54 results about "Viral sequence" patented technology

The invention discloses a virus co-infection detection method and system based on penalty regression and sequencing physical linkage. The method comprises the following steps: collecting virus sequences and corresponding pedigree information thereof; counting and screening high-frequency variation to obtain representative variation of the pedigree; constructing a virus pedigree site variation map as a reference map of a reconstruction model by using representative variation of the pedigree; establishing an index for the reference genome, and comparing a to-be-reconstructed virus sequencing sample with the reference genome by using BWA; converting the compared sequencing sample into a BAM file in a binary format; performing variation detection on the virus sequencing samples after comparison to obtain single nucleotide polymorphism (SNP) and insertion / deletion (INDEL) and frequency information thereof; finally, constructing a regression equation according to the SNP and INDEL of the sample as well as the frequency and the reference map of the sample; and performing regression by using a square error loss function in combination with L0 regularization and L1 regularization, and finally obtaining the spectrum haplotype corresponding to the sample and the frequency thereof.

The invention discloses a fiveleaf virus GI.2 whole genome sequencing primer based on an amplicon sequencing technology and application thereof, and belongs to the technical field of virus gene sequencing. The primer group composed of 18 pairs of primers is preferably obtained according to an amplicon sequencing technology, the primer group can effectively improve the primer amplicon coverage degree, it is guaranteed that the primer amplicon coverage degree is 1.5-1.8 times that of a virus sequence, the amplification time is shortened, the amplification difficulty is reduced, the primer amplicon coverage degree and the whole genome capture efficiency are improved, and the primer amplification efficiency is improved. And the method has important significance on epidemic situation traceability and propagation trend of the fiveleaf virus GI.2.

The invention discloses a visual detection method of Seneca valley virus based on CRISPR-Cas13d (clustered regularly interspaced short palindromic repeats-associated protein 13d), and belongs to the technical field of molecular biology diagnosis. The method comprises the following steps: firstly, amplifying a target virus sequence by utilizing an isothermal amplification technology RAA, specifically identifying and amplifying signals by utilizing a CRISPR-Cas13d system, specifically identifying and amplifying signals by utilizing a crRNA sequence as shown in SEQ ID NO.13, and finally, visually observing a detection result by utilizing various visualization methods, according to the detection method provided by the invention, the SVA virus can be accurately and quickly detected in time on site, the detection capability of the SVA virus is remarkably improved, and the biological safety risk is reduced.

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 discloses a PCR (Polymerase Chain Reaction) primer for amplifying a reverse transcription product of dengue virus, a PCR kit and a method for detecting the dengue virus, and belongs to the technical field of virus detection. Comprising an upstream primer DENV-F as shown in SEQ ID NO. 1 and a downstream primer DENV-R as shown in SEQ ID NO. 2. The dengue virus detection method based on the PCR amplification reaction, the three-generation sequencing and the phylogenetic analysis is established by utilizing the characteristics that the PCR reaction is efficient, rapid and accurate, the three-generation sequencing is rapid, the full-length sequencing of about 10,000 basic groups can be provided, one sequencing result represents one complete virus sequence, the phylogenetic analysis is accurate in strain analysis and the like. The kit plays a role in rapid and accurate detection of dengue viruses. The PCR primer used in the invention has high sensitivity, can reach 0.01 pg / reaction, has good specificity, and does not react with other sequences while amplifying the dengue virus.

The invention provides a probe group and a kit for capturing a full-length genome of a respiratory syncytial virus (RSV), and belongs to the technical field of virus detection. The invention discloses a probe group for capturing a full-length genome of a respiratory syncytial virus. The probe group comprises probes with nucleotide sequences as shown in SEQ ID NO: 1-SEQ ID NO: 900. According to the invention, the probe group and next-generation sequencing are combined to obtain an RSV full-length genome sequence. Compared with a primer or a probe used in a sequencing method in the prior art, the single probe in the probe group is short and high in specificity, the same site is designed by using the imbricate probe, all variations of the site are covered, and when a virus sequence is captured, the probe group has the characteristics of high sensitivity, high coverage rate and high flux.

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.

The invention provides systems and methods for analyzing viruses by representing viral genetic diversity with a directed acyclic graph (DAG), which allows genetic sequencing technology to detect rare variations and represent otherwise difficult-to-document diversity within a sample. Additionally, a host-specific sequence DAG can be used to effectively segregate viral nucleic acid sequence reads from host sequence reads when a sample from a host is subject to sequencing. Known viral genomes can be represented using a viral reference DAG and the viral sequence reads from the sample can be compared to viral DAG to identify viral species or strains from which the reads were derived. Where the viral sequence reads indicate great genetic diversity in the virus that was infecting the host, those reads can be assembled into a DAG that itself properly represents that diversity.

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.

The invention discloses a positive control sequence screening method of strawberry mottle virus. The method comprises the following steps: S1, preparing a strawberry material; s2, sampling: collecting a positive control sample and a negative control sample; s3, RNA extraction and sequencing: extracting RNA by adopting a kit, detecting the RNA, and performing RNA sequencing; and S4, preparing RNA positive control plasmids. The virus sequence is deeply analyzed and sequenced by adopting a genomics method, a positive control sample is designed and developed, and the detoxification effect of strawberry seedlings can be accurately evaluated. Compared with a traditional strawberry seedling phenotype detection method, the method has the advantages that the virus detection is rapid and efficient, the detection time is shortened, and the false detection rate is reduced. Technical guarantee is provided for healthy development of the strawberry industry, the application range is wide, the blank of domestic strawberry virus-free seedling detection standards is filled, and the method has remarkable popularization and application potential in the global strawberry planting market and is wide in application prospect.

The invention provides a capturing probe group and a kit for a measles virus full-length genome and a sequencing method of the measles virus full-length genome, and belongs to the technical field of virus detection. The invention relates to an inactive probe group of a measles virus full-length genome. The inactive probe group comprises probes with nucleotide sequences as shown in SEQ ID NO: 1-SEQ ID NO: 742. The capturing probe group and next-generation sequencing are combined to obtain a measles full-length genome sequence. Compared with a primer or a probe used in a sequencing method in the prior art, the single probe in the probe group is short and high in specificity, the same site is designed by using the imbricate probe, all variations of the site are covered, and when a virus sequence is captured, the probe group has the characteristics of high sensitivity, high coverage rate and high flux.

The invention relates to the technical field of bioinformatics and artificial intelligence, in particular to a classification algorithm for identifying prokaryotic and eukaryotic viruses based on viral genomics, which can effectively distinguish prokaryotic and eukaryotic virus sequences in a virus group and provide technical support for virus classification and genome analysis. According to the algorithm, a dual feature extraction mechanism of nucleotide to frequency statistics and a mask semantic model is innovatively constructed, local nucleotide combination preference of a virus sequence is quantified through weighted distance statistics, and a non-linear dependency relationship of a genome sequence is captured by using the mask semantic model; and constructing a dual-channel deep learning model fusing statistical features and global semantic features. According to the classification algorithm, the calculation efficiency of the virus group sequence can be improved, rapid analysis of million-level virus sequences can be supported, and an efficient solution is provided for virus host traceability and omics big data analysis.

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.

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 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 a method for omics analysis of environmental RNA viruses, and particularly provides a method for analyzing RNA viruses in a sample. The method comprises the following steps: 1) performing macro transcriptome sequencing on a sample to obtain macro transcriptome data, then analyzing the macro transcriptome data through a homologous iteration method based on RdRp and a geNomad method based on a machine learning model, screening RNA virus sequences in the sample, and obtaining an RNA virus operation classification unit and a sequence thereof; 2) respectively performing abundance analysis, classification identification and host analysis on the RNA virus operation classification unit obtained in the step 1); wherein the classification identification comprises the following steps: respectively analyzing sequences of the RNA virus operation classification units through geNomad and CAT methods to obtain classification results of the RNA virus operation classification units in the sample. The method provided by the invention can provide a method technology for detection and analysis of RNA viruses in the environment, and has important practical significance.

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.