59 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 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.

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 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.

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 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.

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.

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.

The invention discloses a single cell sequencing data analysis method based on Bayesian modeling, which is suitable for low expression gene identification and biological heterogeneity detection. The method comprises the following steps: S1, data acquisition and preprocessing; s2, establishing a Bayesian hierarchical model, and modeling technical noise and biological heterogeneity at the same time; s3, introducing a Poisson model and Poisson-negative binomial model combined strategy, and fitting and expressing a mean value and variance relationship by using a Gaussian radial basis function so as to accurately describe gene variation; s4, carrying out posterior inference by utilizing Markov chain Monte Carlo sampling to identify HVG and LVG genes; and S5, under the condition of lack of the spike-in gene, introducing a batch effect and a cross-batch sharing prior, and constructing a non-spike-in expansion model. Compared with a traditional method, the method has the advantages that the accuracy and robustness of expression variation recognition are improved, and the method is suitable for single cell data analysis of complex biological systems such as tumors and immune microenvironments.

To provide a method for producing a recombinant antibody having a unique sugar chain spectrum from a genome-edited CHO host cell.SOLUTION: The method of the present invention is a method for editing fut8 genes in CHO cells suitable for suspension culturing in a serum free medium by TALEN technology, so that the edited CHO host cells can produce recombinant antibodies with unique glycan profiles. The unique glycan profile is characterized by having mainly non-fucosylated N-linked oligosaccharide chains, a low degree of heterogeneity of N-glycosylation, and uniform glycan chains. The antibodies prepared by the methods of the invention have increased ADCC activity and antibody stability.SELECTED DRAWING: Figure 10

The invention provides a method for identifying limb regeneration heterogeneity between salamander and xenopus laevis, and relates to the technical field of biology. Comprising the following steps: respectively extracting samples of salamander and xenopus at different stages, and carrying out RNA extraction and high-throughput sequencing on the samples to obtain original data; evaluating the quality of the original data, performing quality control on the original data, and removing low-quality basic groups or linker pollution sequences; constructing a salamander reference genome index, comparing the quality-controlled data with a reference genome, and outputting comparison data; outputting an original count matrix from the comparison data, and calculating a TPM (Trusted Platform Module) value by using the original count matrix in an R language environment; using sequence alignment to extract common genes of the salamander and the xenopus; and performing principal component analysis and correlation analysis on the acquired data. Through transcriptome analysis and sequencing, the molecular mechanism of the limb regeneration capacity heterogeneity of the salamander and the xenopus laevis is analyzed, and guidance is provided for research of other regeneration capacities.

The application belongs to the technical field of biological pharmacy, and provides a set of esophageal cancer methylation early screening markers and application thereof, the markers being any one or a combination of two or more of ZNF693, MMP14, JAG1, RUNX1 or NOTCH1 gene sequences or gene fragments containing at least one CpG methylation site; the application also discloses application of the set of biomarkers in preparation of a detection kit for diagnosing and / or evaluating the methylation degree of esophageal cancer or in screening of drugs for treating and / or relieving esophageal cancer. The application can effectively identify cancer patients by quantifying the dynamic change of the methylation level, and reduce the risk of missed detection caused by tumor heterogeneity; by establishing a high-throughput and standardized detection process, the application can provide a precise molecular typing tool for early intervention of esophageal cancer; the application overcomes the limitations of traditional technology, such as insufficient detection sensitivity for early cancer and dependence on invasive operation, and can promote clinical transformation of "early screening and early diagnosis".

RNA molecules comprising a guide sequence portion having 17-20 nucleotides in the sequence of 17-20 contiguous nucleotides set forth in any one of SEQ ID NOs: 1-1990 and compositions, methods, and uses thereof.

A method for predicting responsiveness to a HER2-directed therapy by assessing HER2 heterogeneity in a tumor includes contacting a sample of the tumor with a biomarker-specific reagent that specifically binds to HER2 protein and detecting HER2 protein in the sample, contacting the sample of the tumor with a first nucleic acid probe that specifically binds HER2 genomic DNA and detecting HER2 gene amplification status in the sample, contacting the sample of the tumor with a second nucleic acid probe that specifically binds HER2 RNA and detecting HER2 RNA status in the sample scoring the HER2 protein (IHC), HER2 gene (DISH), and HER2 RNA (RNA-ISH), predicting that the tumor is responsive to the HER2-directed therapy if the tumor reveals a first foci having a first score and a second score, in which the first score and the second score are not the same.

The present disclosure provides anti-CD38 IgG class antibody having improved ability to be manufactured at higher yields compared to the parent antibody having the original wild type sequence. The present disclosure provides a mutated antibody light chain that reduces cleavage heterogeneity for improved production of a homogeneous population of antibody light chains.

Compositions and methods discussed herein provide for treatment or prevention of a disease or disorder, or its symptoms, using a modified herpes simplex virus (mHSV) vector comprising at least two transgenes, wherein at least a first transgene encoding a gene-editing system, gene-deletion system, or bridge-editing system is expressed to knock out or delete an endogenous target gene and at least a second transgene encoding a corrected copy of the endogenous targeted gene, wherein the gene product of the second transgene replaces the gene product of the endogenous target gene.

The invention discloses a CpG methylation single cell whole genome analysis method and related equipment based on sequencing reads, and the method comprises the following steps: obtaining to-be-analyzed single cell data, and carrying out methylation data sequencing read comparison on the to-be-analyzed single cell to obtain a methylation site statistical result; screening a methylation specific interval based on a random sampling strategy and the methylation Hamming distance of the sequencing read; calculating a Hamming distance matrix of every two to-be-analyzed single cell CpG sites according to a methylation site statistical result and a methylation specific interval, and performing initial clustering based on the Hamming distance matrix; and obtaining cell DNA methylation heterogeneity measurement according to an initial clustering result, extracting a cell type or state specific DNA methylation mode panorama, calculating the similarity between a read segment and each cell type according to a specific methylation mode, and calculating the proportion of cells from a mixed sample sequencing result. According to the embodiment of the invention, the cell methylation analysis efficiency and sensitivity can be improved. The method can be widely applied to the technical field of gene analysis.

The invention relates to the technical field of application of Congo red derivative photocatalytic probes, in particular to a Congo red derivative photocatalytic probe as well as a preparation method and a use method thereof.The Congo red derivative photocatalytic probe comprises a compound I, the compound I is selected from at least one of the Congo red derivative photocatalytic probes, and the Congo red derivative photocatalytic probe comprises two arenes, arene represents a heteroaromatic ring, and the heteroaromatic ring is selected from one of a benzene ring, furan, thiophene and a naphthalene ring. The Congo red derivative photocatalytic probe provided by the invention can specifically mark amyloid plaques in AD brain tissue slices, the binding affinity is remarkably improved (Kd of Abeta1-40 is equal to 0.07 mu m), the molecular heterogeneity of amyloid deposition and the key regulation and control effect of mitochondrial autophagy-lysosomal axis are disclosed, a new tool is provided for AD pathological mechanism research, and the Congo red derivative photocatalytic probe has a wide application prospect. And cross-brain region (hippocampus / cortex) proteome analysis and pathological mechanism analysis are supported, and the molecular structure and application in neurodegenerative disease research are simulated to be protected.

The application discloses a sequencing read-based CpG methylation single-cell whole genome analysis method and related equipment, and the method comprises the following steps: obtaining single-cell data to be analyzed, performing methylation data sequencing read alignment on the single cells to be analyzed, and obtaining methylation site statistical results; screening a methylation-specific interval based on a random sampling strategy and a methylation Hamming distance of the sequencing read; calculating a Hamming distance matrix of CpG sites of each pair of single cells to be analyzed according to the methylation site statistical results and the methylation-specific interval, and performing initial clustering based on the Hamming distance matrix; obtaining a cell DNA methylation heterogeneity metric according to the initial clustering result, extracting a cell type or state-specific DNA methylation pattern panorama, calculating the similarity of a read and each cell type according to the specific methylation pattern, and calculating the proportion of cells from a mixed sample sequencing result. The embodiment of the application can improve the efficiency and sensitivity of cell methylation analysis. The application can be widely applied in the technical field of gene analysis.

The invention relates to the technical field of biology, in particular to a method for determining multiple functional indexes and mutations of single-cell mitochondria. At the single cell level, the scheme can realize simultaneous detection of a plurality of mitochondrial indexes; in the cell level, the scheme can comprehensively evaluate the mitochondrial function and reduce the requirement for the sample size, and the problems that the number of rare sample cells is low and data acquisition is difficult are effectively solved. The mitochondrial DNA sequence of the cell is subjected to phenotype-genotype correlation analysis. According to the scheme, the mutation site screening accuracy can be improved while high mitochondrial heterogeneity is achieved.

The invention discloses a single-cell RNA sequencing data recovery and feature extraction method and system based on collaborative modeling, and relates to the technical field of bioinformatics and artificial intelligence. The method comprises a data acquisition step, a data preprocessing step, a recovery step, an optimization step, a first extraction step, a screening step and a second extraction step. According to the method, the data integrity and the downstream feature extraction accuracy are effectively improved, and the method is suitable for bioinformatics application scenes such as cancer heterogeneity analysis and candidate gene screening.