A data analysis method, system, device and storage medium for virus database

Abstract

The present invention discloses a data analysis method, system, equipment and storage medium for a virus database, comprising the following steps: based on the virus data of a sample, the virus data is compared with the host genome after quality control, host contamination is removed, and the virus data to be analyzed after screening is obtained; the virus data to be analyzed is assembled with macro viruses to obtain virus contigs; the virus contigs are evaluated and screened, false positive viruses are removed, and virus contig screening results are obtained; virus contig screening results are subjected to virus classification to obtain virus contig groups; the virus contig groups are compared to a known virus database to determine whether the virus contig groups are in a known virus database; if not, the virus contig groups are written into a known virus database based on the functional classification and virus abundance of the virus contig groups to update the known virus database. The data analysis method of the present invention can improve the validity and accuracy of virus data and expand the known virus database.

Description

Technical Field

[0001] The present invention relates to the field of gene detection technology, and in particular to a data analysis method, system, device and storage medium for a virus database. Background Art

[0002] Macrovirome is a new branch of metagenomics that studies the genetic material of all viruses in the environment and identifies all viral components in the environment. The research scope includes human or animal intestines, oceans, soil, etc., in order to explore potential hazards to humans and the environment.

[0003] However, unlike other microorganisms such as bacteria or fungi, viruses do not have universal marker genes that can be studied as a whole. Therefore, it is not possible to classify and analyze the corresponding virus community through tag sequence amplicon sequencing, and it is impossible to systematically obtain the diversity of virus composition and the functions performed in the sample. The existing analysis methods for gene macro viruses are analyzed according to the analysis methods of metagenomics, and some are further identified by self-developed software. The comparison database is also based on the NR total database. This results in inaccurate virus classification, low reliability, and deviation in results. Summary of the invention

[0004] The present invention aims at the shortcomings of the prior art and provides a data analysis method, system, device and storage medium for a virus database.

[0005] In order to solve the above technical problems, the present invention is solved by the following technical solutions:

[0006] A data analysis method for a virus database comprises the following steps:

[0007] Based on the virus data of the sample, the virus data is compared with the host genome after quality control to remove host contamination and obtain the screened virus data to be analyzed;

[0008] Assembling a macro virus for the virus data to be analyzed to obtain a virus contig;

[0009] Evaluating and screening the virus contigs, removing false positive viruses, and obtaining virus contig screening results;

[0010] Performing virus classification on the virus contig screening results to obtain virus contig groups;

[0011] Comparing the virus contig group to a known virus database to determine whether the virus contig group is in the known virus database;

[0012] If not, the viral contig group is written into the known virus database based on the functional classification and viral abundance of the viral contig group to update the known virus database.

[0013] Preferably, trimmomatic software is used for quality control, and the virus data is preprocessed, comprising the following steps:

[0014] Filter the bases with a quality value of less than 20 at the tail of the virus data reads. The specific method is as follows: set a 50 bp window, move the window from the head, and if the average quality value in the window is less than 20, cut off the back end base from the window;

[0015] Remove reads containing more than 2 N bases, remove reads contaminated by adapters, and remove reads less than 50 bp. This quality control method screens out high-quality virus data to improve the effectiveness of analyzing virus data.

[0016] Use bwa to align sequencing reads to the host genome, remove reads with a similarity of >90% to the host genome, remove host contamination, and prevent inaccurate analysis results caused by host contamination.

[0017] Preferably, the MetaviralSPAdes module under the metaSPAdes software is used to obtain the virus contig through different kmer assemblies and virus structural characteristics.

[0018] Preferably, Virsorter, Virfinder, CheckV software is used to evaluate and screen the virus contigs to remove false positive virus data, comprising the following steps:

[0019] Virsorter software classifies viral contigs into six categories, taking the first and second categories with the highest credibility;

[0020] The Virfinder software scores each of the first and second categories and sets a threshold: score>0.9 and p-value<0.05. Contigs that meet the threshold are viruses, and those that do not meet the threshold are screened out to obtain the virus contigs to be judged;

[0021] The CheckV software determines the integrity and contamination of the virus contig based on the structural characteristics of the virus genome, screens and removes false-positive viruses in the virus contig, prevents false-positive viruses caused by sample contamination or contamination during the detection process from affecting the analysis results, and improves the reliability of data analysis.

[0022] Preferably, the virus contig screening results are classified using Vcontact and vpf software to obtain the virus contig groups and construct a phylogenetic tree, specifically:

[0023] The Vcontact software was used to automatically perform a clustering algorithm on the viral contig screening results;

[0024] Virus clusters were defined using ClusterONE of Vcontact software;

[0025] The hmmsearch software in vpf software was used to identify virus clusters to obtain virus classification information and virus contig groups;

[0026] The phylogenetic tree is constructed based on the degree of similarity of the global comparison of viral genome sequences, which makes the relationship between viruses clearer intuitively.

[0027] Preferably, the calculation of viral abundance uses Salmon software to map clean data to each viral gene sequence to obtain the viral abundance of the viral contig in the sample using the following formula:

[0028]

[0029] Ng is the number of reads in the alignment, j is all genes, and the calculation of viral abundance serves as the basis for subsequent quantitative analysis of viral function.

[0030] Preferably, the virus contig group is compared to a known database using blast software to determine whether the virus contig group is in the known database. The specific steps are as follows:

[0031] The virus contig groups were aligned to a known virus library using the blast software, and those contigs with alignment similarity > 60% and evalue < 1e-5 were defined as known viruses;

[0032] The viral contig sequences that were not identified as known viruses were compared with the NT library and the NR library using blast software, and the comparison results with evalue < 1e-5 and evalue < 1e-3 were screened out respectively;

[0033] The two alignment results are annotated using NCBI Taxonomy data. If more than 20% of the alignment results in the first 50 alignment results are non-viral sequences, the sequence is a non-viral sequence, otherwise it is a viral sequence; the viral sequence is a new viral group. This method can effectively screen out known viral groups and unknown viral groups.

[0034] Preferably, writing the viral contig group into a known virus database based on the functional classification and viral abundance of the viral contig group to update the known virus database comprises the following steps:

[0035] Download virus information with clear Latin names based on the NR database, and use the virus information in the NR database as the basic information of the organized virus database;

[0036] Classify each virus based on the source in the gbff file. If a virus has multiple sources, it will be divided into multiple different categories.

[0037] Expand and update the virus database based on the information on the expasy virus database to establish a known virus database;

[0038] The unknown virus sequence to be analyzed is compared with the KEGG database using blast software to obtain the functional classification of the virus at the KEGG orthology and pathway levels; the KEGG functional abundance of the unknown virus can be obtained by combining the virus abundance;

[0039] The unknown virus sequence to be analyzed is compared with the uniprot database using blast software to obtain the function information of the virus on DNA polymerase and RNA polymerase; the polymerase abundance information of the unknown virus can be obtained by combining the abundance of the virus;

[0040] The functional information of unknown virus contig groups obtained in this way is expanded into the known virus database to update the known virus database.

[0041] A data analysis system for a virus database, comprising a preprocessing module, an assembling module, a screening module, a classification module, a comparison module and an updating module;

[0042] The preprocessing module preprocesses the virus data based on the virus data of the sample, removes the virus data contaminated by the host, and obtains the virus data to be analyzed;

[0043] The assembly module is used to assemble the macro virus from the virus data to be analyzed to obtain the virus contig;

[0044] The screening module is used to evaluate and screen the virus contigs, remove false positive viruses, and obtain virus contig screening results;

[0045] The classification module is used to classify the virus contig screening results to obtain virus contig groups;

[0046] The comparison module is used to compare the virus contig group with the known virus library to determine whether the virus contig group exists in the known virus library;

[0047] The updating module is configured to: if not, write the virus contig group into the known virus library based on the functional classification and virus abundance of the virus contig group to update the known virus library.

[0048] An electronic device comprises a processor, a storage medium and a computer program, wherein the computer program is stored in the storage medium and implements a data analysis method for a virus database when the computer program is executed by the processor.

[0049] A computer-readable storage medium stores a computer program, which implements a data analysis method for a virus database when executed by a processor.

[0050] The data analysis method, system, device and storage medium of a virus database implemented by the present invention, through strict screening of the virus data of the sample and pretreatment to remove contamination, assembling macro viruses to obtain virus contigs, and only after the evaluation and screening to remove false positive viruses, can the virus contigs continue to be classified and compared to determine whether the virus contig group exists in a known virus library, if it exists, the analysis ends; if it does not exist, the virus contig group is written into the known virus library based on the functional classification and virus abundance of the virus contig group to update the known virus library. This analysis method can effectively improve the accuracy and effectiveness of the data, and can evaluate and identify a large number of unknown viruses, expand the known virus library, and provide effective theoretical guidance in disease prevention and environmental governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0052] Figure 1 It is a schematic diagram of a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the embodiments, which are explanations of the present invention and are not limited to the following embodiments. The various embodiments can be combined with each other to form other embodiments not shown in the following description.

[0054] Example 1

[0055] This embodiment provides a data analysis method for a virus database. Figure 1 , including the following steps:

[0056] Step S101, based on the virus data of the sample, the virus data is compared with the host genome after quality control to remove host contamination, and obtain high-quality virus data to be analyzed after screening;

[0057] Step S102, assembling a macro virus from the virus data to be analyzed to obtain a virus contig;

[0058] Step S103, evaluating and screening the virus contigs, removing false positive viruses, and obtaining virus contig screening results;

[0059] Step S104, classifying the virus contig screening results to obtain virus contig groups;

[0060] Step S105, comparing the virus contig group to a known database to determine whether the virus contig group is in the known database; if so, the analysis ends; if not, proceeding to step S106;

[0061] Step S106: if not, then based on the functional classification and virus abundance of the virus contig group, the virus contig group is written into the known virus library to update the known virus library.

[0062] Step S101 uses trimmomatic software for quality control, and pre-processes the virus data, including the following steps:

[0063] Filter the bases with a quality value of less than 20 at the tail of the virus data reads. The specific method is as follows: set a 50 bp window, move the window from the head, and if the average quality value in the window is less than 20, cut off the back end base from the window;

[0064] Remove reads with N base number > 2, remove reads contaminated by adapters, and remove reads below 50 bp. This quality control method screens out high-quality virus data to improve the effectiveness of analyzing virus data.

[0065] Usually, sequencing data will be mixed with host genome fragments. Before assembly, it is necessary to remove these host-derived sequences, use bwa to align sequencing reads to the host genome, and remove reads with a similarity of >90% with the host genome to prevent host contamination from causing inaccurate analysis results.

[0066] After obtaining the screened virus data, the MetaviralSPAdes module under the metaSPAdes software is used according to step S102. The MetaviralSPAdes is a newly developed module under the metaSPAdes software, which is specifically used to assemble virus contigs from environmental samples; through different kmer assemblies and virus structural characteristics, a virus contig with as long as possible and deep coverage is obtained.

[0067] Next, step S103 uses Virsorter, Virfinder, and CheckV software to evaluate and screen the virus contigs and remove false positive virus data, including the following steps:

[0068] The Virsorter software classified the virus contigs into 6 categories, taking the first and second categories with the highest credibility;

[0069] The Virfinder software scores each of the first and second categories and sets a threshold: score>0.9 and p-value<0.05. Contigs that meet the threshold are viruses, and those that do not meet the threshold are screened out to obtain the virus contigs to be judged;

[0070] The CheckV software judges the integrity and contamination of the virus contig based on the structural characteristics of the viral genome, screens and removes false-positive viruses in the contig. After three steps of screening, it effectively prevents false-positive viruses caused by sample contamination or contamination during the detection process from affecting the analysis results, thereby improving the reliability of data analysis.

[0071] The step S104 uses Vcontact and vpf software to classify the virus contig screening results, obtain the virus contig groups and construct a phylogenetic tree, specifically:

[0072] The Vcontact software was used to automatically cluster the virus contig screening results: the new clustering algorithm, cluster confidence score and network analysis of the Vcontact software were used to automatically cluster the viral amino acid sequences and this algorithm improved the classification assignment and can be applied to larger data sets. This algorithm establishes classification separation units based on confidence scores and distances for hierarchical classification, and optimizes and evaluates scalability and stability using a large-scale viral metagenomic data set. After forming a Markov algorithm clustering protein integration group, the software optimized the protein clusters and established an automated two-step process. ClusterONE was then used to define the virus clusters, and then hierarchical clustering was used to subdivide the problematic areas in the network. This method is more accurate and convenient for clustering viruses;

[0073] The protein sequence predicted by prodigal was compared with the virus sequence in the database using the hmmsearch software in the vpf software. The threshold was set to 1e-3 to obtain virus classification information and virus contig groups. A phylogenetic tree was constructed based on a global comparison of the genome sequences of each virus. The evolutionary relationship of each virus on the phylogenetic tree was used to obtain the relationship between the viruses, making the relationship between the viruses clearer and the classification in the virus database clearer.

[0074] The calculation of virus abundance was performed by mapping the clean data to each virus gene sequence using Salmon software to obtain the virus abundance of the virus contig in the sample using the following formula:

[0075]

[0076] Ng is the number of reads in the alignment, and j is all genes. Calculating viral abundance is the basis for subsequent quantitative analysis of viral function.

[0077] The step S105 uses blast software to compare the virus contig group to a known database to determine whether the virus contig group is in the known database. The specific steps are as follows:

[0078] The obtained virus contig groups are aligned to a known virus library using the blast software, and those contigs with alignment similarity greater than 60% and evalue less than 1e-5 are defined as known viruses;

[0079] The viral contig sequences that were not identified as known viruses were compared with the NT library and the NR library using blast software, and the comparison results with evalue < 1e-5 and evalue < 1e-3 were screened out respectively;

[0080] The two alignment results were annotated using NCBI Taxonomy data, and the annotation results were Eukaryota, Bacteria, and Archaea. If more than 20% of the alignment results in the first 50 alignment results were non-viral sequences, the sequence was a non-viral sequence, otherwise it was a viral sequence; the viral sequence was a new viral group. This method can effectively screen out known viral groups and unknown viral groups to be analyzed.

[0081] The step S106 writes the viral contig group into a known virus database based on the functional classification and viral abundance of the viral contig group to update the known virus database, including:

[0082] Download virus information with clear Latin names based on the NR database, and use the virus information in the NR database as the basic information of the organized virus database;

[0083] Classify each virus based on the source in the gbff file. If a virus has multiple sources, it will be divided into multiple different categories.

[0084] The virus database is expanded and updated based on the information on the expasy virus database, which mainly contains known animal and plant viruses, including virus sequences and classifications. Add in and update the known virus database to the greatest extent;

[0085] The unknown virus group to be analyzed is compared with the KEGG database using blast software to obtain the functional classification of the virus at the KEGG orthology and pathway levels; the KEGG functional abundance of the unknown virus can be obtained by combining the virus abundance;

[0086] The unknown virus group to be analyzed is compared with the uniprot database using blast software to obtain the virus's functional information on DNA polymerase and RNA polymerase; the polymerase abundance information of the unknown virus can be obtained by combining the virus abundance;

[0087] The functional information of the unknown virus contig group obtained in this way is expanded into the known virus database to update the known virus database.

[0088] Example 2

[0089] Embodiment 2 discloses a data analysis system for a virus database of Embodiment 1, including:

[0090] A preprocessing module, based on the virus data of the sample, preprocesses the virus data to remove the virus data contaminated by the host, and obtains the virus data to be analyzed;

[0091] The assembly module is used to assemble the macro virus from the virus data to be analyzed to obtain the virus contig;

[0092] A screening module is used to evaluate and screen viral contigs, remove false positive viruses, and obtain viral contig screening results;

[0093] A classification module is used to classify the virus contig screening results to obtain virus contig groups;

[0094] A comparison module is used to compare the virus contig group with the known virus library to determine whether the virus contig group exists in the known virus library;

[0095] The updating module is configured as follows: if not, then based on the functional classification and virus abundance of the virus contig group, the virus contig group is written into the known virus library to update the known virus library.

[0096] Example 3

[0097] Embodiment 3 provides a data analysis electronic device for the virus database of Embodiment 1, comprising: a processor, a memory, an input device, and an output device; the number of processors in the computer device can be one or more, and one processor is used in this embodiment; the processor, memory, input device, and output device in the electronic device are connected via a bus or other means, and in this embodiment, the connection via a bus is taken as an example.

[0098] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions corresponding to the data analysis method of the virus database in the embodiment of the present invention. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory, that is, the data analysis method of the virus database in Example 1 is implemented.

[0099] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0100] The input device can be used to input virus data, etc. The output device is used to output and display the results of virus data processing, screening, classification, comparison, etc.

[0101] Example 4

[0102] Embodiment 4 of the present invention further provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to implement a data analysis method for a virus database when executed by a computer processor, the method comprising:

[0103] Based on the virus data of the sample, the virus data is compared with the host genome after quality control to remove host contamination and obtain the screened virus data to be analyzed;

[0104] Assembling a macro virus for the virus data to be analyzed to obtain a virus contig;

[0105] Evaluating and screening the virus contigs, removing false positive viruses, and obtaining virus contig screening results;

[0106] Performing virus classification on the virus contig screening results to obtain virus contig groups;

[0107] Comparing the virus contig group to a known virus database to determine whether the virus contig group is in the known virus database;

[0108] If not, the viral contig group is written into the known virus database based on the functional classification and viral abundance of the viral contig group to update the known virus database.

[0109] Of course, the computer executable instructions of a storage medium including computer executable instructions provided in an embodiment of the present invention are not limited to the operations of the method described above, but can also execute related operations in the data analysis method of the virus database provided in any embodiment of the present invention.

[0110] It is worth noting that in the embodiment of the above-mentioned data analysis method for a virus database, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0111] In addition, it should be noted that the basic principles, main features and advantages of the present invention are described in this specification. The shapes, names, etc. thereof may be different. A person skilled in the art of the present invention may make various modifications or supplements to the specific embodiments described or replace them in a similar manner, and as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A data analysis method for a virus database, characterized in that: The following steps are involved: Based on the virus data of the sample, the virus data is compared with the host genome after quality control to remove host contamination and obtain the screened virus data to be analyzed; Assembling a macro virus for the virus data to be analyzed to obtain a virus contig; Evaluating and screening the virus contigs, removing false positive viruses, and obtaining virus contig screening results; The virus contigs are evaluated and screened using Virsorter, Virfinder, and CheckV software to remove false positive virus data, including the following steps: Virsorter software classifies viral contigs into six categories, taking the first and second categories with the highest credibility; The Virfinder software scores each of the first and second categories and sets a threshold: score>0.9 and p-value<0.

05. Contigs that meet the threshold are viruses, and those that do not meet the threshold are screened out; The CheckV software determines the integrity and contamination of the assembled contigs based on the structural characteristics of the viral genome, and removes false positive viruses from the contigs; Performing virus classification on the virus contig screening results to obtain virus contig groups; Comparing the virus contig group to a known virus database to determine whether the virus contig group is in the known virus database; The blast software is used to compare the virus contig group to a known database to determine whether the virus contig group is in the known database. The specific steps are as follows: The obtained virus contig groups are aligned to a known virus library using the blast software, and contigs with alignment similarity greater than 60% and evalue less than 1e-5 are defined as known viruses; The viral contig sequences that were not identified as known viruses were compared with the NT library and the NR library using blast software, and the comparison results with evalue < 1e-5 and evalue < 1e-3 were screened out respectively; Obtain the unknown virus sequence to be analyzed through the comparison results after screening; If not, then based on the functional classification and viral abundance of this viral contig group, this viral contig group is written into the known virus database to update the known virus database; Wherein, based on the functional classification and virus abundance of the virus contig group, the virus contig group is written into the known virus database to update the known virus database, including the following steps: Download virus information with clear Latin names based on the NR database; Classify based on source in gbff file; Expand and update the virus database based on the information in the expasy virus database to establish a known virus database; The unknown virus sequence to be analyzed was compared with the KEGG database using blast software to obtain the functional classification of the virus at the KEGG orthology and pathway levels, and the KEGG functional abundance of the virus was obtained in combination with the virus abundance; The unknown virus sequence to be analyzed is compared with the uniprot database using blast software to obtain the virus's functional information on DNA polymerase and RNA polymerase, and the virus's polymerase abundance information is obtained in combination with the virus abundance; Among them, Vcontact and vpf software were used to classify the virus contig screening results, obtain the virus contig groups and construct a phylogenetic tree, specifically: The Vcontact software was used to automatically perform a clustering algorithm on the viral contig screening results; Virus clusters were defined using ClusterONE of Vcontact software; The hmmsearch software in vpf software was used to identify virus clusters to obtain virus classification information and virus contig groups; A phylogenetic tree was constructed based on the similarity of the viral genome sequences in a global comparison.

2. The data analysis method of a virus database according to claim 1, characterized in that: When trimmomatic software is used for quality control, the virus data is preprocessed, including the following steps: Filter the bases with a quality value of less than 20 at the tail of the virus data reads, set a 50 bp window, move the window from the head, and if the average quality value in the window is less than 20, cut off the back end base from the window; Remove reads containing more than 2 N bases, remove reads contaminated by adapters, and remove reads less than 50 bp; Sequencing reads were aligned to the host genome using bwa, and reads with >90% similarity to the host genome were removed.

3. The data analysis method of a virus database according to claim 1, characterized in that: The calculation of virus abundance uses Salmon software to map clean data to each viral gene sequence to obtain the abundance of virus contigs in the sample. The unknown virus sequence to be analyzed was compared with the uniprot database using blast software to obtain the virus's functional information on DNA polymerase and RNA polymerase, and the virus's polymerase abundance information was obtained in combination with the virus abundance.

4. A data analysis system for a virus database, characterized in that: It includes preprocessing module, assembly module, screening module, classification module, comparison module and updating module; The preprocessing module, based on the virus data of the sample, compares the virus data with the host genome after quality control, removes host contamination, and obtains the screened virus data to be analyzed; The assembly module is used to assemble a macro virus from the virus data to be analyzed to obtain a virus contig; The screening module is used to evaluate and screen the virus contigs, remove false positive viruses, and obtain virus contig screening results; The virus contigs are evaluated and screened using Virsorter, Virfinder, and CheckV software to remove false positive virus data, including the following steps: Virsorter software classifies viral contigs into six categories, taking the first and second categories with the highest credibility; The Virfinder software scores each of the first and second categories and sets a threshold: score>0.9 and p-value<0.

05. Contigs that meet the threshold are viruses, and those that do not meet the threshold are screened out; The CheckV software determines the integrity and contamination of the assembled contigs based on the structural characteristics of the viral genome, and removes false positive viruses from the contigs; The classification module is used to classify the virus contig screening results to obtain virus contig groups; The comparison module is used to compare the virus contig group to a known virus database to determine whether the virus contig group is in the known virus database; The blast software is used to compare the virus contig group to a known database to determine whether the virus contig group is in the known database. The specific steps are as follows: The obtained virus contig groups are aligned to a known virus library using the blast software, and contigs with alignment similarity greater than 60% and evalue less than 1e-5 are defined as known viruses; The viral contig sequences that were not identified as known viruses were compared with the NT library and the NR library using blast software, and the comparison results with evalue < 1e-5 and evalue < 1e-3 were screened out respectively; Obtain the unknown virus sequence to be analyzed through the comparison results after screening; The updating module is configured to: if not, write the virus contig group into the known virus database based on the functional classification and virus abundance of the virus contig group to update the known virus database; Wherein, based on the functional classification and virus abundance of the virus contig group, the virus contig group is written into the known virus database to update the known virus database, including the following steps: Download virus information with clear Latin names based on the NR database; Classify based on source in gbff file; Expand and update the virus database based on the information in the expasy virus database to establish a known virus database; The unknown virus sequence to be analyzed was compared with the KEGG database using blast software to obtain the functional classification of the virus at the KEGG orthology and pathway levels, and the KEGG functional abundance of the virus was obtained in combination with the virus abundance; The unknown virus sequence to be analyzed is compared with the uniprot database using blast software to obtain the virus's functional information on DNA polymerase and RNA polymerase, and the virus's polymerase abundance information is obtained in combination with the virus abundance; Among them, Vcontact and vpf software were used to classify the virus contig screening results, obtain the virus contig groups and construct a phylogenetic tree, specifically: The Vcontact software was used to automatically perform a clustering algorithm on the viral contig screening results; Virus clusters were defined using ClusterONE of Vcontact software; The hmmsearch software in vpf software was used to identify virus clusters to obtain virus classification information and virus contig groups; A phylogenetic tree was constructed based on the similarity of the viral genome sequences in a global comparison.

5. An electronic device comprising a processor, a storage medium and a computer program, wherein the computer program is stored in the storage medium, wherein: When the computer program is executed by a processor, the data analysis method for a virus database according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data analysis method for a virus database according to any one of claims 1 to 3 is implemented.

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