52 results about "Genome variation" patented technology

The invention discloses a genome variation detection method and system based on few-sample learning, and relates to the technical field of natural language processing. A genome variation detection model constructed by the method creatively integrates an adaptive memory module, a supervised contrast learning module and an orthogonal migration module, and the adaptive memory module reinforces effective retention and expression of context features by dynamically storing and updating key information; the supervised comparative learning module optimizes feature representation by means of a comparative learning strategy, and the semantic distinguishing ability of entities and relationships is improved; the orthogonal migration module realizes efficient migration and adaptation of cross-task knowledge by introducing orthogonal constraints, and the generalization ability of the model is remarkably enhanced. According to the method and the system, the entity recognition task is collaboratively optimized, excellent performance can be achieved, particularly, excellent detection precision and robustness are shown in very few sample settings such as 6Shot-1Way, and an efficient and accurate solution is provided for genome variation detection.

The invention discloses a compression and decompression method based on generic genome expression, and relates to the technical field of compression and decompression of DNA next-generation sequencing data, in particular to the compression and decompression method based on generic genome expression. The method aims at solving the problems that in the prior art, the capacity of processing population genetic diversity is insufficient, original sequencing quality information cannot be effectively restored during decompression, and memory occupation is too high during large-scale data processing. Obtaining a to-be-compressed sequencing sequence data file, a reference genome sequence and a thousand-person genome variation sample; obtaining a haplotype list, a variation list and a haplotype offset list corresponding to each window block; storing the window number, the haplotype number, the haplotype offset, the head and tail unmatched sequences, the current sequence name and the quality score character string into a single compression block; carrying out binding storage; completing the compression processing of the mass fraction; and obtaining each to-be-compressed sequencing sequence based on the result of the compressed part.

An AI-based system and method for determining medical therapies is disclosed. The AI-based method includes receiving patient multiomics raw data, extracting one or more reports from the patient multiomics raw data, and detecting one or more genome variants in the patient. Further, the AI-based method includes determining if the one or more genome variants are one or more known variants or one or more unknown variants, determining if the one or more genome variants are one or more coding variants or one or more non-coding variants, and determining if the patient is suffering from a functional loss or a functional excess. The AI-based method includes generating one or more rescue recommendations, determining one or more medical therapies for the patient, and outputting the one or more rescue recommendations and the one or more medical therapies to one or more electronic devices associated with the user.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.

Systems, methods, software and computer-usable media for annotating biomolecule-related data are disclosed. In certain exemplified embodiments, the biomolecules can be nucleic acids and the data can be sequence-related data. In various embodiments, systems can include one or more public or private biological attributes (e.g., annotation information databases, data storage devices and systems, etc.) sources, one or more genomic features data sources (e.g., genomic variant tools, genomic variant databases, genomic variant data storage devices and systems, etc.), a computing device (e.g., workstation, server, personal computer, mobile device, etc.) hosting an annotations module and / or a reporting module, and a client terminal.

The invention discloses a genome variation cold and hot spot region prediction method and device, and the method comprises the steps: S1, carrying out the slicing of a target genome region according to a preset sliding window length, and constructing a multi-modal input tensor corresponding to a window; s2, inputting the multi-modal input tensor into a pre-trained deep learning prediction model, and outputting a cold and hot spot prediction score of each site in the window through a full connection layer; and S3, according to the cold and hot spot prediction scores, identifying a variation cold spot region and a variation hot spot region in the target genome region. According to the technical scheme provided by the invention, the dependence on the existing variation data density is eliminated, and non-blind area coverage in the whole exon group range is realized; meanwhile, the structured output based on the preset transcript coordinates can directly support clinical variation interpretation, a quantitative basis is provided for PM1 and cold spot evidence, and the proportion of unclear significance variation is effectively reduced.

The present invention discloses a genome variation detection method and system based on few-sample learning, which relates to the field of natural language processing technology. The genome variation detection model constructed by this method innovatively integrates an adaptive memory module, a supervised contrastive learning module and an orthogonal transfer module, wherein the adaptive memory module strengthens the effective retention and expression of contextual features by dynamically storing and updating key information; the supervised contrastive learning module optimizes feature representation with the help of contrastive learning strategy, thereby improving the semantic distinction ability of entities and relationships; the orthogonal transfer module realizes efficient transfer and adaptation of cross-task knowledge by introducing orthogonal constraints, significantly enhancing the generalization ability of the model. The above method and system can achieve excellent performance by collaboratively optimizing entity recognition tasks, especially in very few sample settings such as 6Shot‑1Way, showing excellent detection accuracy and robustness, providing an efficient and accurate solution for genome variation detection.

The invention provides a Joint-Calling acceleration method and device for high-performance sequencing data, electronic equipment and a storage medium, and the method comprises the following steps: determining a division mode of genome variation data according to group information of a plurality of samples, and dividing each piece of genome variation data according to the division mode, obtaining a plurality of variation data fragments corresponding to each piece of genome variation data; merging variation data fragments with the same sequence in the genome variation data to obtain merged variation data; and respectively performing group variation detection on each piece of combined variation data to obtain group genome variation data of each piece of combined variation data, and combining each piece of group genome variation data to obtain target group genome variation data of a plurality of samples. Therefore, in the population variation detection process, the efficiency of obtaining complete population genome variation data can be improved by performing population anomaly detection on the multiple pieces of local merged data and merging the detection results.

There is a demand for low-cost efficient robust method for mapping read sequences with genome variation graph in genomic study. This disclosure herein relates to a method and system for mapping read sequences with genome variation graph by constructing a subgraph using a novel combination of graph embedding and graph winnowing techniques. The system processes the obtained plurality of read sequences and a genome variation graph for constructing the subgraph by computing an embedding for the genome variation graph utilizing a graph embedding technique. Further, graph index is generated for the genome variation graph based on the embedding and the genome variation graph utilizing the graph winnowing technique. Then computes gapped alignment score for read sequence (r) with its corresponding subgraph. Thus, enables a reliable method for read sequence with accurate, memory efficient and scalable system for mapping read sequences with genome variation graph.

A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variants from next generation sequencing reads out of a pool of enriched genomic patient samples without suffering from next generation sequencing workflow biases such as those introduced by sequencing errors in particular in repeat patterns regions of the human genome such as homopolymers or heteropolymers. The variant calling module may estimate the probability distribution of the length of the repeat pattern for each patient sample and cross-analyze it against other samples in a single experimental pool to identify best-fit variant models for each pair of samples. The variant calling module may further group samples according to their matching best-fit variant models and identify which group of patient samples carries the wild type reference without the need for control data in the pool. The variant calling module may subsequently characterize the homozygous or heterozygous repeat patterns variants for each patient sample with improved specificity and accuracy even in the presence of next generation sequencing biases.

The invention discloses a dairy cow and live pig high-quality breeding method based on AI genomics, and relates to the field of breeding. Comprising the following steps: multi-dimensional data acquisition: aiming at a target breeding group, acquiring whole genome variation data, various phenotype data and environment management data of each individual, establishing unique identification association for all the data, and storing the data in a central database; data preprocessing and feature enhancement: performing quality control, filling and standardization processing on the genome data, and constructing an effective feature set for model training from the original data based on statistics and machine learning methods; and training an AI prediction model. By introducing the artificial intelligence deep learning model, the complex non-additive effect and gene-environment interaction between the genotype and the phenotype can be efficiently captured, the prediction precision of important economic characters is greatly improved, and earlier and more accurate selection and optimized hybridization are realized, so that the genetic progress is greatly accelerated, the breeding cost is reduced, and the method is suitable for large-scale popularization and application. The breeding efficiency and benefits are comprehensively improved.

Integrated short read and long read sequencing for genomic variant detection is described. A coverage modulation engine selects a short read sequencing coverage and a long read sequencing coverage for a nucleic acid sequencing event based on a sequencing target of the nucleic acid sequencing event and at least one constraint. A sequencing protocol generator determines a short read sequencing protocol based on the selected short read sequencing coverage and determines a long read sequencing protocol based on the selected long read sequencing coverage. An integrated variant calling module outputs a variant call based on short read sequencing data generated via the short read sequencing protocol and long read sequencing data generated via the long read sequencing protocol.

The application discloses a method and system for rapid screening and analysis of homologous genes, and relates to the technical field of biological information and molecular breeding. The method comprises the following steps: obtaining multi-source input files; the multi-source input files comprise a reference genome file, a sample genome file, a reference annotation file and a target gene list file; pre-processing the multi-source input files to obtain pre-processed multi-source input files; the pre-processing comprises standardization processing, integrity detection, format consistency detection, repeated record detection and sequence file information improvement; performing sequence alignment, variation site extraction and variation marking on the pre-processed multi-source input files to obtain gene screening analysis results; and performing visual processing and data management on the gene screening analysis results. The application can reduce manual intervention, improve the efficiency and consistency of data analysis, and realize rapid standardized processing and intuitive result display of genomic variation information.

Systems, methods, software and computer-usable media for annotating biomolecule-related data are disclosed. In certain exemplified embodiments, the biomolecules can be nucleic acids and the data can be sequence-related data. In various embodiments, systems can include one or more public or private biological attributes (e.g., annotation information databases, data storage devices and systems, etc.) sources, one or more genomic features data sources (e.g., genomic variant tools, genomic variant databases, genomic variant data storage devices and systems, etc.), a computing device (e.g., workstation, server, personal computer, mobile device, etc.) hosting an annotations module and / or a reporting module, and a client terminal.

The disclosure relates to comprehensive genomic profiling (CGP) and to consensus-based classification techniques for determining genetically inferred ancestry from CGP of tumor DNA. Aspects are directed towards accessing reference and subject sequencing files and identifying genomic variants using a hybrid variant tool. The reference variant file is consolidated into a datastore formatted file that is queried to perform joint variant calling to generate a final reference variant file. The final reference variant file and the subject variant file are merged. On the merged variant file, principal component (PC) analysis is performed, and the PCs are used by a first and second classification process to generate a first and second ancestry call. The merged variant file is input into a third classification process to generate a third ancestry call. A consensus genetically inferred ancestry (GIA) call is predicted based on the first, the second, and the third ancestry calls.

The invention discloses a whole genome gene-gene and gene-environment interaction detection method based on an interpretable genetic information neural network, which comprises the following steps of: 1, training a deep neural network model by using genotype data and phenotype data to obtain a trained deep neural network model; 2, inputting genome data of a sample, and predicting a phenotype value of the sample by using the trained deep neural network model; and step 3, calculating the xPI value of each genome variation site, and carrying out whole genome association analysis and genetic site detection through the xPI values. By constructing the deep neural network model and combining the genotype data and phenotype data of the sample, the target character is accurately predicted. Through trained network analysis, the xAI-GWAS not only quantifies the importance of each genotype data, but also can detect the additive effect, the gene interaction effect and the gene-environment interaction effect of the genotype data.

The invention discloses a method and a kit for rapidly screening gene editing positive monoclone based on qPCR (quantitative polymerase chain reaction), and belongs to the field of gene engineering. The method comprises the following steps: preparing a monoclonal sample: preparing a single-cell suspension from cells subjected to gene editing, and carrying out single-cell sorting and culture until monoclone is formed; collecting monoclonal cells, and directly cracking by using a cracking solution to prepare a qPCR template; qPCR detection: carrying out qPCR amplification and detection on the sample by using a specific primer and a probe designed aiming at a target gene editing event; and result judgment: judging whether the monoclone is gene editing positive clone or not by analyzing the Ct value of the qPCR. According to the present invention, the specific primers and the probe of the target gene editing site are designed, and the qPCR is combined to directly detect the genome variation of the monoclonal cell so as to achieve the early rapid screening;

Embodiments of the invention utilize a graph-based approach for simulating genomic datasets from large scale populations. Genomic data may be represented as a directed acyclic graph (DAG) that incorporates individual sample data including variant type, position, and zygosity. A simulator may operate on the DAG to generate variant datasets based on probabilistic traversal of the DAG. This probabilistic traversal reflects genomic variant types associated with the subpopulation used to build the DAG, and as a result, the generated variant datasets maintain statistical fidelity to the original sample data.

Current machine learning methods for target binding oligonucleotides design are limited to considering natural sequences in the targets. Here, Applicants generated novel target binding oligonucleotides—with multiple mismatches to any natural sequence—that are optimized for desired properties. These novel target binding oligonucleotides offer more sensitive and specific detection of, for example, pathogen genome variation than baseline design methods, and they illuminate a new, interpretable design rule that broadens nucleic acid sequence targeting.

The invention discloses a vitis amurensis cold-resistant key gene and genome variation map, which comprises a genome screening method of cold-resistant character genes, and the genome screening method of the cold-resistant character genes comprises the following screening steps: step 1, collecting 330 parts of wild vitis amurensis resources; 2, performing whole genome sequencing on 330 wild vitis amurensis samples to obtain genetic marker information of each individual; step 3, downloading environment data such as isothermal temperature, seasonal temperature and warmth seasonal average temperature through a website; and step 4, carrying out correlation analysis on 330 wild grape sample genetic marker information and environmental factors by adopting R packet LEA. The invention relates to the technical field of grape cold resistance character gene screening. According to the vitis amurensis cold-resistant key gene and genome variation map, the efficiency and accuracy of grape cold-resistant breeding are remarkably improved, and genes closely related to cold-resistant characters are screened out through genotype and environment correlation analysis.

To provide compositions, systems, and methods for spatial index sequencing and nucleic acid library preparation. [Solution] Embodiments of systems, methods, and compositions provided herein relate to on-bead tagmentation and droplet indexing. Some embodiments include performing simultaneous assays, including nucleic acid sequencing, indexed PCR, preparation of nucleic acid libraries, determination of methylation status, identification of genomic variants, or protein analysis, on dispensed beads.

The invention discloses an application of a compound or a composition thereof as a semen cryopreservation solution. According to the invention, through whole genome re-sequencing and seminal plasma non-target metabonomics, a regulation mechanism of interaction of genome variation and metabolites on semen traits is analyzed. Key metabolites having a protection effect on sperm motility are screened from 3613 kinds of metabolites and 38 pieces of sperm character analysis of breeding bulls. The function is verified through a standardized freezing process, it is found that after the sperm concentration is adjusted to 6 * 10 < 8 > / mL, and incubation at 37 DEG C, gradient cooling and liquid nitrogen programmed freezing are carried out, it is found through detection that the effect of a treatment group added with 15 [mu] M of 4, 6-dihydroxyquinoline and 5 [mu] M of flavanone is the most remarkable, and the vitality of frozen sperms can be improved. The invention further finds that the 4, 6-dihydroxyquinoline and the flavanone are combined for use, so that a remarkable synergistic interaction effect can be generated. The method is simple and convenient to operate and easy to popularize, and provides a reliable means for cryopreservation of the semen.

The invention discloses a gene variation frequency calculation system based on personalized phenotypic crowds. The gene variation frequency calculation system comprises a data acquisition module, a clinical phenotypic category module, a crowd phenotypic construction module, a data classification module, a data extraction module, a personalized combination module and a mark calculation module. According to the method, gene variation population frequency related to a specific phenotype can be provided, gene variation frequency data rich in phenotype can be provided by combining the genotype with the specific phenotype, reference result data of genome variation frequency of different populations can be obtained through calculation, the data can be stored and transmitted, and an existing database can be perfected.

The invention provides a tumor genome variation detection method, device and equipment and a storage medium, and relates to the technical field of bioinformatics. The detection method comprises the following steps: acquiring sequencing data and comparing the sequencing data to a reference genome to obtain a comparison sequence; and performing somatic mutation detection based on the comparison sequence. According to the method, the problem of noise interference in clinical FFPE sample detection is effectively solved through a dual mechanism of combining statistical model preliminary screening and chain orientation preference scoring. According to the method, the low-frequency mutation detection sensitivity is ensured, meanwhile, the chain preference false positive is accurately eliminated, and the mutation detection specificity and the result reliability are remarkably improved.

The application discloses a whole genome prediction parallel computing and data visualization analysis system and relates to the technical field of gene image data analysis. The system converts whole genome data into a genome heat map, a genome variation map and a gene structure diagram through parallel computing. Through an image processing algorithm, the system accurately extracts gene expression differences, gene variation characteristics and gene region distribution density in the genome image, and generates an expression difference index, a variation distribution map and a gene density index. According to a machine learning model, the system predicts gene expression trends, variation hot spot evolution and functional region distribution, and generates a genome prediction map. Finally, the system displays and analyzes the analysis results through multiple levels of graphics, helps researchers to intuitively identify the expression changes, variation hot spots and region distribution characteristics of genes, and improves the computing efficiency of large-scale genome data processing. Meanwhile, through multi-level visualization display, the system enhances the intuitiveness and interpretability of data analysis.

The present application belongs to the field of information retrieval and big data analysis, and particularly relates to a bitmap index compression method for genomic variation data, comprising: splitting original genomic variation data sequences according to field attributes, and storing in a columnar database; establishing original bitmap indexes for genomic variation data in the columnar database; dividing the original bitmap indexes into several segments; using a run-length algorithm to compress and store the public part of the segmented bitmap data, and using an integer list or a bit sequence to compress and store the non-public part. The present application saves more storage space by merging public affixes in the bitmap sequence, improves the bit operation speed, and makes the bitmap index load into the memory faster.

The embodiment of the invention provides a method and system for analyzing spatial transcriptome data, and belongs to the technical field of data analysis. The system comprises a data uploading and quality control module which is used for uploading spatial transcriptome data, bright field pictures and sample clinical information and performing quality control on the uploaded data; the spatial multi-omics feature identification and integration module is used for identifying spatial multi-omics features, integrating multi-omics data, carrying out batch processing on the data and unifying a data format; the spatial multi-omics data analysis module is used for performing data analysis of gene expression, genome variation, RNA modification, variable polyadenylation and pathogen and cell distribution dynamics according to different omics data types; the user management module is used for providing multi-level authority management; and the analysis report module is used for automatically generating analysis report data. According to the method, the depth and breadth of spatial transcriptome data analysis can be improved, and spatial multi-omics features can be analyzed more comprehensively and accurately.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.

The disclosure relates to comprehensive genomic profiling (CGP) and to consensus-based classification techniques for determining genetically inferred ancestry from CGP of tumor DNA. Aspects are directed towards accessing reference and subject sequencing files and identifying genomic variants using a hybrid variant tool. The reference variant file is consolidated into a datastore formatted file that is queried to perform joint variant calling to generate a final reference variant file. The final reference variant file and the subject variant file are merged. On the merged variant file, principal component (PC) analysis is performed, and the PCs are used by a first and second classification process to generate a first and second ancestry call. The merged variant file is input into a third classification process to generate a third ancestry call. A consensus genetically inferred ancestry (GIA) call is predicted based on the first, the second, and the third ancestry calls.