31 results about "Genomic annotation" patented technology

The present disclosure, among other things, provides machine-learning technologies for identifying and localizing particular genomic elements (e.g., gene elements and / or regulatory elements) within nucleotide sequences, such as DNA and / or RNA sequences. In certain embodiments, similar to the manner in which image processing methods can be used to localize particular objects in images at pixel level resolution, referred to as “segmentation,” systems and methods of the present disclosure predict presence and locations of certain genomic elements within nucleotide sequences, thereby “segmenting” nucleotide sequences. Accordingly, genomic element segmentation technologies described herein may be used to generate annotations that identify and label portions of nucleotide sequences according to their predicted (e.g., via machine learning models described herein) function—e.g., as protein-coding genes, untranslated regions, splice sites, promotors, enhancers, etc. Among other things, these genomic annotations may be used to inform underlying biological processes driving diseases and facilitate development of new therapies.

The invention discloses a conservative non-coding element identification method and system based on multi-species genome alignment, and the method comprises the following steps: establishing an index database based on reference genome sequences of multiple species, completing whole genome alignment, and further processing to obtain a high-credibility chain alignment result; the multi-species chain type comparison results are integrated into multi-sequence comparison data in a unified format; on this basis, a neutral evolution model is constructed based on quadruple degenerate sites, and candidate conservative regions are predicted through conservative scoring; in combination with genome annotation information, a length threshold is set, and a coding region and a UTR region are rejected, so that a high-confidence non-coding conservative element is obtained; and finally, displaying a cross-species conservative distribution diagram of the CNE by utilizing a visual tool. According to the method, the CNE with a potential regulation function can be accurately, efficiently and automatically identified, and technical support is provided for regulation system analysis, functional gene mining and molecular breeding of various organisms.

The invention discloses a Rongchang pig T2T genome assembly method. The method comprises the following steps: 1) collecting and sequencing a sample; 2) genome investigation and assembly; 3) genome annotation; wherein in the sequencing step, three sequencing technical means, namely, a three-generation gene sequencing technology PacBio, Nanopore PromethION 48 short reading and chromatin conception capture (HiC), are adopted, and the Rongchang pig genome is subjected to sequencing and sequence splicing together. The Contig N50 value of the genome is nearly three times that of Sscrofa11.1, and the improvement is mainly embodied in a complex genome region (centromere and telomere regions), so that the genome becomes the most complete genome available at present.

The invention discloses a gene regulatory network construction method and a related device, and relates to the technical field of gene regulatory network construction.The method comprises the steps that position data of candidate regulatory elements are downloaded from an SCREEN database, a genome annotation file and scATAC-seq data are combined, a promoter and an enhancer are accurately positioned, a variational auto-encoder is introduced, and a gene regulatory element is constructed. The method comprises the following steps: accurately determining an enhancer-promoter pair with potential interaction, associating an enhancer with a target gene, and processing a co-expression gene network based on a potential binding site of each transcription factor of a to-be-detected object on the enhancer, the enhancer-promoter pair with potential interaction and the target gene corresponding to each promoter. And obtaining the gene regulatory network. The method can solve the problems that the promoter and the enhancer cannot be accurately positioned, the enhancer and the target gene are difficult to associate, direct and indirect relationships are difficult to distinguish, false positive regulation relationships are excessive and the like.

This invention discloses an efficient RNA data preprocessing method to address the problems of low processing efficiency and low information density in high-throughput sequencing data. Its core steps include: (1) introducing a parallel processing scheme for high-throughput sequence data, rapidly mapping RNA-seq data to a reference genome to generate a BAM file; (2) extracting base sequences and expression levels and storing them as compact PKL format files; (3) extracting all exon position information by parsing the genome annotation file; (4) combining multi-sample expression level data to screen for highly variable exons and constructing a high-information-density feature list based on the sample set; and (5) accurately extracting target sequences from the preprocessed file based on this list. Compared to traditional methods, this innovative approach achieves triple optimization: full-process parallel processing for accelerated computation, high-compression data storage, and adaptive feature selection. Processing speed is increased by 3-5 times, and data volume is reduced by more than 90%, making it suitable for high-throughput RNA-seq data analysis with large sample sizes.

The present disclosure, among other things, provides machine-learning technologies for identifying and localizing particular genomic elements (e.g., gene elements and / or regulatory elements) within nucleotide sequences, such as DNA and / or RNA sequences. In certain embodiments, similar to the manner in which image processing methods can be used to localize particular objects in images at pixel level resolution, referred to as "segmentation," systems and methods of the present disclosure predict presence and locations of certain genomic elements within nucleotide sequences, thereby "segmenting" nucleotide sequences. Accordingly, genomic element segmentation technologies described herein may be used to generate annotations that identify and label portions of nucleotide sequences according to their predicted (e.g., via machine learning models described herein) function – e.g., as protein-coding genes, untranslated regions, splice sites, promotors, enhancers, etc. Among other things, these genomic annotations may be used to inform underlying biological processes driving diseases and facilitate development of new therapies.

The invention discloses a strain specific culture medium prediction method, a medium and computer equipment. The method comprises the following steps: acquiring strain gene function annotation data and encoding the data into a high-dimensional feature vector; performing principal component analysis dimension reduction on the high-dimensional feature vector; obtaining candidate culture medium component information and encoding the candidate culture medium component information into a binary vector; splicing the low-dimensional strain feature representation and the medium component vector to form a joint input feature vector; inputting the joint input feature vector into a pre-trained deep residual network model; and outputting a growth compatibility probability score and sorting the candidate culture media. By integrating high-throughput genome annotation data and a known culture medium formula, a data-driven intelligent prediction model is constructed and is used for efficiently recommending an optimal culture medium composition suitable for a specific microbial strain, and the prediction precision and the screening speed of microbial isolated culture are remarkably improved.

The invention provides a genome annotation method and an electronic device. The genome annotation method comprises the following steps: S1) performing gene structure prediction on a genome by adopting multiple modes to obtain multiple prediction gene sets; s2) performing gene integration on the multiple predictive gene sets by using an EVM tool to obtain an integrated gene set; s3) performing BUSCO evaluation on the integrated gene set to obtain an integrated gene set evaluation file; s4) performing BUSCO evaluation on the genome to obtain a genome evaluation file; and S5) correcting the integrated gene set evaluation file by using the genome evaluation file to obtain a corrected gene set, wherein the gene structure prediction comprises transcriptome prediction, de novo prediction and homologous prediction.The genome annotation method can significantly improve the accuracy and integrity of gene annotation.

The application discloses a SNP genotype coding method, comprising the following steps: obtaining genomic selection data and a genomic annotation file, wherein the data comprises SNP input data; the SNP input data comprises chromosome number and SNP position; sorting columns of a data table according to the chromosome number and the SNP position; preprocessing the data table to obtain SNP coding; extracting the genomic annotation file to obtain SNP type information; forming SNP columns in the data table at the SNP positions, traversing adjacent SNP columns, storing the SNP columns into channels, and completing the channels; and coding the filled SNP based on the SNP type information and the SNP coding to obtain a sample vector. The sample vector can be used as an input of a convolutional neural network, so that information contained in the genomic annotation file can be applied to deep learning.

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*.

Memory and logic devices formed in a wafer-on-wafer manner enable high bandwidth transfer of data directly between a memory die and a logic die. A logic die bonded to a memory die via a wafer-on-wafer bonding process can receive a signal indicative of a gene sequence from the memory die and through wafer-on-wafer bonding. The logic die can also perform genome annotation logic operations to attach biological information to the gene sequence. Annotated gene sequences can be provided as output.

The invention belongs to the technical field of gene engineering and microbial fermentation, and particularly discloses a cytochrome P450 reductase coding gene CPR2 from aspergillus nidulans and application thereof. The nucleotide sequence of the gene is shown as SEQ ID NO: 1, and the amino acid sequence of the protein coded by the gene is shown as SEQ ID NO: 2. According to the invention, the CPR2 gene (OECPR2) is identified as an optimal electron transfer regulatory element by performing function screening on eight CPR genes annotated in a genome, and the CPR2 gene is over-expressed in an echinocandin B production strain aspergillus nidulans, so that the intracellular electron transfer efficiency is remarkably enhanced, the enzyme catalytic activity of cytochrome P450 is driven, and the cytochrome P450 can be used for preparing an echinocandin B cell. And finally, the yield of echinocandin B is efficiently increased. Experiments show that the engineering strain for over-expression of CPR2 can enable the yield of echinocandin B to reach 1869.97 + / -96.98 mg / L in shake flask fermentation, which is 24.37% higher than that of the original strain. The invention provides a key gene element and an engineering strain for industrial efficient production of echinocandin B, and has important economic value.

The present invention discloses a method for accurately annotating three generations of full-length transcripts. The method comprises the following steps: obtaining corrected transcript structure information; parsing a human reference genome annotation file, extracting specific gene-transcript information as reference information; parsing the corrected transcript structure information, obtaining preliminary classification information, converting the abnormal transcripts, and obtaining normal data; annotating the normal data, obtaining specific gene-transcript annotations, and final transcript classification information; arranging the annotation information and classification information, and obtaining accurate annotations of three generations of full-length transcripts. Experiments have shown that the accuracy of annotations is significantly improved by annotating three generations of full-length transcripts using the method provided by the present invention. The present invention has important application value.

This application discloses an improved method for whole-genome selection of maize hybrids, belonging to the field of genetic breeding technology. To address the shortcomings of existing technologies, this application provides a computer device to implement the following steps: (A1) Construction of a maize pan-genome: screening core planting and breeding backbone parents, constructing libraries and sequencing, loading reference genomes and genome annotations, and iteratively assembling to construct a pan-genome to obtain a sequence / gene-based pan-genome; (A2) Construction of a maize haplotype library: setting chromosomal reference segments / genes and statistically analyzing the haplotypes of each reference segment to obtain a whole-genome haplotype library; (A3) Haplotype genotyping of the training population: obtaining haplotype data of the training population; (A4) Whole-genome prediction: training the training model data through evaluation dimensions such as haplotype effect value, parental combining ability, and / or prediction accuracy.

The invention discloses a construction and analysis method of a paracoccus denitrificans genome scale metabolic network model, and belongs to the technical field of system biology. The method comprises the following steps: annotating a whole genome; acquiring global metabolic reaction data of paracoccus denitrificans; the method comprises the following steps: automatically retrieving genome information and constructing a model 1 on the basis of species codes and genome annotation results of paracoccus denitrificans in a KEGG database; on the basis of a pre-trained hidden Markov model, homology search is carried out on protein of a target organism, homologous protein in a paracoccus denitrificans genome is identified, additional metabolic reaction is added, and a model 2 is constructed; and integrating the model 1 and the model 2. According to the method, denitrification engineering can be efficiently designed and modified, accurate regulation and control of the nitrogen degradation process are achieved, compared with an existing metabolic engineering modification method, the workload of an exploration experiment can be effectively reduced, and the method has a great promotion effect on deepening the nitrogen degradation characteristic of paracoccus denitrificans.

The application discloses a gene regulation network construction method and related device, and relates to the technical field of gene regulation network construction. The method comprises the following steps: downloading position data of candidate regulation elements from a SCREEN database, combining a genome annotation file and scATAC-seq data to accurately position promoters and enhancers, introducing a variational autoencoder to accurately determine enhancer-promoter pairs that exist potential interaction, associating enhancers with target genes, processing a co-expression gene network based on potential binding sites of each transcription factor of a to-be-detected object on an enhancer, enhancer-promoter pairs that exist potential interaction, and target genes corresponding to each promoter, and obtaining a gene regulation network. The application can solve the problems of failing to accurately position promoters and enhancers, being difficult to associate enhancers with target genes, being difficult to distinguish direct and indirect relationships, and having too many false positive regulation relationships.

To provide a system and a method for effective compression, representation and decompression of diverse tabulated data.SOLUTION: A method for controlling compression of data includes the steps of: accessing genomic annotation data of one first file format out of a plurality of first file formats; extracting a plurality of attributes from the genomic annotation data; dividing the genomic annotation data into a plurality of chunks; processing the extracted attributes and chunks into correlated information; selecting different compressors for the attributes and chunks to be identified in the information; and generating a file of a second file format, that includes the correlated information, and information indicating the different compressors for the attributes and chunks indicated in the correlated information, and allowing selective development of the attributes and chunks indicated in the correlated information.SELECTED DRAWING: Figure 2

According to the degradation gene for protecting crops from quizalofop-p-ethyl phytotoxicity and the application of the degradation gene, the degradation gene with a quizalofop-p-ethyl degradation effect is transferred into rice through a genetic transformation technology, so that the rice is protected from the quizalofop-p-ethyl phytotoxicity. The gene is obtained by genome annotation and sequence alignment, codes 268 amino acids, has the molecular weight of 30,166.88 Da, and has the capability of degrading quizalofop-p-ethyl. The gene contains a conserved sequence of esterase and a catalytic triad, and the interaction between the gene and quizalofop-p-ethyl, especially the important effect of Ser94, is verified through 3D structure modeling and molecular docking experiments. After the gene is transferred into rice, the resistance of the rice to quizalofop-p-ethyl can be remarkably improved, healthy growth is maintained, and the risk of phytotoxicity is reduced. Therefore, not only is the tolerance of crops enhanced, but also a new strategy is provided for agriculture, the yield and quality are improved, the environmental influence of pesticides is reduced, and the development of sustainable agriculture is promoted.

The invention provides an automatic genome annotation method, device and equipment and a computer readable storage medium, and the method comprises the following steps: constructing a gene annotation analysis model, and obtaining to-be-annotated genome sequence data; setting model annotation parameters, and inputting genomic sequence data into the gene annotation analysis model for structure annotation; and performing function annotation on the genome subjected to structure annotation. According to the scheme provided by the embodiment of the invention, the end-to-end processing from the original genome sequence to the whole genome annotation is realized by constructing the gene annotation analysis model, integrating a plurality of mainstream annotation programs and carrying out systematic optimization, the consistency and comparability of annotation are remarkably improved through the automatic design, and the operation difficulty is reduced; meanwhile, manual intervention is reduced, the efficiency of the annotation process is improved, genome splicing and annotation integrity evaluation can be automatically carried out, annotation accuracy is ensured, operation is simple, and annotation requirements of different species and data types can be met.

A method for controlling compression of data includes accessing genomic annotation data in one of a plurality of first file formats, extracting attributes from the genomic annotation data, dividing the genomic annotation data into multiple chunks, and processing the extracted attributes and chunks into correlated information. The method also includes generating a file in a second file format that includes the correlated information and information indicative of the different compressors for the chunks and attributes indicated in the correlated information. The information indicative of the different compressors is processed into the second file format to allow selective decompression of the attributes and chunks indicated in correlated information. The method also includes generating access control policy information for the correlated information and integrating the access control policy information into the file of the second file format.

This invention belongs to the field of bioinformatics, specifically relating to a hierarchical transposon classification method based on dynamically gated multi-scale features. First, K-mer frequencies and one-hot encoded bimodal features are extracted from the DNA sequence. Second, these features are encoded separately using a gated multi-scale convolutional network and a DNA-specific convolutional pyramid. Then, adaptive feature fusion is performed using an attention-gating mechanism conditioned on sequence features. Finally, a top-down hierarchical decision is made in the classification tree based on probability thresholds. This invention overcomes the shortcomings of existing methods in feature capture, fusion, and hierarchical decision-making, achieving high-precision and high-efficiency transposon classification, providing a more accurate and reliable analytical tool for genome annotation, evolutionary research, and functional genomics.

The invention discloses a method for improving genome selection accuracy by integrating genome annotation information, and belongs to the technical field of molecular breeding. The method comprises the following steps: constructing a reference group and accurately determining target traits; acquiring high-density genotype data by using a whole genome re-sequencing technology; constructing an SNP function annotation matrix containing multi-dimensional information such as a gene structure, codon degeneracy in a coding region, nucleotide diversity and the like; estimating genetic variance weights of different functional regions by using a random Heismann-Elstarton regression (RHE) algorithm and a Monte Carlo REML algorithm; and constructing a weighted genome prediction model based on mixed prior distribution to estimate a breeding value. According to the method, differential modeling is carried out on functional sites and background noise markers through biological prior information, the genome selection accuracy of complex characters and the robustness of cross-population prediction are remarkably improved, and the method has a good application prospect.

Methods for generating and evaluation methylation meta scores and their use for the detection of disease and prediction of tumor tissue of origin are described. The methods may comprise, e.g., generating annotated genomic interval data comprising at least one of: (i) genomic coordinates, and (ii) associated genomic annotation data; providing annotated patient sample data comprising at least one of: (i) methylation sequencing data, and (ii) associated clinical annotation data; generating an aggregated methylation meta score matrix comprising candidate methylation meta scores; inputting (i) the aggregated methylation meta score matrix, and (ii) the associated clinical annotation data for at least a subset of patient samples as training data for training a machine learning model; and training the model using the training data to identify an optimal methylation meta score for disease detection and / or prediction of disease tissue of origin (TOO) for at least one disease type based on methylation sequencing data.

The invention discloses a method for improving pig complex character genome selection accuracy by using large-scale genome annotation information. The method comprises the following steps: integrating a plurality of function annotations and position information of a reference group pig genome; sNPs data of the multiple function annotation areas are extracted, and a genome genetic relationship matrix A of function annotation is constructed; estimating a variance component of each genome genetic relationship matrix A, sorting the plurality of function annotations based on the variance components, and combining the plurality of function annotations to obtain a function annotation set; deleting repeated SNPs data in the function annotation set, constructing a genome genetic relationship matrix B of the function annotation set, and calculating a random genetic effect value of the function annotation set to obtain an estimated genome breeding value of the pig. According to the method, the genomic breeding value is obtained by extracting the various genomic function annotations of the pig complex characters and fitting the mixed linear model containing the multiple random genetic effects, and the accuracy and timeliness of pig genomic breeding value prediction are improved.

The invention belongs to the technical field of microorganisms, and particularly relates to application of schizophyllum commune ergothioneine synthetase genes ScEgt1a and ScEgt2b in synthesis of ergothioneine, and the ergothioneine synthetase genes ScEgt1a and ScEgt2b are obtained by finding out the sequences of the ergothioneine synthetase genes ScEgt1a and ScEgt2b in genome annotation of schizophyllum commune, performing codon optimization and then utilizing a direct synthesis method. The obtained ergothioneine synthase genes ScEgt1a and ScEgt2b are utilized to construct an engineering vector for starting efficient expression of the ergothioneine synthase genes ScEgt1a and ScEgt2b by using a pichia pastoris promoter pGCW14, the engineering vector is transformed and integrated into a pichia pastoris X33 genome, and a recombinant engineering strain for high yield of ergothioneine is obtained through screening; by optimizing the culture medium composition and fermentation conditions, under the optimal conditions, the recombinant pichia pastoris engineering bacteria are subjected to shake-flask fermentation for 96 hours, the yield of ergothioneine reaches 1.67 g / L, and the method has industrial production prospects.

The embodiment of the invention relates to a chromosome detection method and device, computer equipment and a storage medium. A processor of the computer equipment can execute the following steps: obtaining sequencing data corresponding to a target sample; comparing the sequencing data with reference genome data to obtain a comparison result; annotating the comparison result according to a genome annotation file to obtain chimeric information which comprises chromosome information corresponding to the multiple breakpoints respectively; based on the chromosome information corresponding to each breakpoint, screening the plurality of breakpoints to obtain at least one target breakpoint; the target breakpoint is a breakpoint generated by chromosome connection abnormality; and according to the chromosome information corresponding to the at least one target breakpoint, performing chromosome karyotype analysis on the target sample to obtain a karyotype result corresponding to the target sample. According to the chromosome detection method and device, the computer equipment and the storage medium, the sample sequencing accuracy and analysis efficiency can be improved.

The present disclosure, among other things, provides machine-learning technologies for identifying and localizing particular genomic elements (e.g., gene elements and / or regulatory elements) within nucleotide sequences, such as DNA and / or RNA sequences. In certain embodiments, similar to the manner in which image processing methods can be used to localize particular objects in images at pixel level resolution, referred to as "segmentation," systems and methods of the present disclosure predict presence and locations of certain genomic elements within nucleotide sequences, thereby "segmenting" nucleotide sequences. Accordingly, genomic element segmentation technologies described herein may be used to generate annotations that identify and label portions of nucleotide sequences according to their predicted (e.g., via machine learning models described herein) function – e.g., as protein-coding genes, untranslated regions, splice sites, promotors, enhancers, etc. Among other things, these genomic annotations may be used to inform underlying biological processes driving diseases and facilitate development of new therapies.

The invention relates to an agropyron cristatum variety identification method based on chloroplast genome high-throughput sequencing, and belongs to the technical field of plant taxonomy, and the method comprises the following steps: (a) sample collection and DNA extraction; (b) library construction and sequencing; (c) data processing and chloroplast genome assembly; (d) genome annotation and feature analysis; (e) comparison and phylogenetic analysis; (f) variation identification and analysis; (g) comprehensive judgment: performing species identification on the wheatgrass sample according to the phylogenetic tree topological structure, the PCA clustering result and whether the specific marker exists or not; according to the method, the chloroplast genome high-throughput sequencing technology is utilized, the wheatgrass is subjected to variety identification from the molecular level, the variety of the wheatgrass sample can be rapidly and accurately identified, the method is not interfered by morphological variation, hybridization and other factors, the limitation of a traditional identification method is effectively solved, and the identification accuracy is improved.

The invention provides a genome language model training method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring original genome data; constructing a natural language expression sequence of a gene product for the original genome data based on genome annotation; pre-training a basic language model by using the natural language expression sequence of the gene product to obtain a pre-trained model; and performing supervision and fine tuning on the pre-trained model for different gene function tasks to obtain a genome language model subjected to supervision and fine tuning. According to the method, the problems that a current genome language model takes nucleotide or k-mer as a token and has no explicit mapping with biological functions, a prediction result cannot be explained and a nucleotide sequence modeling is too long, so that computing resources required by training are very huge are solved.

The invention belongs to the field of bioinformatics, and particularly relates to a transposon hierarchical classification method based on dynamic gating multi-scale features. The method comprises the following steps: firstly, extracting K-mer frequency and One-hot coding bimodal features from a DNA sequence; secondly, coding is carried out through a gated multi-scale convolutional network and a DNA specific convolutional pyramid; then, self-adaptive feature fusion is carried out by utilizing an attention gating mechanism taking sequence features as conditions; and finally, performing top-down hierarchical decision-making in the classification tree based on a probability threshold. According to the method, the defects of an existing method in feature capture, fusion and hierarchical decision making are overcome, high-precision and high-efficiency transposon classification is achieved, and a more accurate and reliable analysis tool is provided for genome annotation, evolutionary research and functional genomics.