394 results about "Bioinformatics analysis" patented technology

The invention discloses a crested ibis habitat ecological quality trend analysis method and system, and relates to ecological quality monitoring. The method comprises the following steps: S1, constructing an intelligent sensing network, synchronously obtaining multi-source data of a habitat, identifying activity events of crested ibis, and generating a multi-dimensional habitat parameter table; s2, collecting environmental samples, and generating a microbial functional gene abundance matrix through metagenome sequencing and bioinformatics analysis; s3, taking the activity events of the crested ibis as behavior tags, and generating habitat function health indexes by coupling the parameter table and the matrix training machine learning prediction model; s4, performing spatial interpolation and trend analysis based on the habitat function health index to generate an ecological quality space-time evolution graph; and S5, based on the ecological quality space-time evolution graph, performing quantitative analysis by using a spatial differentiation statistical model, and generating a trend analysis report. By fusing multi-source data, real-time dynamic evaluation of habitat ecological quality and quantitative analysis of driving factors are realized, and a direct decision basis is provided for accurate protection.

The invention relates to the field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 8bp oligonucleotide double-fragment characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through bioinformatics analysis; an integrated support vector machine, K-nearest neighbor, extreme gradient lifting, a random forest and a multi-layer perceptron are combined with a logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized, machine learning analysis is carried out, and the preeclampsia risk is predicted. According to the invention, two 8bp oligonucleotide characteristic fragments are specifically screened, and a deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional screening method are effectively broken through.

The invention relates to the technical field of nucleic acid detection, in particular to a product and a method for detecting bifidobacterium longum subsp. Longum 6-1. Bioinformatics analysis is carried out based on a whole genome sequence of the bifidobacterium longum subspecies longum 6-1, the primer pair and the primer probe combination are obtained through optimization, and the primer pair and the primer probe combination have good specificity and sensitivity in the aspect of detecting the bifidobacterium longum subspecies longum 6-1; the bifidobacterium longum subsp. Longum 6-1 and other strains (including other same subsp. Longum strains, other same strains and other strains) can be quickly and accurately distinguished, and the strain level quantitative detection of the strain in a complex biological sample (such as excrement) can be realized.

The invention provides a lung adenocarcinoma prognostic marker and a model, constructs a lung adenocarcinoma prognostic model based on mRNA and lncRNA related to programmed cell death (PCD), and synthesizes bioinformatics analysis and experiments to explore the biological effect of the lncRNA model in lung adenocarcinoma. The prognosis model covers all PCD pathways and various molecules, has stable prognosis prediction accuracy, and can provide a potential molecular target for personalized treatment. According to the method disclosed by the invention, the PCD-related mRNA and lncRNA are integrated to construct a lung adenocarcinoma diagnosis model suitable for Chinese population, so that the identification capability of tumor patient classification is enhanced from multiple aspects, the accuracy and reliability of prognosis prediction are improved, powerful support can be provided for promoting personalized treatment strategies of lung adenocarcinoma patients, and the method is suitable for popularization and application.

The invention discloses a novel coronavirus neutralizing antibody, a detection kit and application of the novel coronavirus neutralizing antibody. The amino acid sequence of a heavy chain variable region of the neutralizing antibody is shown as SEQ ID No.1, and the amino acid sequence of a light chain variable region of the neutralizing antibody is shown as SEQ ID No.2. The antibody with mature affinity is screened through bioinformatics analysis of a single B cell, the antibody screening process is optimized by combining single cell RNA sequencing, VDJ rearrangement analysis and somatic cell hypermutation research, blindness of a traditional method is avoided, and the accuracy and effectiveness of antibody screening are improved. According to the neutralizing antibody GR75 provided by the invention, a heavy chain variable region and a light chain variable region of the neutralizing antibody GR75 can be specifically combined with an RBD structural domain of the SARS-CoV-2 and an S-Trimer structural domain of an Omicro variant, so that a broad-spectrum neutralizing effect on the SARS-CoV-2 virus and the variant thereof is realized. The binding activity of the antibody GR75 to S-Trimer and RBD under 2-fold and 300-fold dilution conditions is obviously superior to that of other antibodies, which indicates that the antibody GR75 has high affinity and dilution stability and is suitable for clinical large-dose administration.

The invention discloses a novel coronavirus neutralizing antibody GR46, a detection kit and application of the novel coronavirus neutralizing antibody GR46. The amino acid sequence of a heavy chain variable region of the neutralizing antibody is shown as SEQ ID No.1, and the amino acid sequence of a light chain variable region of the neutralizing antibody is shown as SEQ ID No.2. The antibody with mature affinity is screened through bioinformatics analysis of a single B cell, the antibody screening process is optimized by combining single cell RNA sequencing, VDJ rearrangement analysis and somatic cell hypermutation research, blindness of a traditional method is avoided, and the accuracy and effectiveness of antibody screening are improved. According to the neutralizing antibody GR46 provided by the invention, a heavy chain variable region and a light chain variable region of the neutralizing antibody GR46 can be specifically combined with an RBD structural domain of the SARS-CoV-2 and an S-Trimer structural domain of an Omicro variant, so that a broad-spectrum neutralizing effect on the SARS-CoV-2 virus and the variant thereof is realized. The binding activity of the antibody GR46 to S-Trimer and RBD under 2-time and 300-time dilution conditions is obviously superior to that of other antibodies, which indicates that the antibody GR46 has high affinity and dilution stability and is suitable for clinical large-dose administration.

The invention provides a CsERECTA gene-based tea tree leaf shape regulation and control method and a tea tree light utilization rate improving method, which are characterized in that a sequence of a tea tree gene is cloned to obtain a complete sequence; then, the CsERECTA is subjected to bioinformatics analysis, expression of a GUS reporter gene is started by constructing a plant expression vector and connecting a target fragment, and a recombinant plant vector is used for genetic transformation of tobacco, so that the function of the CsERECTA is verified; the character of the CsERECTA transgenic line is analyzed, the leaf shape is determined, the method capable of improving the leaf shape of the tea tree is obtained, and a foundation is laid for cultivating a new variety and improving the light utilization rate of the tea tree by using the CsERECTA gene.

The invention relates to a hepatocellular carcinoma gene knockout target library based on multiple omics and a screening method of the hepatocellular carcinoma gene knockout target library, and the gene knockout target library for precise treatment of hepatocellular carcinoma is finally obtained through data collection and integration, data screening and target verification in sequence. According to the invention, through multi-omics data integration and bioinformatics analysis, key driving genes of hepatocellular carcinoma are systematically screened, and the important effects of the genes in occurrence, development, metastasis, drug resistance and immune escape of hepatocellular carcinoma are disclosed; the genes not only deepen the understanding of the hepatocellular carcinoma molecular mechanism, but also provide important theoretical basis and potential intervention targets for the development of targeted therapy and personalized therapy strategies.

The invention belongs to the technical field of biological medicine and molecular biology, and particularly relates to application of circ0005704 and related biological elements thereof in diagnosis and treatment of recurrent spontaneous abortion with unknown reasons. Researches find that differential expression and autophagy level of ULK1 in trophoblast cells of URSA patients are increased. High-throughput transcriptome sequencing is combined with bioinformatics analysis, and it is found that miR-26a-5p is down-regulated in URSA patients, is responsible for up-regulation of ULK1 and promotes autophagy of trophoblast cells, so that occurrence of URSA is increased. Through a high-throughput transcriptional set screening strategy and dual luciferase reporter gene analysis, it is found that circ0005704 enhances the expression of ULK1 through miR-26a-5p. In a word, the research of the invention shows that the circ0005704 / miR-26a-5p / ULK1 signal axis participates in the pathogenesis of URSA by adjusting the migration of the trophoblast cells, and a new target and scientific evidence are provided for the clinical treatment of URSA.

The invention discloses a method for screening a Wilson disease serum exosome miRNA biomarker based on a transcriptomics technology, and belongs to the field of bioinformatics analysis of diseases. According to the invention, the effect of miRNA in WD pathogenesis is discussed by identifying serum exosome miRNA, and a potential biomarker is provided for accurate diagnosis and treatment of the disease. According to the invention, 59 DE-miRNAs are identified by analyzing the expression of differential miRNAs of WD and control group patients, and the DE-miRNAs are related to key approaches such as metabolic regulation, cancer progression, signal transduction and the like. Besides, the reliability of the key DE-miRNA, including miR-451a, miR-204-5p and the like, is confirmed through experimental verification, and the potential of the key DE-miRNA as a non-invasive biomarker is indicated. Research results provide important theoretical basis and new insight for accurate diagnosis of WD and development of potential therapeutic targets.

The present invention provides a quality control analysis method for third-generation sequencing, which includes performing raw sequencing on the extracted gene sequence to obtain a POD5 text of the initial data. The POD5 text is then converted into a FASTQ file, and adapter data is removed. Quality assessment is then performed to obtain read data. The read data is then aligned and analyzed with the target base sequence of theoretical length, impurities are filtered, and finally, visual detection results are obtained. The present invention also provides a gene extraction method, which forms a highly optimized and reliable bioinformatics analysis process from raw data acquisition, preprocessing, quality assessment, alignment analysis, to final report generation.

The invention belongs to the technical field of tumor molecular diagnosis and biological information analysis, and relates to a high-throughput sequencing method and system for monitoring acute lymphocytic leukemia (MRD). Through targeted sequencing with a unique molecular identifier and / or a double-chain tag, error modeling based on a background noise spectrum and statistics / machine learning pseudo variation filtering, ultra-deep accurate detection of IG / TCR cloning and related gene low-frequency variation is realized. And an artificial intelligence recurrence risk prediction model is established by combining a time sequence MRD index, cloning diversity and clinical information, and a structured clinical report is output and docked with LIS / HIS. According to the method, the sensitivity and the specificity of ALL minimal residual disease detection can be remarkably improved, dynamic evaluation on leukemia cloning evolution and recurrence risks is realized, and a reliable basis is provided for individualized treatment decision and long-term follow-up visit.

The invention discloses a virus gene sequence host prediction method and system, and belongs to the technical field of biological information analysis, and the method comprises the steps: dividing a virus gene sequence into ordered k-mer word sequences, and carrying out the vectorization of the k-mer word sequences through a pre-trained BERT language model, and obtaining an embedded vector; mapping the embedded vector of the high-dimensional space into a low-dimensional space with a fixed dimension by adopting an average pooling method, and reducing the embedded dimension to obtain a feature vector representing an original gene sequence; the feature vectors are input into a pre-trained classification network model to predict the likelihood that the sequence is infected with a particular host. According to the method, the complete virus gene sequence is directly used as input, so that information loss possibly caused by dependence on statistical characteristics is avoided, and the accuracy of characteristic extraction is remarkably improved.

The invention discloses application of an ENOPH1 gene, and relates to the technical field of biological medicines. According to the application, a key metabolic gene closely related to CRC prognosis is analyzed and identified by adopting bioinformatics, and the expression of ENOPH1 in KRAS mutant CRC tissues and cell lines is evaluated. The function of the ENOPH1 in the KRASG12D / G13D mutant CRC is verified through CCK8, a wound healing experiment, an in-vivo subcutaneous xenotransplantation tumor model and other in-vitro experiments. The result shows that the ENOPH1 is highly expressed in the KRAS mutant CRC and is subjected to MEK / ERK signal cascade regulation and control. The ENOPH1 is knocked down through shRNA, so that CRC cell proliferation, migration and tumorigenesis can be inhibited, cell apoptosis is induced, and the reaction to chemotherapy is enhanced. The application provides a new strategy and direction for treatment of colorectal cancer, has extremely high clinical application value, and can bring a better treatment effect for patients with colorectal cancer.

The invention relates to the technical field of oral medicine and biological information analysis, in particular to an evaluation system for oral microbiological analysis and decayed tooth risk prediction. The system comprises a sample collection module, a microorganism detection module, a data preprocessing module, a microorganism population dynamic analysis module, a multi-dimensional feature extraction module, a decayed tooth risk assessment module and a personalized suggestion generation module. By constructing microbial population evolution analysis based on a Lotka-Volterra competitive model, diversity dynamic change analysis based on Shannon entropy and evolution trend prediction based on a Lyapunov stability theory, the system deeply reveals a dynamic evolution law of an oral micro-ecosystem, and a multi-dimensional feature adaptive weighting mechanism based on mutual information is adopted, so that the dynamic evolution law of the oral micro-ecosystem is improved. Personalized accurate risk assessment is realized, a causal reasoning theory is introduced to generate personalized intervention suggestions with causal support, and the accuracy and the prevention effect of dental caries risk prediction are remarkably improved.

The invention discloses application of a SmCYP82D47 gene in improvement of eggplant bacterial wilt resistance, and belongs to the technical field of molecular biology. 1, and the nucleotide sequence of the gene is as shown in SEQ ID NO. 2. On the basis of positioning an eggplant bacterial wilt resistance linkage site by GWAS, the eggplant bacterial wilt resistance candidate gene SmCYP82D47 is screened through bioinformatics analysis such as gene sequence analysis and haplotype analysis; the tissue expression specificity, hormone response expression characteristics and the like of candidate genes are analyzed; by silencing candidate genes in a resistant material S027 and overexpressing the candidate genes in a susceptible material S054, the disease resistance conditions of a silent plant and a wild type plant as well as the wild type plant and an overexpressed plant are observed, so that the accuracy of a positioning result can be verified, and the effect of the candidate genes in regulating and controlling the bacterial wilt resistance of the eggplant is determined; important theoretical basis and technical means can be provided for eggplant disease-resistant molecular breeding.

The present invention discloses a protein multimodal joint modeling method and system based on cross-modal alignment, which aims to achieve unified representation and processing of protein text descriptions, sequence information and structural features, so as to improve the accuracy and generalization ability of complex protein analysis and prediction tasks and open research tasks. The present invention first collects and pre-processes protein multimodal data to generate a standardized representation, uses a geometric graph neural network and a protein sequence model to extract features, passes them to a large language model after alignment by a projection module, and then projects and fuses them to generate unified invariant features, and finally combines equivariant features to complete the protein analysis and prediction task. The present invention can effectively integrate the multimodal information of proteins, achieve efficient alignment and fusion of cross-modal features, improve the understanding and prediction capabilities of complex protein properties, and provide strong support for precise bioinformatics analysis and biomedical research.

The invention relates to the technical field of plant genetic engineering, and particularly provides cloning of a GbGELP25D gene of gossypium barbadense and application of the GbGELP25D gene of gossypium barbadense. According to the invention, the GbGELP25D gene with a full length of 1092 bp is cloned from the sea island cotton variety Xinhai No. 7 for the first time, and the gene encodes a secretory lipase protein with a signal peptide and is positioned in an extracellular gap. Through bioinformatics analysis, phylogenetic classification, protein structure modeling and signal peptide function verification, it is clear that the gene belongs to a plant GELP family. Furthermore, a virus-induced gene silencing technology is utilized to prove that the silent GbGELP25D can obviously enhance the resistance of cotton to verticillium wilt, and the mechanism of the silent GbGELP25D is closely related to activation of ethylene synthesis and signal channels and induction of expression of disease-resistant related genes. The resistance gene provided by the invention enriches gene resources of cotton verticillium wilt resistance breeding, and has important theoretical significance and application value.

The present invention relates to the application of phosphopantetheinyltransferase E7X1, its encoding gene, recombinant vectors containing the gene, and recombinant bacteria, belonging to the field of genetic engineering technology. To increase the yield of natural products such as polyketides and non-ribosomal peptides, the present invention utilizes bioinformatics analysis and other means to mine a highly efficient phosphopantetheinyltransferase E7X1 from a database. The amino acid sequence of the enzyme is shown in SEQ ID NO.1, and the nucleotide sequence of the gene encoding the enzyme is shown in SEQ ID NO.2. By constructing a recombinant vector and recombinant bacteria containing the enzyme encoding gene, the present invention discovered that overexpressing the enzyme can effectively increase the yield of natural products such as polyketides, polyethers, and non-ribosomal peptides.

The invention belongs to the technical field of molecular markers, and particularly relates to a QTL gene for regulating and controlling maize fusarium graminearum stem rot resistance, a KASP molecular marker and application of the QTL gene and the KASP molecular marker. A maize high-disease-resistance inbred line H1710 and a high-susceptibility inbred line Huangzao 4 are used for constructing an F2 group, and high-disease-resistance and high-susceptibility samples are selected from the F2 group for constructing a mixed pool for BSA-seq analysis; in combination with bioinformatics analysis, molecular markers and fine positioning, a stable QTL gene for controlling the resistance of the fusarium graminearum stem rot of the corn is positioned from the sixth chromosome bin6.01 of the corn. On the basis, the invention provides a corn genome region capable of improving the corn stem rot resistance and a corresponding DNA (Deoxyribose Nucleic Acid) fragment thereof. In addition, the invention also provides a nuclear KASP molecular marker linked with the QTL locus. After the molecular marker is screened and verified, the SNP molecular marker provided by the invention can be used for efficiently and quickly identifying the genotype of a to-be-detected corn material and screening a stem rot resistant material.

The invention discloses a detection system and method for identifying mycobacteria and drug resistance genes of the mycobacteria based on nanopore sequencing, and relates to the field of biological medicine, the detection system comprises a specific targeted enrichment module, a nanopore sequencing module and a biological information analysis module; the specific targeted enrichment module comprises a probe combination, and the probe combination covers a mycobacterium tuberculosis complex conservative identification gene, species-specific genes of common nontuberculous mycobacteria and mycobacterium leprosy, and full-length or partial sequences of drug resistance related genes in a targeted manner; by utilizing the characteristics of nanopore length reading length and real-time sequencing and a tuberculosis specific targeted enrichment strategy, accurate identification of a mycobacterium tuberculosis complex group, synchronous typing of 42 mycobacteria, analysis of 24 drug-resistant genes, and efficient detection of structural variation and low-abundance heterogeneity drug-resistant mutation are realized, the detection period is shortened, the detection cost is reduced, and the detection efficiency is improved. And a comprehensive and reliable technical basis is provided for accurate diagnosis and treatment of mycobacterium infection.

The invention discloses application of NCCRP1 as a lung adenocarcinoma immunotherapy biomarker, and belongs to the field of biological medicine. Bioinformatics analysis finds that the NCCRP1 is remarkably and highly expressed in lung adenocarcinoma tissues, and the expression level of the NCCRP1 is in negative correlation with prognosis of a patient. Secondly, the expression level of lung adenocarcinoma cells NCCRP1 is in negative correlation with infiltration of anti-tumor immune cells such as CD8 + T cells. Experiments show that the knockout of the NCCRP1 can significantly inhibit the growth of mouse lung tumors by increasing the infiltration quantity of CD8 + T cells and NK cells in a lung adenocarcinoma microenvironment. The invention provides a new target spot for immunotherapy of lung adenocarcinoma, including NCCRP1 gene editing (knockout / knockdown) and application of an inhibitor in preparation of antitumor drugs.

The present application relates to the field of biological medicine, and particularly relates to biomarkers for migraine diagnosis, kits and application thereof. The biomarkers related to migraine disease according to the present application include COL4A2, and also include any one or a combination of multiple of MMP-14, LCAT, ADAMTS13 and CPXM2. The present application is based on proteomic data of clinical healthy controls and migraine patients, and uses bioinformatics analysis and machine learning methods to deeply mine protein combinations with the most early warning value for migraine disease diagnosis, and to verify in a clinical cohort with expanded samples, to provide a good prospect for clinical transformation of newly discovered biomarkers, and also to lay a foundation for subsequent mechanism research.

The application belongs to the technical field of molecular plant pathology, and particularly relates to a peanut disease-resistant gene AhNHL24 and application thereof. Through whole genome identification and bioinformatics analysis, 45 NHL family genes (AhNHL) are identified in peanuts, and gene expression analysis shows that most of the AhNHL genes can respond to the induction of biological stress and abiotic stress. Gene disease resistance identification finds that transient overexpression of AhNHL14, AhNHL24 and AhNHL31 can significantly improve the resistance of tobacco and peanuts to Pseudomonas solanacearum, and the disease resistance of AhNHL24 is the strongest. Through transgenic tobacco heterologous overexpression of AhNHL24 and inoculation experiments, it is shown that the peanut and tobacco plants overexpressing AhNHL24 not only have significantly enhanced resistance to Pseudomonas solanacearum, but also have significantly enhanced resistance to Sclerotium rofsii. The application provides an important basis for the cultivation of plant (peanut and tobacco, etc.) broad-spectrum disease-resistant varieties.

The present invention discloses a piRNA analysis method, comprising steps (1)-(7). The present invention analyzes the original anti-MIWI RIP-seq dataset to obtain comprehensive and ideal analysis results without the need for other processing operations on the piRNA sample through experimental means. This provides a powerful bioinformatics analysis tool for conducting piRNA tailing modification research in the reproductive and RNA fields.

The present invention discloses a protein phosphorylation site prediction method based on federated learning, which relates to the field of bioinformatics analysis technology, including: each client participating in federated learning pre-processes its protein sequence data and establishes its own training set and test set; each client uses a local data set to train a model and outputs the local model, prediction accuracy and local data set feature description to the committee; the committee tests the local models of each client, selects a local model that meets the standards, and optimizes the aggregated global model based on the feature description of each client data set; uses the fixed members of the federated learning committee to test the aggregated global model and determine whether to end federated learning. Therefore, the above-mentioned protein phosphorylation site prediction method based on federated learning can overcome the problem of data imbalance, effectively protect data privacy, and realize the sharing and utilization of multi-party data.

The invention discloses a method and system for synchronously detecting host chromatin openness and in-vivo microbiome based on transposase, and belongs to the technical field of biological sequencing data analysis. According to the method, transposase is used for selectively fragmenting an open chromatin region of a host, and a microbial genome is almost randomly cut, so that synchronous enrichment of host and microbial DNA is realized; after high-throughput sequencing library construction and double-end sequencing, sequencing data is split into host source and non-host source reads through bioinformatics analysis, host chromatin state and microorganism composition are analyzed respectively, and a microorganism-host epigenetic regulation network is constructed; the invention further provides a matched DNA sequencing library and an analysis system, multi-scene research of infectious diseases, intestinal microecology, tumor microenvironment and the like is supported, a public database can be reanalyzed, and potential microbial interaction signals are mined. According to the method, the host-microorganism interaction research efficiency is remarkably improved, and a high-sensitivity and integrated technical scheme is provided for epigenetic regulation mechanism analysis.

The invention relates to a method for evaluating and positioning nucleic acid pollution of a nucleic acid extraction kit, which comprises the following steps: 1) taking nucleic acid-free water as a negative sample, and performing nucleic acid extraction on the nucleic acid-free water by using the nucleic acid extraction kit according to nucleic acid extraction steps in the kit, the obtained nucleic acid is presumed as the polluted nucleic acid in the nucleic acid extraction kit; 2) amplifying polluted nucleic acid, purifying a PCR product, enriching double-chain cDNA, and determining the concentration of the double-chain cDNA so as to perform quantification, sample mixing and quality control treatment before library construction; (3) carrying out high-throughput deep sequencing by adopting a platform of the Illumina company, and then carrying out bioinformatics analysis to obtain classification information and gene sequences of polluted nucleic acid; and 4) designing a specific primer based on the gene sequence of the polluted nucleic acid, and carrying out pollution evaluation and positioning verification on each component of the nucleic acid extraction kit through qPCR (Quantitative Polymerase Chain Reaction).

The present invention is applicable to the field of genetic engineering technology and provides a cloning and application of the Sophora alopecuroides (Solanum sophora) SaPEI7 gene. By screening differentially expressed genes from transcriptome sequencing of Sophora alopecuroides under simulated stress conditions, a Sophora alopecuroides gene related to salt tolerance was identified through bioinformatics analysis as the Sophora alopecuroides (Solanum sophora) pectin methylesterase inhibitor protein gene SaPEI7. Quantitative detection using RT-PCR technology revealed a significant increase in gene expression under salt stress conditions. The gene was functionally verified by constructing a plant expression vector and successfully transformed into Arabidopsis thaliana. The results showed that overexpression of the gene in Arabidopsis thaliana improved its salt tolerance, providing a new resource for improving crop stress resistance through genetic engineering technology.

The invention relates to the technical field of biological detection, and discloses a metabonomics-based caenorhabditis elegans compound pollutant exposure evaluation method which comprises the following specific steps: S1, constructing a compound pollutant exposure system; s2, carrying out nematode culture and exposure treatment; s3, extracting in-vivo metabolites of the nematodes; s4, carrying out metabonomics analysis; s5, carrying out bioinformatics analysis; and S6, verifying the combined toxicity effect. By establishing the metabonomics-based caenorhabditis elegans compound pollutant exposure evaluation method, the limitation of a traditional single pollutant evaluation system is broken through, and systematic and high-sensitivity detection of multi-pollutant combined toxicity is realized; in addition, by constructing a standardized composite exposure system and an efficient metabolite extraction process and combining multi-dimensional mass spectrum data and bioinformatics modeling, a synergistic, antagonistic or additive effect mechanism among pollutants is deeply analyzed from the molecular level.