2333 results about "Expression gene" patented technology

The present disclosure provides methods and compositions for detecting and spatially locating analyte interactions and gene expression in a biological sample. For example, provided herein are methods of determining a location of at least one analyte in a biological sample using analyte-binding moieties, proximity ligation, and an array including capture probes.

Provided herein are methods of detecting a target nucleic acid of interest for spatial gene expression analysis in a sample using barcoded templated ligation probes.

The invention belongs to the technical field of gene engineering and enzyme engineering, and particularly relates to an ornithine decarboxylase mutant as well as a coding gene, an expression vector, a recombinant strain and application thereof. The ornithine decarboxylase mutant is obtained by mutating wild ornithine decarboxylase ODC10 of which the amino acid sequence is shown as SEQ ID NO.1, the catalytic activity of the obtained mutant is improved, and the biosynthesis efficiency of 1, 4-butanediamine is favorably improved. The recombinant strain for expressing the ornithine decarboxylase mutant constructed by the invention can efficiently synthesize 1, 4-butanediamine, and the molar conversion rate of ornithine is remarkably improved, so that the production benefit is improved, and the production cost is reduced.

Provided herein are devices, methods, and systems for in situ gene sequencing of a target nucleic acid in a cell in an intact tissue. Methods of screening a candidate agent to determine whether the candidate agent modulates gene expression of a nucleic acid in a cell in an intact tissue are also provided herein.

Provided herein are methods for analyzing a biological sample that include contacting the biological sample with a non-conductive substrate; and contacting a matrix to a surface of the biological sample. Further methods optionally include performing mass spectrometry analysis; determining presence of a first analyte in the biological sample; and / or analyzing a second analyte on the surface of the biological sample to determine presence of the first and the second analyte in the biological sample.

The invention relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention relates to a gene GmHB1a participating in development of vascular bundles of soybean root nodules and application of the gene GmHB1a. Specifically, the invention relates to a separated gene participating in development of vascular bundles of soybean root nodules or regulation and control of the number of soybean nodules, and a coding region sequence of the gene is shown as SEQ ID NO: 1 or a homologous sequence of the gene. The invention also provides a method for regulating and controlling the nodulation number of the plant, which comprises the following steps: introducing the gene or a sequence for inhibiting the expression of the gene into a target plant cell or tissue to obtain a transgenic plant, the nodulation number of the transgenic plant is changed compared with that of the target plant, the plant is a leguminous plant, preferably soybean, and the nodulation number of the transgenic plant is changed. Optionally, the change in number of nodules is manifested as an increase in number of nodules and / or in number of single nodules, and optionally, the change in number of nodules is manifested as a decrease in number of nodules and / or in number of single nodules.

The invention provides an artificial nucleic acid molecule which is used for improving the expression quantity of target amino acid, polypeptide or protein. The artificial nucleic acid molecule at least comprises a target 5'untranslated region (UTR), a target coding region (CDS) and a target 3 'untranslated region (UTR). Wherein the sequence of the target 5 'UTR is one of the following sequences: 5' UTR of a high-expression gene and a 5 'UTR variant of the high-expression gene. The sequence of the target 3 'UTR is one of the following sequences: 3' UTR of a high-expression gene and a 3 'UTR variant of the high-expression gene. Optionally, the artificial nucleic acid molecule may further comprise, for example, a 5 '-end cap structure (Cap), a PolyA tail. The 5 'UTR and the 3' UTR have regulating effects on translation and stability of nucleic acid molecules, so that the 5 'UTR, the 3' UTR and variants thereof are selected from high-expression genes, the nucleic acid molecules can be further stabilized and are not easy to degrade, and the amount of protein or polypeptide obtained by translation of the nucleic acid molecules can be increased. The invention also provides methods for making, delivering, and using such artificial nucleic acid molecules, as well as the use of the artificial nucleic acid molecules for the treatment and / or prevention of related diseases or disorders.

The invention discloses a spatial domain identification method based on a multi-view weighted fusion GCN network, and the method comprises the steps: taking the gene expression and spatial position of spatial transcriptome data as input, and combining the spatial information based on different similarity measurement modeling with the gene expression after modeling to construct a multi-view weighted fusion graph convolutional network model; and reconstructing a gene expression matrix by using a decoder, capturing global information of ST data, calculating spatial regularization constraint loss by using similarity information and spatial neighbor information, and completing identification of a spatial transcriptome spatial domain. According to the method provided by the scheme, the model can extract the spatial information from the whole situation and the local situation, so that the spatial information is more fully and comprehensively mined, and accurate spatial domain identification is realized.

The embodiment of the invention discloses a single cell transcriptome sequencing data annotation method. The method comprises the following steps: acquiring single cell transcriptome sequencing data associated with a target disease, and clustering a plurality of cells corresponding to the single cell transcriptome sequencing data to obtain a plurality of cell clusters; aiming at each marker gene corresponding to the target disease, according to single cell transcriptome sequencing data, obtaining the cell cluster gene expression quantity of the marker gene in each cell cluster; for each cell cluster and each cell type corresponding to the target disease, accumulating the cell cluster gene expression quantity of each marker gene corresponding to the cell type in all the marker genes in the cell cluster to obtain the total gene expression quantity; and aiming at each cell cluster, according to the total gene expression quantity corresponding to the cell cluster under each cell type, annotating a target cell type for the cell cluster from all the cell types. According to the technical scheme provided by the embodiment of the invention, the accuracy of cell type annotation can be ensured.

The invention provides a key node identification method for a disease marker expression regulation network, and belongs to the technical field of disease markers, and the method comprises the steps: firstly carrying out the preprocessing and quality control of original data, including batch effect removal, abnormal sample identification and the like; then identifying differential expression genes through multiple difference analysis and a pre-training model, and constructing a gene expression correlation network; and integrating multi-source regulation and control data to construct a multi-level weighted network, calculating network node features, and carrying out representation learning and module division. And based on multi-dimensional features such as network topology features, module contribution degree and biological importance, a neural network model is trained to carry out key node identification. And finally, optimizing the model through multi-layer verification such as pathway enrichment, disease gene overlapping, expression stability, time sequence change and network disturbance, and finally obtaining a verified key node set. The problem that in the prior art, the interaction relation between molecules is ignored, and consequently some key regulation and control nodes are possibly missed is solved.

The invention discloses a genomics cross-modal condition generation method and system based on a potential diffusion model, and belongs to the field of bioinformatics, and the method comprises the following steps: respectively designing and constructing an auto-encoder for gene expression and gene mutation data, and carrying out model training; constructing a potential feature space according to an encoder of gene mutation data and training an unconditionally guided potential diffusion model; constructing a cross-modal feature alignment network according to the two trained encoders and training the cross-modal feature alignment network; and performing joint fine tuning on the unconditionally guided potential diffusion model and the cross-modal feature alignment network. According to the method, the problem of sample modal deletion in multi-modal learning is solved, potential correlation between gene expression data and gene mutation data can be captured through cross-modal feature alignment, information flow between different modals is enhanced, and high-quality prediction data is effectively generated when mutation data is deleted through the potential diffusion model.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention discloses a CA9-targeted chimeric antigen receptor and application thereof, and belongs to the technical field of biological medicines. The CA9 targeting chimeric antigen receptor provided by the invention comprises a CD8 alpha leading signal peptide, an anti-CA9 single-chain variable fragment, a CD8 alpha hinge, a CD8 transmembrane structural domain and a CD3 zeta intracellular costimulatory signal transduction structural domain. According to the invention, an anti-CA9 single-chain variable fragment is used as an antigen binding structural domain to construct a chimeric antigen receptor (CAR) molecule, the macrophage expressing the anti-CA9CAR can realize specific targeting and phagocytosis killing of CA9 positive kidney cancer cells, and the constructed lipid nanoparticle delivery system can effectively transfer a gene expression vector, so that the gene expression vector can realize effective target site delivery, and the CA9 positive kidney cancer cells can be effectively targeted and killed. The constructed hydrogel-liposome combined drug delivery system has the performance of relieving toxic and side effects, controllable release and hemostasis.

The invention provides an UTR element NHP1 as well as a construction method and application thereof, and relates to the technical field of mRNA. A 5 'UTR with a good expression effect is designed by integrating dominant sequences of a human high-expression gene and a pathogen natural UTR, a chimeric structure NHP1 with high ribosome load is predicted through a calculation model, a DNA sequence of the NHP1 is as shown in SEQ NO 1, and an RNA sequence of the NHP1 is as shown in SEQ NO 2; an EGFP report system is adopted on the DNA level to rapidly screen UTR; the translation efficiency is quantitatively evaluated on the RNA level through luciferase mRNA (N1-methyl pseudouridine modification); and the particle size is controlled by a microfluidic technology, so that the optimized UTR-mRNA is efficiently expressed after being delivered.

The invention relates to the technical field of spatial transcriptome data clustering analysis, and discloses a spatial transcriptome data clustering method based on progressive learning and multi-modal fusion. According to the method, a high-order structure relationship among sample points is captured by using a multilayer graph convolutional network, and various noises of gene expression data are removed to the greatest extent through comparative learning and a complementary mask mechanism; meanwhile, gene expression data, spatial position information and histological image information are ingeniously fused to the maximum degree through two times of cross attention, and then fusion feature latent representation Latent is obtained. Compared with a mainstream spatial transcriptomics clustering analysis method, the method has the advantages that clustering based on the fusion feature latent representation Latent shows higher capability and excellent clustering performance, and has remarkable robustness.

The invention discloses construction and optimization of a gene editing tool based on CRISPR (clustered regularly interspaced short palindromic repeats)-associated transsposases and application of the gene editing tool in escherichia coli, and belongs to the field of synthetic biology and gene engineering. According to the invention, an efficient gene editing system is established in Escherichia coli, and the burden of a MUSCULAR-CAST system on strains is reduced through element optimization; by eliminating the self-targeting effect, large-fragment (> 10k bp) target gene expression cassettes can be efficiently integrated, and the editing degree of the gene editing system is improved. According to the invention, a MUSCULAR-CAST gene editing system is used for constructing the chassis strains for producing 3-fucosyllactose, L-tryptophan, 2 '-fucosyllactose and neotetraose, so that the chassis strains have equivalent or higher expression yield compared with plasmids, and the growth condition is better or the conversion rate is higher.

The invention discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

The invention discloses a spatial transcriptomics cell clustering method based on multi-scale contrast learning. The method comprises the following steps: S1, carrying out data preprocessing; s2, performing graph construction by using the processed data; s3, performing data enhancement; s4, extracting cell gene expression information by using GCN; s5, enriching node information by using a ContraNorm layer; and S6, further learning information by using multi-scale image comparison learning, and performing biological analysis by using final information obtained by learning. According to the method, computer-aided cell type analysis is utilized, a large amount of high-quality data is not needed, only gene expression data and cell space position information data are utilized, which cells belong to the same category can be predicted, and more effective information is provided to help researchers to identify the onset of cancer and the development of diseases.

This disclosure describes a Machine Learning (ML) framework, including graph convolution neural networks (GCN) for identifying gene expression signatures related to cancer driver events. The model is trained to identify the TP53 mutational state of cancer samples from gene expression, utilizing comprehensive curated graph structures of gene interactions. A quantitative score is generated to rank the severity of a driver event in each sample. Very high AUG results on unseen data across several tumor types are achieved with this method. There is a strong correlation with protein function. The Signatures in Transcriptome Associated with Mutated Protein (STAMP) model can also predict driver events in many combinations of important cancer genes / pathways and several tumor types, based on well-established annotations from the literature. The STAMP model therefore can identify and quantify driver events, which may lead the way to improved targeted therapy selection and prioritization in cancer patients.

The invention provides a crop whole genome prediction method and system based on machine learning, and the method comprises the steps: firstly obtaining a gene expression data set of a target crop in a plurality of growth cycles, the gene expression data set containing a plurality of gene sequence samples composed of genetic markers and phenotypic character data; performing feature extraction on the gene expression data set to obtain gene association features and growth character features, performing fusion prediction on the gene association features and the growth character features by using a preset machine learning model to generate fusion prediction features, and determining a whole genome prediction result according to the fusion prediction features. The whole genome prediction result can indicate character expression trends of crops in different environments, and finally, an adaptability optimization strategy is generated based on the whole genome prediction result and fed back to a crop cultivation system, cultivation parameter adjustment is triggered, crop cultivation precision and intelligence are achieved, and the cultivation efficiency and crop adaptability are improved.

Provided herein are devices, methods, and systems for in situ gene sequencing of a target nucleic acid in a cell in an intact tissue. Methods of screening a candidate agent to determine whether the candidate agent modulates gene expression of a nucleic acid in a cell in an intact tissue are also provided herein.

The invention belongs to the technical field of plant genetic engineering, and particularly relates to an SCRE10 gene for improving rice disease resistance and application of the SCRE10 gene, the base sequence of the SCRE10 gene is shown as SEQ ID NO.1, and the amino acid sequence of the SCRE10 gene is shown as SEQ ID NO.2. Through construction of a dexamethasone induced expression SCRE10 transgenic rice plant, it is found that the transgenic rice can significantly induce PR gene expression and active oxygen outbreak, and the resistance of rice to false smut and bacterial leaf blight can be improved. The invention proves that heterologous inducible expression of the Ustilaginoidea virens SCRE10 gene has the function of positively regulating the disease resistance of the rice, and the SCRE10 gene can be used for improving the disease resistance of the rice, which is of great significance to the creation of disease-resistant germplasm of the rice.

The disclosed technology relates to methods, transcriptomics systems, and non-transitory computer readable media for enabling natural language transcriptomics analysis. In some examples, genomic data including gene expression profiles for cells is transformed into sequences of genes ordered by expression level for each of the cells. The sequences of genes are annotated with metadata in a natural language format. A large language model (LLM) is then fine-tuned using the annotated sequences. The LLM is pretrained for natural language processing (NLP) tasks. The fine-tuned LLM is applied to generate and output a result in response to a received prompt in the natural language format. Thus, the LLMs of this technology advantageously both generate and interpret transcriptomics data and interact in natural language to generate meaningful text from cells and valid genes, among many other types of results.

The invention relates to the technical field of biological medicine, in particular to recombinant III-type humanized collagen as well as a preparation method and application thereof. In one embodiment, the recombinant III type humanized collagen prepared by the invention has an amino acid sequence as shown in SEQ ID NO: 1 or SEQ ID NO: 4. The recombinant III-type humanized collagen prepared by the invention has good biological activity, can promote collagen synthesis related gene expression, promote skin fibroblast proliferation, increase the content of skin collagenous fibers and elastic fibers, and improve skin elasticity and compactness; in addition, the expression of osteogenesis related genes is promoted, and the proliferation of osteoblasts is promoted. The results show that the recombinant III-type humanized collagen prepared by the invention has an extremely wide application prospect in developing related medical and cosmetic products for delaying skin aging and promoting wound repair and bone tissue healing.

The invention relates to a gene GmHB21 participating in development of vascular bundles of soybean root nodules and application of the gene GmHB21. Specifically, the invention relates to a separated gene participating in development of vascular bundles of soybean root nodules or regulation and control of the number of soybean nodules, and a coding region sequence of the gene is shown as SEQ ID NO: 1 or a homologous sequence of the gene. The invention also provides a method for regulating and controlling the nodulation number of the plant, the method comprises the following steps: introducing the gene or a sequence for inhibiting the expression of the gene into a target plant cell or tissue to obtain a transgenic plant, the nodulation number of the transgenic plant is changed compared with that of the target plant, and the plant is a leguminous plant, preferably soybean. In addition, the invention also provides application of the gene or a sequence for inhibiting expression of the gene in cultivation of leguminous plants with changed nodulation number, such as soybean.

The invention relates to the technical field of biology, and particularly provides preparation and application of a dsMpC002-coated ZIF-8 nano complex. According to the invention, a green peach aphid effector dsMpC002 is taken as a target, a ZIF-8 delivery system of the green peach aphid effector dsMpC002 with a stable superexcellent target is constructed, and a dsMpC002-ZIF-8 nano-composite is prepared and obtained. By observing and analyzing the MpC002 gene expression quantity, the death rate and the single female farrowing quantity of the green peach aphid after the pepper variety DYJ sprayed with the dsMpC002-coated ZIF-8 nano-complex with different concentrations is fed, the control effect of the dsMpC002-coated ZIF-8 nano-complex on the green peach aphid is determined. And a new target technology and a new idea are provided for innovating an aphid control method.