79 results about "Genomic information" patented technology

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

The invention discloses an intelligent breeding method for commercial crops based on big data analysis, and relates to the technical field of agricultural breeding, the method comprises the following specific steps: seed multi-modal data acquisition and processing: acquiring physical characteristic data of seeds, acquiring physiological indexes and genome information data through biochemical experiments and gene sequencing, and determining the seed multi-modal data according to the physiological indexes and the genome information data; carrying out pretreatment on the raw materials; according to the method, after multi-modal data are integrated and preprocessed, a seed vigor accurate evaluation model is constructed by using deep learning and data fusion technologies, the seed vigor level can be measured more accurately, and meanwhile, the electric signal change of the early growth stage of crops is monitored in real time by using high-precision plant electric signal acquisition equipment, so that the accuracy of the seed vigor evaluation is improved. And a correlation model of the electric signal characteristics and multiple traits of crops is established, and fusion analysis is performed on seed vigor evaluation data and plant electric signal data, so that the breeding efficiency is greatly improved, and powerful support is provided for cultivation of high-quality varieties.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention discloses a somatic cell structure variation detection method based on a monomolecular long-read length sequence chart hybrid model, which comprises the following steps of: extracting a read length local sequence of a target structure variation interval and a flank region thereof, constructing a local chart genome by utilizing a partial sequence alignment chart algorithm, and encoding a multi-sequence alignment result. And then, a multi-category hybrid model is combined with an expectation maximization algorithm, and clustering analysis is performed on the comparison data, so that the somatic cell structure variation category only from the tumor sample is identified. And finally, generating a consensus sequence of a somatic cell structure variation region in the tumor sample, and taking the consensus sequence as an optimization result of a local genome. Compared with an existing short-read-length sequencing method, the method has the advantages that the problems of comparison errors, low-complexity sequences and genome heterogeneity can be effectively solved, and the detection precision of somatic cell structure variation is remarkably improved. Through the application of a single-molecule long-read-length sequencing technology, more comprehensive and accurate genome information can be provided.

A system is disclosed for discreetly assessing the compatibility of two or more human genomes across diverse elements, activities, and engagement platforms relevant to potential mating scenarios. The genomic data is subjected to encryption, with the option of employing homomorphic encryption to safeguard user privacy and security. Processing of the data is facilitated through a personal health database processing system, which may be cloud-based or edge-based. The application of homomorphic encryption ensures that the genomic information of individual users remains encrypted during processing, with the outcome limited to the display of progeny compatibility to the respective end users.

Examples are described for dynamically encrypting and / or decrypting a file formed of multiple blocks of ordered data. In one example, a method of dynamically encrypting a file to enable partial decryption of the file includes generating, using a secret key and one or more initialization vectors, a keystream for the multiple blocks of ordered data, encrypting the multiple blocks of ordered data of the file by performing a logical operation of the keystream with the multiple blocks of ordered data in a one-to-one correspondence, and building a file index of the file to identify location information of the multiple blocks of ordered data. The method may further include dynamically decrypting at least a portion of the file by decrypting at least one selected block of encrypted data of the file using a portion of the keystream, the portion of the keystream corresponding to the at least one selected block.

The invention discloses a crop germplasm resource data analysis and integration system, which relates to the technical field of germplasm resource management and comprises a data acquisition module and a data processing module, the mapping module is used for building a crop germplasm correlation graph based on a graph theory algorithm, and the integrated retrieval module is used for providing multi-dimensional retrieval for the correlation graph. According to the method, germplasm resources are mapped into nodes by utilizing a graph theory method, and correlation map view angles oriented by different functions such as genotype approximation, phenotype shape approximation or environmental adaptability approximation can be constructed as required by adjusting the proportions of genotype, phenotype characteristics and environmental adaptability vectors in the correlation map in edge weights; and a basis is provided for diversified analysis and retrieval.

The invention discloses a biological breeding method for cultivating high-yield early-maturing rice with low-temperature-resistant and lodging-resistant characteristics by using a molecular recurrent assisted shuttle method. The method comprises the following steps: selecting parents which are resistant to cold, resistant to lodging and high in adaptability for multiple rounds of hybridization, screening cold-resistant and lodging-resistant strains by direct seeding in a Hainan winter (anti-season) group, carrying out stress identification in a Heilongjiang summer (in-season) cold water pool and a high-fertilizer field, and carrying out phenotype high selection pressure assisted by a genome information combined selection technology. And efficient polymerization of cold resistance, lodging resistance and high-yield and early-maturing characters is realized. According to the method, the technical bottleneck of insufficient multi-character collaborative improvement including cold resistance, disease resistance and lodging resistance in traditional early-maturing japonica rice breeding is solved, and the multi-character collaborative improvement breeding efficiency is remarkably improved. The vacant shell rate of the case variety Zhongnong japonica 5205 bred by the method is lower than 12.9% under low-temperature stress, the cold resistance is improved by more than 50% compared with that of parents, and the method has the characteristics of high yield, disease resistance and high quality.

The invention relates to the technical field of intelligent agriculture and crop models, and particularly discloses a corn whole growth period simulation model construction method based on gene information. The method comprises the following steps: firstly, obtaining whole genome re-sequencing data of a target corn population and multi-growth-stage phenotype data of continuous years at the same ecological point, and dividing the population into a plurality of subgroups with consistent genetic backgrounds based on the re-sequencing data; and then, by adopting a strategy of'single family parameter inversion-subgroup template construction-secondary optimization and calibration on a single genotype under the constraint of the subgroup template ', the genetic subgroup characteristics based on the genome are associated with an APSIM crop model, and a high-precision corn growth period prediction model is constructed. According to the method, the defects that a traditional crop model is low in large-scale breeding material parameter calibration efficiency and prone to falling into local optimum are overcome, and the method is particularly suitable for efficient and accurate growth period prediction and adaptability evaluation on a large number of breeding materials in a unified ecological region.

The application discloses a method for improving chromatin DNA accessibility in cells and application thereof, relates to the technical field of gene sequencing and tissue cell sample analysis, and comprises the following steps: providing a sample to be detected; mixing the sample to be detected with an alcohol reagent to permeabilize cells and expose heterochromatin regions of the sample to be detected, so as to obtain a pretreated sample; performing fragmentation treatment on DNA of the pretreated sample, performing reverse transcription reaction on RNA in the pretreated sample, performing labeling on the fragmented DNA and the cDNA after the reverse transcription reaction, and separating labeled genomic DNA and cDNA; and performing library construction and sequencing on the fragments of the labeled genomic DNA and the cDNA product respectively, so as to obtain single-cell genomic information and transcriptome information of the sample to be detected. The alcohol reagent can open the heterochromatin without bias to obtain more nucleic acid fragments, improve the sensitivity of single-cell genome detection, does not affect RNA detection, and realizes high-throughput double-omics analysis of single-cell whole-genome DNA and RNA.

The invention provides a breeding method for improving the yield and quality of chicken, and belongs to the technical field of poultry breeding. The method comprises the following steps: firstly, dividing chicks hatched in a breeding group into a determination group and a candidate group; measuring the total number of pectoralis major muscle fibers (PTNM) and the total number of gastrocnemius muscle fibers (GTNM) of one-day-old chicks of the group; carrying out limited feeding on the candidate groups; based on pedigree records and / or genome information and phenotypic data such as PTNM and GTNM, genetic parameters are estimated by using an optimal linear unbiased prediction model (ABLUP and / or ssGBLUP), and PTNM and GTNM estimated breeding values of candidate group individuals are solved; standardizing the estimated breeding value and then calculating a comprehensive breeding value in combination with a weight coefficient; performing seed selection on the candidate group to establish a family; the subculture individuals breed the next generation, and the steps are repeated until the chicken yield and quality are improved. Through accurate determination and early selection of the total number of muscle fibers, the breeding process is accelerated, and the chicken yield and quality are remarkably improved.

The invention relates to the technical field of plant genetic engineering, in particular to a mango MiZTP291 gene and application thereof in plant disease resistance immunity. The CDS (Coding Sequence) of the mango MiZTP29l gene is as shown in SEQ ID NO. 1. On the basis of whole genome information, a GWAS technology is utilized to explore the anthracnose-resistant candidate gene MiZTP29l and clarify the disease-resistant mechanism of the anthracnose-resistant candidate gene MiZTP29l, the function of the MiZTP29l gene in improving disease-resistant immunity is verified in mangifera indica, tomato and arabidopsis thaliana, and the miZTP29l gene has important significance on breeding of anthracnose-resistant germplasm, creation of resistant germplasm resources and the like.

The invention discloses a genome display and analysis system which comprises a genome information and literature information acquisition module, a gene display module, a gene comparison module, a primer design module and a database established according to the method. The method has the characteristics that similar homologous genes among different species can be quickly obtained, genes or protein sequences among different species can be more accurately and quickly compared and analyzed, the genes can be quickly visualized in genomes, verified gRNA sequences can be displayed, tools can be integrally used, and time and labor can be saved.

This invention provides a method for identifying catfish ( Silurus asotus This invention discloses a specific molecular marker for sex identification. The molecular marker is a DNA fragment located on the Y chromosome of catfish, and its nucleotide sequence is shown in SEQ ID No. 3. A DNA fragment partially homologous to this fragment is located on chromosome 8, and its nucleotide sequence is shown in SEQ ID No. 4. This invention is the first to screen male-specific fragments from catfish genome information and design specific primers for genetic sex identification of catfish. The genetic sex PCR identification method based on this molecular marker is applicable to genetic sex identification and screening of catfish at different growth stages and in different populations in different watersheds in the field, in laboratories, or in aquaculture enterprises. Since female catfish grow faster than males, applying this method to assist in the production of all-female fry helps improve the efficiency of aquaculture.

To obtain an information processing device that collects genome information from a larger number of people.SOLUTION: An information processing device comprises: storage means for storing a user's genomic information and user information; output request acquisition means for acquiring an output request including an output condition; identification means for identifying the genomic information and user information stored in the storage means based on the output request; output means for deleting information unrelated to the output request from the identified genomic information and user information and outputting it; and compensation calculation means for calculating compensation to the user who provided the genomic information and user information.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of fish genetic breeding in aquaculture industry, and discloses a grass carp intensive high-yield character genome selective breeding method, which comprises the following steps: establishing a grass carp intensive high-yield character prediction model; the establishment of the grass carp intensive high-yield character prediction model comprises the following steps: (1) obtaining a grass carp candidate parent fish single nucleotide polymorphism site; (2) constructing an analysis model for predicting breeding values of candidate parents; and (3) constructing a molecular genetic relationship matrix by using the obtained single nucleotide polymorphic sites, and predicting a genome estimation breeding value of the candidate group in combination with the analysis model in the step (2). The improved variety breeding method based on grass carp genome information can be used for screening excellent parents suitable for intensive breeding, the screened parents are directly used for breeding offspring seeds, and the high-density breeding tolerance of grass carp can be directly improved; and a technical support is provided for improving cultivation of new grass carp varieties suitable for intensive and facility cultivation.

The present invention discloses a method for developing and applying specific single-locus IP markers in the radish genome. This method utilizes bioinformatics techniques to develop specific single-locus IP markers in radish based on the genomic information of radish and Brassica crops; and these markers are actually applied to the identification of exogenous introgressed fragments in the progeny of radish-Brassica interspecific hybridization. The present invention realizes the rapid identification of excellent gene fragments in the progeny of distant hybridization between radish as the donor and Brassica crops, effectively assists the precise introgression of excellent gene fragments, accurately tracks the size of the gene fragments involved at the same time, breaks the linkage drag of bad traits, and realizes the efficient creation and utilization of germplasm resources. At the same time, it provides important data support for the identification of chromosomal fragments of genes related to improved traits in hybrid progeny.

The invention discloses a ganoderma lucidum binuclear genome assembly method based on haplotype analysis, and belongs to the technical field of bioinformatics. The purpose of the present invention is to overcome the accuracy and accuracy of high hybrid genome assembly. The invention provides a ganoderma lucidum binuclear genome assembly method based on haplotype analysis, which comprises the following steps: obtaining mononuclear ganoderma lucidum cells, respectively carrying out whole genome sequencing by utilizing Illumina NovaSeq, Nanopore, Hi-C and PacBio HiFi, and obtaining accurate genome information according to the sequencing data of the Illumina NovaSeq and the sequencing data of the Nanopore. And an assembly strategy of haplotype separation is realized.

The present invention discloses a long-term, reliable, low-cost, and precise storage technology for genomic information. The specific solution is to generate a small amount of ECC information based on the sequenced and assembled DNA sequence information, and then synthesize and encode these ECC information into a large number of small DNA fragments through DNA information storage technology. These small DNA fragments can be mixed with the extracted genomic DNA, or stored separately, thereby realizing the storage process of genomic information. During the reading process, the stored samples need to be mixed or sequenced separately. After sequencing, the ECC information is first decoded, and then the genomic sequence is accurately assembled under the guidance of the ECC information. Compared with directly storing genomic DNA molecules, the solution of the present invention can achieve accurate reading and assembly of genomic information and significantly enhance the reliability of long-term storage, and can reduce the coverage requirements of sequencing in the reading process; compared with directly encoding and storing genomic sequence information through DNA information storage technology, the solution of the present invention has significant cost advantages, and can store information on genomic modifications, thereby supporting related research on the genomic DNA molecules themselves.

The invention discloses a method for detecting escherichia coli carrying a Cfr gene based on NGS, and relates to the technical field of biological detection, and the method comprises the following steps: S1, extracting a microbial overall genome in a clinical sample; s2, constructing a sequencing library, and performing high-throughput sequencing; s3, performing comparative analysis; s4, screening out escherichia coli carrying the Cfr gene; s5, performing genotype analysis; s6, carrying out a conjugation transfer experiment; and S7, analyzing the type and genetic environment of the plasmid carrying the Cfr gene through sequencing. By constructing a comprehensive detection process, genome information of the escherichia coli carrying the Cfr gene can be captured, and the problems that in the prior art, when the escherichia coli carrying the Cfr gene is detected, the omission ratio is high, detection is incomplete, the escherichia coli carrying the gene and related characteristics of the escherichia coli cannot be accurately positioned, and the detection accuracy is high can be solved. Therefore, a reliable basis cannot be provided for clinical treatment, and personalized treatment schemes and prevention and control measures are difficult to formulate.

The invention discloses an aegilops variabilis whole genome SNP site combination, a probe, a chip and application of the aegilops variabilis whole genome SNP site combination, the probe and the chip. The aegilops variabilis whole genome SNP site combination is composed of 10K SNP sites composed of 4K Dv subgenome SNP sites and 6K Nv subgenome SNP sites. According to the method, the genome information of the aegilops tauschii can be detected in a high-quality, high-efficiency and high-throughput manner, so that an efficient detection technical means is provided for molecular breeding application of the excellent genes of the aegilops tauschii and the chromosome segments of the excellent genes, and an efficient detection tool is provided for exploration and breeding utilization of the excellent genes.

The invention relates to a strain for degrading beta-carotene, a crude enzyme solution, dioxygenase and application, and relates to the technical field of genetic engineering and microbial fermentation. The invention relates to a strain for degrading beta-carotene, the preservation name of the strain is Escherichia coli LZU-E1, and the strain is preserved in the China General Microbiological Culture Collection Center (CGMCC) with the preservation number of CGMCC No.34862. The invention further discloses a preparation method of the strain for degrading beta-carotene. The method comprises the following steps: screening a strain for efficiently degrading beta-carotene, extracting a crude enzyme solution, researching the enzymatic property of the crude enzyme solution, and mining a new dioxygenase gene for degrading beta-carotene from genome information of the strain by utilizing a molecular biology means, so that the isoprene-reduced natural perfume is efficiently obtained.

The present invention provides a genomic mating method and application of Huaxi cattle based on whole-genome SNP information, belonging to the technical field of animal breeding. The problem to be solved is the increase in inbreeding level and the decrease in genetic progress caused by multi-generation genomic selection in the existing beef cattle breeding population. The specific steps of the present invention are as follows: Step 1, extract DNA from the Huaxi cattle individuals to be mated for genotyping, and process and quality control the data; Step 2, perform genotype data imputation to obtain high-density chip data; Step 3, calculate the additive relationship matrix, use GBLUP to obtain the genomic estimated breeding values of five important economic traits of the Huaxi cattle population to be mated, and then calculate the comprehensive selection index of individuals; Step 4, use a genetic algorithm to construct a list of optimal mating combinations for the population. The present invention greatly saves breeding costs, reduces the inbreeding level of the offspring population, shortens the breeding process, and has good application prospects and economic benefits.

This present disclosure provides an integrated workflow and systems for the efficient deployment of integrated genomic and proteomic diagnostic assays. The diagnostic assays include a proteomic component, a genetic component, liquid handling robots, a LIMS system, and a software classifier component. Also provided herein are systems and diagnostic assays for the detection of lung cancer.

Disclosed herein are methods for single-cell sequencing. In some examples, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. Obtaining sequence information may include generating a molecularly indexed polynucleotide library from the one or more isolated cells. Enriching the sample may include focusing cells of interest in the sample using acoustic focusing.

This invention discloses a method for constructing and analyzing a genome-scale metabolic network model of *Paragonimus denitrifyingus*, belonging to the field of systems biology. The method includes: whole-genome annotation; obtaining global metabolic response data of *Paragonimus denitrifyingus*; automatically retrieving genomic information and constructing Model 1 based on the species code and genome annotation results of *Paragonimus denitrifyingus* in the KEGG database; constructing Model 2 by identifying homologous proteins in the *Paragonimus denitrifyingus* genome through homology searching of proteins in the target organism based on a pre-trained Hidden Markov Model; and integrating Model 1 and Model 2. This invention allows for the efficient design and modification of denitrification engineering, achieving precise control of nitrogen degradation processes. Compared to existing metabolic engineering methods, this invention effectively reduces the workload of exploratory experiments and greatly advances a deeper understanding of the nitrogen degradation characteristics of *Paragonimus denitrifyingus*.

Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based on phenotype and genomics information.

An integrated data repository may be generated that includes genomics information and health insurance claims data information for a common group of individuals. A data processing pipeline may be implemented with respect to information stored by the integrated data repository. The data processing pipeline may include a number of sets of data processing instructions that are executable to analyze specified information stored by the integrated data repository and generate different datasets. The datasets may be analyzed to determine an impact of characteristics of individuals and / or an amount of impact of treatments provided to individuals in which a biological condition is present.

Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based on phenotype and genomics information.

The invention relates to the technical field of plant genomics and gene editing, in particular to an in-vitro genome editing technology aiming at wheat and other complex genome crops, and provides a plant genome repetitive sequence deletion technology and application thereof. According to the method, specific sgRNA is designed, and CAS9 / CAS12 enzyme is guided to precisely shear repetitive sequences in a target genome, so that redundant repetitive sequences in the genome are effectively removed, sequencing data redundancy is reduced, and the efficiency and precision of genome information analysis are improved. The method comprises the following steps: firstly, identifying and annotating a repetitive sequence region by analyzing genome data of crops such as wheat; then, designing an sgRNA sequence aiming at the repetitive sequence, and optimizing the specificity and amplification efficiency of the sgRNA sequence; then, synthesizing an sgRNA library by utilizing a high-throughput DNA synthesis technology, and carrying out an in-vitro shearing reaction together with CAS9 / CAS12 enzyme; and finally, sequencing the sheared genome through a high-throughput sequencing platform, and analyzing the shearing efficiency and accuracy. The technology not only improves the efficiency of genome simplification and redundant sequence removal, but also can provide powerful data support for genome research and application of crops such as wheat.