102 results about "Heteroplasmy" patented technology

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.

The invention provides an ecDNA structure prediction method based on scATAC-seq, a method for identifying cells carrying ecDNA and a medium, and relates to the technical field of biological information. According to the ecDNA structure prediction method based on scATAC-seq, inconsistent readings of ecDNA breakpoint sources are extracted through comparison with a reference genome, enriched peak regions are called in sequence and converted into a graph, a breakpoint graph is combined with a Bayesian model, and accurate prediction of structural information such as ecDNA breakpoints, ecDNA regions and connection modes is achieved. The invention also provides a method for identifying cells carrying the ecDNA, and the method is used for accurately acquiring the cells carrying the ecDNA through standardization treatment, formation of a gene activity matrix and non-single-peak test on the ecDNA structure based on the scATAC-seq. According to the present invention, the ecDNA structure can be predicted, the cells carrying the ecDNA can be accurately identified, and the method can be used for the accurate analysis and research of the tumor heterogeneity, evolution and drug resistance process of the ecDNA.

The present application relates to the field of bioinformatics. In particular, the present application relates to methods and apparatus for speculating variable splicing functionality based on single cell transcriptome data. The method comprises the following steps: determining a variable splicing mode of each gene in a data set in a cell; determining the incidence relation between the variable splicing mode and the gene expression of each gene; a variable splicing mode module is determined according to the incidence relation between the variable splicing modes and the gene expression, and the variable splicing mode module is a variable splicing mode set obtained through clustering according to the correlation between the variable splicing modes and the cell phenotypes; displaying the cell splicing heterogeneity according to the variable splicing mode module; and / or determining a potential regulatory mechanism between the variable splicing mode and the gene expression according to the variable splicing mode module, the potential regulatory mechanism being used for embodying key splicing factors in the gene expression, and a biological approach in which the variable splicing mode affects the cell phenotype.

The invention discloses an engineered artificial vesicle and application of the engineered artificial vesicle in multiple in-situ detection of urine exosome miRNA, and belongs to the field of biosensors. The method comprises the following steps: constructing planar framework nucleic acid simultaneously modified with cholesterol and an aptamer through annealing reaction; a molecular beacon with a fluorophore is designed according to a target gene sequence, and the molecular beacon and a double-strand specific nuclease (DSN) system are jointly encapsulated in an artificial vesicle; the engineered artificial vesicles and a sample to be detected are incubated, the fluorescence modified molecular beacons can specifically recognize target genes, cyclic cutting and signal amplification are achieved under DSN mediation, and the broken molecular beacons release fluorescence signals; all fluorescence signals in the vesicles are collected through a fluorescence imaging technology, and a characteristic fluorescence spectrum of the to-be-detected sample is obtained. According to the present invention, the miRNA heterogeneity analysis at the single vesicle level and the precise multi-target detection of bladder cancer and other diseases can be achieved, and the problems of low throughput, target number limitation and the like of the existing vesicle in-situ detection are effectively overcome.

The invention relates to the technical field of bioinformatics and molecular biology, and discloses a factor analysis system based on long-chain non-coding RNA multi-omics integration analysis. The system comprises: a multi-omics data acquisition module configured to acquire multi-modal omics data related to long-chain non-coding RNA; the data pre-processing module is configured to pre-process the multi-modal omics data to generate a data set in a unified format; the unsupervised factor analysis module is configured to integrate and analyze the data set in the unified format and identify potential factors; the heterogeneity analysis module is configured to construct a mapping relation between the potential factors and multiple omics data features; and the factor annotation and expansion analysis module is configured to perform function annotation on the analyzed potential factors based on biological function enrichment analysis, regulation and control network inference and cross-modal data association, perform missing value estimation on the multi-omics data in combination with the mapping relationship, and output an analysis result containing factor annotation information and complete data.

The present invention relates to: a recombinant RNA molecule having improved expression efficiency of a target protein; and a DNA molecule encoding same. The present invention is based on the discovery of five 5' UTR sequences derived from F2, F9, CFHR3, MBL2, and C3 genes as optimal heterologous 5' UTR sequences that commonly increase protein expression with the greatest efficiency in various tissues in vivo, and these UTRs showed a protein expression increase equivalent to or up to 2 times greater than 5' UTR applied to mRNA vaccines that are currently available commercially. Accordingly, the present invention can be effectively used as an excellent nucleic acid therapeutic composition capable of stably and systemically producing a therapeutically effective amount of a pharmacological protein in the body of a patient.

The invention relates to novel oligonucleotides and their use in inhibiting or reducing senescence. The oligonucleotides target specific sequences in the 3' untranslated region (UTR) of heterogeneous nuclear ribonucleoprotein D (HNRNPD) mRNA and blocks one or more inhibitory microRNA (miRNA) binding sites. This promotes the physiologically regulated expression of HNRNPD and reduces senescence.

Next Generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of approximately 1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when “deep sequencing” genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, a method Duplex Consensus Sequencing (DCS) is provided. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors will result in errors in only one strand. This method uniquely capitalizes on the redundant information stored in double-stranded DNA, thus overcoming technical limitations of prior methods utilizing data from only one of the two strands.

Microscopy imaging that allows for multiple mRNAs, proteins and metabolites to be spatially resolved at a subcellular level provides valuable molecular information which is a crucial factor for understanding tissue heterogeneity as for example within the tumor micro environment. The current invention describes a method (High Density-SUMI-Seq) which combines the use of Spatial Unique Molecular Identifier in situ localization and identification (by in situ sequencing or sequential fluorescence hybridization) of rolonies derived from rolling circle amplification of circular oligonucleotides and in vitro sequencing of target amplified RNA or DNA in combination with SUMI identification at a subcellular level with no optical diffraction limitation in the amount of amplified target information that can be analyzed per cell. Apart from amplified RNA or DNA, the High Density-SUMI-Seq method can also be applied using linear oligonucleotides to spatially resolve proteins and metabolites to provide multiomics results.

The invention belongs to the technical field of biological medicines, and particularly relates to application of Chi3l1 as a target spot in preparation of a medicine for preventing or treating secondary brain injury after cerebral hemorrhage. By integrating space transcriptomics and a mononuclear RNA sequencing technology, the spatial heterogeneity and cell type specificity of gene expression in ICH afterbrain tissue are deeply analyzed, a group of astrocyte subgroups AST1 with neuritis existing around hematoma in the acute stage after cerebral hemorrhage is identified, Chi3l1 is determined as a key inflammatory effect factor of the AST1 subgroups, and the AST1 subgroups with neuritis are used as the key inflammatory effect factor of the AST1 subgroups. And the influence of the gene on the phenotypic transformation of astrocytes and microglial cells is verified. The discovery provides an important theoretical basis for developing a treatment strategy of targeting Chi3l1, and is expected to improve neuroinflammatory response and neurological dysfunction after ICH by regulating and controlling expression of the AST1 subgroup and Chi3l1, and opens up a new direction for treatment of ICH.

The invention discloses a solid tumor taxane / immunotherapy response evaluation system based on integrated machine learning and a working method thereof, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps of: 1, preprocessing acquired solid tumor taxane chemotherapy / immunotherapy response transcriptome data or a source data set, and dividing a training set and a verification set; step 2, extracting overlapped genes as candidate genes from the gene expression level based on the preprocessed data; step 3, carrying out reprocessing based on the candidate genes; 4, constructing a consensus prediction model; 5, training the constructed model by adopting the training set in the step 3; and 6, verifying and screening the trained model to obtain an optimal scheme. According to the method, a stable and generalizable prediction tool for solving the problem of curative effect heterogeneity of complex combined treatment and how to accurately screen potential benefit people and avoid excessive treatment is filled.

The invention relates to the technical field of metagenomics, discloses an automatic analysis method and system for pit mud metagenomic data, and aims at solving the problem that an existing method is poor in efficiency and accuracy, and the scheme mainly comprises the steps that a sequencing data type, a file path and analysis parameters are received; performing quality control on the original offline data; sequence assembly is carried out, and a contigs file is generated; carrying out assembly quality evaluation on the contigs file; carrying out genome binning by using at least two binning tools; integrating output results of the binning tool, and performing optimization based on a preset integrity threshold value and a preset pollution degree threshold value to obtain an optimized binning genome data set; evaluating and optimizing the integrity, the pollution degree and the strain heterogeneity of the binning genome; calculating coverage and relative abundance; performing species classification annotation and function annotation; and integrating the result data of the previous steps to generate an analysis report. According to the method, automatic analysis of metagenome data is realized, and the analysis efficiency and accuracy are improved.

The invention belongs to the cross technical field of biomedical detection and information technology, and particularly relates to a detection kit, system and method for cancer immunotherapy curative effect prediction. The detection kit comprises a combination of a first detection reagent for specifically detecting the LAMC2 gene or protein and a second detection reagent for specifically detecting the SPP1 gene or protein. Research finds that in a complex tumor microenvironment, LAMC < 2 + > basal cells and SPP1 < + > macrophages, which are respectively derived from specific subgroups of tumor cells and immune cells, jointly form a key biological signal capable of remarkably influencing immunotherapy response according to the existence and the quantity of the two specific subgroups of the LAMC < 2 + > basal cells and the SPP1 < + > macrophages. By jointly detecting the pair of biomarkers with a synergistic effect, the problems of insufficient prediction accuracy and incapability of reflecting tumor microenvironment heterogeneity caused by dependence on PD-L1, TMB and other single markers in the prior art are solved, and the technical effect of remarkably improving the sensitivity and reliability of immunotherapy curative effect prediction is achieved.

The invention discloses a mental disease typing method and system integrating DNA methylation and neuroimaging, and the method is a neuroimaging biological annotation method integrating DNA methylation and brain connection group data, and comprises the steps: recognizing brain network features related to a specific molecular phenotype in a whole brain range through a machine learning model; and mechanism-sensitive layering of the mental disorder heterogeneity group is realized. The method does not need to depend on a prior classification or hypothesis mechanism, can be suitable for different types of mental disorder people, provides technical support for exploring potential biological mechanisms and identifying targeted therapy groups, and has high generalizability and clinical application prospects.

Provided herein are, inter alia, are methods and systems for amplifying a chromosomal fragment DNA sequence. The methods and systems provided herein are, inter alia, useful for detecting copy number variation, structural variation, and / or extrachromosomal DNA within the chromosomal DNA of a plurality of cells. In addition, the methods provided herein are, inter alia, useful for jointly analyzing chromatin architecture and gene expression at a single cell level. The methods provided herein can be, inter alia, used to analyze chromosomal heterogeneity, clonal shifts, and / or evolutionary shifts between different cells.

The invention relates to the technical field of medicine, and discloses a transcriptomics-based prognosis prediction model for a super-aglaia standard hepatocellular carcinoma liver transplantation recipient. The prognosis prediction model comprises the steps of standardized sample collection and quality control, standardized RNA-seq library construction and sequencing, data preprocessing and quality control, pathway activity feature extraction, molecular typing model construction, prognosis verification and clinical application. The method can deeply understand the heterogeneity and prognosis of liver cancer liver transplantation recipients, the recipients are divided into different subgroups by analyzing gene expression modes in tumor tissues, an important basis is provided for prognosis evaluation of the recipients, key genes with different expression levels in different subgroups can be identified, a more accurate scheme is provided for individualized treatment, and the method is suitable for popularization and application. The RNA-seq-based molecular typing model provides a comprehensive, accurate and individualized method for prognosis evaluation of standard liver cancer liver transplantation recipients of the super-aglaia, and is helpful for improving clinical outcomes of patients.

Disclosed herein are methods of identifying a guide RNA (gRNA) that induces exon skipping in a target gene. Also disclosed are methods of inducing exon skipping in a target gene methods of controlling heterogeneity of a phenotypic outcome.

The invention discloses a three-level lymphatic structure gene subtype prediction device for image information entropy habitat analysis. According to a gene expression matrix of a three-level lymphatic structure TLS, a glioblastoma GBM sample is divided into two GBM TLS gene subtypes with significantly different overall lifetime; on the basis of a tumor region-of-interest ROI in T2WI image data paired with a glioblastoma GBM sample, the tumor region-of-interest ROI is divided into tumor habitat sub-regions through information entropy related features, and corresponding radiomics features and important radiomics features are screened in a dimensionality reduction manner to establish a corresponding classification prediction model. And the imaging prediction of the two GBM-TLS gene subtypes is realized. According to the method, the blank that the GBM-TLS gene subtype lacks noninvasive imaging biomarker prediction indexes at present is filled up, and the technical limitation that the tumor spatial heterogeneity cannot be objectively reflected by traditional image omics feature extraction based on the whole tumor region of interest is overcome.

The invention relates to two kinds of siRNA (small interfering Ribonucleic Acid) targeting heterogeneous ribonucleoprotein C, wherein the siRNA comprises si-Hnrnpc-1 and si-Hnrnpc-2. Compared with the prior art, the siRNA disclosed by the invention has the advantages that the mRNA accessibility, space occupying factors and gene functional regions are considered, the probability of miss of family and cross-family miss can be reduced, Hnrnpc expression can be effectively reduced, meanwhile, the siRNA has clear targeting and biological effects, and the siRNA has important research value and application prospect for future cancer mechanisms and anti-cancer treatment.

To provide methods for detecting macro-heteroplasmy and / or micro-heteroplasmy in mitochondrial DNA.SOLUTION: The methods can include detecting or monitoring the presence of heteroplasmy, and / or identifying a threshold level of heteroplasmy. In addition, the methods can be used for diagnosing a mitochondrial related disease or disorder, as well as for monitoring the efficacy of a therapy affecting mitochondrial DNA (mtDNA) in a subject having or suspected of having heteroplasmy.SELECTED DRAWING: Figure 1-1

The invention discloses an identification method of a single cell ecDNA cloning trajectory. Potential tumor markers in single cell ATAC data are identified based on ecDNA cloning characteristics. The cancer marker is recognized through the ecDNA, tumor cells can be sensitively recognized, and early diagnosis can be carried out; the copy number, the structure and the distribution of the ecDNA often have great heterogeneity in tumor cells. Different types of ecDNA clones may be carried among different tumor cell populations, so that the evolution trajectory of tumors and the diversity of the cell populations can be disclosed; according to the present invention, the gene structure change of the tumor cell population in different stages can be revealed by researching the clone trajectory of the ecDNA so as to provide the potential marker for the early screening and monitoring of cancers.

The application discloses a Holstein cow breeding probe combination, a gene chip, a kit and application, the probe combination can detect 140290 SNP sites based on the reference genome ARS-UCD1.2. The site detected by the Holstein cow breeding probe combination is obtained from 77887 sites after further screening based on 4600 Holstein cow genome sequencing data, in combination with 62387 SNP sites related to yield, reproduction and body shape and 16 defect-related SNP sites, and can effectively reflect the structural heterogeneity of the genome itself. The site detected by the Holstein cow breeding probe combination has high polymorphism information content, and fully covers high polymorphism sites. Meanwhile, the interpolation accuracy is high, the genotyping consistency is equivalent to that of a high-density chip, the cost is significantly lower, and the economy is better, and the application provides an independent technical tool for efficient and accurate genome breeding of Holstein cows.

To provide: compositions of cells with mitochondrial DNA reduction and / or mitochondrial DNA replacement; methods for producing the same; and methods for treating various diseases associated with genetic or age-related mitochondrial dysfunction.SOLUTION: A method for producing mitochondria replaced cells includes: (a) contacting recipient cells with an agent that reduces endogenous mtDNA copy number; (b) incubating the recipient cells for a period of time sufficient for the agent to partially reduce the endogenous mtDNA copy number in the recipient cells; and (c) co-incubating (1) the recipient cells from the step (b) in which the endogenous mtDNA has been partially reduced with (2) exogenous mitochondria from a healthy donor for a period of time sufficient to non-invasively transfer the exogenous mitochondria into the recipient cells, thereby producing the mitochondria replaced cells.SELECTED DRAWING: Figure 1A

The invention discloses a method for detecting LncRNA-RBP spatial heterogeneity in living cells in real time and application of the method. The functionalized nanoneedle comprises a nanoneedle array chip and a hairpin DNA (Deoxyribose Nucleic Acid) probe. The nanoneedle disclosed by the invention can be used for detecting spatial heterogeneity of long-chain non-coding RNA and RBP in living cells in real time so as to carry out comprehensive quantitative analysis on an lncRNA-RBP compound, and can be used for tracking multiple spectrums of lncRNA and RBP in the living cells and capturing dynamic interaction heterogeneity of lncRNA and RBP in different biological processes.

The application discloses a lung cancer gene mutation prediction method based on unsupervised clustering double-stage attention multi-instance learning, and relates to the technical field of pathological image analysis and gene detection. H&E staining pathological whole section images of non-small cell lung cancer patients and corresponding gene mutation data are collected to construct a data set; the images are preprocessed by using the OTSU method, segmented into blocks and high-dimensional feature vectors are extracted; the block features are grouped into cluster feature sets through unsupervised clustering; a double-stage attention mechanism composed of intra-cluster and inter-cluster is used to hierarchically aggregate and generate global features; finally, a classification model is used to output mutation positive / negative prediction results. The application groups the features through unsupervised clustering and structures the features, combines double-stage attention to strengthen key information, does not need complex manual annotation, adapts to various driver gene mutation prediction requirements, effectively deals with tumor heterogeneity and feature sparsity, improves prediction accuracy and generalization ability, and provides low-cost and efficient targeted therapy preliminary screening technical support for clinics.

The invention discloses an anti-tumor compound screening method and system based on AURKA mutation synergy, and belongs to the technical field of anti-tumor compound screening. According to the method, key action targets related to AURKA mutation are extracted, multi-dimensional historical interaction data are integrated, a unified action feature space is constructed, and dimension differences of different targets and different data types are eliminated; the method comprises the following steps: dividing mutant and wild targets based on a unified feature space, quantifying an action binding difference value and a binding action time efficiency difference value between a compound and the two types of targets, and establishing a collaborative screening and scoring system to realize multi-dimensional quantification of the compound targeting activity; aURKA mutation heterogeneity and pathway fluctuation in a tumor microenvironment are simulated, scoring stability of the compound under complex conditions is verified through dynamic disturbance, and the adaptability of the compound is judged. According to the method, accurate compound screening aiming at AURKA mutation is realized, the screening efficiency and accuracy are improved, and the problems that mutation synergistic effect is ignored and adaptability evaluation is lacked in existing screening are solved.

The invention relates to a cancer subtype unsupervised clustering method based on multi-view contrast fusion and an attention mechanism, and belongs to the field of medical data processing and multi-view clustering. Aiming at the problem of inaccurate recognition caused by data heterogeneity, deletion, noise and subtype fuzziness in the existing method, multi-view depth features are extracted through multi-source data preprocessing (low expression gene deletion and KNN deletion filling) and an attention mechanism feature extraction network, and cross-view consistency information is learned by using a Transform comparison fusion network. And subtype division is realized in combination with contrast loss optimization and clustering analysis. According to the method, the accuracy rate of 90% or above is still kept under the data missing rate of 40%, the recognition rate is increased by 25%-35% compared with that of a single-view method, subtype driver genes can be located, and an efficient and explainable tool is provided for accurate cancer diagnosis and treatment.

Techniques for identifying a tumor heterogeneity of a subject are described. In an example method, sequence read data of a sample obtained from the subject is identified. The sequence read data is indicative of endpoint positions of nucleic acid molecules in the sample. The example method further comprises determining endpoint positions of the nucleic acid molecules, generating input features based on the endpoint positions of the nucleic acid molecules, and classifying, using a classifier, the tumor heterogeneity of the subject based on the input features.