126 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 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 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 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 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.

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.

The invention belongs to the field of new auxiliary materials and new dosage forms of pharmaceutical preparations, and relates to a preparation method and application of a co-assembled nano preparation for targeting glioblastoma. The preparation method comprises the following steps: firstly, synthesizing a nitroimidazole dimer with tumor hypoxia-oxidation-reduction triple response, and assembling the nitroimidazole dimer with AOH1996 and HRO761 to prepare the nano preparation. The molar ratio of the AOH1996 to the HRO761 to the nitroimidazole dimer is 1 to 2 to (0.5 to 2). The key scaffold protein PCNA for DNA repair is blocked by the PCNA inhibitor AOH1996, and recruitment and anchoring functions of the key scaffold protein PCNA on repair factors are destroyed, so that the glioblastoma is induced to enter a mismatch repair defect state. On the basis, a WRN inhibitor HRO761 is further used for promoting large-scale breakage of chromosomes and finally inducing cell death. The nitroimidazole dimer and the nitroimidazole dimer are jointly assembled into the nano preparation, and accurate drug release is realized in response to a hypoxia state and oxidation and reduction signals overexpressed in a tumor microenvironment, so that the heterogeneity of the tumor microenvironment is overcome.

The invention provides a probe group and a kit for detecting drug-resistant gene mutation of bacteria and application of the probe group and the kit. According to the present invention, the drug-resistant gene mutation detection of various bacteria can be simultaneously performed, the mutation frequency can be accurately quantified, the drug-resistant gene subtype can be accurately identified, the drug-resistant heterogeneity can be accurately diagnosed, the sensitivity and the specificity are high, the detection time is short, and the cost is substantially reduced.

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 relates to a lung cancer EGFR (Epidermal Growth Factor Receptor) gene mutation prediction method based on multi-instance learning and Transform technology, which comprises the following steps of: segmenting a lung CT (Computed Tomography) image into image blocks for instance-level feature extraction, providing an extra supervision signal for instance-level features by utilizing a soft pseudo-marking technology in a self-generating manner, guiding a multi-instance learning model to more effectively distinguish instances, and predicting the mutation of the lung cancer EGFR gene mutation. The identification capability of instance-level features is enhanced; unique relative spatial position information is integrated into a self-attention mechanism, so that the dependency relationship between instances can be flexibly and clearly mined, and the expression capability of heterogeneity tumors can be greatly improved; the importance scores of different instance features are automatically learned through the self-adaptive gating instance aggregation module, dynamic weighting of feature aggregation instances is facilitated, and the generalization of the model on different data sets is improved through the method. According to the prediction method provided by the invention, heterogeneity characteristics in the tumor can be caught and analyzed finely, and it is ensured that prediction of the EGFR mutation state is more accurate and high in pertinence.

The invention discloses a method for analyzing and identifying an inflammatory bowel disease related oral cancer biomarker, and belongs to the technical field of bioengineering.The method comprises the following steps that 1, IBD whole genome correlation research summary data is obtained; 2, analyzing a causal relationship between IBD and the oral cancer by using a Mendel randomization method, and determining IBD related genes; 3, collecting an oral cancer tissue sample, and carrying out single-cell RNA sequencing; according to the invention, single-cell RNA sequencing is carried out on an oral cancer specimen, and a cell cluster and a gene expression mode are described. Cell clusters and types are displayed by using t-SNE and UMAP, key modes of gene expression are defined by using a lattice diagram, and pathways related to NFKBIA expression are analyzed through GSEA. The experimental result explains the cell heterogeneity and gene expression kinetics in the oral cancer disease progression process, and reveals possible therapeutic targets related to IBD.

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