31 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 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 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.

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.

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 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 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 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.

The invention provides a circadian rhythm evaluation method and system oriented to cardiovascular states, and belongs to the field of transcriptional gene analysis, and the method comprises the steps: obtaining transcriptome data at a single time point; gene expression data are positioned and extracted from the transcriptome data through a preset rhythm homeostasis gene module, and the rhythm homeostasis gene module is constructed through a weighted gene co-expression network analysis algorithm; and calculating the collaboration, the volatility and the network concentration degree of the gene expression data based on a dynamic network biomarker algorithm, and determining the circadian rhythm disorder index of the to-be-evaluated user according to the collaboration, the volatility and the network concentration degree. According to the method, collaborative expression characteristics are systematically captured by using a rhythm homeostasis gene module, dependence on a small number of core clock gene signals is avoided, and quantitative calculation is carried out on the collaboration, volatility and network concentration ratio of gene expression data in the module based on a dynamic network biomarker algorithm; and an objective and quantifiable molecular evaluation index is provided for an individual rhythm state.

Provided herein are methods of treating cancer, inflammatory diseases, and autoimmune diseases, and methods of modulating related phenotypes and expression levels, by targeting interactions between E3 ligases, E3-like proteins, and interacting partners thereof. Methods of identifying modulators of such interactions are also provided. Also provided herein are altered cell therapies comprising at least two members of a co-functional gene module.

The invention discloses an application of an overexpressed BnaAHL19 gene in regulating and controlling the flowering time of brassica napus. A DNA (deoxyribonucleic acid) nucleotide sequence of the BnaAHL19 gene is shown as SEQ ID NO.1, and a coded amino acid sequence is shown as SEQ ID NO.2. The invention further comprises a module containing the BnaAHL19 gene to the BnaELF-BnaAHL19-BnaFLC gene, and application of the BnaELF-BnaAHL19-BnaFLC gene module to regulation and control of flowering time of the brassica napus, the BnaAHL19 and the interactive protein BnaELF thereof can cooperatively activate expression of a downstream gene BnaFLC, and the BnaAHL19 and the interactive protein BnaELF thereof can be used for regulating and controlling flowering time of the brassica napus. The function excavation of the BnaELF-BnaAHL19-BnaFLC module has important significance on promoting the genetic improvement and breeding of the brassica napus in the growth period.

The application discloses a tylosin A high-yield engineering strain and application thereof, and belongs to the field of genetic engineering and fermentation technology. The strain is obtained by integrating a carbon mycose synthesis gene module (M14) overexpression cassette driven by a constitutive strong promoter into a genome and knocking out a negative regulation gene tylQ , so that the yield is greatly improved. Experiments show that the yield of the engineering strain in FFM culture medium reaches 1183 mg / L, which is 10 times that of a wild type; the yield can reach 1491.5 mg / L after adding alpha-ketoglutaric acid, and the genetic stability is good. The 5L fed-batch fermentation titer reaches 5.63 g / L. The application provides an efficient and stable industrial strain for large-scale production of tylosin A.

The present application relates to the field of disease prediction in bioinformatics, and particularly relates to a cognitive disease classification method based on gene communication mode and multi-view learning. The present application uses a gene network reasoning algorithm to operate on the screened sample-gene expression matrix to obtain a gene regulatory network; combines the importance score of the gene regulatory network and the gene module information to obtain a gene module communication tensor; respectively extracts features from the gene module communication tensor and the sample-gene expression matrix, and puts the extracted features into a Transformer encoder and an interleaved decoder for feature fusion to obtain odd layer and even layer features, splices the features of the two layers and puts them into a multi-layer perceptron to finally classify Alzheimer's disease into different stages. The present application provides a neural network framework for double-view learning of gene communication analysis of Alzheimer's disease, fuses two-view information of gene module communication tensor and transcriptome, and improves the accuracy of brain disease stage classification.

The invention discloses a cancer cell mitochondrial biomarker and a screening method based on a machine algorithm, and when the cancer cell is colorectal cancer, the mitochondrial biomarker comprises OXCT1, CLPB, SLC25A12, MRPL51, SFXN1, GATM and TRMT10C genes. The screening method comprises the following steps: (1) standardizing an expression data set of cancer cells to be screened, and then obtaining the expression quantity of mitochondrial related genes in the expression data of the cancer cells; (2) difference analysis: carrying out correlation analysis on cancer cell mitochondrial genes or proteins subjected to differential expression, and selecting gene modules which simultaneously meet differential genes and belong to mitochondrial related genes; (3) functional enrichment analysis; and (4) respectively screening by selecting at least two machine algorithms to respectively obtain a characteristic gene or protein list, and determining a core intersection target. The biomarker disclosed by the invention is high in accuracy, specificity and sensitivity. The method is efficient and comprehensive.

The invention discloses an application of a 5D006949-1D001661-3C003541 gene module in regulating and controlling the content of crude protein of oat, and belongs to the technical field of plant molecular biology. Wherein the nucleotide sequence of the 5D006949 is as shown in SEQ ID NO. 1, and the amino acid sequence of the 5D006949 is as shown in SEQ ID NO. 2; the nucleotide sequence of the 1D001661 is as shown in SEQ ID NO.3, and the amino acid sequence of the 1D001661 is as shown in SEQ ID NO.4; the nucleotide sequence of the 3C003541 is as shown in SEQ ID NO.5, and the amino acid sequence of the 3C003541 is as shown in SEQ ID NO.6. By analyzing the coding sequence of the gene in the module, a VIGS strain and an overexpression transgenic oat strain are constructed to evaluate the function of the gene in the gene module for regulating and controlling the crude protein content of the oat, and the result shows that the gene module can regulate and control the crude protein content of the oat, so that the content of the crude protein in the oat can be regulated and controlled. A reference basis is provided for genetic improvement of cultivated oats by using a genetic engineering technology, and a new target is provided for cultivation of a new variety of high-yield and high-quality oats.

The invention provides an intelligent forest tree breeding method based on multi-modal data and a large model, and relates to the technical field of forest tree genetic breeding. The method comprises the steps of obtaining and integrating genotype data, phenotype data, multi-omics data and environment time sequence data of a target tree species, and performing preprocessing and feature fusion to form a multi-modal data set for analysis; on the basis of the multi-modal data set, key genes or gene modules for regulating and controlling target characters are mined by adopting a genotype-environment interaction algorithm and multi-omics coupling analysis; constructing a dynamic model fusing genotypes, environmental factors and time dimensions, and predicting breeding values of individuals or combinations on a whole genome level based on mined key genes or gene module information; and based on a prediction result, performing multi-objective optimization and intelligent screening at three levels of parents, combinations and offspring, and outputting a breeding decision scheme. According to the method, a complete, efficient and accurate forest tree intelligent breeding solution is formed.

This invention belongs to the fields of bioinformatics and computational biology, and specifically relates to a method, system, and storage medium for gene functional module mining based on tumor subtype supervision. The method includes the following steps: preprocessing breast cancer data and screening a unified gene set; calculating the difference in gene correlation coefficients before and after adding tumor samples in normal samples to construct a gene differential expression network; constructing a subtype-level gene graph structure through gene correlations, inputting it into a graph neural network to obtain an updated gene embedding representation; performing end-to-end training based on the gene embedding representation using differentiable minimum cut loss and subtype-supervised joint loss; and fusing the clustering results of samples within a subtype through majority voting to identify subtype-specific functional gene modules. This invention achieves the joint mining of cancer shared gene modules and subtype-specific gene modules, effectively improving the accuracy and biological interpretability of functional module identification.

The application artificially designs and creates a functional module SyKerMb with high efficient degradation of poultry feather waste and synthesis of new heme protein by using synthetic biology method. The application constructs a recombinant vector of the functional module, and assembles and expresses the same in a chassis microbial cell Escherichia coli. Experiments prove that the engineered strain integrated with the conversion functional module has the biological synthesis capacity of degrading animal waste feather and efficiently synthesizing artificial heme protein, and can be used in artificial protein preparation and future artificial food and other related industrial production.

This invention belongs to the field of disease evolution trajectory prediction in precision medicine, and discloses a method for predicting and modeling the evolution of Parkinson's disease based on high-order gene communication tensors. First, based on intercellular communication mechanisms, a high-order gene module communication tensor is constructed as spatial topological data to characterize the information interaction patterns between gene modules. Second, an adaptive Transformer module is designed to simultaneously extract gene expression features and high-order spatial topological features. Finally, a multi-perspective collaborative fusion mechanism is used to integrate the multi-perspective interaction information between the high-order gene module communication tensor and whole blood transcriptome data, achieving effective feature fusion and utilization. Experimental results show that, compared with traditional gene maps, the high-order spatial representation proposed in this invention has more refined resolution and significantly improves multiple evaluation indicators, providing a new technical means for the accurate staging of Parkinson's disease.

The application discloses a high gene module score region identification method, device and system, and relates to the technical field of spatial transcriptomics. The identification method comprises the following steps: acquiring spatial transcriptome data, dividing cells into a plurality of unit grids according to spatial coordinate information of the cells and determining corresponding grid spatial 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 score index; and dividing the unit grids into regions and determining a high gene module score region. The method analyzes a plurality of genes as a functional whole, overcomes the limitation that traditional single-gene analysis cannot reveal synergistic regulation, realizes spatial quantification of the synergistic effect of a gene set, and solves the problem that the prior art cannot locate a gene set enrichment region.

The invention relates to a method for promoting biosynthesis of ginsenoside (Rg3 / Rh2) based on cooperative expression of an MVA pathway and a saponin synthesis module. According to the method, efficient synthesis is realized through a double-vector synergistic expression strategy: an MVA pathway module vector is constructed, and terpene precursor IPP supply is enhanced; an Rg3 synthesis module carrier is constructed, and a ginsenoside synthesis route is directionally catalyzed. A gene module is assembled by adopting a TransGene Stacking II system, and the nicotiana benthamiana is transiently co-transformed through agrobacterium tumefaciens mediation. Through MVA module single expression, the IPP content of tobacco leaves reaches 533 ng / g and is improved by 43% compared with that of a wild type (372 ng / g); the metabolic flux is synergistically activated through double-module co-expression, the yield of Rg3 reaches 23.70 mu g / g and is increased by 53.90% compared with that of a single Rg3 module (15.04 mu g / g), and Rh2 (0.37 mu g / g) is synthesized in tobacco for the first time. The bottleneck of insufficient supply of plant chassis precursors is broken through, efficient synthesis of ginsenoside Rg3 / Rh2 is achieved through modular design, and a new normal form is provided for green biological manufacturing of rare triterpenoid saponin.

The present application relates to a kind of gene co-expression network identification method and system based on graph convolutional neural network, the method includes steps: S1.data preprocessing;S2.based on gene expression correlation constructs gene network diagram;S3.captures node information by graph convolutional neural network GraphSAGE model and extracts feature;S4.cluster analysis is carried out using Gaussian mixture model;S5.according to clustering result, gene module is divided.This method can help researchers more effectively from mass gene expression data extract valuable information, in-depth understanding of gene regulatory network, discover new gene function and regulatory mechanism, provide important guidance for the treatment and prevention of disease.Also have good scalability and flexibility, can adapt to different scale and type of gene expression data analysis needs.

The invention discloses application of D74 as a biomarker in preparation of a kit for evaluating or diagnosing bullous pemphigus. The CD74 expression of the skin lesion part of a to-be-detected subject and the CD74 expression of the corresponding position of the normal skin are compared, bullous pemphigus is positive if the CD74 at the skin lesion part is linearly deposited along the true epidermis junction, and the normal tissue is not positively stained. A BP characteristic gene module is identified by adopting a multi-omics integration analysis strategy, and it is found that the CD74 expression level is remarkably related to the eosinophilic granulocyte infiltration degree; the potential of the aptamer as a diagnostic biomarker is verified in patient tissues through immunohistochemistry, the detection sensitivity is 100%, the specificity is 83.3%, the positive predictive value is 78.9%, and the accuracy is 89.7%.

The invention relates to an intelligent canvas construction system and method for an insurance marketing scenario, the system comprises a hierarchical component gene pool, an AI auxiliary dynamic layout engine and a behavior triggering type interaction rule engine, the method comprises the following steps: combining and packaging insurance basic elements into a gene module, and defining corresponding component DNA (Deoxyribose Nucleic Acid), performing parameterization adjustment on the gene module to generate a corresponding component variant; configuring a layout rule library and establishing an interaction rule resource library; canvas elements are built by using a gene module, characteristics of the canvas elements are scanned in real time, and a scenarized layout scheme is generated based on a layout rule base and a marketing target selected by a user; and collecting user behavior data on the canvas, determining an interaction action through judgment of the conditional logic tree in combination with the interaction rule resource library, and updating the state of the canvas. Compared with the prior art, the method has the advantages that the problems of poor component reuse, inflexible layout and deep rule coupling of a traditional marketing scheme can be solved, and agile construction and accurate conversion of activities are realized.