202 results about "Gene screening" patented technology

The invention relates to a fluorescent protein selection marker staygold-levEr gene as well as a construction method and application thereof, and belongs to the technical field of biological engineering. The invention provides a fluorescent protein selection marker staygold-levEr gene for screening an L-valine production strain and a construction method of a gene fragment, compared with other similar gene selection markers, the selection marker provided by the invention has the advantages that the fluorescence intensity is positively correlated with the concentration of L-valine, and the screening marker has the advantages that the screening marker can be used for screening the L-valine production strain; according to the screening marker, the screening efficiency of the L-valine high-yield strain can be obviously improved, the screening time and cost can be saved, the screening efficiency can be improved, and the screening marker has a wide application prospect.

The invention discloses a whole-genome low-density chip for breeding high-yield and long-life characters of dairy cows and application of the whole-genome low-density chip. The invention firstly discloses a whole-genome low-density chip for breeding the high-yield and long-life character of the dairy cow, the whole-genome low-density chip comprises 10000 SNP (Single Nucleotide Polymorphism) loci, ARS-UCD1.2 is used as a reference genome, and the information of the 10000 SNP loci is shown in a table 6. The invention further discloses application of the whole-genome low-density chip. The 10,000 SNP loci contained in the whole genome low-density chip are uniformly distributed on chromosomes, cover the whole genome and important character loci, can effectively improve the longevity and the accuracy of milk yield breeding, and can be applied to gene screening, gene positioning, marker-assisted breeding and the like. In addition, the detection cost of the low-density chip is greatly reduced, and the low-density chip is more suitable for commercialized dairy farms and has important significance on dairy cow breeding and dairy cow group improvement.

The present invention relates to the field of biomedicine, and specifically is a method for detecting platelet markers of non-small cell lung cancer, comprising the steps of: A. acquiring a gene data source; B. preprocessing gene data; C. analyzing gene differences; D. screening prognostic genes; E. selecting prognostic characteristic genes; and F. displaying analysis results. The present invention can determine characteristic genes that are closely related to the prognosis of patients with non-small cell lung cancer: by analyzing differential genes between different subtypes, the present invention determines ten characteristic genes that are closely related to the prognosis of patients with non-small cell lung cancer, providing new clues for further prognosis evaluation.

The invention relates to the technical field of machine learning, and discloses a cancer treatment effect prediction method and device, equipment and a storage medium. The method comprises the following steps: carrying out radiomics feature extraction on a CT scanning image to obtain radiomics features; performing interested pathological feature extraction based on an attention mechanism and local pathological feature extraction based on an interested pathological feature extraction result on the pathological image to obtain pathological omics features; performing differential expression gene analysis on the transcriptomics data, and performing differential expression gene screening based on a differential expression gene analysis result to obtain transcriptomics characteristics; and inputting a fusion feature obtained by fusing the radiomics feature, the pathological omics feature, the transcriptomics feature and the clinical data into a cancer treatment effect prediction model to obtain a cancer treatment effect prediction result. According to the embodiment of the invention, the cancer treatment effect is predicted by combining the radiomics characteristics, the pathomics characteristics, the transcriptomics characteristics and the clinical characteristics, and the prediction accuracy can be improved.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

The invention discloses an automatic grading and grouping system and method for boars, and relates to the technical field of boar breeding, and the system constructs a multi-modal biological characteristic data set by collecting gene, semen quality, health examination and growth performance data. Secondly, extracting gene features by using a convolutional neural network, calculating a gene screening coefficient JYX, and classifying the gene screening coefficient JYX into an A class level and a B class level through a first threshold Q1; then, semen quality analysis is conducted on the A-class boars, a semen quality screening coefficient ZLX is calculated, and the A-class boars are divided into the A1-class boars and the A2-class boars through a second threshold Q2; and when the A2-class boar triggers health early warning, further evaluating a health index JCZ, and subdividing the health index JCZ into A21, A22 and A23 classes according to a third threshold Q3. And finally, grouping the boars according to the class levels of the boars, and formulating an accurate feeding strategy to optimize the reproductive performance and improve the breeding benefits.

The invention relates to the technical field of biology, and provides a screening method and application of susceptible SNP site target genes for AML diagnosis, and the method comprises the following steps: obtaining AML susceptible SNP sites, three-dimensional genome data and AML patient queue data; preprocessing the three-dimensional genome data to obtain a DNA interaction section; predicting a potential target gene regulated and controlled by the AML susceptible SNP locus according to the DNA interaction segment; according to potential target genes regulated by AML susceptible SNP sites, high-credibility transcriptional regulation target genes are screened, and a high-credibility target gene set is obtained; the transcriptional regulation activity of the SNP of the high-credibility target gene set is verified by applying an in-vitro cell experiment. According to the invention, understanding of a regulatory network between AML susceptible SNP and a target gene is expanded, and a new biomarker and a new drug target are provided.

The invention discloses a method for high-purity sorting and capturing of rare cells. According to the method for high-purity sorting and capturing of the rare cells, the cells are spread firstly, then the positions of the target cells are searched and determined, then a probe is used for discharging a buffer solution outwards to blow away non-target cells around the target cells, and then fluid power is used for capturing the target cells without damage. Finally, the target cells can be released into a receiving container, and high-purity sorting and capturing of the rare cells are achieved. A device matched with the method is simple in structure and easy to operate, and the method has the advantages that repeated and massive dilution is not needed, the cell sample spreading speed is high, the target cell sorting purity is high, the omission rate is low, in-situ capture is achieved, and repeatability is good. The method is suitable for the fields of single cell screening and analysis, single cell culture, single cell drug screening, single molecule analysis, gene screening, sample purification, antibody screening, microbiological research and the like.

The invention relates to the technical field of gene screening, in particular to application of GOT1, MT3, SETD1B, FH and HRAS genes and a method for screening Alzheimer's disease related genes, and five genes related to the Alzheimer's disease, namely the GOT1, MT3, SETD1B, FH and HRAS genes, are obtained through screening. The five genes obtained by screening can be used for constructing a model for predicting the probability of the Alzheimer's disease and a drug model for predicting and treating the Alzheimer's disease, and important reference is provided for diagnosis, mechanism research and treatment of the Alzheimer's disease in the later period.

The invention discloses a genomics-based biomarker screening method and system, and relates to the technical field of biological detection.The method comprises the steps that biomarker expression data related to significant genes are determined according to transcriptome sequencing data of a biological sample of a cancer disease, the posterior distribution of the biomarker associated with the cancer disease is estimated through a logistic regression model; determining significant interaction genes according to the posterior distribution and the interaction network between the genes; and based on the significant interaction genes, screening to obtain a biomarker list related to cancer diseases. According to the application, the posterior distribution and significant interaction genes are screened through the transcriptome sequencing data of the biological sample of the cancer disease, so that the situation of missing detection caused by low biomarker specificity in the traditional biomarker screening process is avoided, and a biomarker list highly related to the cancer disease can be obtained; and the sensitivity of the biomarker screening process is improved.

This invention discloses a protein and gene related to resistance to neonicotinoid acetylcholine receptor competitive regulator insecticides in peach aphids, and their applications. It relates to the field of screening technology for neonicotinoid acetylcholine receptor competitive regulator insecticides in peach aphids. This invention obtains and clarifies an MpeABCC_4G protein and its encoding gene that is closely related to the resistance of peach aphids to neonicotinoid acetylcholine receptor competitive regulator insecticides. By knocking down the expression of MpeABCC_4G in resistant peach aphids using RNAi technology, the control efficacy of neonicotinoid acetylcholine receptor competitive regulator insecticides is significantly improved.

The invention discloses a double-gene screening expression vector of a CHO monoclonal cell strain and a preparation method and application of the double-gene screening expression vector, the double-gene screening expression vector comprises a glutamyl synthetase GS screening gene and an antibiotic screening gene, the antibiotic screening gene is subjected to weakening expression treatment, that is, an IRES sequence exists in front of the antibiotic screening gene, and an IRES sequence exists in front of the antibiotic screening gene. A target expression gene and an antibiotic screening marker gene share one set of promoter and terminator, and when the double-gene screening expression vector disclosed by the invention is used for screening CHO monoclonal cell strains, the screening efficiency can be remarkably improved, the screening time is saved, and the target gene has high expression quantity.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

The invention provides a phytophagous insect food web DNA molecular data automatic analysis method and device based on high-pass sequencing and a storage medium, and relates to the field of molecular biological information detection.The method comprises the steps that sequence splicing, screening and species identification are carried out on obtained double-end sequencing data and local and downloaded DNA databases through an automatic system, and a DNA molecular database is obtained; generating an Excel table containing species names and a DNA bar code sequence file; performing comparative analysis on the double-end sequencing data by adopting matching splicing, and generating a contiguous group sequence based on a local DNA database; if the matching splicing cannot generate the effective sequence, generating a new gene file by adopting non-parameter splicing, and performing gene annotation in combination with the downloaded DNA database; all analysis steps are connected in series through standardized parameter input, including gene screening through threshold values and generation of insect recipe identification results. According to the method, the sequencing data can be subjected to full-process automatic analysis through a one-key command, and the efficiency of food web authentication high-throughput sequencing data processing is greatly improved.

The invention belongs to the field of lung cancer detection, and particularly relates to a platelet marker for detecting lung cancer and a detection kit. The method comprises the following steps: firstly, carrying out transcriptome sequencing on platelets of a healthy (HC) individual and a lung cancer (Lung) individual, then, carrying out differential expression analysis to identify genes which are specifically and differentially expressed in the platelets of a lung cancer patient for the first time, and screening out ZYX, GMPR, RPL9 and RPL11 genes. A logistic regression model is constructed based on mRNA expression levels of the four genes, and in subject working characteristic curve analysis of a training set, the area under a curve is up to 0.97. The application of mRNA expression levels of platelet genes ZYX, GMPR, RPL9 and RPL11 in diagnosis of lung cancer is defined mainly in a liquid biopsy mode, the marker is used for predicting occurrence of the lung cancer, and the invention aims to identify a new marker of the lung cancer and prepare a lung cancer diagnosis kit based on the marker.

The invention provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention belongs to the technical field of single cell data analysis, and particularly relates to a single cell pseudo-trajectory identification method based on a gene co-expression network. Comprising the following steps: S1, constructing a single cell transcription data matrix of behavior gene names and cell names; s2, preprocessing the single-cell transcription data matrix, constructing a cell expression matrix, and performing characteristic gene screening on the cell expression matrix to obtain a single-cell gene expression matrix; s3, constructing a gene co-expression network; s4, constructing a gene rank differential expression network in combination with the single-cell gene expression matrix and the gene co-expression network; and S5, carrying out integrated clustering on the gene rank differential expression network, fusing by using a similarity network to obtain a final clustering result, determining an initial cluster, distributing pseudo time for cells in each cluster, and deducing a cell pseudo trajectory. According to the method, the differentiation track of the cell can be identified, and compared with an original gene expression matrix, a more stable identification result can be obtained by utilizing a relative expression sequence.

The invention relates to the technical field of microbiology, in particular to a biological reagent research and development method based on microbial functional gene screening. The method comprises the following steps: acquiring a microorganism sample, and performing genome sequencing extraction treatment and gene transcription analysis to generate transcriptome data of the microorganism sample; obtaining a biological reagent research and development target function, and performing target function gene screening treatment and gene targeted knockout treatment to generate a target function gene targeted knockout experimental group; carrying out gene cloning on the target functional gene targeted knockout experimental group, transferring into a preset host strain, carrying out function expression evaluation analysis to obtain a target functional expression level corresponding to each target functional targeted knockout gene, and carrying out excellent function screening and verification to generate a biological reagent excellent function verification gene, and the method is applied to a research and development process corresponding to a biological reagent so as to generate a target functional biological reagent product. The functional screening efficiency and the specificity of biological reagent products can be improved.

The invention discloses a carbapenemase OXA-58-like positive plasmid and a host bacterium propagation risk assessment method thereof, which are used for carrying out integrated assessment on the plasmid and the host bacterium in three dimensions of drug resistance, pathogenic potential and propagation potential, and carrying out comprehensive risk grading and classification by taking the OXA-58-like positive plasmid and the host bacterium thereof as a plasmid-host complex. The invention develops a set of OXA-58-like gene screening and systematic analysis process based on a public plasmid database aiming at the defects that the existing research of the OXA-58-like gene is mostly limited to a single species or a local sample and a standardized automatic analysis method is lacked. According to the method, all OXA-58-like gene positive plasmids in a database can be subjected to unified and efficient drug-resistant gene mining, the association rule between the drug-resistant genes and plasmid pedigree, host strains and geographical distribution is systematically disclosed, and important technical support is provided for drug resistance propagation monitoring and risk assessment.

The invention discloses a mulberry important character QTL fine positioning method based on a chromosome segment replacement line, and relates to the technical field of molecular breeding. Comprising the following steps: S1, constructing a genotype-phenotype incidence matrix: screening backcross populations through high-heterozygosity parent hybridization and multi-generation backcross, and marking genomes of the screened backcross populations through targeted sequencing and recombination hot spots; s2, phenotype determination modeling: performing sample collection according to a test plant, and obtaining a hybrid model according to the environmental effect of the test plant and a covered CSSL line; and S3, gene screening: carrying out hub gene screening through the constructed co-expression network model and the hybrid model. According to the method, through multi-generation backcrossing, targeted sequencing and recombination hotspot analysis, dynamic labeling is performed on the genome, and a stepped coverage CSSL library is constructed, so that a QTL interval is effectively compressed, and the problem of fuzzy positioning caused by limited recombination events of a traditional F2 or RIL population is solved.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

The invention discloses an organizational structure division method for spatial transcriptome data, which comprises the following steps of: firstly, performing coordinate calibration, hypervariable gene screening and gene expression standardization preprocessing on original spatial transcriptome data, and constructing a standardized data set containing a spatial adjacency relation and a standardized gene expression profile; an initial hyperedge is generated based on a k-nearest neighbor algorithm, cross-domain noise is dynamically eliminated in combination with a hyperedge decomposition algorithm guided by gene expression, and a hypergraph structure with double constraints of spatial proximity and gene expression homogeneity is formed; designing an auto-encoder architecture comprising a hypergraph attention layer, compressing high-dimensional data to a low-dimensional potential space, reconstructing a loss optimization architecture by using gene expression, and generating low-dimensional representation with topology retentivity and function consistency; and finally, organizational structure division is carried out on the low-dimensional representation clustering based on a Gaussian mixture model. According to the method, the spatial continuity of organization structure division is remarkably improved, and a semantic gap of cross-resolution data is effectively bridged.

The invention provides a kit for detecting Leber hereditary optic neuropathy and application, and relates to the technical field of biology. The kit comprises: (1) an alkaline lysis solution for releasing mitochondrial DNA in a blood sample; (2) an RPA isothermal amplification reaction system for amplifying the target sequence, wherein the RPA isothermal amplification reaction system comprises an RPA amplification primer; (3) a CRISPR-Cas12a (Clustered Regularly Interspaced Short Palindromic Repeats / Cas12a) system for carrying out high-specificity cutting on a mutation site, wherein the CRISPR-Cas12a system comprises crRNA (Complementary Ribonucleic Acid); and (4) lateral flow chromatography test paper for realizing visual detection. By simplifying the detection process, the kit greatly improves the accessibility of gene diagnosis, so that the gene screening technology can break through the limitation of traditional equipment and is popularized to a wider application scene, and the development of the gene diagnosis technology in the direction of portability, low cost and high precision is promoted. The innovation not only brings a convenient detection tool for gene mutation screening, but also lays a foundation for future gene therapy and personalized medical treatment.

The present invention discloses a whole-genome low-density chip for the selection of high-yield and long-lived traits in dairy cows and its application. The present invention first discloses a whole-genome low-density chip for the selection of high-yield and long-lived traits in dairy cows, comprising 10,000 SNP sites, with ARS-UCD1.2 as the reference genome, and the information of the 10,000 SNP sites is shown in Table 6. The present invention further discloses the application of the above-mentioned whole-genome low-density chip. The 10,000 SNP sites contained in the whole-genome low-density chip of the present invention are relatively evenly distributed on the chromosomes, covering the whole genome and important trait sites, which can effectively improve the accuracy of longevity and milk production selection, and can be applied to gene screening, gene positioning, marker-assisted breeding and other directions. In addition, the detection cost of the low-density chip of the present invention is greatly reduced, and it is more suitable for use in commercial dairy farms, which is of great significance to dairy cow breeding and dairy cow population improvement.

The invention discloses a saline-alkaline tolerance gene introduced rice high-yield breeding method. The method comprises the following steps: S1, gene screening and cloning: screening related genes with saline-alkaline tolerance; s2, vector construction: inserting the screened saline-alkaline tolerant gene into a proper expression vector; s3, transforming rice cells: introducing the vector carrying the saline-alkaline tolerant gene into the rice cells; s4, screening positive plants: confirming that the rice cells with the saline-alkaline tolerant genes are successfully transferred, and selecting the positive plants from the rice cells for next-step culture; s5, tissue culture and development: placing the positive plants on a sterile culture medium for tissue culture, and enabling the positive plants to develop into complete plants; s6, planting and harvesting: transplanting and planting the obtained transgenic rice plants, and harvesting the transgenic rice plants to obtain rice seeds. The saline-alkali soil-resistant gene disclosed by the invention is stronger in saline-alkali resistance, the yield is not influenced after the gene is planted in saline-alkali soil, and high-yield rice can be obtained.

The invention belongs to the technical field of biological feature recognition, and relates to a rice blast pathogenic gene recognition method suitable for small sample high-dimension data, the method comprises the following steps: processing collected data to obtain the expression quantity of a sample corresponding to each gene, then carrying out difference analysis to recognize a difference expression gene, and determining the expression quantity of the sample corresponding to each gene; constructing a PPI network according to the differentially expressed gene, and determining a key gene related to rice blast; and finally, constructing a CGAN-SAE deep learning model, and training a CGAN module by using the determined key gene, so that the CGAN module can generate simulated gene sample data which is difficult to distinguish from real data. And training the SAE module by using the simulated gene sample and the real sample, so that the SAE module can capture global features, and finally, further processing the extracted features through PCA to identify the core gene in the rice blast infection process. The method solves the problem of gene screening in a rice data set with a small number of samples and high dimensionality.

This invention relates to soybean pod-resistant trait loci. qPD08‑1 This invention relates to the soybean pod-bomb resistance gene locus and its applications, belonging to the field of plant molecular biotechnology. qPD08‑1 Located in soybeans Glyma The reference genome of .Wm82.a2.v1, located between 35983663 and 36106921 bp on chromosome 8, has a total physical interval length of 123.258 kb. The molecular marker detection method of this invention can be used for identification of soybean pod-resistant germplasm resources, screening and functional studies of pod-resistant genes, marker-assisted selection breeding for pod-resistant soybeans, and natural resource applications. It features convenient detection, stable amplification products, and high specificity, helping to broaden the sources of superior breeding parents and improve the efficiency of soybean breeding selection.

The invention belongs to the technical field of gene engineering, and discloses application of an F-box protein gene OsFBX235 in improvement of bacterial blight resistance. The invention specifically discloses an application of knocking out OsFBX235 gene in improving the bacterial blight resistance of rice or cultivating a bacterial blight resistant rice variety. The F-box protein coding gene OsFBX235 is separated and cloned from rice, and the gene is proved to participate in the defensive reaction of the rice to the bacterial blight for the first time through functional analysis, and is an important negative regulation factor for regulating the resistance of the rice bacterial blight. The gene OsFBX235 is knocked out through target gene screening in combination with a CRISPR / Cas9 technology, and the importance of the gene OsFBX235 on resistance regulation and control of the rice bacterial blight is proved. Meanwhile, knockout of the gene can be used for creating rice germplasm resources with high bacterial leaf blight resistance and can be applied to breeding of new varieties with the bacterial leaf blight resistance of the rice.

The present invention belongs to the field of biotechnology, and discloses a gene for enhancing the hardness of tomato fruits and its application. Specifically, it discloses the application of the Solyc02g077940 gene in regulating the hardness of tomato fruits, and the amino acid sequence of the Solyc02g077940 gene is shown in SEQ ID NO: 1. The present invention discloses for the first time the application of Solyc02g077940 in regulating the hardness of tomato fruits. By screening target genes and combining with the CRISPR / Cas9 technology to knockout / knockdown Solyc02g077940, it proves the importance of Solyc02g077940 in enhancing the hardness of tomato fruits.