18 results about "Viral sequence" patented technology

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 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.

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 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 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.

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 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.

Disclosed are compositions, assays, methods, diagnostic methods, kits, and diagnostic kits for the specific and discriminatory detection of SARS-CoV-2 and / or other viruses from samples, including veterinary samples, clinical samples, food samples, forensic samples, environmental samples (e.g., obtained from soil, garbage, sewage, air, water, food processing and manufacturing surfaces, etc.), or biological samples obtained from humans or non-human animals.

The invention relates to a poliovirus primer combination, a kit and a whole-gene capturing method. The primer combination disclosed by the invention comprises primers with nucleotide sequences as shown in SEQ ID NO: 1-12, and highly conserved regions targeting PV1, PV2 and PV3 viruses, and can be specifically combined with poliovirus RNA (Ribonucleic Acid). Through targeted enrichment, library construction and sequencing signal analysis, efficient capture of the whole genome (about 7400 nucleotides) of the virus can be realized, the capture amplicons cover 99% of the virus sequence, and the virus nucleic acid with the virus load greater than or equal to 1 * 10 < 6 > copies / mL can be detected.