpolation;Three-dimensional space and trajectory curve are rendered to display device and provide interactive interface.Can simultaneously integrate four-dimensional information of genetic background, multi-phenotypic trait performance, time dynamics and environmental response, realize the dynamic visualization of phenotypic development process and the automatic identification of abnormal variety, significantly improve the analysis efficiency and cognitive intuitiveness of potato germplasm resources.

The rate of evolution may be estimated for any length of genomic fragments (or amino acid sequences) from a single codon (or amino acid position) to the entire genome or protein. Under the condition that a target genome reference sequence at initial time and each genome specific sequence of one or more target strains within a certain time period after the initial time are given, some genome fragments can be selected; a genetic differentiation parameter for a reference sequence fragment may then be calculated based on the genetic distance between the fragment and a corresponding fragment in each strain-specific sequence. Based on the genetic differentiation parameters, the evolution rate or evolution stability parameter of the fragment may be calculated and applied to the design of antiviral therapy or antiviral vaccines and / or the estimation of tumor growth.

The application relates to the technical field of gene SNP molecular markers, in particular to a gene SNP molecular marker method and system for watermelon breeding, which comprises the following steps: extracting DNA solutions of watermelon samples of multiple varieties respectively, and performing SNP typing-based fingerprint construction; obtaining 16S rRNA sequences of watermelon microbial communities, dividing the 16S rRNA sequences into base sequences; determining mutation correlation parameters of the bases; determining a transition probability matrix of the 16S rRNA sequences; correcting sampling step lengths of the 16S rRNA sequences; correcting the transition probability matrix; determining characteristic vectors of the 16S rRNA sequences; determining genetic distances between the 16S rRNA sequences of the watermelon microbial communities and soil samples, constructing a phylogenetic tree, and determining strongly correlated microbial communities of the breeding environment of the watermelon samples of the multiple varieties. The adaptability of the watermelon breeding environment is improved.

This application relates to the fields of molecular genetics and biotechnology, particularly to SNP marker sets and their applications for upland cotton variety identification. Through analysis of 10K SNP microarray data from 1205 upland cotton varieties (1194 species), this application identified a core marker set containing 1553 SNP loci. These markers exhibit high polymorphism, low linkage disequilibrium, and uniform chromosome distribution among upland cotton varieties, enabling efficient and accurate identification. This application is the first to determine a genetic distance threshold of 0.3 for identifying the authenticity of upland cotton varieties using this 1553 SNP marker set. This marker set demonstrates strong identification capabilities and high cost-effectiveness, and can be widely applied in areas such as intellectual property protection, breeding, and variety management of upland cotton varieties.

The application discloses a method for ancestral haplotype typing in a fine genomic interval across tetraploid and hexaploid wheat. The application provides a method, steps of which are as follows: 1) obtaining whole genome resequencing data of each wheat sample to be tested; 2) obtaining filtered DNA sequence alignment files of each sample to be tested; 3) SNP identification and CNV variation identification; 4) obtaining SNP polymorphism site density distribution; 5) fitting the density distribution of the SNP polymorphism sites to obtain a normal distribution, and determining a genetic distance hard threshold according to a boundary; 6) obtaining an initial ancestral haplotype; 7) obtaining an initial priority order of the sample; and 8) obtaining an ancestral haplotype file. The application does not need resequencing data of a reference population as input, which reduces the total detection cost. The application also does not need to limit the phylogenetic relationship between populations to which the samples to be detected belong, which can improve the detection accuracy in crops with complex evolutionary history.

The present invention discloses a primer combination for KASP markers used in the identification of Elymus sibiricus germplasm resources and application thereof, belonging to the technical field of the biotechnology. The primer is designed to amplify 31 SNP sites. Nucleotide sequences of KASP-SNP primers are provided sequentially as SEQ ID NOs: 1-93. Using the primer, the genetic distance matrix and phylogenetic tree of Elymus sibiricus germplasm can be constructed to determine genetic relationships among individuals. It can be applied to screen materials required for the Elymus sibiricus research and utilization, such as core germplasm construction, breeding population screening and genetic diversity studies, etc. The method provided by present invention offers advantages of high throughput, high accuracy, low cost, simplicity of operation and savings in manpower and material resources, and thus has broad application prospects.

The present application relates to the field of animal genetic breeding and intelligent animal husbandry, in particular to a crossbreeding improvement method for Simmental cattle, comprising: obtaining whole genome sequence data, phenotypic data and target area environmental climate time series data of a target population; inputting the three types of data into a pre-trained multi-modal dynamic model, extracting features and quantifying the output of recessive genetic load index; judging the relationship between the index and the preset safety threshold; when the index is lower than the safety threshold, generating a first selection scheme based on a pre-constructed deep reinforcement learning model, with the goal of maximizing the intergenerational genetic progress rate of production performance; when the index is higher than or equal to the safety threshold, generating a second selection scheme with the goal of maximizing genetic distance; the present application realizes the dynamic balance between short-term improvement efficiency and long-term breeding safety.

This invention discloses a method for constructing a giant panda genetic map, belonging to the field of genetic map construction technology. The method includes the following steps: retrieving DNA samples, performing sequencing processing to obtain a sequencing BAM file; performing SNP+SV dual-type genetic marker targeted mining and marker hierarchical filtering; using an improved partial least squares regression algorithm to correct errors in the marker filtering genotype matrix and label pedigree relationships; using an improved minimum spanning tree-based genetic map construction algorithm to construct linkage groups and calculate preliminary genetic distances between markers; correcting the preliminary genetic distances between markers to construct the giant panda genetic map; and performing genome anchoring and functional annotation to obtain the final giant panda genetic map. This application improves the accuracy, coverage, and species suitability of the giant panda genetic map by combining SNP+SV dual-type genetic marker targeted mining, an improved error correction algorithm, and an improved genetic map construction algorithm.

This application discloses a novel algorithm for constructing a genetic map of a MAGIC population, comprising: collecting species data; segmenting chromosomes based on the collected data to form chromosome fragments; calculating the parental genetic similarity index of chromosome fragments to determine parental origin; obtaining a definite origin matrix and a fuzzy origin matrix; using definite fragments from a single parental origin in the definite origin matrix as anchor points; using the anchor points as starting points, examining adjacent fragments of fuzzy fragments in the fuzzy origin matrix to determine parental origin; merging fragments with the same parental origin and adjacent positions into a larger fragment to form a parental origin matrix; calculating recombination rate and genetic distance; constructing a genetic map; and using the novel algorithm to analyze the whole-genome resequencing data of a MAGIC population of a specified crop, thereby effectively detecting minor genes controlling complex quantitative traits, clarifying the parental origin of each variation site, and finding the required high-quality parents, thereby improving the breeding process.

This invention belongs to the field of spectral applications and mammalian evolution, specifically relating to a rapid prediction method for reference genetic distances between different mammals based on the mid-infrared spectroscopy (MIRS) of milk. The applicant has, for the first time, used milk MIRS to predict reference genetic distances between different mammalian populations, establishing a regression model that can predict the genetic distances between new populations of known mammals, and even new populations of unknown mammalian species, and known mammalian populations, thereby providing a preliminary assessment of evolutionary differences between mammalian populations. The method exhibits high accuracy and clear trends, approaching the results obtained from proteomics or other omics analyses.

The application relates to the technical field of virus tracing, and specifically discloses an abnormality identification and tracing method based on high-throughput detection of coronaviruses, which comprises the following steps: S1, obtaining high-throughput original sequencing data of a target sample; S2, inputting the original sequencing data into a dynamic learning type identification model to output each virus lineage and the credibility of each lineage; S3, performing noise perception variation detection on the original sequencing data; S4, determining variation sites for each virus lineage; S5, constructing a Bayesian network to determine the genetic relationship between the variation sites, and generating a virus haplotype sequence based on the genetic relationship; S6, calculating the genetic distance between abnormal variation sites and a reference database, and deducing a transmission path through a maximum likelihood method; and outputting a tracing report, wherein the tracing report comprises a geographical position and a transmission timeline of the abnormal variation source. The dynamic learning type identification model is trained through an incremental learning mechanism, so that the identification capability for new variants can be continuously optimized.