996 results about "Transcriptome" patented technology

Provided herein are methods of identifying genetic variants in biological samples. In particular, the present disclosure uses spatial transcriptomic templated ligation methods and compositions to identify the presence or absence, and the location of genetic variants in the transcriptome or genome of a biological sample.

The invention belongs to the technical field of biology, and discloses a reagent combination or kit for constructing intestinal organs and application of the reagent combination or kit. According to the reagent combination or the kit, intestinal organs can be obtained from cell-derived epithelial cells obtained from a donor in a non-invasive manner, and the obtained intestinal organs can be cryopreserved and recovered and can be amplified in vitro for a long time; transcriptome characteristics are similar to those of real human small intestine tissues, and typical marker genes of various small intestine pedigree cell types are highly expressed; compared with intestinal organs obtained through induction of pluripotent stem cells, the intestinal organs have more intestinal pedigree characteristics, and the intestinal function development is more mature; after being promoted to be mature, the intestinal organ also highly expresses genes related to drug absorption and metabolism, has drug absorption capability similar to that of an immortalized intestinal cell line, but more prominently shows intestinal cell lineage characteristics, is closer to an intestinal environment in a real human body, and can be used for screening intestinal disease drugs; the intestinal barrier function is realized.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

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 invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

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 provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

The invention discloses a gastric cancer multi-omics marker detection method, system and device, and the method comprises the following steps: S1, collecting a public database open-source space transcriptome, a single cell sequencing sample and bulk-RNAseq data for pre-processing, and collecting a primary tissue sample of a gastric cancer patient in the center for data processing; s2, integrating different modal data samples to obtain a patient label of an input end, and constructing a marker detection model and training the marker detection model; and S3, extracting a target feature value from the input external pathological section by using the trained model, and generating a diagnosis prediction result. Through multi-modal data chimerism, algorithm optimization and AI system development, subpopulation cell marker proportion prediction and prognosis diagnosis and marker evaluation with population prognosis information are realized, and an integrated diagnosis scheme for breaking through molecule-space-prognosis information is constructed.

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 discloses a spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

The invention discloses a construction method of a microplastic toxicity prediction model based on transcriptomics and a QSAR (Quantitative Synthetic Aperture Radar) model, which comprises the following steps: measuring actual observation values of cell survival rates of different microplastic samples after contamination and changes of genes in the cells, and screening out the genes as specific genes; selecting a physicochemical descriptor and then determining a gene expression descriptor of the specific gene; establishing a training set and a test set; a plurality of machine learning algorithms are utilized to construct QSAR models respectively, and an optimal QSAR model is selected according to a performance evaluation result; and carrying out performance verification on the optimal QSAR model, and screening out a microplastic toxicity prediction range suitable for the optimal QSAR model. The construction method of the microplastic toxicity prediction model can realize reliable prediction of toxicity, and can effectively support safety evaluation and biotoxicity mechanism research of microplastics.

The invention discloses a method for screening and identifying a cucumber aphid-resistant gene as well as a screened gene and application thereof, and provides a brand-new method for screening and identifying the cucumber aphid-resistant gene, which is used for screening the cucumber aphid-resistant gene based on four omics technologies. The four types of omics comprise transcriptome, proteome, methylation group and metabolome before and after inoculation of aphids on cucumbers, and the cucumber aphid resistance regulation gene is screened through combined analysis, so that a new gene resource is provided for improving the aphid resistance of the cucumbers, and a new method and a new idea are provided for designing molecular genetic breeding. The CsPGD2 gene obtained through screening is an aphid resistance gene, the dynamic change of a metabolic substance 5-ribulose phosphate in a metabolome is consistent with that of a transcriptome gene CsPGD2, the aphid resistance function of the gene CsPGD2 is verified through VIGS, and the result proves that the gene CsPGD2 is an important regulation factor for the aphid resistance of cucumbers. The invention provides a new gene resource for improving the aphid resistance of cucumbers, and has a good application prospect.

The invention discloses a space transcriptome data analysis method based on edge weighted graph attention and multi-view comparative learning, which comprises the following steps: preprocessing space transcriptome data to obtain preprocessed space transcriptome data; wherein the spatial transcriptome data comprises histological information, gene expression information and spatial position information; constructing a cell adjacency matrix, a disturbance matrix and a gene co-expression matrix based on the preprocessed space transcriptome data; based on the cell adjacency matrix, the perturbation matrix and the gene co-expression matrix, a multi-view comparison learning framework is constructed, and multiple views comprise a front view, a space view and a negative view; the multiple views are mapped to a unified potential representation space through an edge weighted graph attention auto-encoder, and node embedding is obtained; downstream analysis is performed based on node embedding. According to the method, different space structure modes can be accurately identified in space transcriptome data analysis, so that the clustering accuracy, compactness and separability of space data are improved.

The invention discloses a drug perturbation diffusion prediction method fused with cell description, and relates to the technical field of drug perturbation prediction.The method comprises the steps that firstly, a cell perturbation transcriptome database is preprocessed, and a cell-drug combination containing drug characteristics, cell line gene expression and cell line description characteristics is obtained to serve as training data; and then constructing a drug disturbance prediction diffusion model based on cell description, training the drug disturbance prediction diffusion model by using the training data, and finally inputting Gaussian white noise, cell line gene expression before disturbance, drug characteristics and cell line description characteristics into the trained model to predict cell line gene expression after drug disturbance. According to the method, cell line description characteristics are introduced, so that the perception capability of the model on intercellular biological differences is enhanced, and the generalization performance of cross-drug and cross-cell lines is improved.

The present disclosure, in some aspects, provides one or more biomarkers (e.g., plasma biomarkers or circulating biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., plasma biomarkers or circulating biomarkers) described herein is regulated by DUX4. Methods (e.g., non-invasive methods) of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided. The present disclosure, in other aspects, provides one or more biomarkers (e.g., transcriptome biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., transcriptome biomarkers) described herein is regulated by DUX4. Methods of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided.

The invention provides a method for predicting a compound target based on a compound perturbation transcriptome and a protein sequence and application, and the method creatively combines the characteristics of the compound perturbation transcriptome and the characteristics of the protein target sequence as input characteristics and combines a deep learning model to realize prediction of the compound target. Specifically, perturbation transcriptome and active and inactive target spot information data of a compound are collected, transcriptome features are extracted through principal component analysis and linear discriminant analysis, meanwhile, a protein language model is used for representing target spot protein features, the transcriptome and a target spot of the compound are converted into learnable embedding, and the target spot information of the compound is obtained. And the classification model is used for training and learning potential association of the two, and is finally used for compound target prediction. According to the method, an external test set is used for proving high accuracy and practical applicability of the model. The method has the advantages of being low in cost, high in efficiency and wide in range, the compound target spot discovery efficiency can be remarkably improved, and the research and development process of drugs can be accelerated.

The invention relates to the technical field of plants, in particular to a paraffin embedding method for reducing plant RNA degradation. In the plant paraffin embedding method provided by the invention, a stationary liquid for fixing plant tissues is prepared from absolute ethyl alcohol and acetic acid in a volume ratio of (2-5): (1-4); buffer I used for dehydration is absolute ethyl alcohol and n-butyl alcohol with the volume ratio of (1-3): (1-2); buffer II used for waxing is n-butyl alcohol and low-temperature paraffin wax with the volume ratio of (1-3): (1-2), and the melting point of the paraffin wax is 20-50 DEG C. According to the method, low-temperature paraffin with the melting point of 20-50 DEG C is adopted, and a reagent for paraffin embedding and a paraffin embedding process are adjusted, so that the plant section with relatively high RNA integrity and relatively high tissue structure integrity is obtained. The paraffin embedding method provided by the invention is suitable for different types of molecular biology experiments of space transcriptome, immunohistochemistry, immunofluorescence and the like.

The invention discloses an inference method and an inference system of a gene regulatory network. The inference method comprises the following steps: acquiring transcriptome data and prior gene network data of a single cell; extracting a gene sub-network related to the transcriptome data from the prior gene network data; obtaining a first view and a second view for the gene sub-network according to a first random deletion strategy and a second random deletion strategy; providing the first view and the second view to a neural network model, and performing comparative learning based on the neural network model to obtain incoming features and outgoing features of each node in the gene sub-network; and constructing a regulation score matrix based on the incoming features and the outgoing features, wherein the regulation score matrix displays the regulation association degree between genes in the transcriptome data of the single cell. According to the technical scheme, the direct causal relationship and the indirect association relationship can be effectively distinguished, so that the gene co-expression network more accurately reflects the real regulation relationship.

The invention develops a method and a model for predicting mRNA (messenger ribonucleic acid) translated protein products based on deep learning. Specifically, the invention designs a method for accurately predicting a transcriptome horizontal translation product based on a deep learning method, and the method is used for predicting a translation site and a translation product of a transcript sequence to form a TRANSAID model. The TRANSAID model adopts a unique sequence coding strategy and a deep learning architecture, local sequence features and a long-distance dependency relationship can be captured at the same time, and the prediction accuracy is greatly improved; transcript sequences of different species can be predicted, and cross-species prediction ability appearing for the first time in the field is achieved; and different influences of 5 'UTR and 3' UTR regions on translation regulation and control can be identified.

The invention discloses a variable polyadenylation dynamic analysis method and device. The method comprises the following steps: acquiring initial space transcriptome data and an initial space bright field image; performing first preprocessing on the initial space transcriptome data to obtain target space transcriptome data; performing second preprocessing on the initial space bright field image to obtain a target space bright field image; performing data analysis and integration processing to obtain spatial variable polyadenylation data, spatial gene expression data and spatial cell distribution dynamic data; constructing a gene expression regulation network; generating an integrated analysis result; performing space-time analysis to obtain a space-time analysis result; performing trajectory analysis to obtain a trajectory analysis result; performing disease process analysis to obtain a disease process analysis result; and generating a variable poly-adenosine acidification dynamic analysis result. According to the invention, dynamic analysis of variable polyadenylation is realized, the accuracy is improved, and multi-angle biological understanding is provided. The method can be applied to the technical field of gene analysis.

The invention discloses application of a substance for detecting the expression level of APOE or MT2 in evaluating the ovarian aging degree. According to the invention, cell and molecular changes related to ovarian aging are explored in spatial resolution by adopting a spatial transcriptome technology, a first non-human primate space-time transcriptome map is established, and potential diagnostic biomarkers and therapeutic targets of age-related human ovarian diseases are disclosed; the method not only promotes the understanding of primate ovarian aging mechanism determinants, but also provides abundant resources for identifying cells and molecular programs which can be used for evaluating ovarian aging and formulating intervention measures for human ovarian aging and related diseases.

The present invention relates to the intersection of bioinformatics and computational biology, and discloses a transcriptome annotation method and system based on a large language model, including: fusing single-cell spatial coordinates and gene expression values ​​into pseudo-image modal data, extracting spatial topological features of the pseudo-image modal data; performing cross-modal alignment between the spatial topological features and a preset medical database, and analyzing the cell type probability of the cross-modal embedding vector; constructing a functional semantic space of functional description texts, and projecting non-model species into the functional semantic space; calculating the semantic similarity between gene expression embeddings and homologous genes of reference species, and using a large language model to convert gene expression embeddings into semantic mapping relationships. The present invention reduces the core problems faced in single-cell spatial transcriptome annotation, such as extensive spatial topological modeling, low cross-modal alignment accuracy, limited non-model species annotation, and rigid semantic mapping.

The invention relates to cloning of a stress-resistant gene LpbHLH75 of Lilium pumilum DC (Lilium pumilum DC.) and application of the stress-resistant gene LpbHLH75 of Lilium pumilum DC. The lilium tenuifolium stress resistance key genes are mined through transcriptome data analysis, and the functions of the lilium tenuifolium stress resistance key genes are studied. Comprising the following steps: (1) cloning a target gene; (2) constructing a plant overexpression vector; and (3) detecting physiological indexes of the transgenic plant. According to the experiment, lilium tenuifolium is taken as a research object, and the LpbHLH75 gene is cloned by applying an RT-PCR technology; transforming tobacco by constructing an overexpression vector; the function of overexpression of the LpbHLH75 on tobacco salt and alkali and drought stress is identified through transgenic tobacco, the function of the lilium tenuifolium bHLH gene under adversity stress is disclosed, a new gene source is provided for lilium tenuifolium genetic engineering breeding, a reference is provided for later lilium tenuifolium stress-resistant germplasm resource research, and the lilium tenuifolium bHLH gene is developed. And a foundation is laid for exploring a gene capable of enhancing stress resistance in the lilium tenuifolium and a stress resistance mechanism of the gene.

The invention relates to a colorectal cancer space transcriptome prediction method and device based on deep learning and a storage medium. The prediction method comprises the following steps: collecting paired digital pathological images and spatial transcriptome data to form a data set; preprocessing the spatial transcriptome data; the method comprises the following steps of: constructing an STFormer model; the Style-Aug module is used for performing feature space conversion on an input digital pathological image; the dense connection convolutional network DenseNet module is used for constructing a deep mapping relation between a pathological image and a spatial transcriptome; the inter-batch full-image crossing Transform module is used for effectively avoiding overfitting of the model due to data volume limitation through an inter-batch attention mechanism; and performing spatial transcriptome prediction on the input digital pathological image by using the trained STFormer model to obtain a prediction result. Under the condition of a small sample, the performance and the general stability of a pathological image prediction space transcriptome are remarkably improved, and an efficient and reliable deep learning tool is provided for analyzing a tumor microenvironment.

The invention relates to the technical field of image analysis, in particular to a targeted drug curative effect prediction method based on image recognition, which comprises the following steps: acquiring tissue images and nuclear morphological parameters by a microscope, establishing a database in combination with transcripts, extracting an injury area, recognizing image features through a convolutional neural network, and constructing a prediction model; and inputting candidate drug molecular structures for molecular docking, calculating a repair progress by combining animal verification to establish a curative effect model, predicting drug scores and response time based on the curative effect model to generate a ranking list, screening high-score drug cells, verifying monitored survival, comparing, predicting and outputting a result. The method comprises the following steps: extracting a cell nucleus form, revealing a relation between damage and molecular abnormality in combination with a transcriptome, identifying a target spot corresponding to an abnormal mode and pathological change through deep learning, performing affinity prediction and animal verification on a drug structure, quantifying the repair progress by adopting image difference, and evaluating the curative effect with two dimensions of structure and function. And curative effect scores and response prediction are output to realize system sequencing, so that drug screening is more accurate and practical.

The invention discloses a prediction marker and a prediction method for acute upper respiratory infection susceptibility. The prediction marker and the prediction method are used for solving the problem that current acute upper respiratory infection susceptibility risk prediction is low in accuracy. The method comprises the following steps: firstly, providing a prediction marker for acute upper respiratory infection susceptibility, wherein the prediction marker is a prediction feature combination obtained by comprehensively considering human health state blood routine data and baseline blood transcriptome data and carrying out feature processing; secondly, providing a prediction method, and obtaining a to-be-detected blood sample; and performing blood analysis based on a to-be-detected blood sample, inputting the obtained blood routine sample and the gene expression sample into a susceptibility prediction model pre-trained based on a prediction marker for susceptibility risk judgment, and obtaining a susceptibility risk assessment result of acute upper respiratory infection of a human body corresponding to the to-be-detected blood sample in a future period of time, therefore, the acute upper respiratory infection susceptibility risk of the human body in a period of time in the future can be simply, quickly and accurately predicted.

The invention discloses a single cell space transcriptome data analysis method, device and system, and a storage medium. The method comprises the following steps: acquiring single cell space transcriptome data; the spatial transcriptome data comprises a gene expression map and spatial site coordinates; constructing a graph structure considering gene expression similarity and spatial position continuity on the basis of the data, and performing data representation on a gene expression graph by using an adversarial auto-encoder; in combination with a graph neural network, robust potential characterization is learned, training loss is constructed through reconstruction of a gene expression map, and meanwhile, a clustering prediction result of spatial sites is obtained in combination with mcluster clustering. By adopting the technical scheme of the invention, the spatial clustering analysis with higher precision can be realized, and the key spatial functional region in the biological tissue can be identified.

The present invention provides MMLV reverse transcriptase enzymes with increased thermal stability as compared with wild type MMLV and AMV reverse transcriptases. The improved thermal stability allows for reverse transcription of RNA to cDNA at temperatures above 37° C., thereby reducing error rates introduced during cDNA synthesis. As a result, the reverse transcriptases of the invention allow for increased accuracy in the determination of transcriptomes of living organisms.