447 results about "Sequence alignment" patented technology

According to the D-psicose 3-epimerase mutant and the application thereof, the D-psicose 3-epimerase mutant is obtained through protein C-terminal sequence modification, software analysis and sequence alignment of different sources of D-psicose 3-epimerase, the half-life period of the mutant enzyme at 60 DEG C, 70 DEG C and 80 DEG C is prolonged, and the activity of the mutant enzyme is improved. The substrate affinity and conversion efficiency of the enzyme to D-fructose as a substrate for synthesizing D-psicose are improved, and the Tm value of the mutant is 73.23 DEG C and is improved by 11.98 DEG C compared with that of a wild type; the half-life period at the temperature of 60 DEG C is 255.35 min and is 17.60 times that of a wild type, the problem that the half-life period of the wild type is too short at the high temperature is solved, the relative action time of the mutant enzyme is long, the yield of the product D-psicose is remarkably increased by applying the genetically engineered bacterium containing the modified enzyme for biotransformation, the product yield is increased by 4.3%, the equilibrium conversion rate reaches 33%, and the yield of the product is greatly increased. And a green, environment-friendly, sanitary and safe biological preparation method is provided for the preparation process of the D-psicose.

The invention relates to a high-yield lipase strain and application thereof, and belongs to the technical field of bioengineering. According to the invention, grease-rich samples from different sources are subjected to enrichment treatment, a strain with lipase activity reaching 80.8 + / -1.8 U / mL is obtained through neutral red grease plate screening, the strain is identified as Burkholderia cepacia OCRI-Lip100 through sequencing and sequence comparison, the Burkholderia cepacia OCRI-Lip100 is named as Burkholderia cepacia OCRI-Lip100, and the Burkholderia cepacia OCRI-Lip100 is preserved in China Center for Type Culture Collection on July 3, 2024 with a preservation number of CCTCC M20241369. Lipase obtained by fermentation of the strain is sequenced, enzymatic properties, catalytic activity and substrate preference of the lipase are studied, the optimum catalytic temperature is 60 DEG C, the specific enzyme activity is higher than that of commercial lipase, the catalytic activity is further improved to 116 + / -0.5 U / mL when a C14 substrate is catalyzed, and triglyceride can be hydrolyzed into diglyceride. The strain provided by the invention and the lipase produced by the strain enrich the resource library of the existing lipase, and provide a basis for improving the industrial application of the lipase and realizing the high utilization rate of resources.

The invention discloses a power system protocol analysis method and system based on association rule analysis and sequence alignment, particularly relates to the field of private protocol analysis, is used for solving the problems of dynamic field feature extraction and boundary recognition, and aims at extracting features of dynamic fields through separation of static fields and the dynamic fields. Then, hidden frequent items and associated features are mined by using an association rule algorithm, so that dynamic fields are displayed in a structured manner in a feature matrix, which becomes the basis of subsequent clustering and inference. The feature matrix is clustered through a DBSCAN algorithm, protocol formats are classified, noise and abnormal data are processed, and a preliminary format is provided for field boundary recognition. According to the method, the Nedleman-Wunsch sequence comparison and information entropy verification technology is combined, the boundary position is accurately identified, the fuzzy field is dynamically adjusted by using the field boundary ambiguity index and the sequence alignment error correction index, and finally, the boundary division precision is enhanced through mutual information correction, so that the accuracy and processing efficiency of protocol analysis are improved, and the method is suitable for popularization and application. And automatic analysis of a complex protocol is realized.

The invention provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention discloses an alfalfa SNP (Single Nucleotide Polymorphism) molecular marker combination and application thereof. The SNP molecular marker combination is composed of 61351 SNP molecular markers, the physical positions of the 61351 SNP molecular markers are determined based on sequence alignment of a medicago sativa reference genome ZM-4alfalfa gene, the SNP molecular markers are good in site specificity, high in universality and high in polymorphism, medicago sativa germplasms of different sources can be effectively distinguished, and the application prospect is wide. The alfalfa whole-genome liquid-phase gene chip prepared from the SNP molecular marker can be used for alfalfa germplasm resource genetic diversity evaluation, genetic map construction, functional gene positioning, whole-genome association analysis and the like, and provides important tools and technical supports for alfalfa molecular design breeding and variety authenticity identification. The method has important significance on high-quality development of alfalfa seed industry in China.

The invention discloses a silver carp 20K liquid phase chip and application thereof, the liquid phase chip comprises a probe combination of 20,909 SNP molecular markers covering a whole genome, the physical positions of the 20,909 SNP molecular markers are determined based on sequence alignment of the silver carp genome (GCA041475455.1), and the site information of the 20,909 SNP molecular markers is shown in the specification table 1. The invention provides a silver carp first type whole genome SNP liquid phase breeding chip, which effectively reduces the application cost of silver carp in genetic diversity analysis, QTL positioning, GWAS analysis and the like in scientific research, solves the problem that no applicable product exists in silver carp molecular auxiliary breeding and whole genome selective breeding, accelerates the progress of silver carp basic research and breeding, and has a wide application prospect. The method is of great significance in improving the utilization efficiency of silver carp germplasm resources, accelerating the breeding process of excellent characters and improving the economic value and environmental adaptability of bred varieties.

The invention provides a three-generation sequencing read analysis method, application and device. The method comprises the steps that a read segment is obtained, the read segment is obtained by sequencing a sequencing library through a three-generation sequencing platform, and molecules of the sequencing library comprise a sequencing primer recognition sequence, a tag sequence and a UMI sequence; based on the sequencing primer recognition sequence in the read, determining an identity marker area of the read; comparing the identity marking area with at least one standard tag sequence, and determining the tag sequence of the read section in the identity marking area based on the comparison result; and determining the UMI sequence of the read based on the tag sequence in the identity marking area. According to the method, the characteristic of high insertion and deletion error rate in a three-generation full-length transcriptome sequencing result is fully considered, the utilization rate of three-generation sequencing data is improved based on a sequence alignment method, and the accuracy of transcript quantification is improved based on K-mer UMI sequence correction.

The invention provides an enzyme modification method based on artificial intelligence. The enzyme modification method comprises the following steps: training an AlphaFold2 model to obtain a glycosyltransferase tertiary structure prediction model; performing multi-sequence comparison on the protein and nucleic acid sequence database and the non-redundant protein feature sequence database based on the active point location recognition model to obtain a comparison result; initializing a matrix of which the length is equal to that of a comparison result, so as to construct a PSSM matrix and screen out potential mutation sites; according to the screened potential mutation sites, constructing an initial mutation library to form a virtual screening mutation library; performing structure reconstruction on the sequence in the virtual screening mutation library based on a glycosyl transferase tertiary structure prediction model so as to predict the glycosyl transferase tertiary structure and perform molecular docking and dynamic simulation on the glycosyl transferase tertiary structure; the method is used for analyzing binding pockets of proteins and ligands and determining dynamic interaction of amino acid residues and ligands in the binding pockets, and the contact number of each amino acid residue and the ligands is used for modifying the tertiary structure of the glycosyltransferase.

The invention belongs to the technical field of biological probiotic screening, and particularly relates to a probiotic screening method based on a convolutional neural network, which comprises the following steps: reading an original probiotic related DNA sequence file, matching the DNA sequence file by adopting a regular expression, removing all non-ATCG base characters to obtain a normalized DNA sequence, and screening the normalized DNA sequence; segmenting the screened DNA sequence by adopting a sliding window method to generate a k-mer fragment set; constructing a k-mer frequency vector according to the occurrence frequency of each k-mer fragment in the sequence; performing normalization processing on the k-mer frequency vector; inputting the k-mer frequency vector after normalization processing into the trained convolutional neural network to obtain a DNA sequence classification result; determining whether a strain corresponding to the sequence is a probiotic or not according to a DNA sequence classification result; according to the method, the k-mer frequency characteristics are extracted by adopting the sliding window, and the calculation complexity of a traditional sequence alignment method is reduced from O (n2) to O (n), so that the consumption of calculation resources is reduced.

The invention provides a low-frequency mononucleotide variation detection method and device, and relates to the technical field of nucleic acid sequencing, and the method comprises the following steps: obtaining sequencing data and a corresponding reference genome, comparing the sequencing data to the reference genome, and carrying out stacking analysis processing to obtain comparison information; potential variation positions are extracted based on the comparison information to serve as candidate sites; extracting sequence information covering the candidate sites as candidate sequences; removing the same gap vacancies according to the coordinate positions; the alignment treatment of the basic groups in different directions is repeatedly carried out until the alignment treatment results of two adjacent times do not change, so that an optimized comparison result is obtained; and outputting a low-frequency variation detection result. According to the method, false positive results are reduced through multi-sequence alignment and re-alignment, the result accuracy is ensured, the minimum number of reads supported by mutation is not limited, the identification sensitivity of low-frequency mutation is kept, and missing detection is avoided; low-frequency variation sites are comprehensively recognized through accumulation analysis, and the method is suitable for third-generation sequencing data.

The invention discloses an electric power infrastructure line operation behavior intelligent identification method, and belongs to the technical field of mode identification and data processing, and the method comprises the steps: constructing a heterogeneous space-time scene map, and carrying out the multi-modal structural perception of an operation site; segmenting the continuous behavior flow into behavior primitives in an unsupervised manner, and encoding the behavior primitives into observation sequences; analyzing the security operation regulations offline and compiling the security operation regulations into normal form behavior genomes; performing local sequence alignment on the observation sequence and the normal form genome to quantify behavior deviation; and identifying a jump critical point of the risk state based on the deviation disturbance and generating a structured early warning event. According to the method, a heterogeneous space-time scene map is constructed to fuse multi-modal perception data, an actual operation behavior is abstracted into an observation behavior primitive sequence, and the observation behavior primitive sequence and a normal form behavior genome compiled from a safety regulation are subjected to sequence alignment, so that behavior deviation can be quantified, a jump critical point of a risk state can be identified, and the accuracy of the risk state is improved. And accurate and foresight intelligent identification and early warning of operation behaviors are realized.

The invention discloses a molecular biological method capable of simultaneously detecting duck-derived and goose-derived components and application of the molecular biological method, after genome DNA of a sample to be detected is subjected to PCR amplification and capillary electrophoresis through primers shown in SEQ ID NO.1 and SEQ ID NO.2, sequence comparison is carried out, when the peak value is 999bp, it is indicated that the sample contains the duck-derived component, and when the peak value is 992bp, it is indicated that the sample contains the goose-derived component, and when the peak value is 992bp, it is indicated that the sample contains the goose-derived component, it is indicated that the sample contains the duck-derived component. When the peak values are 999bp and 992bp, the duck-derived component and the goose-derived component are contained at the same time. The detection kit developed on the basis of the method can generate considerable economic benefits and good social values.

The invention belongs to the technical field of protein structure prediction, and relates to a protein structure prediction method, system and device based on a light quantum computer, and the method comprises the steps: 1) obtaining a multi-sequence alignment MSA matrix of a target sequence based on the target sequence alignment of a to-be-predicted protein; 2) encoding the obtained multi-sequence alignment MSA matrix into a {0, 1} matrix; 3) converting the amino acid interaction of the protein into an undirected graph model of {0, 1} state nodes, and training by adopting a Boltzmann machine training mechanism and using a light quantum computer to obtain a weight coefficient of a connecting edge between the nodes in the undirected graph model; and 4) obtaining an interaction coefficient between different amino acids of the protein based on the weight coefficient. According to the method, the calculation efficiency and the solving result of protein structure prediction are improved, and the problems that complex scheduling problems are difficult to calculate and accurate solutions cannot be obtained in the prior art are solved.

Compressing files is disclosed. An input file to be compressed is first aligned. Aligning the file includes splitting the file into sequences that can be aligned. When splitting the file into sequences or when performing subsequent recursive splitting, the splitting is based on a longest sequence match. The result is a compression matrix, where each row of the matrix corresponds to part of the file. A consensus sequence is determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.

The invention discloses a pattern recognition method and device for medical gene sequencing data, and belongs to the technical field of data processing.The method comprises the steps that a target gene sequence obtained through sequencing is preprocessed, a self-adaptive scoring matrix is constructed, and site scores are dynamically adjusted through neural network training; by means of the self-adaptive scoring matrix, a sliding window is adopted to scan sequence differences, and a Gaussian mixture model is combined for analysis, and a variation region information file is generated; reading a variation region information file, constructing a base conversion matrix for sequence comparison, and generating a variation classification result by analyzing a comparison path; and based on a variation classification result, performing standardization processing on the variation region feature vector, and generating a mutation mode type through an improved clustering algorithm. Through the scheme of the invention, the filtering quality of the sequence data can be improved, and the capability of processing a complex variation mode is enhanced.

The invention provides a method for predicting binding affinity of human leukocyte antigen I-type molecules and peptide fragments, and a construction method of the method comprises the following steps: acquiring antigen binding site residues of the human leukocyte antigen I-type molecules, and extracting antigen binding region sequences of the human leukocyte antigen I-type molecules through secondary multi-sequence alignment; respectively generating a human leukocyte antigen class I molecule coding matrix and a peptide fragment coding matrix based on the paired human leukocyte antigen class I molecule-peptide fragment; and then constructing a model of a convolutional neural network to quantify the binding affinity score of the human leukocyte antigen class I molecule and the peptide fragment. The invention develops a method for predicting binding affinity of human leukocyte antigen class I molecules and peptide fragments based on a convolutional neural network by combining physicochemical characteristics of amino acids, complementary epitope sequences of the human leukocyte antigen class I molecules and an interaction relationship among the sequences.

The invention discloses a low-abundance pathogen detection method and system based on metagenome sequencing data, and belongs to the technical field of bioinformatics. The system comprises a species specificity sequence screening module (BactAlert2SSRs), a reference genome database construction module (BactAlert2Lib), a sequence alignment reference genome module (BactAlert2Map), a sequence alignment result analysis module (BactAlert2ID), and an SSRs posterior probability correction module (BactAlert2Rec). The detection system realizes efficient, accurate and reliable detection of low-abundance pathogens, and is suitable for analyzing third-generation metagenome sequencing data.

The invention relates to the technical field of data assimilation, and particularly provides a data assimilation method and equipment based on digital twinning and virtual-real verification, and a medium. The data assimilation method based on digital twinning and virtual-real verification comprises the following steps: constructing an agent model of an observation object, and obtaining a prediction result output by the agent model and an actual observation result of the observation object; consistency verification is carried out on the prediction result and the actual observation result, and the prediction result and the actual observation result which pass verification are obtained; performing sequence alignment processing on the predicted result and the actual observation result which pass the verification to obtain a processed predicted result and an actual observation result; and performing data assimilation on the processed prediction result based on the processed actual observation result to obtain a target prediction result of the observation object. According to the method, the technical problems that actual observation data has noise and has sparsity are solved through consistency verification and sequence alignment processing, so that the accuracy and reliability of an agent model prediction result are improved.

The invention relates to the technical field of molecular detection, and particularly discloses a liquid chip for identifying opisthopapus taihangensis and Hebei province as well as a preparation method and application of the liquid chip. Genotyping sites of the liquid chip comprise 1140 SNP sites; the physical positions of the 1140 SNP loci are determined based on comparison of a whole genome sequence of a bovine reference genome, and the version number of the whole genome sequence of the bovine reference genome is ARSUCD 1.2; wherein the physical position information of the 1140 SNP loci is as shown in a table 1. The liquid chip can realize genetic typing of the opisthopapus taihangensis and the Hebei province cattle, has the advantage of high detection rate, and can obviously cluster the opisthopapus taihangensis and the Hebei province cattle into two classes when clustering analysis is carried out on the opisthopapus taihangensis and the Hebei province cattle, so that a marker combination of the liquid chip can represent whole genome re-sequencing genes, assists in distinguishing the opisthopapus taihangensis and the Hebei province cattle, and is high in accuracy and high in accuracy. Therefore, the variety identification of the opisthopapus taihangensis and the Hebei Hebei is realized.

A machine learning-assisted intelligent device for optimizing personalized cancer treatment based on genomic data, the device comprising the following: a genome data input unit configured to receive and preprocess genome sequences from a variety of external data sources, the preprocessing including noise reduction, sequence alignment, and filtering of irrelevant genome segments; a mutation detection unit comprising a deep learning-based processing module, wherein the mutation detection unit is configured to compare the preprocessed genome sequences with a database of known cancer-related mutations and biomarkers and to create a mutation profile associated with a patient's cancer; an optimization unit for machine learning models comprising at least one neural network architecture, wherein the optimization unit is configured to receive the mutation profile, analyze the identified mutations, correlate the mutations with historical patient outcomes, and generate an initial treatment recommendation; a treatment recommendation engine configured to use reinforcement learning algorithms, wherein the engine continuously learns from real-time patient responses to previous treatments and dynamically adapts treatment strategies based on updated genomic data and evolving patient conditions, thus providing an optimized treatment plan; an outcome prediction module configured to analyze potential outcomes of the recommended treatments based on predictive analysis models, whereby the module generates a probability value for treatment success and presents a detailed explanation of the correlation between genomic markers and the expected efficacy of the treatment; a secure data transmission interface configured for bidirectional communication with hospital networks and genome research centers, with the interface complying with data protection standards to protect sensitive patient information; a display interface configured to visually represent the results of genomic analyses, treatment recommendations, and outcome predictions, wherein the display interface allows user interaction to adjust treatment parameters based on clinical inputs; and a power management unit configured to regulate energy consumption, wherein the power management unit includes a cooling system to maintain the operating temperature of the device during high-performance data processing tasks.

The application discloses a kind of electric power infrastructure line operation behavior intelligent identification method, belong to pattern recognition and data processing technical field, it includes constructing heterogeneous space-time scene atlas to carry out multi-modal structured perception to operation site;Continuous behavior flow is unsupervised and is divided into behavior base element and is encoded as observation sequence;Offline analysis safety operation regulation and is compiled as paradigm behavior genome;Observation sequence and paradigm genome are locally sequenced to be aligned to quantify behavior deviation;The jump critical point of risk state is identified based on deviation disturbance, and structured early warning event is generated.The application constructs heterogeneous space-time scene atlas to fuse multi-modal perception data, abstracts actual operation behavior as observation behavior base element sequence, and carries out sequence alignment with paradigm behavior genome compiled from safety regulation, can quantify behavior deviation and identify the jump critical point of risk state, realize accurate, forward-looking intelligent identification and early warning to operation behavior.

The invention provides a protein language model pre-training and protein mutation method and related products. According to one specific embodiment of the protein language model pre-training method, a sample protein data set is obtained; generating a multi-sequence alignment probability distribution sequence corresponding to the sample protein data according to the amino acid residue probability distribution of multiple sequences in each sample protein data alignment at each site; the sample protein sequence in each sample protein data and the corresponding multi-sequence comparison probability distribution sequence and structure sequence are sequentially connected in series in the forward direction or the reverse direction, and a multi-modal sequence corresponding to the corresponding sample protein data is generated; and finally, performing autoregression pre-training on the protein language model based on the multi-modal sequence corresponding to each sample protein data to obtain a pre-trained protein language model. Namely, the prediction performance of the model is improved by introducing a multi-sequence comparison probability distribution sequence as an independent intermediate reasoning mode and thinking chains in two directions.

The invention discloses a method, a system and equipment for detecting internal tandem repeat and a storage medium, and the key points of the technical scheme are as follows: obtaining a first reference sequence according to at least one target exon sequence corresponding to a protooncogene, and obtaining a second reference sequence according to at least one target intron sequence corresponding to the protooncogene; comparing the sequencing data of the to-be-detected sample to the second reference sequence to obtain a first comparison result, extracting an uncompared sequence from the sequencing data according to the first comparison result, and comparing the uncompared sequence to the first reference sequence to obtain a second comparison result; and determining a first detection result according to the second comparison result and a first reference sequence, and performing false positive filtering on the first detection result to obtain a second detection result. According to the invention, false positive can be reduced so as to ensure the accuracy and reliability of subsequent analysis.

A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.

The invention relates to a probe design of a high-throughput single cell space transcriptome based on multi-round imaging coding. The invention provides a design method of a readout probe for a high-throughput single-cell space transcriptome, which is simultaneously suitable for more than two species tissues, and comprises the following steps: generating a candidate DNA sequence library with a sequence length of n, filtering out a sequence capable of being compared to a genome of a target species, filtering out a candidate DNA probe containing a short sequence appearing on the genome for more than one time, and obtaining a read-out probe for the high-throughput single-cell space transcriptome. The method comprises the steps of filtering candidate DNA probes capable of being compared to transcriptomes of target species through local BLAST, repeating the steps S20-S40 for all target species, performing probe internal mutual binding screening through the local BLAST, performing probe secondary structure inspection and probe internal mutual binding inspection through two-sequence comparison, performing online BLAST inspection on the probes, and determining the target species according to the BLAST. The final DNA probe sequence is obtained.

The invention discloses a molecular marker combination for analyzing goat varieties and application, relates to the technical field of biology, and provides 13947 molecular markers capable of analyzing goats, and the physical position information of the molecular markers is determined based on genome sequence comparison of a goat reference genome ARS1.2. A molecular probe combination, a gene chip and a kit prepared from 13947 SNP molecular markers can be used for carrying out genetic evaluation on goat individuals, carrying out individual selection on goat varieties which are difficult to determine in the early stage, shortening the generation interval and accelerating the breeding process, so that a large amount of breeding cost is saved.

Provided are a Medicago sativa SNP molecular marker combination and a use thereof. The SNP molecular marker combination consists of 61351 SNP molecular markers. The physical positions of the 61351 SNP molecular markers are determined by sequence alignment on the basis of the Medicago sativa reference genome ZM-4 alfalfa genome. The SNP molecular markers have good site specificity, strong versatility, and high polymorphism and can effectively discriminate Medicago sativa germplasms from different sources. A Medicago sativa genome-wide liquid-phase gene chip is prepared from the SNP molecular markers, and can be used for genetic diversity assessment of Medicago sativa germplasm resources, genetic map construction and functional gene mapping, genome-wide association study, etc., providing an important tool and technical support for Medicago sativa molecular design breeding and variety authenticity identification, and achieving important significance for high-quality development of the alfalfa seed industry in China.

The present invention discloses a SNP molecular marker combination of poplar, a whole-genome liquid chip prepared therefrom, and applications thereof. The SNP molecular marker combination of poplar includes 60,944 SNP molecular markers, and the physical positions of the 60,944 SNP molecular markers are determined by sequence alignment based on the poplar reference genome GCA_015852605.2. The SNP molecular markers of poplar in the present invention can be used for specific identification of five major sections of poplar species. Using the SNP molecular markers of poplar to prepare a whole-genome liquid chip for poplar tree species can achieve efficient, accurate genotyping detection of low-cost poplar germplasm resource populations, and is specific to the sections of Populus, such as Leuce, Aigeiros, Tacamahaca, Leucoides, and Turanga, and has important application values in multiple fields of poplar biological breeding.

The invention discloses a group of shark nano antibodies targeting infectious subcutaneous and hematopoietic necrosis viruses of prawns and a preparation method of the shark nano antibodies. The amino acid sequence of the nano antibodies is any one amino acid sequence selected from SEQ ID NO.8-14, and the nano antibodies can be used for detecting cp, nsp1 and nsp2 antigen proteins of the infectious subcutaneous and hematopoietic necrosis viruses. The preparation or screening method of the nano antibody comprises the following steps: A, expression and purification of cp, nsp1 and nsp2 antigen proteins; b, constructing a shark natural library and / or a shark immune library; c, assisting in phage rescue, and purifying to obtain a phage library; d, panning a phage library of anti-cp, anti-nsp1 and anti-nsp2 antigen proteins; and E, carrying out phage-ELISA identification and sequencing, and carrying out sequence comparison on the cp nano antibody, the nsp1 nano antibody and the nsp2 nano antibody with different CDR3 region sequences. The nano antibody provided by the invention can specifically recognize infectious subcutaneous and hematopoietic necrosis viruses, can detect whether aquaculture animals and aquaculture feeds in aquaculture water are polluted by viruses or not, and realizes rapid detection.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.