60 results about "Gene module" patented technology

The invention discloses a key gene identification method related to tobacco nitrogen response, which comprises the following steps: S1, setting four nitrogen fertilizer gradient treatments on the basis of same phosphorus and potassium fertilization by adopting a field experiment of a completely random block; s2, randomly taking the 6th to 8th leaves of the five plants in each area, and dividing a sample into two parts: quickly freezing one part with liquid nitrogen, and storing at-80 DEG C for RNA (Ribonucleic Acid) extraction and transcriptome sequencing; one part is used for measuring the nitrogen content, and after baking and drying treatment, a KjeltecTM8100 automatic nitrogen analyzer is used for measuring; s3, nitrogen content determination: determining the nitrogen content in the treated sample by using an automatic nitrogen analyzer KjeltecTM8100; according to the method, high-throughput transcriptome sequencing and weighted gene co-expression network analysis (WGCNA) are combined, so that not only can gene expression maps of flue-cured tobacco leaves treated by different nitrogen fertilizers be comprehensively captured, but also gene modules with similar expression modes can be mined from a global perspective.

The invention discloses an interpretable cancer survival prediction method based on multi-modal data fusion, and the method comprises the following steps: carrying out the preprocessing of a full-section pathological image, and obtaining an image feature matrix; preprocessing a pathological report text corresponding to the pathological image, and constructing a text feature matrix; preprocessing the high-dimensional gene expression data of the patient to generate a plurality of survival-related gene modules and feature vectors thereof; performing dynamic fusion on the obtained multi-modal features through a self-adaptive multi-modal expert hybrid module to obtain final fusion feature representation; and training a deep learning model by using fusion feature representation in combination with a negative log-likelihood loss function and a Cox proportional risk model, and performing prognosis analysis on the cancer patient. According to the explainable cancer survival prediction method based on multi-modal data fusion, the prediction accuracy is improved, good model interpretability is achieved, and biomarkers closely related to cancer prognosis can be automatically recognized.

The invention provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.

The invention belongs to the technical field of biology, and particularly relates to application of a GmNIP5-GmPKM20 gene module in regulation and control of nodulation of leguminous plants, the GmNIP5-GmPKM20 gene module is one or more of GmNIP5 and genes interacting with the GmNIP5, by analyzing the influence of the transgenic GmNIP5 and the genes GmPKM20 interacting with the GmNIP5 on soybean nodulation, it is clear that the GmNIP5 promotes infection of nodule bacteria and formation of root nodules, and the GmNIP5-GmPKM20 gene module can be used for regulating and controlling nodulation of leguminous plants. GmPKM20 inhibits symbiotic nodulation signals of soybeans, and it is verified that GmPKM20 and GmPKM20 can directly interact with each other.

The invention relates to a gene co-expression network identification method and system based on a graph convolutional neural network. The method comprises the following steps: S1, data preprocessing; s2, constructing a gene network diagram based on gene expression correlation; s3, node information is captured through a GraphSAGE model of the graph convolutional neural network, and features are extracted; s4, performing clustering analysis by using a Gaussian mixture model; and S5, dividing gene modules according to a clustering result. The method can help researchers to more effectively extract valuable information from massive gene expression data, deeply understand a gene regulatory network, find new gene functions and regulatory mechanisms, and provide important guidance for disease treatment and prevention. And meanwhile, the method also has good expandability and flexibility, and can adapt to analysis requirements of gene expression data of different scales and types.

The invention provides a construction method of engineering saccharomycetes for efficient biosynthesis of chelerythrine. Specifically, the invention provides an engineering bacterium for producing chelerythrine, the engineering bacterium is saccharomycetes, and the engineering bacterium contains exogenous and / or endogenous gene modules, such as BBE, SMT, TDC, TNMT, MSH, P6H, CPR and BUP1 gene modules. The invention also provides a construction method, application and a construction kit of the engineering bacterium, and a production method and a fermentation system of chelerythrine. The engineering bacteria can efficiently synthesize chelerythrine, the yield is far higher than that in the prior art, and the highest yield reaches 4.5 mg / L. Therefore, the engineering bacterium and the construction method provided by the invention can overcome the defect of long production period of chelerythrine in the field, and a new method is provided for synthesis of chelerythrine.

The invention provides an oxazomycin high-yield engineering strain as well as a construction method and application thereof. The method for constructing the oxazomycin high-yield engineering strain comprises the following steps: driving high expression of a gene cluster by using a strong promoter in streptomyces longsheng, enhancing transcription of a key resistance gene module (ozmS-oxaA), and optimizing ribosome engineering of strain chassis cells and a culture medium. According to the invention, the rational synthetic biology and metabolic engineering technology is combined with the irrational ribosome engineering technology to gradually increase the yield of the streptomyces longsheng oxazomycin, and the established efficient biosynthesis system lays a foundation for further development of the oxazomycin.

The invention discloses a construction method of immune-related key gene prognosis indexes of metastatic melanoma and related equipment, and belongs to the field of bioinformatics, the method comprises the following steps: obtaining an immune-related differential expression gene list, and constructing a weighted gene co-expression network to divide gene modules, two gene modules with the highest correlation degree with the matrix score and the immune score are screened out, and gene intersection is taken to recognize immune related key genes; and determining immune-related key genes significantly related to the lifetime, and sequentially screening through univariate Cox regression and LASSO regression to obtain genes of which the regression coefficients are not zero so as to construct and obtain immune-related key gene prognosis indexes of metastatic melanoma. The method can solve the problems that in the prior art, when a prognosis index is constructed, an immune-related gene pair is only determined by an LASSO regression coefficient, key disease treatment genes are difficult to recognize, too many immune-related genes are contained, and clinical application convenience is low.

The invention belongs to the field of disease evolution trajectory prediction in precision medicine, and discloses a Parkinson's disease evolution prediction modeling method based on high-order gene communication tensor. According to the method, firstly, based on an intercellular communication mechanism, a high-order gene module communication tensor is constructed as spatial topological data, so that an information interaction mode between gene modules is described; secondly, a self-adaptive Transform module is designed to extract gene expression features and high-order space topology features at the same time; and finally, integrating multi-view interaction information between the high-order gene module communication tensor and the whole blood transcriptome data through a multi-view collaborative fusion mechanism to realize effective fusion and utilization of features. Experimental results show that compared with a traditional genetic map, the high-order space representation provided by the invention has finer resolution capability, a plurality of evaluation indexes are remarkably improved, and a new technical means is provided for accurate staging of Parkinson's disease.

Methods and systems for constructing gene modules with regulator genes and target genes are provided. A Gaussian mixed model can construct gene modules using RNA sequencing data. Sets of gene modules can be compared to identify shared or unique biological processes.

The invention relates to the technical field of biology, in particular to a gene co-expression network-based sepsis marker screening method and application. The screening method comprises the following steps: screening differential expression genes; performing hierarchical clustering analysis on each differential expression gene to combine each differential expression gene into a plurality of target gene modules; and screening out hub genes from each target gene module. According to the method, gene expression data and clinical feature data are combined, the accuracy of screening sepsis marker genes is improved, possible abnormal genes irrelevant to the pathophysiological process of sepsis are avoided, gene modules are divided more finely through primary clustering analysis and secondary clustering analysis, GO function analysis is combined, and the accuracy of screening the sepsis marker genes is improved. According to an analysis result, selection of a GO term subset is optimized, and a step length set by primary clustering is corrected, so that the fine granularity and accuracy of analysis are improved, division of gene modules is optimized, and more refined treatment and screening of markers of sepsis are realized.

The invention belongs to the technical field of molecular breeding, and particularly relates to application of an E1-SOC1 gene model in cultivation of wide-adaptability soybean varieties. Research finds that three genes (E1 and homologous genes E1La and E1Lb) of an E1 family and two genes (SOC1a and SOC1b) of an SOC1 family are knocked out from soybeans at the same time by utilizing a gene editing technology, and the obtained homozygous five mutants show wide adaptability when planted under field conditions of different latitude regions; and the method has certain wide adaptability in the aspects of plant height, section number, flowering period, mature period and yield. It is indicated that the E1-SOC1 gene module (E1, E1La, E1Lb, SOC1a and SOC1b genes) is expected to be applied to molecular design breeding for breeding wide-adaptability soybean models, and has important application prospects in soybean molecular breeding research.

Dysfunctional or exhausted T cells arise in chronic diseases including chronic viral infections and cancer, and express high levels of co-inhibitory receptors. Therapeutic blockade of these receptors has clinical efficacy in the treatment of cancer. While co-inhibitory receptors are co-expressed, the triggers that induce them and the transcriptional regulators that drive their co-expression have not been identified. The immunoregulatory cytokine IL-27 induces a gene module in T cells that includes several known co-inhibitory receptors (Tim-3, Lag-3, and TIGIT). The present invention provides a novel immunoregulatory network as well as novel cell surface molecules that have an inhibitory function in the tumor microenvironment. The present invention further provides the novel discovery that the transcription factors Prdm1 and c-Maf cooperatively regulate the expression of the co-inhibitory receptor module. This critical molecular circuit underlies the co-expression of co-inhibitory receptors in dysfunctional T cells and identifies novel regulators of T cell dysfunction.

The invention discloses an identification method, device and system for a high gene module scoring area, and relates to the technical field of space transcriptomics. The identification method comprises the following steps: acquiring space transcriptome data, dividing cells into a plurality of unit grids according to space coordinate information of the cells, and determining corresponding grid space coordinates for each unit grid; calculating a module score value of each cell and determining a high-score cell set; calculating an integrated gene set scoring index; and carrying out region division on the unit grid and determining a high gene module scoring region. According to the method, a plurality of genes are analyzed as a functional whole, the limitation that traditional single-gene analysis cannot reveal collaborative regulation is overcome, spatial quantization of a gene set synergistic effect is realized, and the problem that a gene set enrichment region cannot be positioned in the prior art is solved.

The present invention discloses a gene module N-miR172- BraNPY5 and its application, the precursor sequence of cabbage N-miR172 is shown in SEQ ID No.1, and its target gene BraNPY5 The sequence is shown in SEQ ID No. 2. The miRNA overexpression vector and the STTM inhibition expression vector were transformed into Col-type Arabidopsis by Agrobacterium floral transformation to obtain heterologous expression Arabidopsis strains. The results showed that the inhibition of N-miR172 expression led to delayed development of young leaves, delayed flowering, and decreased pollen vitality in Arabidopsis, while 3-fold overexpression of N-miR172 led to poor development of Arabidopsis, short plants, and decreased seed germination vitality. This shows that cabbage N-miR172 plays an important role in the development of floral organs and the transition from vegetative growth to reproductive growth, and this gene module can be applied to the breeding of cabbage and other horticultural plants.

The invention belongs to the technical field of cell data processing, and relates to a single cell gene characterization system and method based on deep learning. The system comprises a gene information input module, a data processing module, a context sensing interpolation module, a gene feature fusion module, a deep representation learning and joint optimization module and a cell typing and differentiation track inference module. The input module integrates a data source; the processing module preprocesses and enhances the original expression matrix; the interpolation module performs weighted interpolation by using a conditional Gaussian graph model; the gene feature fusion module generates a gene module activity matrix through 1 * 1 convolution and the like; the fusion module fuses the sample meta-information with the active matrix, the sequencing depth and the gene feature vector; the depth representation module captures high-dimensional dependence through a multi-layer Transform network, and generates cell high-order potential representation; the typing module outputs a cell population division and differentiation trajectory based on the deduced pseudo-time trajectory. The method has the beneficial effects that cell expression characteristics are recovered under high sparseness and noise, and a new way is provided for cell type identification and the like.

Provided herein are stoichiometric Gas Vesicle Expression Systems (GVES), sets of polynucleotide constructs, and related vectors, cells, compositions, and methods configured for robust expression of Gas Vesicle Gene Clusters (GVGCs) in mammalian cells, particularly primary and immune cells. The GVES comprises distinct gene modules for the primary structural protein (gvpA / B) and assembly factors (AF1, AF2), operably configured to achieve a stoichiometric expression ratio, quantified by Dosage Index (DI), wherein the gvpA / B module is expressed at least 2-fold higher than the AF1 and AF2 modules.

The application discloses a method for constructing a gene regulation network based on meta-analysis, and aims to solve the problem that a gene regulation network has a large error and a low accuracy because different research data are not completely homogeneous when the gene regulation network is expanded by combining data of multiple studies to enlarge a sample size, and the method comprises the following steps: performing meta-analysis on each transcriptomic gene expression dataset obtained to obtain a significant differential gene list; obtaining a transcription factor gene list according to a species to which the transcriptomic gene expression data belong, and generating a core gene list according to the transcription factor gene list and the significant differential gene; calculating a Pearson correlation coefficient of each core gene and each significant differential gene, and constructing a co-expression network according to the Pearson correlation coefficient; clustering the co-expression network to obtain a plurality of differential gene modules with high correlation of the core genes; and combining biological knowledge and a structural equation model to construct a corresponding gene regulation network according to each module. The application belongs to the field of gene regulation networks.

Provided herein are stoichiometric Gas Vesicle Expression Systems (GVES), sets of polynucleotide constructs, and related vectors, cells, compositions, and methods configured for robust expression of Gas Vesicle Gene Clusters (GVGCs) in mammalian cells, particularly primary and immune cells. The GVES comprises distinct gene modules for the primary structural protein (gvpA / B) and assembly factors (AF1, AF2), operably configured to achieve a stoichiometric expression ratio, quantified by Dosage Index (DI), wherein the gvpA / B module is expressed at least 2-fold higher than the AF1 and AF2 modules.

The invention relates to a method for analyzing and excavating key genes for regulating and controlling non-structural carbohydrate distribution of emmenopterys henryi under a shading condition based on a weighted gene co-expression network. The method comprises the following steps: S1, obtaining root, stem and leaf samples of emmenopterys henryi under different illumination conditions; s2, non-structural carbon index determination and transcriptome sequencing; s3, establishing a weighted gene co-expression network model, and dividing the weighted gene co-expression network model into a plurality of gene modules; calculating the correlation between the feature gene of the gene module and a non-structural carbon index, and screening a key gene module; and performing gene connectivity calculation and gene function annotation on the key gene module, and screening key genes. The 19 key candidate genes participating in regulation and control of the non-structural carbon distribution of the emmenopterys henryi under the shading condition are successfully excavated and identified, a foundation is laid for further clarification of a molecular mechanism of highlight stress response of the emmenopterys henryi, theoretical support is provided for development of an effective emmenopterys henryi conservation technology and promotion of emmenopterys henryi population recovery, and the important significance is achieved.

The invention discloses a method, equipment and device for identifying osteoarthritis gene characteristics and a storage medium, belongs to the technical field of medical treatment, and solves the problem that large-scale analysis of genome data is not available in the prior art. The invention discloses a method for identifying osteoarthritis gene characteristics. The method comprises the following steps: S1, processing data in a database to obtain differential genes, and identifying a gene module which has high synergistic change with osteoarthritis; s2, carrying out intersection operation on the differential gene obtained in the step S1 and a gene module with high synergistic change with osteoarthritis to obtain an intersection gene; s3, obtaining candidate genes according to the intersection genes obtained in the step S2 in combination with an integration machine learning method; and S4, determining gene characteristics according to the candidate genes and expression data of the candidate genes in the database. According to the invention, the importance of the characteristic gene in the pathogenesis process of osteoarthritis is verified, and gene characteristics related to osteoarthritis are provided for clinic, so that more accurate diagnosis can be realized.

The application discloses application of a 5D006949-1D001661-3C003541 gene module in regulating oat crude protein content and belongs to the technical field of plant molecular biology. The nucleotide sequence of 5D006949 is shown in SEQ ID NO. 1, and the amino acid sequence is shown in SEQ ID NO. 2; the nucleotide sequence of 1D001661 is shown in SEQ ID NO. 3, and the amino acid sequence is shown in SEQ ID NO. 4; and the nucleotide sequence of 3C003541 is shown in SEQ ID NO. 5, and the amino acid sequence is shown in SEQ ID NO. 6. By analyzing the coding sequences of the genes in the module, VIGS strains and overexpression transgenic oat strains are constructed to evaluate the functions of the genes in the gene module in regulating the crude protein content of oats. The results show that the gene module can regulate the crude protein content of oats, thereby providing a reference basis for genetically improving cultivated oats by using genetic engineering technology and providing a new target for cultivating new oat varieties with high yield and high quality.

The invention relates to the technical field of gene engineering, in particular to a bidirectional promoter Para *, an araa * expression system, application and an engineering strain. The araa * expression system comprises the bidirectional promoter Para *, a repressor protein araR gene module is arranged on one side of the bidirectional promoter Para *, and a fluorescent protein gene module is arranged on the other side of the bidirectional promoter Para *. Wherein the repressor protein araR gene module comprises a repressor protein araR gene and a first RBS sequence corresponding to the repressor protein araR gene; and the fluorescent protein gene module comprises a fluorescent protein gene and a second RBS sequence corresponding to the fluorescent protein gene. By characterizing and designing an exogenous inducible promoter, an ara * expression system capable of realizing inducible expression in xanthomonas campestris is obtained, and a basic element is provided for construction of a xanthomonas campestris genetic engineering strain.

The invention discloses a Chinese rose HSF gene and application thereof, the Chinese rose HSF gene comprises an RcHSFA2 gene or an RcHSFC1 gene, and the nucleotide sequences of the RcHSFA2 gene or the RcHSFC1 gene are respectively shown as SEQ ID No.1 and SEQ ID No.3; the amino acid sequences are respectively as shown in SEQ ID No.2 and SEQ ID No.4; the two genes are subjected to high-temperature stress induced expression, and the protein RcHSFA2 and the protein RcHSFC1 interact with each other; the two genes are respectively and independently overexpressed in tobacco, so that the heat resistance of the transgenic tobacco is remarkably improved; the two genes are respectively and independently overexpressed in the Chinese rose petals, so that the heat resistance of the transgenic Chinese rose petals is also remarkably improved; according to the present invention, the RcHSFA2-RcHSFC1 gene module is co-expressed in the Chinese rose petal so as to significantly improve the heat resistance of the Chinese rose compared to the single overexpression of RcHSFA2 or RcHSFC1;

A method for processing cancer transcriptome data based on gene co-expression network analysis, which relates to the field of data processing, includes obtaining an original data set; preprocessing the original data; identifying differentially expressed genes; constructing a gene co-expression network; mining gene modules; performing correlation analysis between gene modules and clinical indicators; performing enrichment analysis of gene modules; identifying key genes; exploring the functions of key genes; and performing survival analysis of key genes. From the results of the enrichment analysis, it can be seen that the gene modules divided by this method have significant biological significance; from the verification results of the key genes by the Disgenet database, it can be seen that most of the key genes identified by this method are related to tumor diseases. This method has good effects in the mining of gene modules and the identification of key genes. This method can be used as an important tool for cancer disease transcriptome data, and its application also provides a new direction for further understanding the pathogenesis of cancer diseases.

The present invention discloses a method for constructing a novel 9-gene RISK acute myeloid leukemia prognosis model and a kit and a nomogram model based on the prognosis model. The present invention uses the acute myeloid leukemia transcriptome to screen the gene module related to disease prognosis through weighted gene co-expression network analysis, uses univariate Cox regression analysis combined with WGCNA module analysis to determine the AML prognosis core-related genes, and uses Lasso regression to construct a model for the above-screened AML prognosis core genes to obtain a novel 9-gene RISK acute myeloid leukemia prognosis model. After various verifications, the new prognosis model has a better evaluation effect.

The invention relates to the technical field of plant genetic breeding, in particular to a directional pyramiding breeding method for improving the pungency degree and color value of processed chilies. Comprising the steps of parent screening (molecular marker identification of high-pungency and high-color-value germplasm based on capsaicin synthesis key genes Pun1 and Capana06g001204 and capsorubin regulatory genes CaBBX20 and CCS), hybridization polymerization (multi-generation backcross and molecular marker-assisted selection integration of pungency degree / color value genes), ploidy breeding accelerated homozygosis (a microspore culture combined chromosome doubling technology), and cross breeding of the high-pungency and high-color-value germplasm. And performing phenotype verification (detecting the content of capsaicin substances by high performance liquid chromatography and determining the color value by spectrophotometry). According to the method, gene modules for controlling the pungency degree and the color value are directionally polymerized, a special dry processing variety with the capsaicin content being larger than or equal to 3% (the corresponding pungency degree being larger than or equal to 30,000 SHU) and the color value being larger than or equal to 300 ASTA is bred, the breeding period is shortened to 1 / 3 of that of a traditional method, and the requirement of the pepper processing industry for high-quality raw materials is met. The method is suitable for large-scale pepper breeding, and is particularly suitable for planting in high-altitude and arid regions.

The invention provides a preparation method and application of a microorganism for producing 1-phenethyl isoquinoline. Specifically, the invention provides an engineering microorganism for producing 1-phenethyl isoquinoline, and the engineering microorganism contains exogenous and / or endogenous gene modules, such as gene modules of TyrH, PCD, DHPR, DODC, 4CL, TAL, CCR, AER, NCS and the like. The invention also provides a carrier, a kit and a fermentation system for producing the 1-phenethyl isoquinoline, a method for constructing the engineering microorganism and a method for producing the 1-phenethyl isoquinoline. According to the invention, a heterologous biosynthesis approach is successfully established, efficient de novo synthesis of the 1-phenethyl isoquinoline is realized by utilizing a simple carbon source, the process flow is simplified, the yield of the 1-phenethyl isoquinoline is increased, and a foundation is laid for large-scale preparation of PIA natural products.

The application relates to the field of bioinformatics and clinical auxiliary decision-making technology, in particular to a high-dimensional weighted membranous nephropathy gene co-expression network construction method, which integrates biomolecular interaction, disease pathways and health baseline data to construct an initial topological matrix, uses a Riemann manifold optimization algorithm to inject high-dimensional transcription sequencing data into the matrix and update edge weights, generates a high-dimensional weighted network of a target biological sample, accurately locks an abnormal gene module by calculating the topological residual error of the network and the health baseline and the dynamic offset of the eigenvector centrality, compares module features with a target point database to screen candidate drug targets, and stores network weight eigenvalues into a vector database, so that gene co-expression network construction and drug target screening based on biological priori and data driving are realized; and the application accurately retains weak but key local nonlinear cascade signals.

The invention relates to a single cell data enhanced representation method and device and a storage medium. The method comprises the following steps: acquiring an original counting matrix and a gene module data set of single cell gene expression; performing gene feature conversion based on the original counting matrix to obtain a gene feature matrix; determining a cell module activity matrix based on the gene feature matrix, the original counting matrix and the gene module data set; processing the gene feature matrix through a gene encoder to generate a gene implicit vector, and processing the cell module activity matrix through a module encoder to generate a module implicit vector; performing interactive fusion by taking the gene implicit vector and the module implicit vector as input of a cross attention fusion model and taking the weighted sum of the first loss item and the second loss item as an optimization target to obtain an output fusion implicit vector; and projecting the fusion implicit vector to a target embedding space to generate single cell enhanced representation. Implicit vectors are integrated through a cross attention fusion model, and the interpretability of unicellular organisms is improved.