19 results about "Genome variation" patented technology

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 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.

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.

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 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;

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.

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 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 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 invention discloses a wheat breeding method, and relates to the technical field of crop intelligent breeding, and the method comprises the following steps: S1, obtaining a whole genome DNA sample of a wheat germplasm to be bred and plant tissues in different growth periods, detecting genome variation sites through a liquid phase breeding chip, and analyzing a gene space expression mode by combining a space-time transcriptome technology, a multispectral unmanned aerial vehicle and a root system in-situ sensor are used for collecting phenotype data, and a genotype and phenotype association database is established. According to the wheat breeding method provided by the invention, by integrating genomics, phenotypic omics and artificial intelligence technologies, the precision and intelligence of the breeding process are realized, the breeding efficiency and the achievement quality are remarkably improved, the dependence of traditional breeding on phenotypic selection is broken through, a genetic mechanism formed by crop traits can be comprehensively analyzed, and the method is suitable for popularization and application. And the matching relationship between the genetic loci and the environmental factors is accurately evaluated, so that a more scientific and reasonable breeding scheme is designed.

The application discloses a model and method for predicting biological traits by comprehensively integrating genetic background and variation information, and the model comprises: a Siamese network which generates and extracts genetic background and genetic correlation information between samples, establishes an embedding vector for each sample to represent the genetic background information; a genetic variation embedding network which uses a deep learning network to extract complex high-order information using whole genome variation information of each sample, establishes an embedding vector to represent the cumulative effect of genetic variation on traits; and a fusion and trait prediction module which realizes accurate prediction of traits by organically fusing the embedding vectors output by the above two modules. The application can solve the problem that genetic background information and genomic variation information cannot be effectively fused in the prior art, effectively capture the nonlinear effect between genes, and greatly improve the accuracy of genomic selection and the genetic architecture analysis capability of complex traits.

The invention discloses an information encryption method and system based on strain genome coding and identification. The method comprises the following steps: extracting target gene sequence features from a strain genome database, including conservative marker gene sequences, necessary functional gene sequences and genome variation site distribution, and generating a strain specific key matrix; after to-be-encrypted information is converted into binary codes, a dynamic mapping rule is established according to the strain specificity key matrix, the binary codes are converted into quaternary pseudo-gene sequences, and strain identity identification codes are embedded into the quaternary pseudo-gene sequences; a hierarchical coding mechanism is adopted to allocate different security levels according to strain system development levels, and multi-level key management and authority control are achieved; inserting a strain specific marker sequence and an integrity check code into the pseudo-gene sequence to generate a final biological ciphertext; according to the method, high-security quantum resistant encryption is realized by utilizing natural diversity and randomness of microbial genomes.

The invention belongs to the field of coconut breeding, and particularly relates to a perfumed coconut aroma substance 2AP whole genome selective breeding method based on machine learning. The invention provides a perfume coconut 2AP content prediction method based on whole genome variation sites and a machine learning algorithm, and finally, 21 molecular marker sites are screened out as key features to construct a 2AP content prediction model for early germplasm screening. According to the method, efficient modeling of complex aroma traits is achieved, a reliable tool is provided for early screening of high-quality germplasm of Xianja, a key basis is provided for construction of a molecular prediction model, the screening efficiency is greatly improved, the cost is reduced, the aroma potential of filial generations can be predicted in the seedling stage, and the method is suitable for popularization and application. Therefore, the breeding period is effectively shortened, and cultivation and popularization of high-quality varieties of the perfume coconuts are accelerated.

This invention discloses a method for obtaining SSR molecular marker information based on high-throughput sequencing technology, belonging to the field of molecular biology. The method includes: selecting a reference genome in a genome database; collecting biological samples of the target species for high-throughput sequencing; obtaining raw sequencing data; analyzing the raw data to obtain a genome variation record file; performing a whole-genome scan of the reference genome; identifying and obtaining all SSR loci that meet the screening parameters; generating an analysis background file; performing joint analysis with the genome variation record files of each individual; extracting SSR loci information that have mutated relative to the reference genome in each individual; generating a cleaned SSR locus information file; constructing a whole-genome SSR molecular marker identification file; or performing joint analysis of multiple locus information files to generate population genetic data. This invention can provide efficient and highly adaptable core technical support for molecular genetic research and breeding applications in various species.

The application discloses a genome penetration identification method fusing parent-specific SNPs and population allele frequencies, and relates to the technical field of bioinformatics and data processing. The method is based on the whole genome variation data of a parent population with a known source, uses the difference in population allele frequencies to screen parent-specific diagnostic SNP sites, and constructs a diagnostic SNP library. For the whole genome variation data of a sample to be measured, parent sources are determined in a preset sliding window through a physical path and a statistical path. On this basis, the double-path determination results are fused according to the decision rules of physical priority and loss completion, and the final determination results of each sliding window are obtained. Then, through continuous window merging and chromosome physical coordinate mapping, a phase block list and a visual karyotype map are output. The method is beneficial to improving the accuracy and robustness of parent source determination of chromosome segments.