Virus species identification method, identification system, equipment and medium

Abstract

The present invention discloses a method, system, device, and medium for viral species identification. The method comprises: obtaining an original reference database comprising non-redundant viral species and subspecies sequences; breaking sequences in the original reference database and comparing them with the original reference database, replacing sequences that match more than one instance in the original reference database to obtain a first reference database; aligning raw data from second- and third-generation sequencing to the first reference database, and identifying species based on abundance; breaking sequences in the original reference database and comparing them with the original reference database, replacing matched sequences to obtain a second reference database; aligning the spliced ​​genomic data to the second reference database, and identifying species based on bit fractions. The present invention designs a standardized analytical process that directly utilizes a reads data alignment strategy for viral species identification. The present invention can identify a wide range of species with high accuracy down to the subspecies level, and achieves a high and rapid identification rate.

Description

Technical Field

[0001] The present invention relates to the technical field of species identification, and in particular to a virus species identification method, identification system, equipment and medium. Background Art

[0002] Accurate identification of viral species is crucial for disease prevention, control, and treatment. However, the vast variety of viruses, rapid genetic variation, and high host specificity and environmental adaptability of many viruses make viral species identification a complex and arduous task.

[0003] Currently, viral species identification mainly relies on traditional microscopic imaging techniques, immuno-serological detection, and molecular biological detection methods. Although these methods are currently commonly used, they also have significant limitations.

[0004] Although microscopic imaging technology has the advantage of intuitively displaying the morphology of viruses and the infection process in virological research, its application is subject to multiple limitations. First, the morphological characteristics of viruses are extremely small and diverse, and traditional optical microscopes often cannot achieve sufficient resolution to clearly identify all types of viruses. Although electron microscopes can provide higher resolution, the sample preparation process is complicated and has high requirements for sample integrity. It is easy to introduce artifacts or destroy the natural state of the virus during the preparation process. In addition, the dynamic process of viruses in cells is difficult to fully capture through static microscopic images, which limits the application of microscopic imaging technology in the study of viral life cycles.

[0005] Immuno-serological testing relies on the specific binding between antibodies and antigens, but this specificity is not absolute. The antibody preparation process may be affected by a variety of factors, such as the purity of the antigen, the design of the immunization program, etc., resulting in insufficient antibody specificity or non-specific cross-reactions. This means that antibodies may not only bind to the target virus, but may also react with other microorganisms or host components in the sample, producing false positive results. In addition, for emerging or mutated viruses, immuno-serological testing may not be carried out in a timely and effective manner due to the lack of corresponding antibodies. The preparation and validation of antibodies also takes time, which is particularly disadvantageous in emerging epidemics.

[0006] Molecular biology detection methods, with their high sensitivity and high specificity, play an important role in virus detection. However, the application of this method also faces challenges. Molecular biology detection requires the design of primers based on known viral sequence information, which means that for new or unknown viruses, it may not be implemented or may not be effective due to the lack of sequence information. In addition, genetic variation of the virus may cause the match between the primer and the viral sequence to decrease, affecting the accuracy of the detection. Even for known viruses, if the viral sequence mutates significantly, the primers may need to be redesigned, which increases the detection time. In addition, molecular biology detection may also be affected by factors such as inhibitors, contamination or degradation in the sample, resulting in test failure or inaccurate results.

[0007] Since microscopic imaging technology is limited by the morphological characteristics of the virus and sample preparation; immune-serological testing may be affected by antibody specificity and cross-reaction; molecular biological detection methods, such as PCR, although highly sensitive, usually require known viral sequence information as the basis for primer design, and have blind spots in the detection of new or unknown viruses, there is an urgent need to develop a virus species identification method, identification system, equipment and medium. Summary of the Invention

[0008] In view of the above problems, the present invention provides a virus species identification method, identification system, device and medium.

[0009] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0010] In a first aspect, the present invention provides a method for identifying virus species, comprising:

[0011] Obtaining an original reference database, wherein the original reference database includes non-redundant viral species genome sequences and representative functional fragment sequences of non-redundant viral subspecies;

[0012] All sequences in the original reference database are interrupted to obtain a number of first fragments, and each first fragment is aligned with the original reference database using the BWT algorithm. When there is more than one sequence aligned in the first fragment, all bases in the aligned sequence segment in the original reference database are replaced with N to obtain a first reference database;

[0013] The raw data from the second-generation or third-generation sequencing was aligned to the first reference database using the BWT algorithm to obtain unique aligned reads. The species abundance of the unique aligned reads was counted, and the species with the highest aligned abundance was used as the identified species.

[0014] Sliding in steps of a third length, all sequences in the original reference database are broken into a plurality of second segments, and each second segment is aligned with the original reference database using the BLAST algorithm. If the similarity between the second segment and the sequence alignment in the original reference database reaches a first threshold, and the length of the aligned sub-segment is not less than the third length at more than one position, then all bases in the aligned sequence segment in the original reference database are replaced with N to obtain a second reference database;

[0015] The spliced ​​genome data were aligned to the second reference database using the BLAST algorithm, and the species to which the sequence with the highest bit-score value belonged was identified as the species.

[0016] In a preferred embodiment, the step of breaking all sequences in the original reference database to obtain a plurality of first segments comprises: sliding at a step length of the first length to break all sequences in the original reference database into a plurality of first segments according to a second length;

[0017] The step of breaking all the sequences in the original reference database specifically includes breaking all the sequences in the original reference database into a plurality of second segments according to a fourth length.

[0018] In a preferred embodiment, the first length is 10 bp, the second length is 50 bp, the third length is 50 bp, the fourth length is 500 bp, and the first threshold value range is [98%, 100%].

[0019] In a preferred embodiment, the identification method further comprises:

[0020] Obtain genetic data of the virus species to be identified;

[0021] Determine the data type of the genetic data of the virus species to be identified, wherein the data type includes the spliced ​​genome data, the original data from the second-generation sequencing, and the original data from the third-generation sequencing.

[0022] In a second aspect, the present invention provides a virus species identification system, comprising:

[0023] An acquisition module is used to acquire an original reference database, wherein the original reference database includes non-redundant viral species genome sequences and representative functional fragment sequences of non-redundant viral subspecies;

[0024] A first construction module is used to construct a first reference database, specifically for breaking all sequences in the original reference database into a plurality of first fragments, aligning each first fragment with the original reference database using the BWT algorithm, and if there is more than one sequence aligned in the first fragment, replacing all bases in the aligned sequence segment in the original reference database with N to obtain the first reference database;

[0025] The first identification module is used to align the raw data from the second-generation or third-generation sequencing to the first reference database using the BWT algorithm to obtain unique aligned reads, perform species abundance statistics on the unique aligned reads, and use the species with the highest aligned abundance as the identified species;

[0026] a second construction module for constructing a second reference database, specifically for sliding with a step size of a third length, breaking all sequences in the original reference database into a plurality of second fragments, aligning each second fragment with the original reference database using a BLAST algorithm, and if the similarity between the second fragment and the sequence alignment in the original reference database reaches a first threshold, and the length of the aligned sub-fragment is not less than the third length at more than one position, replacing all bases in the aligned sequence segment in the original reference database with N, to obtain a second reference database;

[0027] The second identification module is used to compare the spliced ​​genome data to the second reference database using the BLAST algorithm, and the species to which the sequence with the highest bit-score value belongs is used as the identified species.

[0028] In a third aspect, the present invention provides an electronic device comprising: a memory; one or more processors; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods for identifying a virus species according to the first aspect and its preferred embodiments.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium comprising instructions, which, when executed on a computer, enable the computer to execute a virus species identification method as described in any one of the first aspect and its preferred embodiments.

[0030] The present invention provides a virus species identification method, identification system, equipment and medium. The original reference database is composed of non-redundant genome sequences of virus species and subspecies. A sequence that replaces the homologous sequence is designed as a reference database and a processing method is specifically designed. The BWA or BLAST algorithm is then used to compare and analyze the second-generation and third-generation original offline data or the spliced ​​genome data, and the final species result is determined based on the comparison results. With such a design, it is possible to achieve the virus species or subtype level, and accurate identification can be achieved between strains with extremely high genome sequence similarity. When outputting the results, the strain information with the highest identification rate is directly given. The implementation principle is different from the existing technology, and the species that can be identified exceed the existing technology, and the identification accuracy is high. The species of new viruses or unknown viruses can also be detected. The present invention realizes simple identification, accurate results and short time consumption of virus identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A schematic diagram of a method for identifying virus species;

[0033] Figure 2 A specific flow chart of a virus species identification method;

[0034] Figure 3 A schematic diagram of a virus species identification system;

[0035] Figure 4 This is a structural framework diagram of a virus species identification system;

[0036] Figure 5 is a schematic diagram of an electronic device;

[0037] Among them, 10, acquisition module, 20, first construction module, 30, first identification module, 40, second construction module, 50, second identification module, 60, output module, 70, test data acquisition module, 80, type determination module. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0039] The commonly used methods for existing virus species identification are mainly microscopic imaging technology, immuno-serological testing and molecular biological detection methods, but these methods have very prominent limitations. Microscopic imaging technology is limited by the morphological characteristics of the virus and sample preparation; immuno-serological testing may be affected by antibody specificity and cross-reaction; molecular biological detection methods, such as PCR, although highly sensitive, usually require known virus sequence information as the basis for primer design, and have blind spots for the detection of new or unknown viruses.

[0040] To this end, the present invention provides a virus species identification method, identification system, equipment and medium. The present invention replaces the non-specific region by comparison, thereby solving the problem of low specificity of the comparison results based on the original offline data of short read length or the shorter genome splicing data. The present invention can achieve the virus species or subtype level, and can also achieve accurate identification between different strains with extremely high genome sequence similarity, and directly give the strain information with the highest identification rate when outputting the results. The implementation principle is different from the existing technology, and the species that can be identified exceed the existing technology, and the identification accuracy is higher. For new viruses or unknown viruses, their species can also be detected, and the identification is simple. At the same time, the present invention can directly use the original sequencing data for identification, omitting the process of data splicing and assembly, and greatly shortening the analysis time.

[0041] The following embodiments of the present invention can be implemented independently or in combination with each other, and the present invention does not impose any specific restrictions on this. In the embodiments of the present invention, "first," "second," and the like are used to describe various components, but these components should not be limited by these terms. These terms are only used to distinguish one component from another. The term "and / or" mentioned in the present invention refers to any and all combinations of one or more of the relevant listed items.

[0042] Below, the virus species identification method, identification system, equipment and medium and their technical effects are described.

[0043] Figure 1 A schematic diagram of a process for identifying virus species provided in one embodiment is shown in FIG. Figure 1 As shown, the method includes the following steps:

[0044] Obtaining an original reference database, wherein the original reference database includes non-redundant viral species genome sequences and non-redundant viral subspecies representative functional fragment sequences;

[0045] All sequences in the original reference database are broken into several first fragments, and each first fragment is aligned with the original reference database using the BWT algorithm. If there is more than one sequence aligned in the first fragment, all bases in the aligned sequence segment in the original reference database are replaced with N to obtain a first reference database;

[0046] The raw data from the second-generation sequencing or third-generation sequencing is aligned to the first reference database using the BWT algorithm to obtain unique aligned reads. The species abundance statistics are performed on the unique aligned reads, and the species with the highest aligned abundance is used as the identified species.

[0047] Sliding in steps of a third length, all sequences in the original reference database are broken into several second segments, and each second segment is aligned with the original reference database using the BLAST algorithm. If the alignment similarity between the second segment and a sequence in the original reference database exceeds (≥) a first threshold, and the length of the aligned sub-segment in the second segment is not less than the third length at more than one position, then all bases in the sequence segment in the original reference database whose alignment similarity is ≥ the first threshold are replaced with N, to obtain a second reference database;

[0048] The spliced ​​genome data were aligned to the second reference database using the BLAST algorithm, and the species to which the sequence with the highest bit-score value belonged was identified as the species.

[0049] It should be understood that the above method does not represent or imply that all steps must be performed in this order. Ordinary technicians in this field can transform or change the execution order of the above steps based on the present invention. Some embodiments of the above method are illustrated below.

[0050] In one embodiment, see Figure 2 , the virus species identification method includes the following steps:

[0051] S1. Obtain the original reference database;

[0052] We use the genome sequence of viral species and representative functional fragment sequences of viral subspecies as the basic data for constructing a reference database. Viral species and subspecies are non-redundant. Here, the genome sequence of a viral species is generally the complete viral gene sequence, while the sequence of a viral subspecies is generally an incomplete viral gene sequence. There is no limit to the number of representative functional fragment sequences of a viral subspecies; a viral subspecies may have several functional fragment sequences, the number of which is determined specifically by the viral subspecies. Representative functional fragment sequences include, for example, fragment sequences that can characterize genetic characteristics or pathogenicity.

[0053] S2. constructing a first reference database based on the original reference database;

[0054] All sequences in the original reference database are fragmented into segments of a second length (preferably 50 bp) in steps of a first length (preferably 10 bp) to obtain a number of first segments, and a fastq file is simulated. It is understood that not all first segments will be the same length as the second length; the final segment of the sequence may be shorter than the second length after fragmentation. Similarly, the following second segments can be generated.

[0055] The first fragment of each fastq file is aligned with the original reference database using the Burrows-Wheeler Transform (BWT) algorithm. For each first fragment, if the sequence alignment result between the first fragment and the original reference database is that there is more than one aligned sequence, all the bases of the aligned sequence segment in the original reference database are replaced with N. Through alignment and replacement, the original reference database after the replacement is used as the first reference database to obtain the first reference database. That is, after all first fragments are aligned, all the bases of the aligned sequence segment in the original reference database are replaced with N when the alignment result is more than one aligned sequence, and the replaced original reference database is used as the first reference database.

[0056] S3. Building a second reference database based on the original reference database;

[0057] All sequences in the original reference database are segmented into segments of a fourth length (preferably 500 bp) at a sliding step length of a third length (preferably 50 bp) to obtain a plurality of second segments. Each second segment is then aligned with the original reference database using the BLAST algorithm. If the alignment similarity between the second segment and the sequence in the original reference database is greater than or equal to 98% (i.e., the similarity between the second segment and a sequence segment in the original reference database is greater than or equal to 98%), and if the aligned subsegment (a subsegment can be understood as a partial segment within the second segment) has a length of at least one position less than the third length, all bases in the aligned sequence segment in the original reference database are replaced with N, thereby obtaining a second reference database. Specifically, after all fourth segments are aligned, all bases in the sequence segments with alignment similarity ≥ 98% are replaced with N, and the replaced original reference database serves as the second reference database. In other embodiments, the first threshold value can also be other values, typically a value in the range [98%, 100%]. It is understood that the order in which S2 and S3 are performed is not limited.

[0058] S4. Obtaining genetic data of the virus species to be identified;

[0059] S5. Determine the data type of the genetic data of the virus species to be identified, where the data type includes the spliced ​​genome data, the original data from the second-generation sequencing, and the original data from the third-generation sequencing.

[0060] If the data type is the original data from the second generation sequencing or the original data from the third generation sequencing, proceed to S6; if the data type is the spliced ​​genome data, proceed to S7.

[0061] S6. Align the raw data (FASTA format) from the second-generation or third-generation sequencing to the first reference database using the BWT algorithm to obtain unique aligned reads, perform species abundance statistics on the unique aligned reads, and use the species with the highest aligned abundance as the identified species for S8.

[0062] S7. Use the BLAST algorithm to align the assembled genome data (FASTQ format) to the second reference database, sort the BLAST results in reverse order according to the bit-score value, and use the species to which the sequence with the highest bit-score value belongs as the identified species for S8.

[0063] S8. Output the identified species.

[0064] like Figure 3 As shown, the present invention also provides a virus species identification system, comprising:

[0065] An acquisition module 10 is used to obtain an original reference database including genome sequences of non-redundant virus species and representative functional fragment sequences of non-redundant virus subspecies;

[0066] A first construction module 20 is configured to construct a first reference database, specifically, to break all sequences in the original reference database into a plurality of first segments, align each first segment with the original reference database using the BWT algorithm, and if more than one sequence in the first segment aligns with a sequence in the original reference database, replace all bases in the aligned sequence segment in the original reference database with N to obtain the first reference database;

[0067] The first identification module 30 is configured to align the raw data from the second-generation or third-generation sequencing to the first reference database using the BWT algorithm to obtain unique aligned reads, perform species abundance statistics on the unique aligned reads, and use the species with the highest aligned abundance as the identified species;

[0068] A second construction module 40 is configured to construct a second reference database, specifically to break all sequences in the original reference database into a plurality of second segments by sliding in steps of a third length, and to align each second segment with the original reference database using a BLAST algorithm. If the similarity between the second segment and the sequence alignment in the original reference database reaches a first threshold, and the length of the aligned sub-segment is not less than the third length at more than one position, then all bases in the aligned sequence segment in the original reference database are replaced with N, thereby obtaining a second reference database.

[0069] The second identification module 50 is used to compare the spliced ​​genome data to the second reference database using the BLAST algorithm, and the species to which the sequence with the highest bit-score value belongs is used as the identified species.

[0070] The step of breaking all the sequences in the original reference database to obtain a number of first fragments is specifically as follows: sliding with a step size of the first length, breaking all the sequences in the original reference database according to the second length to obtain a number of first fragments; the step of breaking all the sequences in the original reference database is specifically as follows: breaking all the sequences in the original reference database according to the fourth length to obtain a number of second fragments.

[0071] The first length is 10 bp, the second length is 50 bp, the third length is 50 bp, the fourth length is 500 bp, and the first threshold value range is [98%, 100%].

[0072] See also Figure 4 , the identification system further comprises:

[0073] The test data acquisition module 70 is used to obtain the genetic data of the virus species to be identified;

[0074] The type determination module 80 is used to determine the data type of the genetic data of the virus species to be identified, and the data type includes the spliced ​​genome data, the original data from the second-generation sequencing, and the original data from the third-generation sequencing.

[0075] Here, the identification system also includes:

[0076] The output module 60 is used to output the identification results of the first identification module and the second identification module.

[0077] See Figure 5 The present invention also provides an electronic device, comprising: a memory; one or more processors; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods for identifying a virus species.

[0078] The present invention also provides a computer-readable storage medium, which includes instructions. When the instructions are executed on a computer, the computer executes each step of a virus species identification method described in any of the above embodiments.

[0079] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0080] Based on a non-redundant original reference database of viral species and subspecies genome sequences, this method uses a masked sequence as a reference database. Based on specific circumstances, a homology comparison method is designed. Using the BWA or BLAST algorithms, the second- and third-generation raw data or the spliced ​​genomic data are compared and analyzed, and the final species is determined based on the comparison results. In one specific embodiment, species identification can be achieved for 15,327 viral species.

[0081] Based on the above-designed method and system, it is possible to achieve virus species or subtype level, and accurate identification can be achieved between different strains with highly similar genomes. When outputting the results, the strain information with the highest identification rate is directly given.

[0082] Compared with microscopic imaging technology, the present invention has a different principle, so it is not limited by the morphological characteristics of the virus and sample preparation, there is no problem of complete image capture, and the identification accuracy is high; compared with the immune-serological detection technology, the principle is different, the identification accuracy is higher and the identification is faster; compared with molecular biology detection methods, the detection principle is different, the present invention is easy to implement, the detection accuracy is higher, the failure rate is extremely low, and the identification speed is stable and fast.

[0083] The present invention not only encompasses species that can be detected by microscopic imaging technology, immune-serological detection technology and molecular biological detection methods, and has high identification accuracy, but can also adapt to the identification of virus species that cannot be identified by microscopic imaging, immune-serological detection and molecular biological methods, and can also detect the species of new viruses or unknown viruses. The present invention directly uses raw sequencing data for identification, which achieves simple identification, accurate results and short time consumption for virus identification, and solves the problem of specificity of comparison results of short-read raw offline data or shorter genome splicing data. The present invention is the first in the field to develop a standardized analysis process that can directly use reads data comparison strategies and is used for virus species identification. It can identify many species, can be accurate to the subspecies level, and has a high and fast identification rate.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. The present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, systems, and electronic devices according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0089] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for identifying virus species, characterized in that: include: Obtaining an original reference database, wherein the original reference database includes non-redundant viral species genome sequences and representative functional fragment sequences of non-redundant viral subspecies; Breaking all sequences in the original reference database into several first fragments, aligning each first fragment with the original reference database using the BWT algorithm, and replacing all bases in the aligned sequence segments in the original reference database with N when there is more than one aligned sequence in the first fragment, thereby obtaining a first reference database; The raw data from the second-generation or third-generation sequencing was aligned to the first reference database using the BWT algorithm to obtain unique aligned reads. The species abundance of the unique aligned reads was counted, and the species with the highest aligned abundance was used as the identified species. Sliding with a step size of a third length, all genomic sequences in the original reference database are broken into a plurality of second segments, and each second segment is compared with the original reference database using a BLAST algorithm. If the similarity between the second segment and the genomic sequence in the original reference database reaches a first threshold, and the length of the aligned sub-segment is not less than the third length at more than one position, then all bases in the aligned sequence segment in the original reference database are replaced with N to obtain a second reference database; The spliced ​​genome data were aligned to the second reference database using the BLAST algorithm, and the species to which the genome sequence with the highest bit-score value belonged was identified as the species; The identification method further comprises: Obtain genetic data of the virus species to be identified; Determine the data type of the genetic data of the virus species to be identified, the data type including the spliced ​​genome data, the raw data from the second-generation sequencing, and the raw data from the third-generation sequencing; The step of breaking all the sequences in the original reference database to obtain a plurality of first segments specifically comprises: sliding with a step length of the first length, breaking all the genome sequences in the original reference database into a plurality of first segments according to a second length; The step of breaking all genomic sequences in the original reference database specifically includes: breaking all sequences in the original reference database into a plurality of second segments according to a fourth length; The first length is 10 bp, the second length is 50 bp, the third length is 50 bp, the fourth length is 500 bp, and the first threshold value range is [98%, 100%].

2. A virus species identification system, characterized in that: include: An acquisition module is used to acquire an original reference database, wherein the original reference database includes non-redundant viral species genome sequences and representative functional fragment sequences of non-redundant viral subspecies; A first construction module is used to construct a first reference database, specifically for breaking all sequences in the original reference database into a plurality of first fragments, aligning each first fragment with the original reference database using the BWT algorithm, and if there is more than one sequence aligned in the first fragment, replacing all bases in the aligned sequence segment in the original reference database with N to obtain the first reference database; The first identification module is used to align the raw data from the second-generation or third-generation sequencing to the first reference database using the BWT algorithm to obtain unique aligned reads, perform species abundance statistics on the unique aligned reads, and use the species with the highest aligned abundance as the identified species; a second construction module for constructing a second reference database, specifically for sliding with a step size of a third length, breaking all sequences in the original reference database into a plurality of second fragments, aligning each second fragment with the original reference database using a BLAST algorithm, and if the similarity between the second fragment and the sequence alignment in the original reference database reaches a first threshold, and the length of the aligned sub-fragment is not less than the third length at more than one position, replacing all bases in the aligned sequence segment in the original reference database with N, to obtain a second reference database; The second identification module is used to align the spliced ​​genome data to the second reference database using the BLAST algorithm, and the species to which the sequence with the highest bit-score value belongs is used as the identified species; The identification system further comprises: The test data acquisition module is used to obtain the genetic data of the virus species to be identified; A type determination module is used to determine the data type of the genetic data of the virus species to be identified, wherein the data type includes the spliced ​​genome data, the raw data from the second-generation sequencing, and the raw data from the third-generation sequencing; The step of breaking all the sequences in the original reference database to obtain a plurality of first segments is specifically: breaking all the sequences in the original reference database into a plurality of first segments according to a second length by sliding with a step size of the first length; the step of breaking all the sequences in the original reference database is specifically: breaking all the sequences in the original reference database into a plurality of second segments according to a fourth length; The first length is 10 bp, the second length is 50 bp, the third length is 50 bp, the fourth length is 500 bp, and the first threshold value range is [98%, 100%].

3. An electronic device, characterized in that: include: Memory; one or more processors; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method for identifying a virus species according to claim 1.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium includes instructions, and when the instructions are executed on a computer, the computer is caused to execute the method for identifying virus species according to claim 1 .

Images