18 results about "Virus identification" patented technology

The application discloses a macrovirus group analysis method based on second-generation and third-generation sequencing technologies, which comprises the following steps: performing quality control and filtering on second-generation sequencing raw data to obtain clean reads, performing single-sample and multi-sample assembly on the quality-controlled data to obtain contigs sequences; performing correction and quality control on third-generation sequencing raw data to obtain clean long sequences, performing assembly on the quality-controlled third-generation data to obtain contigs sequences; performing mixed assembly on the quality-controlled data of the second-generation and third-generation sequencing to obtain contigs sequences; merging all contigs to construct a non-redundant contigs set; and finally performing virus identification and determination, virus species annotation and functional annotation. The application provides a reliable macrovirus group analysis method based on second-generation and third-generation sequencing technologies, and the implementation method is simple and the application range is wide.

The application belongs to the cross field of virology and bioinformatics, and particularly relates to a virus identification model construction method and system based on capsid proteins. The method collects capsid protein sequence data and non-capsid protein sequence data, and trains a capsid protein identification model; obtains capsid protein sequences with known structures, structure information and hierarchical classification information, and identifies a few-sample category with a sample quantity lower than a preset threshold; generates supplementary data of the few-sample category through a protein sequence design model and screens the supplementary data from the collected capsid protein sequence data, predicts the three-dimensional structure of the sequence in the supplementary data, and together with the corresponding classification information, forms a capsid protein supplementary data set for training a capsid protein classification model. The application alleviates the problems of data imbalance caused by insufficient data of the few-sample category and high classification difficulty of remote homologous proteins caused by single feature extraction, and is helpful to realize efficient preliminary identification of viruses.

The invention provides a physical AI virus identification method and system based on a Bayesian neural network, and belongs to the technical field of network security. According to the method, aiming at the fact that a target recognition object is a physical AI virus (including a physical confrontation sample, a physical backdoor, an unknown physical domain AI virus and the like), the physical confrontation sample is taken as an example, and the confrontation sample and a clean sample collected in a physical real world are creatively adopted to construct a training data set. According to the recognition framework provided by the invention, a multi-modal data fusion mechanism under simulation of real environment disturbance is utilized, higher robustness and generalization ability are shown in a complex physical scene, and AI viruses such as physical adversarial samples in the real world can be better recognized.

The application relates to the technical field of data processing, and discloses a multi-modal attention deep learning method for enhancing virus identification in metagenomic data, which comprises five main steps of data preprocessing, sequence embedding, multi-modal feature extraction, dynamic feature fusion and prediction, and hyperparameter optimization and training; through four model paths of GAT, a self-encoder, a convolutional long short-term memory network and a Transformer, the graph structure features, the latent features, the space-time features and the global features of sequences are respectively extracted, the self-attention mechanism is introduced, the contribution weights of the paths are adaptively adjusted according to the features of the input sequences, the sequence embedding is carried out by using the GAT and the self-encoder, the long-distance dependence relationship and the latent features are captured, the space-time and global features are extracted by using the ConvLSTM and the Transformer model paths, and the hyperparameter optimization and the training strategy optimization are carried out; the application improves the virus sequence identification capability, improves the feature fusion accuracy, and further improves the performance and the robustness of the model.

A method and system for virus detection in tumor RNA sequencing data are disclosed. The method includes preprocessing the raw tumor RNA sequencing data; inputting the processed tumor RNA sequencing data into sequence-information-based channels and codon-based channels for feature extraction to generate a feature matrix; constructing a sequence information prediction model and a codon information prediction model, and inputting the feature matrices generated from the sequence information-based channels and codon-based channels into the sequence information prediction model and codon information prediction model, respectively, for training and optimization; predicting the virus probability of each sequencing read to obtain a model score; and selecting viral sequencing reads based on the model scores to assemble viral contigs. This invention improves the accuracy and robustness of virus monitoring by introducing a multimodal deep learning method, and can adaptively process sequencing data from different sources and of different lengths, thereby better meeting the needs of medical and research fields for virus identification in tumor sequencing data.

The application discloses a macrovirus group data analysis method, and belongs to the technical field of macrovirology. The method comprises the following steps: sequence quality control, sequence assembly, sequence clustering, virus sequence identification, virus sequence checking, virus abundance calculation, species annotation, virus lifestyle judgment, virus host prediction and virus auxiliary metabolism gene analysis. The application uses Trimmomatic software, BWA-MEN, Megahit software, CD-HIT and other tools to execute the analysis process of macrovirus group data. Practice proves that the application can accurately identify and annotate virus species, comprehensively and systematically deeply analyze and mine macrovirus group data, the steps are simple and clear, the analysis time is short, and the effect of macrovirus identification research is greatly improved.

The invention discloses a micropterus salmoides rhabdovirus gene engineering vaccine preparation method, which comprises: S1, recombinant plasmid construction: carrying out PCR amplification on a micropterus salmoides rhabdovirus G protein gene with a 6 * His tag, cloning the amplified gene between BamH I and Hind III restriction enzyme cutting sites of a pVL1393 vector to obtain a pVL1393-G-His recombinant plasmid, S2, preparing a transfection system, and S3, carrying out purification on the transfection system to obtain the micropterus salmoides rhabdovirus gene engineering vaccine. S3, recombinant baculovirus preparation and identification, S31, cell transfection, S32, P1-generation virus harvesting, and S33, virus identification, the G protein of the micropterus salmoides rhabdovirus (MSRV) is directionally expressed through a genetic engineering technology, a large number of pathogenic viruses do not need to be cultured, the biological safety risk is greatly reduced, meanwhile, the defect that a prokaryotic expression system lacks protein post-translational modification is overcome, and the method is suitable for industrial production. The sf9 insect cell is used as an expression host, so that the recombinant G protein can be correctly folded to form a functional structure domain, and the immunogenicity is remarkably improved.

A real-time fluorescence RT-PCR detection method and a kit for a novel coronavirus 2019-nCoV. Specifically, the present invention relates to a kit and a method for detecting the nucleic acid of an E gene of the novel coronavirus 2019-nCoV. The kit and the method have extremely high sensitivity and specificity, and can significantly improve the accuracy of virus identification.

This invention discloses a primer design method and primer set. Through in-depth analysis of influenza surveillance network data nationwide, this application makes significant contributions to improving the accuracy and sensitivity of influenza virus identification. The primer revisions not only achieved significant results in experiments but also provided valuable insights for future influenza surveillance and research. A deeper understanding of the genetic characteristics and epidemiological patterns of influenza viruses is crucial for developing scientifically effective prevention and control strategies. In disease prevention and public health management, continuous improvement of surveillance methods and strategies will bring greater benefits to timely responses to infectious diseases such as influenza.

The application belongs to the technical field of biology. More particularly, it relates to a method for accurately identifying the genotype of African swine fever virus based on high-throughput sequencing. The application selects 29 SNP sites in the B646L (P72) gene region that are highly conserved and genotype-specific, calculates the proportion of specific sites by parallel alignment with double reference sequences and base depth quantification statistics, and realizes quantitative determination of the proportion of type I, type II and mixed infection of African swine fever virus. The application breaks through the limitation that the traditional method cannot determine the mixed infection of African swine fever virus, realizes the objective determination of mixed strains, and provides a reliable tool for virus identification under complex infection scenarios. The application is completed based on high-throughput sequencing data, the analysis process is standardized, the subjective error caused by manual judgment is reduced, the consistency and repeatability of the results are strong; at the same time, the output statistical table and the results are intuitive and traceable, and have good popularization and application value.

The application discloses a Teensy virus identification and protection method of a mobile hard disk, relates to the technical field of virus identification and protection, and breaks through the limitation of traditional single feature detection by combining hardware ripple characteristics with dynamic protocol response to effectively identify high-simulation attack equipment through composite equipment fingerprint construction. Meanwhile, a hidden Markov chain is used for real-time analysis of a protocol state transition probability model to accurately capture microsecond-level protocol switching abnormalities and solve the problem of missed detection caused by coarse time granularity of an existing scheme. In addition, a window context instruction association mechanism binds a system focus state to HID operation to block a hidden attack chain formed by a combination of legal instructions. A hierarchical fuse strategy integrates protocol endpoint control and physical layer isolation to realize attack blocking while ensuring the availability of storage functions, thereby avoiding the influence of normal use caused by full-port disabling in traditional schemes.

The application discloses an Internet Trojan virus detection method and device, the method comprises the following steps: configuring a Trojan virus environment, and collecting traffic data under the Trojan virus environment; dividing the collected traffic data according to five tuples in a bidirectional manner, and extracting features to obtain traffic feature data; obtaining training set data obtained by processing the traffic feature data, training a machine learning model by using the training set data, and obtaining a Trojan virus identification model; wherein the processing comprises: normalizing the traffic feature data; and identifying the hiding mode of the Trojan virus and the activity stage of the Trojan virus by using the Trojan virus identification model. The application realizes the detection of Trojan virus traffic, the locking of the hiding mode of the Trojan virus, and the identification of the activity stage of the Trojan virus under high concealment.

The embodiment of the invention relates to the technical field of virus recognition, in particular to a virus classification method, device and equipment based on hierarchical Transform and a computer readable storage medium, and the virus classification method based on hierarchical Transform comprises the following steps: obtaining a to-be-classified virus genome; extracting protein characteristics of a protein coding gene sequence in the virus genome as local input expression; extracting overall tissue information of the virus genome as global context representation; inputting the local input representation and the global context representation into the trained hierarchical Transform for feature processing and classification calculation to obtain a plurality of virus classification hierarchies and corresponding matching degrees; and determining a matched virus category label according to each virus classification level and the corresponding matching degree, and taking the matched virus category label as a virus classification result. According to the virus classification method based on the hierarchical Transform, multi-dimensional features are fused, dependence on a single reference can be reduced, and robustness and accuracy of virus classification are improved.

The present application relates to the technical field of virus identification, in particular to a virus identification method in NGS sequence based on multi-dimensional feature fusion, comprising the following steps: S1, non-parametric assembly analysis and non-parametric feature extraction; S2, sample-specific reference sequence library construction; S3, parametric coverage analysis and feature extraction; S4, machine learning model design and pathogen accurate determination; S5, virus identification in NGS sequence. The present application performs non-parametric assembly analysis on NGS sequence, constructs a reference sequence for parametric comparison according to the results obtained by non-parametric assembly, carries out three kinds of parametric coverage analysis based on the constructed reference sequence library, finally designs a machine learning algorithm, constructs a training label based on qPCR verification data, screens sequence features and trains a model, uses the trained model as the final determination basis, and realizes accurate virus identification.

This invention discloses a method for identifying and analyzing unknown pathogenic microorganisms, comprising the following steps: filtering out genomic sequences with low integrity, contamination, and incorrect labeling to establish a high-quality virus identification database; constructing a virus host prediction model using machine learning algorithms; and performing identification and analysis on the unknown pathogenic microorganisms to identify their potential hosts or pathogenicity, further determining whether they are unknown pathogenic viruses or bacteria. This invention, based on metagenomic data analysis, can more accurately identify potential unknown pathogenic microorganisms in human and environmental samples.

A method for identifying exogenous viruses based on metagenomic sequencing, comprising the following steps: S1, a data quality control step, filtering the quality of the metagenomic sequencing raw data of the sample to obtain clean sequencing data; S2, a host removal step, aligning the clean sequencing data with a host reference genome and a ribosomal RNA database, removing the host sequences on the alignment, and obtaining enriched microbial sequences. The method integrates kraken2 species identification, RVDB / nt_core database alignment, virsorter2 / checkV verification and other multiple links to form a multi-dimensional identification system, improve the accuracy and reliability of virus identification, introduce a multi-process parallel processing mechanism to support the synchronous analysis of batch samples, greatly improve the analysis efficiency, adapt to the high-throughput detection needs of large-scale cell banks, biological products intermediates and finished products, and solve the problem of low throughput of traditional methods and existing mNGS methods.

This disclosure provides a primer set, kit, and application for detecting MNP marker sites of zaruvirus, belonging to the field of biotechnology. The primer set includes at least one pair of primers from the first to the third primer pair, each primer pair including a forward primer and a reverse primer. The forward primer of the first primer pair, the reverse primer of the first primer pair, the forward primer of the third primer pair, and the reverse primer of the third primer pair are shown sequentially as SEQ ID NO: 1 to SEQ ID NO: 6 in the sequence listing. This disclosure utilizes the designed multiplex primer set for multiplex PCR amplification and integrates a next-generation sequencing platform for sequencing of the amplified products, achieving high specificity, high throughput, high efficiency, and high accuracy detection of zaruvirus, providing technical support for zaruvirus identification, mutation monitoring, and database construction.

The invention discloses a method for identifying viruses infected with dormant bacteria in fluvial sediments and application of the method. The invention relates to the technical field of microbiology and bioinformatics, and solves the problems that dead bacteria DNA interference cannot be effectively distinguished, a dormant host in an environmental sample is difficult to accurately recognize and the false positive rate of virus-host matching is relatively high in the prior art. The method comprises the following steps: carrying out propionium bromide azide treatment on a river sediment sample to obtain active microbial community DNA (Deoxyribonucleic Acid), and carrying out high-throughput sequencing; carrying out multi-module cooperative dormancy characteristic analysis on the assembled bacterial genome, and defining a dormancy host candidate genome; identifying a candidate virus sequence by adopting a triple cross validation mode; and extracting a CRISPR interval sequence in a dormant host genome, and comparing the CRISPR interval sequence with a candidate virus sequence to establish an infection relationship between the virus and the dormant host. According to the method, the false positive rate in the virus-host matching process can be reduced, and accurate identification of the virus infected with the dormant bacteria in a complex environment sample is realized.