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

The invention relates to the field of cell classification, in particular to a cell classification and function evaluation method and system based on nucleolus morphology. The method provided by the invention comprises the following steps: S101, acquiring microscopic image data of a biological sample; s102, acquiring a first region of interest, a second region of interest and a third region of interest; s103, extracting kernel feature data; s104, carrying out first classification on cells in the biological sample; s105, carrying out second classification on the C-type cells in the biological sample; and S106, calculating the number and / or proportion of the A-type cells and the B-type cells in the biological sample so as to evaluate the functional state of the tissues or organs corresponding to the biological sample. The method can accurately and sensitively reflect and evaluate the functional state of the tissue or organ corresponding to the biological sample, and is especially suitable for the biological sample which is lack of a specific marker and has strong heterogeneity.

The invention discloses a somatic cell structure variation detection method based on a monomolecular long-read length sequence chart hybrid model, which comprises the following steps of: extracting a read length local sequence of a target structure variation interval and a flank region thereof, constructing a local chart genome by utilizing a partial sequence alignment chart algorithm, and encoding a multi-sequence alignment result. And then, a multi-category hybrid model is combined with an expectation maximization algorithm, and clustering analysis is performed on the comparison data, so that the somatic cell structure variation category only from the tumor sample is identified. And finally, generating a consensus sequence of a somatic cell structure variation region in the tumor sample, and taking the consensus sequence as an optimization result of a local genome. Compared with an existing short-read-length sequencing method, the method has the advantages that the problems of comparison errors, low-complexity sequences and genome heterogeneity can be effectively solved, and the detection precision of somatic cell structure variation is remarkably improved. Through the application of a single-molecule long-read-length sequencing technology, more comprehensive and accurate genome information can be provided.

The invention belongs to the field of oncology and computational biology, and discloses a method for reconstructing tumor clone heterogeneity and evolutionary relationship based on tumor DNA sequencing data. Comprising the steps of mutation multiplicity and cancer cell fraction calculation, subcloning clustering and recognition, clustering post-processing, tumor cloning evolutionary tree inference, subcloning proportion calculation and tumor sample evolutionary tree inference. According to the method, the mutation multiplicity of SNV is calculated through the Euclidean distance to accurately represent the CCF, and the subcloning clustering result is post-processed to evaluate the credibility and rationality of the subcloning clustering result. Secondly, quickly deducing a tumor cloning evolutionary tree by adopting a greedy strategy, and calculating a subcloning proportion according to an evolutionary relationship;

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 invention relates to a construction method of a macrophage specific fluorescence report mouse model, and belongs to the technical field of genetic engineering and genetic modification. According to the invention, a brand-new C1qc-CreChy gene knock-in mouse model is obtained after a C1qc gene locus is edited through a CRISPR / Cas9 genome editing technology for the first time, the expression of Cre and mCherry is directly driven by a C1qc promoter, and flow cytometry verifies that mCherry has very high expression quantity only in microglial cells in the brain of a mouse and macrophages in the spleen of the mouse. And mCherry expression is not detected in other immune cells, so that the two macrophages are traced and used for researching the functions of the macrophages. The invention provides a usable animal model for researching heterogeneity of macrophages, and provides a new animal model for researching complex immune functions in a body.

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 provides a tumor infiltration boundary analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: analyzing space transcriptome data, and determining a tumor region and a non-tumor region; boundary division is carried out on the tumor area and the non-tumor area, and a boundary line between the tumor area and the non-tumor area is determined; dividing the areas on the two sides of the boundary line to obtain a preset number of grid areas; numbering the preset number of grid regions to obtain an infiltration boundary region; and according to the spatial transcriptome data and the infiltration boundary region, analyzing heterogeneity statistical information of the boundary region and enriched gene information of the boundary region. The space transcriptome data is processed, and the boundary line of the tumor area and the non-tumor area is determined; after the areas on the two sides of the boundary line are divided, the divided infiltration boundary areas are analyzed, and quantitative analysis on the tumor infiltration boundary areas can be achieved through space transcriptome data. And the invasion and infiltration process of the tumor can be analyzed.

The invention provides a method for evaluating cell population heterogeneity based on an energy index, and belongs to the technical field of biological recognition, and the method comprises the following steps: S1, preprocessing single cell transcriptome data; s2, carrying out feature selection on the preprocessed data, and defining a cell state; s3, constructing a gene local network based on a Hupfield neural network; s4, calculating gene local network energy and cell energy, and carrying out normalization processing on the cell energy; s5, introducing a Gaussian mixture model, identifying the potential state of the cells by analyzing the energy distribution difference of the cells, and clustering the cells in the same differentiation potential state; and step S6, evaluating the heterogeneity of the cell population. By adopting the method for evaluating the heterogeneity of the cell population based on the energy index, the complexity and the difference in the cell population can be disclosed more accurately.

The invention discloses a method and a device for constructing a cancer early screening model based on cfDNA difference statistics. The construction method of the cancer early screening model comprises the following steps: performing difference statistics on tumor-derived cfDNA and healthy individual-derived cfDNA in a low-depth whole-genome sequencing mode, and establishing the cancer early screening model to realize non-invasive early screening of cancers; the difference statistics comprises cfDNA fragment size characteristic difference statistics, cfDNA upstream and downstream continuous three groups of 3bp base combination category distribution frequency change trend difference statistics at a fracture position and sequencing data coverage depth mode difference statistics of the cfDNA near TSS. The method has the advantages of being minimally invasive, high in early diagnosis sensitivity, capable of achieving dynamic monitoring and the like, the limitation of cfDNA gene mutation detection in the early stage of the tumor is overcome, and information lag or deletion caused by tumor heterogeneity is avoided.

The invention discloses a DNA tracer migration prediction and site flow field description method, and belongs to the field of environmental rock and soil. According to the method, Laplace transform, Fourier transform and numerical inversion are used for obtaining the expression of the concentration of the DNA tracer in a fast region and a slow region. On the basis, necessary model parameters such as a tracer molecular diffusion coefficient, aquifer thickness, dispersity and tracer injection attenuation rate are determined according to hydrogeological conditions disclosed by a stratum and a tracer concentration monitoring result of an injection well in a field tracer experiment. And an analytical solution provided by genetic algorithm coupling is adopted, and the fitting degree of a tracer output curve of a monitoring well and a prediction result is taken as a control quantity, so that the average linear flow velocity and the porosity of the fast area and the slow area of the field soil are determined, and the fine description of the flow field in the field heterogeneous soil is realized. The method combines an analytical model, a genetic algorithm and a field tracer experiment, the obtained prediction result is accurate and objective, calculation is convenient and fast without depending on software, and the method is suitable for engineering application.

The invention discloses a phenotype-based gene mutation analysis method, system and device and a computer readable storage medium, and the method comprises the following steps: data acquisition: acquiring phenotype and gene mutation data of a subject; phenotype description: describing the phenotype of the subject as a standard entry; gene assignment: assigning each gene of gene mutation of the subject according to genetic heterogeneity, genetic pattern and phenotype comparison to obtain a score of each gene; and sorting: sorting the gene mutation of the subject based on the score. According to the application, the phenotype correlation between the phenotype of the subject and the gene where the mutation is located is analyzed, the gene mutation is assigned according to the correlation, and then the mutation is sorted according to the score, so that medical interpretation personnel can preferentially pay attention to the pathogenic gene mutation possibly related to the phenotype of the subject; and the working efficiency of medical interpretation personnel can be improved.

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.

Disclosed herein is a custom platform called NSC-seq that can be used for cell lineage and cell division tracking in a temporal fashion but also with single-cell resolution. With simultaneous integration of single-cell transcriptomics with lineage barcoding and temporal recording information, NSC-seq facilitates the generation of multidimensional datasets for elucidating functional heterogeneity and clonal events in vivo. We apply this platform (i) to decipher lineage branching of mouse embryonic development, (ii) to record clonal dynamics of the adult intestinal epithelium, and (iii) to track clonal composition of the mouse intestinal tumors. In addition, we apply NSC-seq to assess a comprehensive gene expression phenotype for individual gene knockout at the single-cell level using existing whole-genome CRISPR knockout screening plasmid libraries. Overall, NSC-seq enables in vivo temporal recording of mammalian development and cancer at single-cell resolution.

The present disclosure provides methods for detecting macro-heteroplasmy and / or micro-heteroplasmy in mitochondrial DNA. 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.

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.

There is described a compound for use in the treatment of a mitochondrial DNA disorder, wherein the compound is a glycolysis inhibitor, an inhibitor of glutamine consumption or is L-asparaginase or pegaspargase. The glycolysis inhibitor may be a glucose analogue such as 2-deoxy-D-glucose (2DG) or 5-thioglucose (5TG). Also described is a method of treating a mitochondrial DNA disorder comprising administering a therapeutically effective amount of the compound above to a patient suffering from a mitochondrial DNA disorder. In addition, there is described a method of reducing the mtDNA heteroplasmy in the cells of a patient comprising administering a therapeutically effective amount of the compound above to the patient.

The invention discloses a method for determining RNA in-vitro transcription and purification and RNA folding in a three-dimensional structure of RNA, which comprises the following steps: S1, RNA in-vitro transcription and purification: carrying out enzyme digestion preparation on a linearized DNA template after polymerase chain reaction or plasmid amplification, and introducing extra ribozyme sequence and primer modification to ensure the sequence consistency of 5'and 3 'terminals; t7RNA polymerase or a mutant of the T7RNA polymerase is used for in-vitro transcription under optimized conditions, so that the maximum yield and uniformity are obtained; the uniformity of RNA is rapidly evaluated through denatured gel electrophoresis, and the folding state is evaluated through in-vitro non-denatured gel electrophoresis; co-transcribed and folded RNA is directly concentrated through buffer exchange or purified through size exclusion chromatography so as to separate different conformations; if the IVT product is heterogeneity, conventional denatured gel purification and RNA refolding under appropriate conditions are required. S2, screening RNA folding conditions: screening the RNA folding conditions through non-denaturing gel electrophoresis or performing high-throughput screening on nanoDLS of in-vitro transcription and RNA folding conditions. The present invention assists in implementing cryo-EM structure assays, assisted by a cryo-EM data processing flow, to parse RNA dynamics and conformational variations, and an RNA modeling algorithm to generate atomic coordinates based on a medium to high resolution cryo-EM density map.

The invention relates to the technical field of single-cell multi-omics sequencing, and discloses a method for analyzing CIN epigenetic characteristics of tumor cells, which comprises the following steps: preparing a single-cell RNA library and a DNA library, and sequencing; the method comprises the following steps: respectively preprocessing DNA and RNA sequencing data, comparing to obtain a human genome reference sequence, identifying a differential methylation region, carrying out cell karyotype analysis, dividing tumor cells into different subgroups, and quantifying a gene expression level; and analyzing the correlation among DNA methylation difference, gene expression difference and karyotype heterogeneity among different subgroups. According to the method, DNA methylation, gene expression and karyotype of a single cell can be measured at the same time, the correlation among DNA methylation, gene expression and karyotype heterogeneity of tumor cells is analyzed on the basis, and the mechanism of tumor generation and evolution can be understood more deeply.