355 results about "Transcriptome" patented technology

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 invention discloses a laying hen genetic disease molecular marker screening system based on data fusion and AI prediction, the system comprises six modules, a multi-omics data acquisition module obtains laying hen genome and transcriptome data, and a FineDataLink data fusion module carries out feature alignment and association mapping to generate a fusion feature matrix; the dynamic time sequence diagram neural network processing module constructs a time sequence association diagram and outputs a time sequence feature vector, and the attention enhancement deep forest analysis module evaluates feature importance and outputs a screening result; the federal variation auto-encoder modeling module constructs a federal training framework to generate a molecular marker probability distribution model, and finally the molecular marker screening output module extracts key molecular markers. The system realizes deep fusion of multi-omics data and efficient application of an AI algorithm through multi-module cooperation, improves the molecular marker screening efficiency and accuracy, and provides technical support for disease-resistant breeding of laying hens.

The invention belongs to the technical field of biosynthesis of plant secondary metabolites, and particularly relates to a key gene for biosynthesis of large-fruit hawthorn flavonoid compounds and a screening method and application of the key gene. Through combined analysis of metabolome and transcriptome, a key gene combination containing seven structural genes and three transcription factor genes is screened out; the expression of the genes is remarkably positively correlated with the accumulation of a target flavone metabolite [6]-gingerol, and the genes are core factors for regulating and controlling the synthesis of the flavonoid compounds of the big-fruit hawthorns through experimental verification. According to the invention, the key gene for regulating and controlling the synthesis of flavone substances such as [6]-gingerol in the big hawthorn fruit is systematically identified for the first time, and a target spot is provided for analyzing a quality formation mechanism from a molecular level; the gene can be used for molecular marker-assisted breeding so as to cultivate a new variety of large-fruit hawthorn with high flavone content, and also can provide gene resources and technical support for the development of functional food and health care products.

The invention provides a method for jointly deducing dynamic cell communication and a cell state transition rate. Relates to the technical field of biological information. The method comprises the following steps: extracting candidate ligands, candidate receptors and characteristic genes from space transcriptome data to be processed; the method comprises the following steps: screening nodes which have an interaction relationship with candidate ligands, candidate receptors and characteristic genes from a pre-constructed prior database, and constructing a multi-layer signal network of which the structure is ligand-receptor-transcription factor-target genes; establishing a gene regulation kinetic model according to a ligand-receptor action relationship, a receptor-transcription factor action relationship and a transcription factor-target gene action relationship in the multilayer signal network; iteratively optimizing parameters to be estimated in the gene regulation and control kinetic model; and obtaining the change rate of the expression quantity of the target gene in the receiving cell based on the potential time after iterative optimization and the parameters to be estimated after iterative optimization. And combined analysis of dynamic cell communication and cell differentiation tracks can be realized.

Systems and methods for multi-modal subcellular segmentation using photolysable biomarkers and / or transcriptomic readout density maps are disclosed. The systems and methods improve the accuracy of cell segmentation of the nucleus, cytoplasm, and cell membrane regions by using optical and bleach correction from a variety of photolysable morphological markers in combination with high quality 3D images acquired with high dynamic range scans and spatial transcriptomic readout density maps.

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 medical biology, in particular to a chronic pancreatitis complication risk grading system based on exosome transcriptome data, and aims to solve the problems that existing chronic pancreatitis (CP) typing lacks molecular basis, complication risk prediction is weak and clinical transformation is poor. According to the system, plasma exosomes are extracted and sequenced, a functional characteristic gene set is constructed in combination with pancreas single cell data, CP is divided into three risk increasing subtypes by using a COCA algorithm, and finally 12 core miRNAs are screened to construct a BPNN diagnosis model. The invention proves that the plasma exosome can be used for staging classification of chronic pancreatitis for the first time, miRNA non-invasive accurate layering illness conditions can be detected through qPCR, the risk of fatty diarrhea and 3c type diabetes mellitus can be predicted, and the non-invasiveness, convenience, classification accuracy and result repeatability of the plasma exosome have clinical application and transformation advantages.

The invention discloses a gene expression map generation method and device based on spatial omics hierarchical reconstruction, a storage medium and equipment, relates to the technical field of biological information, and mainly aims to solve the problem of poor generation accuracy of an existing gene expression map. Comprising the following steps: acquiring original transcriptome data of a tissue gene; the gene data are predicted based on a hybrid neural network model, target slice space transcriptome data are obtained, the hybrid neural network model comprises a regulation and control niche network, a cell niche network and a cell communication prediction network, and a contrast diffusion bridge is constructed in the regulation and control niche network; a multi-modal condition diffusion bridge is constructed in the cell ecological niche network, and an optimal transport stream matching diffusion bridge is constructed in the cell communication prediction network; and taking the target slice space transcriptome data as an anchor slice to carry out space stream matching alignment to obtain an alignment result, and reconstructing the target slice space transcriptome data based on the alignment result to obtain a gene expression map of the tissue gene.

The application discloses a single-cell biopsy and dynamic transcriptome tracking system and method, and belongs to the technical field of single-cell sequencing. The system comprises the following steps: culturing single cells to be detected in a cell culture dish; puncturing the single cells by using an amino-modified quartz nanocapillary to extract trace cytoplasm samples; recovering the single cells in a culture environment; applying specific stimulation to the recovered single cells; at one or more time points after the stimulation is applied, puncturing the same single cells again by using the quartz nanocapillary to extract trace cytoplasm samples, and recovering the single cells after each extraction; and respectively performing reverse transcription, amplification, library construction and sequencing on the cytoplasm samples extracted at different time points to obtain dynamic transcriptome data of the single cells at different time points. The application can perform low-damage multiple longitudinal biopsies on the same single living cell, and can construct a dynamic transcriptome atlas of the single cell on a time axis in combination with downstream sequencing.

The present application relates to the use of recombinant collagen in the preparation of a preparation for increasing the expression of skin anti-wrinkle related genes, wherein the amino acid sequence of the recombinant collagen comprises the sequence shown in SEQ ID NO. 1. The present application detects the expression of a series of skin anti-wrinkle related genes by transcriptomics, and proves that the recombinant collagen provided by the present application can promote the expression of skin cell anti-wrinkle genes, and can be used for the development and application of various cosmetic products, and endows the cosmetic products with the efficacy and effect of promoting skin anti-wrinkle ability. The above-mentioned genes can also be used as a reference for evaluating the skin anti-wrinkle ability of other raw materials or cosmetics in the future.

The invention provides a management method and system for standardized organ-like sample library information, and is applied to the field of data processing application. The method comprises the following steps: preprocessing organoid sample source data and manufacturing process data to generate sample basic features, manufacturing process features and culture condition features; performing feature extraction processing on the quality control data to generate morphological features, molecular marker features, functional features, genome transcriptome features and vigor apoptosis features; processing the sample basic features, the manufacturing process features and the culture condition features based on a graph database model to generate an organoid data storage and association system; organoid generation and quality control processes are managed, key parameters and reagent batch numbers are recorded, operation is automatically triggered, data are associated, and standardized operation process records are generated; and processing the morphological characteristics, the molecular marker characteristics, the functional characteristics, the genome transcriptome characteristics and the vigor apoptosis characteristics to generate an organoid quality evaluation result.

Provided are a reagent combination or a kit for constructing an embryoid and the use thereof. The reagent combination or the kit can be used for inducing a stem cell to produce a blastocyst-like lineage precursor cell, a blastocyst-like cell and / or an embryoid, which are based on small-molecule induction and independent of transgenes, and have a single cell source. The produced blastocyst-like lineage precursor cell and blastocyst-like cell have a full blastocyst lineage, and are seed cells having balanced developmental competence and totipotency, which solves the problem of unbalanced cell maturation in the prior art. The produced embryoid is highly similar to natural embryos in terms of morphological characteristics and transcriptomic characteristics, has the capability of highly reproducing post-implantation embryonic development and / or gastrulation, which is beneficial for research on embryonic development in vitro. The method for inducing the stem cell to produce the blastocyst-like lineage precursor cell, the blastocyst-like cell and / or the embryoid by using the reagent combination or the kit is simple, and has high applicability and high embryoid construction efficiency.

The invention belongs to the field of plant engineering technology and crop germplasm resource innovation, and particularly relates to a rice BEIIb gene mutant, application of the rice BEIIb gene mutant in improving rice resistant starch and an improving method. A mutant material with high resistant starch content is created by knocking out the rice BEIIb gene, and compared with a wild type, mutant seeds show an obvious chalky phenotype, composite starch particles are abnormal in a spherical or ellipsoidal shape, the amylose content is remarkably increased, the protein content is remarkably reduced, the resistant starch content is remarkably increased, and the resistant starch content is remarkably reduced. The hardness of cooked rice is remarkably increased, and the elasticity is remarkably reduced. Through transcriptomics analysis, the protein mainly participates in biological pathways such as carbohydrate metabolism, chloroplast photosynthesis, calmodulin combination and secondary metabolite synthesis, and the expression level of endosperm starch synthesis related genes is obviously changed. The method can provide theoretical basis and method reference for breeding of high-quality rice varieties with high resistant starch.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The present application relates to the field of biomedical technology, and discloses an immune infiltration desert tumor benefit ICI treatment method based on a weighting strategy. The method obtains spatial transcriptome data of tumor tissue of a patient, divides the tumor microenvironment into functional regions through an adaptive grid division algorithm, and extracts a multi-scale feature vector; calculates dynamic weight coefficients of each functional region using a graph attention network, and generates a comprehensive response index after weighted integration; and finally outputs an individualized ICI treatment decision according to the comparison result of the index and a preset threshold. The present application realizes dynamic evaluation of tumor microenvironment heterogeneity based on spatial transcriptome data, and provides ICI treatment response prediction and clinical decision support for patients with immune infiltration desert tumors.

The application discloses a biomarker combination and application thereof in evaluation of chemoradiotherapy efficacy of esophageal squamous cell carcinoma. The biomarker combination comprises STOX2, TENM4, AC069277.1, B4GALNT3, CASC15, PPFIA1, NTN4, RDH10, CYYR1 and RNF165. The application performs niche analysis on samples through spatial transcriptome data, identifies an immune niche, and then screens the above-mentioned biomarker combination related to chemoradiotherapy efficacy, and a scoring model for evaluating chemoradiotherapy efficacy of esophageal squamous cell carcinoma is constructed based on the biomarker combination. The scoring model has high prediction accuracy and clinical applicability, provides a new tool for objective evaluation of chemoradiotherapy efficacy of esophageal squamous cell carcinoma, and has significant clinical transformation potential and application value.

This invention discloses a ferroptosis target identification system and method based on an attention mechanism, belonging to the field of artificial intelligence technology. The system includes modules for extracting multi-source features of ferroptosis regulation, labeling weak signal targets, calculating attention potential energy mapping, and ranking ferroptosis targets. It extracts multimodal features from transcriptome expression, molecular interaction networks, and ferroptosis pathway annotations. Targets with expression intensities below a preset threshold that participate in the ferroptosis pathway are labeled with weak signals. Weak signal characteristics are injected, and a potential energy bias is constructed to complete attention potential energy mapping. Then, through pathway mapping and cross-pathway linkage identification and fusion weights, the target linkage degree is calculated and ranked. This system solves the problem of traditional methods easily neglecting weak signal targets, and can identify targets with insignificant expression changes in the ferroptosis regulatory network. Through attention and pathway-level linkage modeling, the comprehensiveness of target identification is improved, and the obtained ranking results have stability and discriminative power, making it suitable for the identification and research of ferroptosis-related targets.

The invention relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.

The invention provides a spatial transcriptome tissue specific expression gene identification method and system based on a multi-dimensional statistical index, and belongs to the technical field of gene identification, the method comprises the following steps: obtaining gene expression data corresponding to a plurality of genes in different cells, different cells belonging to different types of tissues; the gene expression data is data processed based on a space transcriptome; for each gene, determining a basic statistical feature and a differential expression feature corresponding to the gene based on the gene expression data corresponding to the gene in different cells; determining specific index characteristics of the gene based on the basic statistical characteristics of the gene; and determining a tissue specific expression gene from the plurality of genes based on the basic statistical characteristics, the differential expression characteristics and the specific index characteristics corresponding to each gene. According to the invention, the accuracy of tissue specific expression gene recognition can be improved.

The invention provides a fusion transcript recognition method, device and system based on transcriptome multiple comparison data and a medium, and the core of the method is to construct a set of full-chain analysis framework oriented to multiple mapping reads to realize fusion gene detection. Comprising a pairing read segment preliminary screening module based on a loose mapping strategy, a candidate fusion construction module based on an exon graph structure, a remapping module based on an enhanced reference transcriptome, a transcript abundance estimation module based on a probability generation model, and a fusion scoring module based on fusion site specificity support degree. And a false positive inhibition module based on coverage consistency and biological filtration. According to the method, the dependence of a traditional fusion gene detection method on a unique mapping read is broken through, multiple comparative analysis on the fuzzy mapping read abandoned due to sequence homology is proposed for the first time, and high sensitivity and low false positive rate of the fuzzy mapping read are verified in simulated data and real tumor samples; the method has remarkable technical advancement, clinical applicability and expandability.

The invention discloses a colon cancer diagnostic marker screening method and system based on transcriptome data, and belongs to the technical field of biological information, and the method comprises the steps of data preparation, pathological hierarchical modeling, diagnostic marker screening and screening report generation. According to the method, pathological hierarchical modeling based on dynamic discrimination and double-layer feature fusion is adopted, on the basis of comprehensively considering gene expression and pathological morphology information, pathological subtypes of colon cancer samples are adaptively recognized, stable and representative subtype features are obtained, and therefore the accuracy and biological representativeness of diagnostic marker screening are improved; diagnostic marker screening based on pathological subtype difference analysis is adopted, and on the premise of considering pathological subtype characteristics, a stable and reliable diagnostic marker set with remarkable expression difference is screened in a targeted manner, so that the biological representativeness, screening robustness and diagnostic specificity of markers are improved.

The invention discloses a detection method and application of a molecular marker related to cattle cold adaptability. On the basis of large-batch whole genome re-sequencing data of northern cattle and southern cattle, a genome selection signal analysis method is adopted, and multi-omics verification such as transcriptome and epime is combined, so that the molecular marker related to the cold environment adaptability is screened out. The molecular marker can be used for cold adaptive molecular breeding of cattle population, and provides scientific basis and technical support for genetic improvement and resource utilization of cattle species adaptive to cold environment.

The invention relates to the technical field of biological information, and particularly discloses a multi-omics genome combined genetic assessment method and device. According to the method, multi-source data such as a genome, a transcriptome, a proteome, a metabolome, a microbiome and a high-throughput phenotype are integrated by constructing a multi-omics fusion similar matrix, an adaptive dichotomy is adopted to optimize omics weights, a grid search and cross validation system is introduced, and global optimal estimation of parameters is achieved. Verification of the method on livestock and poultry breeding and plant breeding data shows that the prediction accuracy of complex characters can be remarkably improved, and particularly, the prediction precision in low heritability characters is remarkably improved. The invention further provides a corresponding device which comprises a data acquisition module, a matrix construction module, a parameter optimization module and a breeding value calculation module, comprehensive utilization of multi-omics information can be efficiently achieved, and the accuracy and calculation efficiency of breeding value estimation are improved.

The invention belongs to the technical field of gene engineering, and particularly relates to an application of a BnaA02g04730D gene in improving the salt tolerance of oilseed rape. According to the invention, a high-salt-tolerance germplasm 'Xiangong saline-alkali oil No.1' is obtained by screening, and a key candidate gene BnaA02g04730D responding to salt stress is excavated by combining absolute quantitative transcriptomics analysis. Yeast stress phenotype identification and transgenic rape verification both prove that the gene positively regulates the salt tolerance of rape. Functional verification shows that under the stress of 0.24% NaCl, the SOD activity, the CAT activity and the POD activity of the overexpressed plant are remarkably improved compared with those of a wild type, and the MDA content is remarkably reduced. The invention preliminarily illustrates a molecular mechanism for endowing rape with salt tolerance by regulating and controlling an anti-oxidation defense system and a hormone signal channel. A new perspective is provided for analysis of a salt-tolerant molecular mechanism of the rape, and a gene resource with application value is also provided for molecular breeding of a new salt-tolerant variety.

The invention provides a crop allergen dynamic baseline construction and trusted sharing system based on a multi-dimensional heterogeneous data cube, and the system comprises a collection layer which is used for collecting physical information metadata of a to-be-detected sample based on an agricultural environment monitoring terminal, the method comprises the following steps: performing physical detection on a to-be-detected sample through a high-throughput sequencing terminal to generate original mass spectrum data and transcriptome data, and calling meteorological metadata of a region where the to-be-detected sample is located through a third-party meteorological server; the core processing layer is used for generating a dynamic baseline by adopting a central processing server cluster based on the physical information metadata, the original mass spectrum data, the transcriptome data and the meteorological metadata; and the application layer is used for checking the dynamic baseline based on the user query terminal and checking the GLP log based on the checking and auditing terminal. By constructing the multi-dimensional heterogeneous data cube and the dynamic baseline model, standardized data calculation service is provided for safety evaluation of transgenic crops and biotechnology products.

This application provides a method, electronic device, and storage medium for analyzing the microenvironment of biological tissues. The method includes: acquiring spatial transcriptome data of biological tissues; determining window data corresponding to multiple sliding windows from the spatial transcriptome data using sliding windows, each sliding window containing multiple detection regions; constructing an adjacency graph for each sliding window based on the adjacency relationships between the multiple detection regions; obtaining a first interaction matrix for each sliding window based on the adjacency graph; constructing a tensor matrix based on the first interaction matrix; and decomposing the tensor matrix to obtain the analysis result of the microenvironment of the biological tissue corresponding to the biological tissue. This application can improve the accuracy of analyzing the microenvironment of biological tissues.

A spatial in situ sequencing method, belonging to the field of biology, is proposed. It utilizes an electric field-assisted directed migration of mRNA and in-situ capture with primers on a microarray surface to enrich tissue and release mRNA. In-situ reverse transcription generates covalently fixed cDNA, ensuring high positional stability during multiple rounds of hybridization and imaging. After reverse transcription, tissue is digested to remove tissue, reducing spatial hindrance and background interference, while the cDNA remains at its original coordinates due to covalent anchoring. Combining coding probe hybridization and RCA, single-molecule-level signal amplification and recognition are achieved. Through decoding and single-cell segmentation, transcripts are mapped to their respective cells, constructing a single-cell resolution spatial gene expression map. This method does not rely on multi-round DAPI mapping or other endogenous morphological marker-based multi-cycle image registration methods, making it suitable for high-throughput spatial in situ sequencing and significantly improving robustness and versatility in complex imaging scenarios such as thick tissue sections and low signal-to-noise ratios. It is applicable to high spatial resolution, high-throughput spatial transcriptome research.

The invention discloses an odor receptor gene SinfOR21 of sesamia inferens and application of the odor receptor gene SinfOR21. Belongs to the technical field of biology. According to the invention, sex pheromones of Zhejiang and Guangdong sesamia inferens strains and expression and functional verification of olfaction receptor genes of the sesamia inferens are identified and compared. The method comprises the following steps: firstly, identifying pheromone mixtures with region specificity, and verifying the behavior effectiveness of the pheromone mixtures in field trials; through transcriptome and function analysis, it is proved that a SinfOR21 receptor of the Zhejiang strain is specifically combined with Z11-16: OH, and a homologous receptor of the Guangdong strain does not have the function. And determining that the sex pheromone mixture of the sesamia inferens should be composed of Z11-16: Ac, 16: Ac and Z11-16: Ald. Different from reported sex pheromone mixtures of sesamia zhejiangensis, the sex pheromone mixture of sesamia zhejiangensis is composed of Z11-16: Ac, Z11-16: OH and Z11-16: Ald.

The invention discloses a gene level DNA methylation, transcriptome and proteome conjoint analysis method based on a snkemake framework, which comprises the following steps: preprocessing original data of DNA methylation and / or transcriptome and / or proteome, analyzing gene methylation, analyzing transcriptome and / or proteome, and analyzing the transcriptome and proteome. Based on gene methylation analysis data, the preprocessed transcriptome data and the preprocessed proteome data, correlation analysis and / or difference intersection analysis and / or enrichment pathway conjoint analysis are / is carried out, finally analysis results are sorted, and a visual report is generated. The method has comprehensive results, and relates to quantification, difference analysis and pathway enrichment analysis of gene methylation, and correlation analysis, difference intersection analysis and enrichment pathway joint analysis of integrated DNA methylation and / or transcriptome and / or proteome data; automatic arrangement, verification, visualization and report generation of analysis results are realized; all operation steps can be traced, and corresponding analysis log records are generated. The invention further discloses a related system and application.

The application provides a prostate cancer biochemical recurrence prognosis risk prediction model based on fatty acid metabolism and cancer cell stemness genes and a construction method thereof, wherein the construction method comprises the following steps: S1: data collection: obtaining prostate cancer sample transcriptome data with biochemical recurrence information from a database, and dividing the data into a model training set and a model test set; S2: stemness score analysis; S3: fatty acid metabolism score analysis; S4: based on the analysis results of S2 and S3, identifying a co-expression gene module related to fatty acid metabolism and stemness characteristics in prostate cancer by a co-expression similarity algorithm and a hierarchical clustering algorithm, and obtaining a fatty acid metabolism and stemness-related gene set; S5: constructing a prostate cancer BCR prognosis risk prediction model; S6: constructing a nomogram model; S7: extracting RNA of a to-be-tested sample, constructing a cDNA library, quantifying the expression of the above genes, and calculating the prognosis risk level of prostate cancer through the expression level.