21 results about "Gene prediction" patented technology

The application discloses a gene editing system CRISPR-Cas12p and application thereof. Based on microbial genomes and metagenomic data, the CRISPR-Cas12p protein of the CRISPR-Cas protein family is obtained by preliminary screening by using a Prodigal gene prediction tool, a Pfam database and HMMER software, and a gene editing system CRISPR-Cas12p is constructed. PAM preference and interference function identification show that the editing system has a PAM preference of 5'-TTC-3', can effectively realize targeted cutting by using long transcripts and double RNA hybrid chain transcripts respectively, and can realize editing of a target gene in prokaryotic and eukaryotic cells. The CRISPR-Cas12p gene editing system obtained by the application has a small protein component, is beneficial to delivery, can realize gene editing in prokaryotic and eukaryotic cells, and has a wide application prospect.

The invention relates to the technical field of gene detection, in particular to an opioid analgesic drug gene detection method based on multiple targeted amplification, which comprises the following steps: extracting DNA from a peripheral blood sample; the method comprises the following steps: setting a primer pool aiming at a preset site of a specific gene related to opioid drugs, and carrying out multi-targeted amplification sequencing by using the primer pool to obtain sequencing data; comparing the sequencing data with a human reference genome to obtain a data comparison rate, and judging the eligibility of the multi-target amplification sequencing process according to the data comparison rate; performing variation detection on the qualified sequencing data, identifying the base type of each SNP site, and determining the genotype of the subject in combination with a dbSNP database; and inputting the genotype and clinical data into a pre-trained multi-gene prediction model, and obtaining the medication guidance of the subject for using the opioid drugs. The gene detection efficiency is improved.

This invention provides a gene prediction and identification method, apparatus, device, and storage medium, belonging to the field of gene annotation and prediction. The method includes: acquiring the gene text to be annotated and a set of promoter-terminator pairs; preprocessing the gene text to generate a raw genome sequence; searching the raw genome sequence for base sequences that fuzzy search matches the set of promoter-terminator pairs to generate a base sequence to be aligned; and comparing the base sequence to be aligned with base sequences in a bioinformatics database based on the BLAST gene alignment method to generate gene prediction and identification results. This invention, by separating base sequences from promoter-terminator pairs and then performing BLAST gene sequence alignment, achieves coarse gene localization followed by BLAST gene sequence alignment, providing a method for predicting new genes, improving alignment efficiency and comprehensiveness, as well as the accuracy and comprehensiveness of gene prediction results.

The present application relates to the technical field of artificial intelligence and deep learning, in particular to a tomato salt tolerance prediction method and system based on deep learning, specifically as follows: obtaining a tomato salt tolerance genome, inputting it into a tomato salt tolerance prediction system based on deep learning for detection, sequentially passing through a salt-tolerant small sample gene recombination module, an epigenetic gene multi-granularity mining module, a heterogeneous salt-tolerant gene feature integration module, a salt-tolerant feature point fusion module and a tomato salt tolerance gene prediction module, and calculating the probability of having relevant tomato salt tolerance genes in the input tomato salt tolerance genome. The present application improves the prediction accuracy and generalization ability of salt tolerance genes under complex genetic background, and provides an intelligent prediction method for tomato salt-tolerant variety breeding.

The invention provides a dairy cow ketosis regulatory gene prediction system based on multi-omics analysis and machine learning, which belongs to the technical field of molecular breeding and disease prevention and control, and comprises a data acquisition and preprocessing module, a feature set establishment module, a machine learning model establishment module and a result evaluation module, the method comprises the following steps: integrating dairy cow genome, transcriptome and metabolome data, screening candidate regulatory genes through whole genome association analysis, gene differential expression analysis, cis-eQTL positioning and co-positioning analysis, and constructing a gene expression feature set; core regulation genes are screened through L1 regularization penalty by means of a Lasso model, weights are distributed, model parameters are optimized in combination with grid search and cross validation, and model performance is evaluated through an ROC curve and an AUC value. According to the invention, an integrated technical system from gene screening to risk prediction is constructed, efficient screening of the core regulation gene and accurate prediction of ketosis risk are realized, and the economic loss of breeding is effectively reduced.

Provided are a gene prediction method and apparatus, a computer device, and a computer readable storage medium. The method comprises: acquiring a template gene sequence, and a genetic gene sequence and a free gene sequence which correspond to a subject under test (step S102); determining a target gene site in the genetic gene sequence and the free gene sequence (step S104); extracting feature data corresponding to the target gene site, wherein the feature data is used for representing attribute features of the target gene site, and the feature data comprises first input data and second input data (step S106); acquiring a target gene prediction model, wherein the target gene prediction model comprises a first network, a second network, and a third network, and an output of the first network and the second network is an input of the third network (step S108); and respectively inputting the first input data and the second input data into the first network and the second network to obtain a gene prediction result outputted by the third network and corresponding to said subject (step S110).

The application discloses a method for eRNA transcription identification, regulation target prediction and function annotation based on high-throughput transcriptome sequencing data, and is characterized in that the method comprises the following steps: identifying part of non-coding RNA in which a transcription start site is located in an enhancer region as eRNA; obtaining eRNA-related protein coding genes which simultaneously exist in an eRNA-protein coding gene co-expression network and an eRNA-centered regulation network, constructing an eRNA-protein coding gene relationship network, and extracting protein coding genes which are directly connected or closely connected to the eRNA, so as to predict potential regulation targets of the eRNA; and finally, performing function enrichment analysis on the potential regulation targets of the eRNA, so as to obtain the results of eRNA function annotation, and the method has the advantages of wider application range, and can be applied to all eRNAs and more accurately obtain the action forms between the eRNA and the protein coding gene.

The application relates to the technical field of gene analysis, and provides an Alzheimer disease gene prediction method and equipment based on alternative splicing, which comprises the following steps: performing difference analysis on the expression amount of all genes and the expression amount of transcripts to obtain expression amount difference characteristics, expression amount difference characteristics and expression proportion difference characteristics of the genes, and performing difference analysis on alternative splicing events of all sample gene signals to obtain alternative splicing difference characteristics of each gene; splicing the expression amount difference characteristics, the transcript expression amount difference characteristics, the expression proportion difference characteristics and the alternative splicing difference characteristics of each gene to obtain final characteristics of each gene; constructing a function correlation network according to the final characteristics of all genes; and performing gene disease prediction on the function correlation network to obtain the probability that each gene is related to Alzheimer disease. The method can improve the accuracy and reliability of Alzheimer disease gene prediction.

This invention relates to a biomarker for predicting treatment response to immunotherapy and a gene prediction model utilizing the same, wherein the gene prediction model utilizing the biomarker is based on the expression patterns of key genes that reveal the characteristics of a patient's tumor-microenvironment, thereby predicting treatment response, thus avoiding unnecessary surgery and determining the optimal treatment regimen.

The invention discloses a hereditary disease candidate gene sorting method and device based on heterogeneous graph embedding, and relates to the field of biological information. The method comprises the following steps: constructing a phenotype-gene heterogeneous network, and determining an edge weight in the heterogeneous network according to an association frequency of genes and phenotypes in a clinical medical record; capturing heterogeneous neighbor nodes based on meta-path weighted random walk according to the types of the neighbor nodes, and obtaining node embedding in the heterogeneous network; and according to the node embedding corresponding to the phenotypic node and the node embedding corresponding to the gene node, evaluating the possibility that the candidate gene is a pathogenic gene, and according to an evaluation result, sorting the priority of the candidate gene. Through the method, heterogeneous information in a biological network is effectively captured, the priority ranking accuracy of candidate genes is improved, the historical medical record data is introduced to generate the edge weight, and the expression ability of a heterogeneous graph and the credibility of a virulence gene prediction result are improved.

The invention discloses a prognosis model for predicting cervical cancer based on autophagy-related genes and a construction method of the prognosis model, and belongs to the technical field of biomedicine. The model contains four characteristic genes related to prognosis of cervical cancer: BCL2, SPNS1, TM9SF1 and TP73, and the characteristic genes can become biological markers related to cervical cancer; the calculation formula of the prognosis model is as follows: risk score = (-0.411 * BCL2 gene expression quantity) + (0.753 * SPNS1 gene expression quantity) + (0.669 * TM9SF1 gene expression quantity) + (-0.398 * TP73 gene expression quantity). The prognosis model provided by the invention can evaluate the prognosis of the cervical cancer patient, improve the prognosis prediction capability of the cervical cancer patient, effectively identify the high-risk patient, assist in predicting the curative effect of immunotherapy, detect and intervene the high-risk patient earlier in clinic, improve the survival rate and life quality of the patient, and improve the clinical application prospect. A reference is provided for individualized diagnosis and treatment of cervical cancer; and tests and external verification prove that the model is stable and effective.

The invention provides a method for cloning a broad-spectrum resistance gene ETD1 of rice blast through map-based cloning. The method comprises the following steps: phenotypic screening and population construction: extracting DNA from screened individuals; constructing a DNA pool; performing high-throughput sequencing; controlling data quality; performing comparison and variation detection; analyzing variation sites; performing correlation analysis; and predicting candidate genes. The broad-spectrum resistance gene ETD1 cloned by the method has a nucleotide sequence as shown in SEQ ID NO: 1, and / or a cDNA sequence of the gene is as shown in SEQ ID NO: 2, and / or a coding protein of the ETD1 gene has an amino acid sequence as shown in SEQ ID NO: 3, and the gene belongs to a super-efficient allele of OsCNGC13, obtains stronger calcium ion transport capacity, and can be used for preparing a calcium ion transport agent. The function of remarkably accelerating cell calcium ion influx is realized. Therefore, the rice resistance can be improved by normal expression or overexpression of the gene in a gene knockout type plant, and the rice blast resistance can be positively regulated and controlled.

The invention discloses a pathogenic gene prediction method and device based on phenotypic fingerprints, equipment and a medium. The method is executed by a computer, systematic integration and quantification are carried out on associated information between genes and phenotypes of multiple dimensions, phenotype fingerprints with specific genes are constructed on the group level, and complex effects of the genes on different phenotype dimensions can be captured more comprehensively; a multi-phenotype score value taking genes as the center is calculated through gene phenotype fingerprints, that is, multi-dimensional clinical phenotype information of a target object is converted into quantitative scores taking the genes as the center on the object level, and two types of key output of pathogenic variation carrying risk assessment and candidate gene priority ranking are achieved through observation phenotypes of the target object; and the integrating degree of each candidate gene and the actual phenotype of the target object can be objectively and efficiently evaluated. According to the method, phenotype fingerprints are introduced, a multi-phenotype scoring mechanism is combined, the efficiency and objectivity of complex disease pathogenic gene recognition are remarkably improved, and the method has important clinical application value.

The application relates to the technical field of genome selection, and provides a multi-omics genome selection method and application thereof in livestock and poultry breeding, which comprises the following steps: obtaining reference population data and target population data and carrying out pretreatment; training an optimal linear unbiased prediction model and an elastic network model by using the reference population data; inputting cis-single nucleotide polymorphism site genotype data of the target population into the trained gene expression prediction model to obtain predicted gene expression of each individual in the target population; estimating the cis-heritability of each gene based on the reference population data, and screening gene prediction expression data greater than a cis-heritability threshold; integrating the screened gene prediction expression data and genome data to construct a multi-omics genome selection model, and obtaining a genome breeding value of each individual based on the multi-omics genome selection model. The application can adapt to genetic regulation structure differences of different genes and obtain high-quality prediction results.

The invention belongs to the field of gene prediction, and discloses a livestock and poultry breeding value prediction method based on model evaluation and a related device.The method comprises the steps that firstly, genome historical data is preprocessed to form a training set; adopting parallel cross validation to synchronously train a plurality of statistical basic models, and outputting prediction accuracy indexes of each model under different characters; then, combined with economic weights of breeding target traits, comprehensive selection indexes of all the models for specific samples are calculated; and finally, screening the model with the highest index as an optimal prediction model to estimate a breeding value. According to the method, data feature changes are responded through comprehensive selection indexes, and it is ensured that the model is adaptively optimized under the condition of small groups or complex characters. And finally, the precision stability and the practical value of breeding value prediction in livestock breeding are remarkably improved.

The invention relates to a pathogenic fungus generic genome analysis method, device and equipment and a readable storage medium. The method comprises the following steps: obtaining to-be-analyzed sequencing genome data containing a plurality of contig sequences, removing the contig sequences of human and bacteria in the sequencing genome data to obtain cleaned sequencing genome data, performing gene prediction on the cleaned sequencing genome data to obtain a gff protein sequence file corresponding to the cleaned sequencing genome data, and analyzing the gff protein sequence file according to the gff protein sequence file. And finally, performing generic genome clustering analysis on the gff protein sequence file to obtain a clustering analysis result of sequencing genome data. According to the method, the to-be-analyzed sequencing genome is subjected to cleaning of human and bacterial sequences, only fungal gene sequences are reserved, the cleaned data are further predicted, so that the corresponding gff protein sequence file is obtained, clustering analysis is executed on the basis of the gff protein sequence file, and the accuracy and integrity of analysis are improved.

The invention relates to the technical field of artificial intelligence and deep learning, in particular to a tomato salt tolerance prediction method and system based on deep learning, and the method specifically comprises the following steps: obtaining a tomato salt tolerance genome, and inputting the tomato salt tolerance genome into the tomato salt tolerance prediction system based on deep learning for detection; and calculating the probability of related tomato salt-tolerant genes in the input tomato salt-tolerant genome through a salt-tolerant small sample gene recombination module, an epigenetic gene multi-granularity mining module, an isomeric salt-tolerant gene feature integration module, a salt-tolerant feature point fusion module and a tomato salt-tolerant gene prediction module in sequence. The prediction precision and generalization ability of the salt-tolerant gene under the complex gene background are improved, and an intelligent prediction method is provided for tomato salt-tolerant variety breeding.

The invention provides a gene abundance calculation method, device and equipment for metagenomes, and the method comprises the following steps: processing a sample to obtain clean reads and contigs; traversing the clean reads, performing mapping processing on related contigs in the contigs, storing all the clean reads of which the scores meet a first preset threshold value and comparison information of the corresponding contigs into a first SAM file, and splitting all the clean reads of which the scores do not meet the first preset threshold value into a plurality of subsets according to a preset numerical value; traversing each subset, carrying out mapping processing on the subset and each contig in the contigs, and storing all the clean read of which the comparison quality score meets a second preset threshold in each subset and the comparison information of the corresponding contig in a second SAM file; combining the SAM files, sorting the SAM files according to the comparison quality scores, and determining contig corresponding to the highest score of each clear read and corresponding comparison information to obtain a coverage result; and determining the gene abundance according to the obtained gene prediction result and coverage result of the metagenome. And the gene abundance calculation accuracy can be improved.

The invention discloses a rice drought-enduring gene prediction method and system based on knowledge graph embedding. The method comprises the following steps: constructing an ontology layer of a rice knowledge graph; collecting multi-source data of rice genes from different databases, performing knowledge extraction, fusing the extracted knowledge according to the specification of the ontology layer to obtain different knowledge triads, and importing the different knowledge triads into the graph database, thereby constructing a data layer of the rice knowledge graph; training an Att-CompGCN model by using a rice knowledge graph to learn low-dimensional embedded representation of entities and relationships; using the trained Att-CompGCN model to predict the association probability of all rice genes and drought-enduring traits, and carrying out descending sorting to obtain a candidate gene list; and carrying out GO enrichment analysis and protein interaction network verification on the candidate genes, and screening high-confidence drought-tolerant genes. According to the invention, a more accurate prediction result can be obtained.

This application discloses a method, apparatus, device, and medium for predicting pathogenic genes based on phenotypic fingerprinting. This method, executed by a computer, systematically integrates and quantifies the association information between genes and multi-dimensional phenotypes, constructing gene-specific phenotypic fingerprints at the population level. This allows for a more comprehensive capture of the complex effects of genes across different phenotypic dimensions. Furthermore, it calculates gene-centered multi-phenotypic scores using gene phenotypic fingerprints, transforming the multi-dimensional clinical phenotypic information of the target subject into gene-centered quantitative scores at the object level. Utilizing the observed phenotypes of the target subject, it achieves two key outputs: assessment of the risk of carrying pathogenic variants and priority ranking of candidate genes. This enables an objective and efficient evaluation of the fit between each candidate gene and the actual phenotype of the target subject. By introducing phenotypic fingerprinting and combining it with a multi-phenotypic scoring mechanism, this application significantly improves the efficiency and objectivity of identifying pathogenic genes for complex diseases, possessing significant clinical application value.

Systems and methods are provided for selecting a patient for intervention relating to genetically predicted lipoprotein (a) (“Lp(a)”) levels. A method may obtain or having obtained a biological sample from the patient. A method may perform or having performed sequencing on the biological sample, comprising. A method may acquire reads for the patient. A method may mask at least one portion of a gene LPA at a reference genome to facilitate alignment of the reads to a reference genome. A method may determine a pseudocount of copy number within the gene LPA at a genome of the patient. A method may in an event that the pseudocount is not an expected amount, selecting the patient for the intervention. A method may in an event that the pseudocount is an expected amount, omitting selection of the patient for the intervention.