35 results about "Viral sequence" patented technology

Described herein are compositions and methods for enriching library fragments comprising viral sequences prepared from a variety of samples. These methods may incorporate microfluidics and flowcells for greater ease of use. Libraries enriched with the present methods may be used for sequencing. Also described are probes and methods for enzymatic depletion of unwanted RNA.

The invention provides a rapid processing method for HPV (human papillomavirus) gene sequence data. The method relates to the technical field of data processing, and comprises the following steps: acquiring and integrating HPV sequence data, host transcriptome data and clinical association data to form a comprehensive input matrix; performing feature screening on the comprehensive input matrix by adopting a bee colony optimization algorithm based on reinforcement learning to obtain an optimal feature subset; according to the bee colony optimization algorithm, global search of the bee colony algorithm is combined with a dynamic decision-making mechanism of reinforcement learning, and a feature selection strategy is dynamically adjusted; and constructing an HPV subtype accurate typing and variation detection model based on multi-task deep learning, performing deep coding and weighted fusion on the optimal feature subset through an unstable attention mechanism, and outputting two key prediction results of subtype probability and mutation site probability. According to the method, the accuracy and biological interpretability of virus typing and variation detection are improved, and efficient extraction and multi-task accurate recognition of high-dimensional features are realized.

The invention provides a capture primer group and a kit for targeted Salivirus genome sequencing and application of the capture primer group and the kit, and belongs to the technical field of genome sequencing. The targeted capture primer group of the Salivirus virus genome comprises a primer 1 and a primer 2, the nucleotide sequence of the primer 1 is as shown in SEQ ID NO. 1, and the nucleotide sequence of the primer 2 is as shown in SEQ ID NO. 2. According to the invention, a universal reverse transcription oligo-T primer and the Salivirus specific primer group provided by the invention are put into the reverse transcription step at the same time, so that not only is a macro transcription effect achieved, but also a Salivirus sequence in a sample is synchronously identified for specific capture. According to the method, the complete Salivirus genome sequence can be efficiently obtained, and the method has good coverage and sequencing adaptability to the virus genome.

Provided herein are methods and systems for detecting polynucleotide sequences encoding viral capsids in metagenomic data. The methods and systems disclosed herein may include a sequence alignment-based module, a gene-based data processing module, and further characterization of putative viral sequences to identify novel polynucleotide sequences encoding viral capsids in metagenomic data.

Proteases of Group IV (+)ssRNA viruses were found to act on a human sequences in addition to the viral sequences. The identity of the cleavable human sequences is disclosed. Detection of these sequences can act as a diagnostic of infection. It is contemplated that these findings could be employed to facilitate post-translational silencing at the level of protein (e.g., removal of existing proteins), thus serving as a protein analog to CRISPR / Cas9 and RNAi / RISC, and further to enable sequence-specific silencing of host functions without the modification of the host genome.

The application provides an intelligent analysis platform for risk assessment of wild bird flu virus recombination, relates to the technical field of influenza virus monitoring and risk assessment, and comprises a multi-source monitoring data acquisition module, a data preprocessing module, a virus sequence intelligent analysis module, a recombination risk assessment module, a spatial situation analysis and visualization module and a report generation and early warning module connected in sequence through an API interface. The intelligent analysis platform for risk assessment of wild bird flu virus recombination can realize multi-source monitoring data acquisition, virus sequence intelligent analysis, recombination risk quantitative assessment and spatial visualization early warning, and provides whole-process technical support for influenza virus recombination prevention and control.

The invention belongs to the technical field of tumor detection, and particularly relates to a probe composition for nasopharyngeal carcinoma MRD detection, a kit and application, the probe composition for nasopharyngeal carcinoma MRD detection comprises the following probes, the sequences of the probes are SEQ IN NO.1-106; according to the targeted probe capture technology, the probe is designed according to the whole genome sequence of the EB virus, so that all EB virus sequences in a sample are captured, and the detection sensitivity and reliability are improved.

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.

Compositions, assays, methods, diagnostic methods, kits, and diagnostic kits are disclosed for the specific and differential detection of SARS-COV-2 and / or other viruses from samples, including veterinary samples, clinical samples, food samples, forensic sample, environmental samples (e.g., obtained from soil, garbage, sewage, air, water, food processing and manufacturing surfaces, or likewise), or biological sample obtained from a human or non-human animal.

This invention relates to an omics analysis method for environmental RNA viruses. Specifically, the invention provides a method for analyzing RNA viruses in samples, comprising the following steps: 1) performing metatranscriptomic sequencing on the sample to obtain metatranscriptomic data, and then analyzing the metatranscriptomic data using the RdRp-based homology iteration method and the geNomad method based on a machine learning model to screen RNA virus sequences in the sample and obtain RNA virus operational taxonomic units and their sequences; 2) performing abundance analysis, classification identification, and host analysis on the RNA virus operational taxonomic units obtained in step 1); wherein, the classification identification includes analyzing the RNA virus operational taxonomic unit sequences using both geNomad and CAT methods to obtain the classification results of the RNA virus operational taxonomic units in the sample. The method of this invention provides a methodological technique for the detection and analysis of RNA viruses in the environment and has significant practical implications.

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 application discloses a method for detecting viruses and virus replication in media based on Small-RNA high-throughput sequencing. The application belongs to the technical field of virus detection, and the method for identifying virus types in a to-be-detected sample by Small-RNA sequencing developed by the application comprises the following steps: 1) extracting total RNA from a virus-infected to-be-detected sample, constructing a cDNA library, and separating small RNA sequence fragments from the cDNA library; 2) downloading virus sequences and classification databases from GenBank, and comparing the small RNA sequence fragments with virus sequence files; 3) comparing a contigs3 file with a virus nucleic acid database, selecting sequences with the minimum e value in the comparison results in the contigs3 file, calculating the coverage of the contigs3 sequences in corresponding virus genomes, distinguishing virus information with a coverage threshold, and deducing the virus source of the small RNA sequence.

The invention relates to the technical field of biological information, and discloses a virus host prediction model and a training method and device.The virus host prediction model comprises a basic model and a classification model, and the basic model comprises an autoregression pre-training model and a low-rank adaptation unit inserted into the autoregression pre-training model; on the basis of the human genome sequence, the sequence characteristic relation of the genome is learned in an autoregression mode, and the sequence characteristic relation is represented as the general representation of the gene sequence; furthermore, a low-rank adaptation unit is added when transfer learning is carried out on the autoregression pre-training model, and the low-rank adaptation unit continuously learns subtle features which are difficult to perceive in a virus sequence by utilizing a causal relationship before and after a gene sequence, so that the sensitivity and the adaptability to virus sequence variation are improved. The virus host prediction model has good generalization ability after being fully trained, and can perform effective potential host prediction on unknown or new virus sequences.

The invention discloses a virus qPCR (quantitative polymerase chain reaction) detection method and application, belongs to the technical field of biology, discloses a primer group, a primer / probe group and a product of a water-borne pathogenic virus, and further provides an amplification method, a detection method and application of a water-borne pathogenic virus sequence. The invention also provides a library for detecting a water-borne pathogenic virus.

The present disclosure generally relates to viral-based expression systems suitable for the production of molecule of interests in recombinant host cells. The disclosure particularly relates to nucleic acid constructs, such as expression vectors, containing a modified arterivirus genome or replicon RNA in which at least some of its original viral sequence has been deleted. Also included in the disclosure are viral-based expression vectors including one or more expression cassettes encoding heterologous polypeptides. In some embodiments, the expression cassettes are configured and positioned at defined locations on the viral genome so as to enable expression of the heterologous polypeptides in a tunable manner.

Provided are compositions, systems, and methods useful for effecting gene editing in eukaryotic cells. Compositions include plasmids that encode one or more viral fusion proteins in which one or more viral proteins are fused with an aptamer-binding protein. Compositions also include plasmids that encode a non-viral nucleic acid sequence, wherein the non-viral nucleic acid sequence encodes a CRISPR system component. In some instances, the non-viral nucleic acid sequence also includes an aptamer sequence. The plasmids can be used to generate viral particles, including lentivirus-like particles that contain a viral fusion protein and a non-viral RNA sequence. Systems of producing such viral particles are provided. Also provided are methods of using the viral particles of the disclosure to effect gene editing in eukaryotic cells.

The present invention provides, among other things, a novel and improved method for generating “mosaic” influenza antigenic polypeptides including hemagglutinin (HA) and neuraminidase (NA) polypeptides based on unique combination of epitope patterns that maximize exposure to epitopes present across multiple HA or NA sequences and therefore improved influenza strain coverage. In particular, the present invention provides engineered influenza B hemagglutinin (HA) polypeptides that are comprised of novel combinations of protective epitopes and antigenic regions from multiple influenza B viral strains. Such engineered HA polypeptides have improved properties over HA polypeptides developed through conventional approaches that rely on consensus alignments of viral sequences.

The invention provides an analysis method and system for virus metagenome sequencing data, and relates to the field of biological information.The method comprises the steps that quality filtering is conducted on original sequencing data, and filtered data is obtained; according to the filtered data, a sequence is assembled through a De Bruijn graph and an overlapping layout, and a virus metagenome sketch is constructed; comparing the virus metagenome sketch with a known virus database to identify known virus types; and identifying a new virus by combining the unknown sequence which is not compared with the virus sequence through clustering analysis so as to realize the sequence comparison of the virus metagene. By improving data quality and accuracy, enhancing virus identification and classification capability, improving variation detection and function annotation efficiency and optimizing gene prediction and community structure analysis, analysis of virus metagenome sequencing data is realized.

The invention relates to the technical field of single cell transcriptomics and microbiomics, in particular to a method for detecting viral load based on single cell transcriptome sequencing, which comprises the following steps: extracting single cell transcriptome sequencing data to form a single cell gene expression matrix, and comparing the single cell gene expression matrix with a host comparison file to extract reading data; dynamically translating the reading data to generate an amino acid sequence; comparing and scoring the amino acid sequence with a virus protein sequence in a virus database, obtaining a weighted comparison value through distance weight correction, and forming a virus sequence-cell matrix by the weighted comparison value and reading data; inputting the virus reading data, the host cell immune response index and the cell type sensitivity score into a cell infection tendency index calculation formula, and obtaining the cell infection probability through a logistic regression model. The technical problem that the sensitivity to RNA viruses with high mutation rate is poor is solved, quantitative estimation of the single cell infection probability is achieved, and the method is suitable for detecting distant viruses or new viruses which are not recorded yet.

The application provides a virus sequence genome structure and function annotation method and system, and the method comprises the following steps: identifying terminal repeat sequences in a virus genome sequence, deleting or retaining the terminal repeat sequences according to a first parameter set by a user; predicting open reading frames in the virus genome sequence, and deleting or retaining the open reading frames according to a second parameter set by the user; performing annotation on the virus genome sequence by using a blastp tool, identifying open reading frames with virus protein functions according to the function annotation of the open reading frames, determining virus classification information, and adjusting the direction of the virus genome sequence according to the annotation; identifying ambisense viruses according to the virus classification information, retaining overlapping open reading frames in different translation frames of the ambisense viruses according to an overlapping degree of a tolerated open reading frame set by the user, and preferentially retaining open reading frames in the same direction. The application realizes efficient, flexible and more adaptable virus genome annotation.

The invention discloses a metagenome virus classification method based on a weighted graph network and label propagation, and relates to a metagenome virus classification method. The method aims at solving the problems that in the prior art, the dynamic adaptability of ICTV classification standards is insufficient, data categories are unbalanced, and dark matter classification is difficult. The method comprises the following steps: in a database updating and constructing process, acquiring latest virus classification sample data, and constructing a latest virus protein database; in the weighted graph network construction process, firstly, a to-be-detected virus nucleotide sequence is translated into a protein sequence, then a protein cluster is obtained through Markov clustering, finally, the weight of an edge between the to-be-detected virus sequence and a reference sequence in a database is calculated through an edge weight calculation algorithm, and a weighted graph network is constructed; in a label propagation virus classification process, comprehensive virus species classification is carried out on metagenome viruses in three stages through a category weight normalization label propagation algorithm. The invention belongs to the technical field of gene engineering.

The invention belongs to the technical field of bioengineering, and particularly relates to a banana streak virus vaccine and application thereof in prevention and treatment of banana streak virus diseases. Through research and exploration, the banana streak virus vaccine constructed based on an endogenous banana streak virus sequence carried by a banana genome is developed, and after the banana streak virus vaccine is applied to banana plants, the replication of the banana streak virus can be remarkably inhibited, and the infection of the banana streak virus and the development of the banana streak virus can be controlled. The invention provides a new strategy for simply, conveniently and effectively preventing and treating banana streak virus diseases, and is particularly suitable for large-scale banana planting areas.

The present application relates to the technical field of single cell transcriptomics and microbiomics, in particular to a method for detecting viral load based on single cell transcriptome sequencing, comprising the following steps: extracting single cell transcriptome sequencing data to form a single cell gene expression matrix, and aligning the read data with a host alignment file; dynamically translating the read data to generate amino acid sequences; comparing and scoring the amino acid sequences with viral protein sequences in a virus database, and correcting the weighted alignment value by distance weight, which, together with the read data, forms a virus sequence-cell matrix; inputting the viral read data, host cell immune response index and cell type sensitivity score into a cell infection tendency index calculation formula, and obtaining the cell infection probability through a logistic regression model. The present application avoids the technical problem of poor sensitivity to RNA viruses with high mutation rate, realizes quantitative estimation of single cell infection probability, and is suitable for detecting distantly related viruses or new viruses that have not been included.

This invention discloses a method and apparatus for mining viral micro-interference RNA using multiple algorithms, relating to the field of biological detection. The method includes: removing adapters from an input sequence using multiple adapter removal algorithms, merging and deduplicating the resulting sequences to obtain an adapter-removed sequence; mapping the adapter-removed sequence to a ribosomal RNA database using multiple mapping algorithms in a cascaded manner, outputting a first incompletely mapped sequence; mapping the first incompletely mapped sequence to a host genome using the same mapping method, outputting a second incompletely mapped sequence; mapping the second incompletely mapped sequence to a viral genome using multiple mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch number ≤ 1, it is determined to originate from a viral sequence. This invention significantly improves the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data.

Disclosed are compositions, assays, methods, diagnostic methods, kits and diagnostic kits for the specific and differential detection of SARS-CoV-2, including SARS-CoV-2 variants, or other coronaviruses from samples including veterinary samples, clinical samples, food samples, forensic sample, an environmental sample (e.g., soil, dirt, garbage, sewage, air, or water), including food processing and manufacturing surfaces, or a biological sample.

The invention relates to an RPA (recombinase polymerase amplification) detection primer group for otter flavivirus in Asian and application, and relates to the technical field of virus detection. The RPA detection primer group is used for amplifying a target sequence, the target sequence is a conserved region of a virus sequence of the otter flavivirus, and the virus sequence of the otter flavivirus is shown as SEQ ID NO: 1. On the basis of the RPA detection primer group, specific, stable, sensitive and efficient detection of the otter flavivirus can be realized, and the RPA detection primer group can be applied to primary field detection and has important value in the aspect of detection of the otter flavivirus.

The application discloses a virus-host RNA sequence classification method and device based on AI, and relates to the field of biological detection.The method comprises the following steps: mapping preprocessed short read RNA sequences to a host genome twice, assembling short read RNA sequences which are not mapped to the host genome into continuous RNA sequences, and screening RNA sequences with a length greater than 1000bp from the continuous RNA sequences; and performing AI classification on the RNA sequences with a length greater than 1000bp to obtain virus RNA sequences.The application combines host filtering, rapid assembly and AI classification, can significantly reduce the calculation amount and hardware pressure, realizes efficient and accurate virus sequence classification, and can quickly distinguish unknown viruses.

The application provides a kind of real-time monitoring system and method based on wild animal host influenza virus, it is related to animal influenza virus monitoring technical field, comprising the following steps: step S1, dynamic monitoring point is laid out;Step S2, sample collection and pretreatment;Step S3, real-time detection and typing virus;Step S4, data real-time transmission and sharing;Step S5, virus sequence alignment and variation analysis;Step S6, dynamic risk assessment and early warning.The real-time monitoring system and method based on wild animal host influenza virus, through portable real-time fluorescent RT-PCR detection equipment realizes field real-time detection, combines double mode communication and cloud real-time analysis, eliminates the time lag of sample transportation and laboratory detection, realizes the real-time feedback of virus information.

The application provides a rapid processing method for HPV virus gene sequence data. It relates to the technical field of data processing, and the method comprises the following steps: obtaining HPV virus sequence data, host transcriptome data and clinical correlation data and integrating them to form a comprehensive input matrix; adopting a bee colony optimization algorithm based on reinforcement learning to perform feature screening on the comprehensive input matrix to obtain an optimal feature subset; wherein the bee colony optimization algorithm combines global search of the bee colony algorithm with dynamic decision mechanism of the reinforcement learning to dynamically adjust the feature selection strategy; constructing an HPV subtype accurate typing and variation detection model based on multi-task deep learning, performing deep coding and weighted fusion on the optimal feature subset through an unstable attention mechanism, and outputting two key prediction results of subtype probability and mutation site probability. The application improves the accuracy and biological interpretability of virus typing and variation detection, and realizes efficient extraction of high-dimensional features and multi-task accurate identification.

The application discloses an analysis method for high-throughput prediction of bacteriophage hosts based on a second-generation sequencing technology, which comprises the following steps: performing quality control on sequencing data respectively to obtain clean reads of bacteria and clean reads of viruses, performing assembly on the quality-controlled bacterial data to obtain bacterial contig sequences, performing binning on the bacterial contig sequences to obtain bins, performing quality control and redundancy removal on the bins to obtain non-redundant high-quality metagenome assembly genomes (MAGs) and performing species annotation on the non-redundant high-quality metagenome assembly genomes (MAGs), performing assembly on the quality-controlled viral clean reads to obtain viral contig sequences, performing quality control on the viral sequences to obtain high-quality viral contigs and performing species annotation on the high-quality viral contigs, and finally predicting bacteriophages and hosts thereof and performing precision evaluation on the prediction results. The application provides a reliable analysis method for high-throughput prediction of bacteriophage hosts based on a second-generation sequencing technology, and the implementation method is simple and the application range is wide.