43 results about "Gene recognition" patented technology

The invention discloses an efficient specific sgRNA recognition site guide sequence for pig gene editing and a screening method thereof. The screening method includes: screening functional genes, performing ORF analysis, predicting a functional gene sgRNA recognition site guide sequence, detecting whole-genome off-target sites, grading predicted target sites according to off-target information and target site positions, sequencing, screening and statistically counting results, optimizing algorithms and developing software. The efficient specific sgRNA recognition site guide sequence and the screening method thereof have the advantages that the pig specific sgRNA recognition site guide sequence is obtained through strict screening and inspection and includes the sgRNA recognition site guide sequences, for CRISPR-Cas9 gene editing, of all pig protein encoding genes; the authenticating, grading and inspecting algorithms for specific sgRNA recognition and software corresponding to the algorithms and used for predicting and evaluating the pig functional gene sgRNA recognition site guide sequence are widely applicable to the sgRNA specific site prediction of non-model species with whole-genome sequences.

This invention provides human mtDNA polymorphisms that are diagnostic of all the major human haplogroups and methods of diagnosing those haplogroups and selected subhaplogroups. This invention also provides methods for identifying evolutionarily significant mitochondrial DNA genes, nucleotide alleles, and amino acid alleles. Evolutionarily significant genes and alleles are identified using one or two populations of a single species. The process of identifying evolutionarily significant nucleotide alleles involves identifying evolutionarily significant genes and then evolutionarily significant nucleotide alleles in those genes, and identifying evolutionarily significant amino acid alleles involves identifying amino acids encoded by all nonsynonymous alleles. Synonymous codings of the nucleotide alleles encoding evolutionarily significant amino acid alleles of this invention are equivalent to the evolutionarily significant amino acid alleles disclosed herein and are included within the scope of this invention. Synonymous codings include alleles at neighboring nucleotide loci that are within the same codon. This invention also provides methods for associating haplogroups and evolutionarily significant nucleotide and amino acid alleles with predispositions to physiological conditions. Methods for diagnosing predisposition to LHON, and methods for diagnosing increased likelihood of developing blindness, centenaria, and increased longevity that are not dependent on the geographical location of the individual being diagnosed are provided herein. Diagnosis of an individual with a predisposition to an energy metabolism-related physiological condition is dependent on the geographic region of the individual. Physiological conditions diagnosable by the methods of this invention include healthy conditions and pathological conditions. Physiological conditions that are associated with haplogroups and with alleles provided by this invention include energetic imbalance, metabolic disease, abnormal energy metabolism, abnormal temperature regulation, abnormal oxidative phosphorylation, abnormal electron transport, obesity, amount of body fat, diabetes, hypertension, and cardiovascular disease.

The embodiment of the invention provides a gene detection method, a model training method, a device, equipment and a system. The gene detection method comprises the following steps: acquiring to-be-processed gene data, wherein the average number of gene segments corresponding to each position in the to-be-processed gene data is smaller than or equal to a preset threshold value; inputting the to-be-processed gene data into a feature generation network layer for feature extraction operation to obtain gene features corresponding to the to-be-processed gene data and enhanced features corresponding to the gene features; and inputting the to-be-processed gene data and the enhanced features into a gene identification network layer for gene detection operation to obtain a detection result. According to the technical scheme provided by the embodiment, feature extraction operation is carried out through low-depth gene data, the gene features and the enhanced features corresponding to the gene features are obtained, and detection operation is carried out based on the enhanced features, so the accuracy of a gene detection result is ensured, and the data processing cost and the data processing amount are also reduced.

The invention discloses a gene expression data analysis method based on a PAM clustering algorithm, and relates to the field of data analysis. The method comprises the steps of data acquisition, data preprocessing, gene module identification, GO enrichment analysis, PPI network construction, HUB gene identification and HUB gene verification. According to the method, on the basis of fully utilizing information contained in gene expression data, the optimal membership module can be searched for each gene through multiple iterations, so that the identified gene module is more reliable. The hidden information contained in the gene module can be better mined, so that the bioinformatics problem to be solved is comprehensively analyzed. According to the method, data preprocessing is performed on the gene expression data, so that the problems of much noise, many irrelevant genes, sparse data and the like in the gene expression data are solved. Through the downstream bioinformatics analysis process, a series of bioinformatics analysis can be completed, and the bioinformatics problems to be solved can be comprehensively analyzed and explained.

The invention provides a miRNA causal regulation network identification method and device, and relates to the technical field of gene identification. The miRNA causal regulatory network identification method includes: obtaining a miRNA single-cell transcriptome matrix and a target gene single-cell transcriptome matrix of a given matched sample. Obtain the miRNA causal regulatory network of miRNA single-cell transcriptome matrix and target gene single-cell transcriptome matrix. The i-th single cell in the miRNA single-cell transcriptome matrix and the target gene single-cell transcriptome matrix was eliminated to obtain the miRNA causal regulatory network after the i-th single cell was eliminated. According to the miRNA causal regulatory network and the miRNA causal regulatory network after removing the i-th single cell, the miRNA causal regulatory network of the i-th single cell was determined. Since the heterogeneity of each single cell is considered, the single-cell miRNA causal regulatory network is identified at the single-cell level, reflecting the effect of the real miRNA causal regulatory mechanism.

The invention relates to the technical field of drug research and development, and discloses a comprehensive analysis method for predicting anti-cancer drug reaction related genes, which comprises the following steps: 1) carrying out drug sensitivity prediction and feature gene identification; 2) carrying out drug sensitivity prediction based on a collaborative filtering method; and 3) identifying a drug sensitivity related gene of a specific cancer type. According to the method, a complete calculation pipeline is established to identify characteristic genes related to drug sensitivities and drug resistance of different cancer types, different drug sensitivities in a cancer cell line are predicted by using a collaborative filtering-based algorithm, and a group sparse model is used for selecting the characteristic genes of the drug sensitivities of a specific cancer type. The simultaneous integration of multiple drug cell line data and the expandability for further important gene identification can be realized, and meanwhile, the collaborative filtering algorithm has the advantage that the sensitivity of heterogeneous drug cell lines among multiple drugs can be modeled at the same time.

The invention provides a method for identifying miRNA sponge interaction pairs, and relates to the technical field of gene identification. The method for identifying the miRNA-sponge interaction pair includes: obtaining the sponge gene expression matrix, the target gene expression matrix and the miRNA expression matrix of the sample. And calculate the miRNA sponge interaction pair recognition parameters of the sponge gene of the matching sample and the target gene. If the identification parameters of the miRNA-sponge interaction pair between the sponge gene of the matching sample and the target gene meet the preset conditions, it is confirmed that the sponge gene and the target gene of the matching sample are miRNA-sponge interaction pairs. Due to the fusion of heterogeneous data sources in the calculation of miRNA-sponge interaction identification parameters, and when determining the miRNA-sponge interaction pair, it is based on the miRNA competition mechanism to measure the competition strength between miRNA sponges to identify the non-linearity between miRNA sponges Correlation relationship, and then determine miRNA sponge interaction pairs, realize the appropriate time complexity, and can identify the non-linear correlation relationship between miRNA sponges.

The invention provides an identification method and device for a miRNA causal regulation and control network, belonging to the technical field of gene identification. The identification method for themiRNA causal regulation and control network comprises the following steps: acquiring a miRNA single-cell transcriptome matrix and a target gene single-cell transcriptome matrix of a given matching sample; acquiring a miRNA causal regulation and control network of the miRNA single-cell transcriptome matrix and the target gene single-cell transcriptome matrix; removing the i single cell in themiRNA single-cell transcriptome matrix and the target gene single-cell transcriptome matrix, and acquiring a miRNA causal regulation and control network after the i single cell is removed; and determining the miRNA causal control network of the i single cell according to the miRNA causal control network and the miRNA causal control network after the i single cell is removed. Because the heterogeneity of each single cell is taken into consideration, the single-cell miRNA causal regulation and control network is identified at a single-cell level, and the effect of a real miRNA causalregulation and control mechanism is reflected.

The present invention provides a method for identifying heterogeneous cancer driver genes based on a mutually exclusive constrained graph Laplacian. First, obtain cancer genome variation data and gene interaction network; then, use a matrix model to describe the heterogeneity of cancer, and use mutually exclusive constraint matrix decomposition to estimate the sample parameters of heterogeneity cancer; Then, construct the mutual exclusion constraint matrix decomposition optimization function regularized by the joint association and interaction network, and correct the parameters of the driving genes affected by the interaction in the local samples through iterative solution; finally, use the outlier test method to identify the driving genes . The invention can solve the problem of differential estimation of parameters of cancer samples and effective identification of driver genes in local samples affected by interaction, and realizes the identification of driver genes mutated in local samples from gene variation data of heterogeneous cancer samples.

The invention discloses a preparing method for MC3R gene knockout pigs. The preparing method for MC3R gene knockout pigs includes the steps that a CRISPR / Cas9 system is adopted, the CRISPR / Cas9 system comprises an encoding gene of gRNA1 and an encoding gene of gRNA2, and the target sequence of a MC3R gene identified by the gRNA1 is a sequence 1 in a sequence table; the target sequence of the MC3R gene identified by the gRNA2 is a sequence 2 in a sequence table. An experiment shows that the MC3R gene knockout efficiency is 29.16% with the preparing method for MC3R gene knockout pigs; a target gene can be rapidly and efficiently knocked out in a large-fragment mode, exogenous gene fragments are not left, and the preparing method can be used for researching the action mode and mechanism of the MC3R gene, and can be further used for animal breeding.

The invention discloses a tumor key gene identification method based on particle swarm optimization and a marking criterion. The method comprises the steps that one gene marking criterion is defined to acquire gene classification ability information so as to filter genes with a low correlation to a tumor category; and the Metropolis criterion is utilized to improve a particle swarm optimization (PSO) algorithm in combination with the gene classification ability information so as to realize identification of tumor key genes. The new method for gene classification information and PSO improvement overcomes the defect that a traditional method for identifying tumor key genes based on PSO is prone to fall into a locally optimal solution, gene subsets in a smaller number and with a high correlation to the tumor category can be selected, and therefore the method is beneficial for improving subsequent tumor identification.

The invention provides a method and device for rapid detection of malware genes based on semantic segmentation. The semantic segmentation model trained by the gene bank can greatly improve the matching detection efficiency of real-time samples, and the trained semantic segmentation model does not need to carry genes The features of the library can even be embedded in offline real-time security products; the features of automatic abstract feature extraction and subsequent mature optimization technology can improve the accuracy rate of genetic identification of malicious deformation.

The invention provides a method for indentifying non-toxic effect protein to identify signal peptide having a secretion function by using an anti-disease gene. The method comprises the following steps: 1) the to-be-identified signal peptide is connected with a C terminal construction expression carrier of phytophthora infestans nontoxic effect protein Avr3a, wherein the C terminal construction expression carrier of phytophthora infestans nontoxic effect protein Avr3a is shown as SEQ ID NO.3; 2) transforming the expression vector constructed in the step 1) to competent cell; and 3) injecting the competent cell in the step 2) and the competent cell containing the anti-disease gene in plant cells, and observing the necrotic condition of an injection site. If the injection site is necrotic, the to-be-detected signal peptide does not have the secretion function, and if the injection site has no necrotic condition, the to-be-detected signal peptide has the secretion function.