Virus SNP (Single Nucleotide Polymorphism) detection method and device, computer equipment and readable storage medium

Abstract

The invention relates to a virus SNP (Single Nucleotide Polymorphism) detection method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring virus genome data of a to-be-detected virus genome, acquiring reference genome data preset for the virus genome, and performing sequence comparison on the virus genome data and the reference genome data to obtain a genome data comparison file of the virus genome data and the reference genome data; and performing SNP mutation site identification on the genome data comparison file to obtain a plurality of SNP mutation sites, and finally performing mutation annotation on the plurality of SNP mutation sites and the virus genome data according to the reference genome data to obtain mutation information of the plurality of SNP mutation sites. By simplifying the SNP detection process, the detection cost is reduced, and experimental environment conditions required to be prepared during SNP detection of viral genome data are reduced, so that the influence degree of environmental factors on detection is reduced, and the detection accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of viral genome SNP detection, and in particular to a viral SNP detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of viral genome SNP detection technology, detection methods based on PCR (Polymerase Chain Reaction) have emerged. High-resolution melting curve (HRM) analysis based on PCR SNP (Single Nucleotide Polymorphism) detection technology distinguishes different genotypes by real-time monitoring of the changes in the melting curve of PCR products during heating.

[0003] However, the current PCR-based SNP detection method has the problem of low detection accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a virus SNP detection method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of SNP detection in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting viral SNPs, comprising:

[0006] Obtaining viral genome data of the viral genome to be detected, and obtaining reference genome data pre-set for the viral genome;

[0007] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0008] Identify SNP mutation sites on the genome data comparison file and obtain multiple SNP mutation sites;

[0009] Based on the reference genome data, mutation annotations were performed on multiple SNP mutation sites and viral genome data to obtain mutation information of multiple SNP mutation sites.

[0010] In conjunction with the first aspect, in one embodiment, when the viral genome data belongs to the read sequence of the second / third generation original offline data;

[0011] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data, including:

[0012] Build a reference genome index based on the reference genome data;

[0013] Use each read sequence contained in the viral genome data to search the reference genome index to obtain the comparison position of each read sequence on the reference genome;

[0014] According to the comparison position, each read sequence is sorted, and the sorted read sequences are used as genome data comparison files.

[0015] In conjunction with the first aspect, in one embodiment, using each read sequence contained in the viral genome data to search the reference genome index to obtain the comparison position of each read sequence on the reference genome includes:

[0016] Use each read sequence to search the reference genome index to obtain multiple matching positions of each read sequence on the reference genome;

[0017] From multiple matching positions, the target matching position with the greatest matching degree with each read sequence is determined, and each target matching position is determined as the comparison position of each read sequence on the reference genome.

[0018] In conjunction with the first aspect, in one embodiment, each read sequence is sorted according to the comparison position, and each sorted read sequence is used as a genome data comparison file, including:

[0019] The chromosome to which the comparison position belongs is determined as the chromosome to which the read sequence belongs;

[0020] Based on the attribution information of the reads sequence and the chromosome information of the chromosome, each read sequence is sorted to obtain the sorted read sequence;

[0021] The sorted read sequences are used as genome data comparison files.

[0022] In conjunction with the first aspect, in one embodiment, the chromosome information includes chromosome number information and starting base position information of the read sequence on the chromosome;

[0023] Based on the attribution information of the reads sequence and the chromosome information of the chromosome, each read sequence is sorted to obtain the sorted read sequences, including:

[0024] When the attribution information indicates that each read sequence belongs to a different chromosome, the read sequences are sorted in ascending order based on the chromosome number information to obtain sorted read sequences;

[0025] When the attribution information indicates that at least two target read sequences belong to the same chromosome, the read sequences are sorted in ascending order according to the chromosome number information to obtain the initially sorted read sequences, and the target read sequence is determined from the initially sorted read sequences;

[0026] Sort the target reads according to the starting base position information of the target reads on the chromosome to which they belong, and obtain the sorted target reads;

[0027] Based on the sorted target reads sequence, each read sequence after the initial sorting is updated to obtain each sorted read sequence.

[0028] In conjunction with the first aspect, in one embodiment, SNP mutation sites are identified on the genomic data comparison file to obtain multiple SNP mutation sites, including:

[0029] Identify SNP mutation sites on the genome data comparison file to obtain multiple initial SNP mutation sites;

[0030] Based on pre-set screening parameters, multiple initial SNP mutation sites are filtered to obtain multiple SNP mutation sites.

[0031] In conjunction with the first aspect, in one embodiment, when the viral genome data is spliced ​​genome sequence data;

[0032] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data, including:

[0033] Compare and analyze the viral genome data with the reference genome data to obtain the alignment between the viral genome data and the reference genome data;

[0034] Generate a genome data comparison file based on the alignment.

[0035] In conjunction with the first aspect, in one embodiment, mutation annotation is performed on multiple SNP mutation sites and viral genome data based on reference genome data to obtain mutation information of multiple SNP mutation sites, including:

[0036] Based on the reference genome data, multiple SNP mutation sites and the proteins encoded by the genes at their locations on the viral genome were annotated to obtain the mutation location information, mutation nucleotide information, gene name, amino acid mutation location, functional region, and mutation type of multiple SNP mutation sites; mutation types include insertion, deletion, transition, and transversion;

[0037] The mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region and mutation type are used as the mutation information of multiple SNP mutation sites.

[0038] In a second aspect, the present application also provides a viral SNP detection device, comprising:

[0039] A data acquisition module is used to obtain viral genome data of the viral genome to be detected, and to obtain reference genome data preset for the viral genome;

[0040] A sequence alignment module is used to perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0041] SNP identification module, used to identify SNP mutation sites in genome data comparison files and obtain multiple SNP mutation sites;

[0042] The SNP annotation module is used to perform mutation annotation on multiple SNP mutation sites and viral genome data based on the reference genome data to obtain mutation information of multiple SNP mutation sites.

[0043] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0044] Obtaining viral genome data of the viral genome to be detected, and obtaining reference genome data pre-set for the viral genome;

[0045] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0046] Identify SNP mutation sites on the genome data comparison file and obtain multiple SNP mutation sites;

[0047] Based on the reference genome data, mutation annotations were performed on multiple SNP mutation sites and viral genome data to obtain mutation information of multiple SNP mutation sites.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0049] Obtaining viral genome data of the viral genome to be detected, and obtaining reference genome data pre-set for the viral genome;

[0050] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0051] Identify SNP mutation sites on the genome data comparison file and obtain multiple SNP mutation sites;

[0052] Based on the reference genome data, mutation annotations were performed on multiple SNP mutation sites and viral genome data to obtain mutation information of multiple SNP mutation sites.

[0053] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0054] Obtaining viral genome data of the viral genome to be detected, and obtaining reference genome data pre-set for the viral genome;

[0055] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0056] Identify SNP mutation sites on the genome data comparison file and obtain multiple SNP mutation sites;

[0057] Based on the reference genome data, mutation annotations were performed on multiple SNP mutation sites and viral genome data to obtain mutation information of multiple SNP mutation sites.

[0058] The above-mentioned viral SNP detection method, apparatus, computer device, computer-readable storage medium and computer program product obtain viral genome data of the viral genome to be detected, and obtain reference genome data pre-set for the viral genome, perform sequence comparison on the viral genome data and the reference genome data, obtain a genome data comparison file of the viral genome data and the reference genome data, identify SNP mutation sites on the genome data comparison file, obtain multiple SNP mutation sites, and finally, perform mutation annotation on the multiple SNP mutation sites and the viral genome data based on the reference genome data to obtain mutation information of the multiple SNP mutation sites. By comparing and analyzing the viral genome data and the corresponding reference genome data, generating a genome data comparison file for the two, identifying mutation sites on the genome data comparison file, identifying multiple SNP mutation sites, and finally performing mutation annotation on the multiple SNP mutation sites to determine mutation information of the multiple SNP mutation sites, the above-mentioned comparison and analysis process is simple to operate, has low identification cost, is adaptable to mutation detection of different types of viral genome data, reduces the experimental environment conditions required for SNP detection in viral genome data, thereby reducing the impact of environmental factors on detection, thereby improving the accuracy of SNP detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is a diagram of an application environment of a method for detecting viral SNPs in one embodiment;

[0061] Figure 2 Schematic diagram of a process for detecting viral SNPs in one embodiment;

[0062] Figure 3 Schematic diagram of a process for detecting viral SNPs in another embodiment;

[0063] Figure 4 This is a structural block diagram of a viral SNP detection device in one embodiment;

[0064] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0066] In the field of viral genome SNP (single nucleotide polymorphism) detection, accurate and efficient identification of subtle variations within the viral genome is crucial. This not only has far-reaching implications for understanding viral evolution, tracing transmission pathways, and assessing vaccine effectiveness and drug sensitivity, but is also directly related to the precise diagnosis and treatment of diseases. Currently, commonly used technologies for viral genome SNP detection include PCR-based detection methods, sequencing technologies (including first-, second-, and third-generation sequencing), and gene chip technology. Each of these technologies has its advantages, but also significant disadvantages. For example, high-resolution melting curve analysis (HRM) for PCR-based SNP detection distinguishes different genotypes by monitoring the melting curve changes of PCR products during heating in real time. There is also molecular beacon technology based on real-time PCR quantification, which uses a dual-labeled oligonucleotide probe with a fluorescent group and a quencher at its 5' and 3' ends, respectively, to form a stem-loop structure. When the probe hybridizes to the target sequence, the stem-loop structure opens, separating the fluorescent group and quencher, generating a fluorescent signal, which is characterized by highly specific SNP detection. Gene chip-based methods also exist, such as oligonucleotide chip detection. , oligonucleotide sequences containing specific SNP sites can be fixed to the chip surface. Each sequence contains a specific SNP. The target region in the viral genome is amplified by PCR, fluorescently labeled, and then hybridized with the probe on the chip. If a specific SNP is present, the fluorescently labeled genomic probe will display a positive signal at the corresponding position. This method can be used for high-throughput detection of multiple SNP sites in the viral genome.

[0067] While PCR-based detection methods are mature and widely used, they have low throughput, making it difficult to simultaneously detect a large number of SNPs. They can also result in false-negative and false-positive results, and their cost increases significantly with the number of loci tested. For example, high-resolution melting curve analysis (HRM) requires no sequence-specific probes and is inexpensive. However, melting curve analysis can be affected by various factors, such as DNA (deoxyribonucleic acid) sequence length and GC (guanine-cytosine) content, necessitating careful optimization of experimental conditions to ensure accuracy. Molecular beacon technology, on the other hand, faces challenges with complex probe design and high cost, and can be susceptible to interference from nonspecific components in the sample, affecting the accuracy and stability of the fluorescence signal. While sequencing technologies can comprehensively analyze genomic polymorphisms, first-generation sequencing (NGS) suffers from low throughput and high cost. Second-generation sequencing (NGS) has shorter read lengths and requires complex assembly processes, potentially compromising accuracy. Third-generation sequencing (NGS) faces challenges with high error rates and high costs. While gene chip technology offers high throughput and high accuracy, it is complex to operate, requires long detection times, and remains costly for large-scale application. For example, while oligonucleotide chip detection based on gene chips can achieve high-throughput detection, the chip preparation cost is high, the operation process is relatively complex, and requires strict experimental conditions and environmental requirements. These factors can affect the sensitivity and accuracy of SNP detection. Therefore, in the field of viral genome SNP detection, continuous exploration and optimization of new technologies are needed to overcome the limitations of existing technologies and improve detection accuracy and efficiency.

[0068] The viral SNP detection method provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The server 104 obtains the viral genome data of the detected viral genome from the terminal 102, and obtains the reference genome data pre-set for the viral genome from the data storage system, performs sequence comparison between the viral genome data and the reference genome data, obtains a genome data comparison file of the viral genome data and the reference genome data, identifies SNP mutation sites in the genome data comparison file, obtains multiple SNP mutation sites, and annotates the multiple SNP mutation sites and the viral genome data based on the reference genome data to obtain mutation information of the multiple SNP mutation sites. The terminal 102 can be, but is not limited to, various personal computers, optical microscopes, and other various laboratory instruments. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0069] In an exemplary embodiment, Figure 2 As shown, a method for detecting viral SNPs is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps S201 to S204.

[0070] Step S201 : obtaining viral genome data of a viral genome to be detected, and obtaining reference genome data preset for the viral genome.

[0071] Viral genome data can be understood as the complete genetic information of the virus, primarily including the DNA or RNA sequences of viral genes. This data is typically presented as sequence text (e.g., in FASTA format) and contains all the genetic instructions of the virus. Reference genome data can be understood as either a self-selected, proprietary reference genome in FASTA format or an accurate reference genome previously verified by a laboratory.

[0072] Optionally, server 104 obtains viral genome data of the viral genome to be detected from terminal 102 and obtains pre-set reference genome data for the viral genome from a data storage system. The reference genome data here can be independently selected reference set data or accurate reference genome data verified by a laboratory. Selecting accurate reference genome data lays a data foundation for subsequent sequence alignment between the viral genome data and the reference genome data. Due to the higher accuracy of the reference genome data, the accuracy of the alignment is improved.

[0073] Step S202 , performing sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data.

[0074] Among them, sequence alignment can be understood as comparing the sequence of viral genome data with the sequence of reference genome data.

[0075] Exemplarily, server 104 performs sequence alignment on the viral genome data and the reference genome data. If the viral genome data is a read sequence of the second / third generation original offline data, an index is first generated based on the reference genome data. Then, the index is searched based on each read sequence contained in the viral genome data to obtain a comparison file of each read sequence on the reference genome. Each read sequence is sorted according to the comparison position to obtain a genome data comparison file. If the viral genome data is a spliced ​​genome sequence data, the viral genome data and the reference genome data are compared and analyzed to obtain the alignment between the viral genome data and the reference genome data. Based on the alignment, a genome data comparison file is generated. Based on the data type of the viral genome data, a corresponding sequence alignment method is selected. After the alignment analysis, a corresponding genome data comparison file is obtained, which speeds up the sequence alignment process. The generation of the comparison file also lays the data foundation for the subsequent SNP detection.

[0076] Step S203: performing SNP mutation site identification on the genome data comparison file to obtain multiple SNP mutation sites.

[0077] Step S204: perform mutation annotation on multiple SNP mutation sites and viral genome data based on the reference genome data to obtain mutation information of the multiple SNP mutation sites.

[0078] Among them, the SNP mutation site can be understood as a single nucleotide variation at a specific position in the DNA sequence, including information such as the mutation position and mutation type. The mutation information can be understood as relevant variation information for the SNP mutation site, including mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region, mutation type, including insertion, deletion, transition and transversion, etc.

[0079] Optionally, server 104 uses Samtools software to identify SNP mutation sites on the genome comparison file, obtaining multiple SNP mutation sites. Finally, based on the reference genome data, the multiple SNP mutation sites and the proteins encoded by the genes at their viral genome locations are annotated. Finally, the mutation location information, mutation nucleotide information, gene name, amino acid mutation location, functional region, and mutation type, including insertion, deletion, transition, and transversion, of each SNP mutation site are obtained. This result information is output in a text file format. Finally, based on the impact of the SNP on the protein structure, the impact of each mutation site on the protein structure and its amino acid function changes is predicted.

[0080] Through the aforementioned SNP identification and annotation process, the following technical effects were achieved:

[0081] 1. Use Samtools comparison files to accurately detect SNP mutation sites in the viral genome to ensure the reliability of subsequent analysis.

[0082] 2. Compare SNPs with reference genomes and viral genes to identify the gene to which they belong, the protein they encode, the location and type of mutation (such as insertion, deletion, transition, and transversion), and annotate them in the functional region of the protein to help understand the potential impact of the mutation.

[0083] 3. Output the nucleotide changes of the mutation, the amino acid mutation position, the structure and functional region information of the target protein, and provide basic data for subsequent protein function and structure analysis.

[0084] In the above-mentioned viral SNP detection method, viral genome data of the viral genome to be detected is obtained, and reference genome data pre-set for the viral genome is obtained, the viral genome data and the reference genome data are sequence-aligned to obtain a genome data comparison file of the viral genome data and the reference genome data, the genome data comparison file is subjected to SNP mutation site identification to obtain multiple SNP mutation sites, and finally, based on the reference genome data, the multiple SNP mutation sites and the viral genome data are subjected to mutation annotation to obtain mutation information of the multiple SNP mutation sites. By comparing and analyzing the viral genome data and the corresponding reference genome data, and generating a genome data comparison file for the two, mutation site identification is performed on the genome data comparison file to identify multiple SNP mutation sites, and finally, mutation annotation is performed on the multiple SNP mutation sites to determine the mutation information of the multiple SNP mutation sites. The above-mentioned comparison and analysis process is simple to operate, has a low identification cost, is adaptable to mutation detection of different types of viral genome data, and reduces the experimental environment conditions required for SNP detection of viral genome data, thereby reducing the degree of influence of environmental factors on detection, thereby improving the accuracy of SNP detection.

[0085] In one embodiment, when the viral genome data belongs to the read sequence of the second / third generation original offline data;

[0086] Perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data, including:

[0087] A reference genome index is constructed based on the reference genome data; the reference genome index is searched using the read sequences contained in the viral genome data to obtain the comparison position of each read sequence on the reference genome; the read sequences are sorted according to the comparison position, and the sorted read sequences are used as genome data comparison files.

[0088] Among them, the reads sequence can be understood as a gene fragment at a specific position on the chromosome.

[0089] Exemplarily, server 104 uses BWA (Burrows-Wheeler Aligner) software to construct an index for the reference genome, obtaining a corresponding reference genome index. Using BWA, the software searches the reference genome index using the read sequences contained in the viral genome data, finding the alignment position of each read sequence on the reference genome. Finally, the read sequences are sorted by determining the alignment position on the reference genome, and the sorted read sequences are used as a genome data comparison file. By constructing the reference genome index, the alignment and analysis process of the read sequences contained in the viral genome is accelerated, thereby increasing the speed of constructing the genome data comparison file. Furthermore, the reads are sorted according to the alignment position, providing a structured genome data comparison file for subsequent variant detection, assembly, and annotation.

[0090] In one embodiment, each read sequence contained in the viral genome data is used to search a reference genome index to obtain a comparison position of each read sequence on the reference genome, including: using each read sequence to search the reference genome index to obtain multiple matching positions of each read sequence on the reference genome; from the multiple matching positions, determining a target matching position with the greatest degree of matching with each read sequence, and determining each target matching position as a comparison position of each read sequence on the reference genome.

[0091] Optionally, server 104 uses BWA software to search the reference genome index using each read sequence to obtain possible matching positions for each read sequence. From the possible matching positions, the target matching position with the highest degree of match with each read sequence is determined, and each target matching position is determined as the comparison position of each read sequence on the reference genome. A score is calculated for each possible matching position, and the position with the highest score is ultimately obtained as the comparison position of each read sequence on the reference genome. By calculating the corresponding matching degree for each possible position and selecting the matching position with the highest matching degree as the comparison position of the read sequence on the reference genome, the reliability of the comparison is enhanced; the optimal matching position is screened through the matching degree mechanism, effectively reducing false positive matches, ensuring the accuracy of sequence positioning, and thereby improving the detection accuracy of SNP detection.

[0092] In an exemplary embodiment, each read sequence is sorted according to the comparison position, and each sorted read sequence is used as a genome data comparison file, including: determining the chromosome to which the comparison position belongs as the chromosome to which the read sequence belongs; sorting each read sequence based on the attribution information of the read sequence and the chromosome information of the chromosome to obtain each sorted read sequence; and using each sorted read sequence as a genome data comparison file.

[0093] The chromosome information may be understood as information related to the chromosome, and may include chromosome number information of the chromosome, base position information of the bases constituting the chromosome, and the like.

[0094] For example, server 104 uses the chromosome to which the comparison position belongs as the chromosome to which the corresponding read sequence belongs, and sorts each read sequence based on the read sequence's attribution information and the chromosome information of the chromosome: for different chromosomes, sort from small to large according to the chromosome number information in the chromosome information; for the same chromosome, sort by the base position of the chromosome itself, and finally obtain the sorted read sequences, and use the sorted read sequences as the genome data comparison file. The read files obtained after sorting have a consistent and systematic arrangement, which facilitates subsequent analysis, visualization, and interpretation. The standardized sorted read sequences serve as the basic files for subsequent SNP detection, which helps to simplify the SNP detection process and thus speed up the SNP detection speed.

[0095] In one embodiment, the chromosome information includes chromosome number information and starting base position information of the read sequence on the chromosome;

[0096] Based on the attribution information of the reads sequence and the chromosome information of the chromosome, each read sequence is sorted to obtain the sorted read sequences, including:

[0097] When the attribution information indicates that each read sequence belongs to a different chromosome, the read sequences are sorted in ascending order based on the chromosome number information to obtain sorted read sequences;

[0098] When the attribution information indicates that at least two target read sequences belong to the same chromosome, the read sequences are arranged in ascending order according to the chromosome number information to obtain the initially sorted read sequences, and the target read sequences are determined from the initially sorted read sequences; the target read sequences are sorted according to the starting base position information of the target read sequences on the chromosome to which they belong to obtain the sorted target read sequences; and based on the sorted target read sequences, the initially sorted read sequences are updated to obtain the sorted read sequences.

[0099] Optionally, when the attribution information indicates that each read sequence belongs to a different chromosome, the server 104 arranges the read sequences in ascending order based on the chromosome number information, that is, the read sequence with a smaller number has an earlier arrangement number, to obtain the sorted read sequences; when the attribution information indicates that at least two target read sequences belong to the same chromosome, the server 104 first arranges the read sequences in ascending order according to the chromosome number information to obtain the initially sorted read sequences, and determines the target read sequences with the same arrangement number due to the same chromosome from the initially sorted read sequences, and then sorts the target read sequences from small to large according to the starting base position information of the target read sequences on the chromosome to which they belong, to obtain the sorted target read sequences, and finally updates the initially sorted read sequences based on the sorted target read sequences to obtain the sorted read sequences.

[0100] Based on the aforementioned implementation, sequencing is prioritized by chromosome number (small to large), and then by base position within the same chromosome to ensure biologically meaningful continuity. Multi-level sequencing ensures logical arrangement of reads at different levels, reduces errors, and improves analysis accuracy. The resulting sequence of sorted reads helps improve detection accuracy during SNP detection.

[0101] In one embodiment, SNP mutation sites are identified on a genomic data comparison file to obtain multiple SNP mutation sites, including: SNP mutation sites are identified on a genomic data comparison file to obtain multiple initial SNP mutation sites; based on pre-set screening parameters, the multiple initial SNP mutation sites are filtered to obtain multiple SNP mutation sites.

[0102] Among them, the screening parameters can be understood as parameters such as alignment quality, base quality, sequencing depth, and mutation frequency.

[0103] For example, server 104 uses Samtools software to identify SNP mutation sites on each sorted read sequence to obtain multiple initial SNP mutation sites. Based on pre-set screening parameters, the multiple initial SNP mutation sites are filtered to remove SNP mutation sites that do not meet the screening parameters to obtain multiple SNP mutation sites. When the screening parameter is a mutation frequency of 1%, the initial SNP mutation sites with a mutation frequency of less than 1% are filtered, and the initial SNP mutation sites with a mutation frequency of greater than or equal to 1% are retained to form multiple SNP mutation sites. The identified SNP mutation sites are screened using the pre-set screening parameters to effectively remove low-frequency mutations, reduce false positives and background noise, ensure that the SNP mutation sites finally annotated have a high degree of credibility, and achieve improved detection accuracy of SNP detection.

[0104] In an exemplary embodiment, when the viral genome data is spliced ​​genome sequence data;

[0105] Performing sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data, including: performing alignment analysis on the viral genome data and the reference genome data to obtain an alignment between the viral genome data and the reference genome data; and generating a genome data comparison file based on the alignment.

[0106] Optionally, if the viral genome data is spliced ​​genome sequence data, the server compares and analyzes the viral genome data with the reference genome data to obtain alignment between the viral genome data and the reference genome data, and generates a genome data comparison file based on the alignment. This accurately aligns the spliced ​​viral genome with the reference genome, reveals sequence similarities and differences, and generates a genome data comparison file based on the comparison, laying the data foundation for subsequent SNP detection and improving the accuracy of SNP detection.

[0107] In one embodiment, mutation annotation is performed on multiple SNP mutation sites and viral genome data based on reference genome data to obtain mutation information of multiple SNP mutation sites, including: based on the reference genome data, annotating multiple SNP mutation sites with the proteins encoded by the genes at their viral genome locations to obtain mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region, and mutation type of multiple SNP mutation sites; mutation types include insertion, deletion, transition, and transversion; and using the mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region, and mutation type as mutation information of multiple SNP mutation sites.

[0108] Here, transversion can be understood as inversion followed by exchange or reversal followed by transposition.

[0109] For example, based on the reference genome data, server 104 annotates multiple SNP mutation sites and the proteins encoded by the genes at their locations on the viral genome, ultimately obtaining the mutation location information, mutation nucleotide information, gene name, amino acid mutation location, functional region, and mutation type of each SNP mutation site, including insertion, deletion, transition, and transversion. The mutation location information, mutation nucleotide information, gene name, amino acid mutation location, functional region, and mutation type of the SNP mutation site are used as the mutation information of the SNP mutation site. The mutation information of the SNP mutation site is output in a text format. Finally, based on the effect of the SNP on the protein structure, the effect of each mutation site on the protein structure and its amino acid function changes is predicted.

[0110] According to the above implementation method, combined with gene and protein information, the location, nucleotide change, gene to which it belongs, amino acid variation location, functional region, and mutation type (insertion, deletion, transition, transversion) of each mutation are provided, comprehensively revealing the biological significance of the mutation; detailed mutation information is output in text format to facilitate archiving, browsing and subsequent analysis, ensuring the readability and usability of the information; based on the mutation information, the potential impact of the mutation on protein structure and functional changes is evaluated, which helps to understand the biological role and potential pathogenicity of the mutation.

[0111] In an exemplary embodiment, Figure 3 As shown, a specific implementation of a viral SNP detection method is provided, and the specific contents are as follows:

[0112] 1. SNP detection for the short reads (FASTQ format) of the original single-end or paired-end sequencing data. The specific steps are as follows:

[0113] 1) Align and analyze the raw second-generation / third-generation off-machine data reads with the reference genome. First, use BWA software to build an index for the reference genome and output multiple index files. BWA software then searches the reads against the reference genome index to identify possible matches. A score is calculated for each possible alignment position, and the position with the highest score is the alignment position of the read on the reference genome.

[0114] 2) Sort all reads by their alignment position on the reference genome. The sorting rule is: for different chromosomes, sort by chromosome number from smallest to largest; for the same chromosome, sort by base position within the chromosome itself.

[0115] 3) Identification of SNP mutation sites: Using Samtools software, all sequenced reads were converted and extracted for information such as mutation sites, mutation frequencies, and mutation quality, generating a bcf (Binary Call Format) file.

[0116] 4) SNP mutation site filtering: Mutation sites are filtered by controlling parameters such as alignment quality, base quality, sequencing depth, and mutation frequency. The default filtering parameter for this tool is a mutation frequency ≥ 1%, but users can also set custom filtering parameters to adjust the frequency filtering parameters as needed.

[0117] 5) Functional Annotation and Effect Prediction of SNP Mutation Sites: Based on the reference genome gff (General Feature Format) file, filtered mutation sites are annotated with the proteins encoded by the genes at their genomic locations. This provides information on each SNP's mutation location, including the mutated nucleotide, gene name, amino acid mutation location, functional region, and mutation type, including insertions, deletions, transitions, and transversions. This information is exported in TXT (Text (plain text) file) format. Finally, the impact of each SNP on protein structure and amino acid function is predicted based on its effect on protein structure.

[0118] 2. SNP detection for assembled viral genome data. If the user has completed the assembly of the complete viral genome, SNP identification can be performed by directly uploading the assembled genome data. The specific steps are as follows:

[0119] 1) Sequence alignment: Compare the assembled genome sequence data (FASTA / GBK / EMB format) with the reference genome to determine the alignment between the genome sequence and the reference genome.

[0120] 2) Identification of SNP mutation sites: Using Samtools software, the genome sequence data is analyzed for mutation sites and information such as mutation quality is annotated.

[0121] 3) Functional Annotation and Effect Prediction of SNP Mutation Sites: Based on the reference genome gff file, all mutation sites are annotated with the proteins encoded by the genes at their genomic locations. Ultimately, each SNP is provided with information on its location, mutated nucleotide, gene name, amino acid mutation location, functional region, and mutation type, including insertions, deletions, transitions, and transversions. This information is exported in TXT format. Finally, the impact of each SNP on protein structure and its amino acid function is predicted based on its effect on protein structure.

[0122] Compared with the existing technology, this application has the following technical advantages:

[0123] 1. By simplifying the SNP detection process, the detection cost of viral SNPs is reduced, thus enabling large-scale viral SNP detection.

[0124] 2. The experimental environment conditions required for SNP detection of viral genome data are reduced, thereby reducing the impact of environmental factors on the detection, and thus improving the detection accuracy of SNP detection.

[0125] 3. Output mutation details in text format for easy archiving, browsing and subsequent analysis, ensuring readability and usability of the information.

[0126] 4. By predicting the impact of SNPs (single nucleotide polymorphisms) on protein structure, we can determine whether mutations will disrupt the normal function of proteins, such as affecting enzyme activity, receptor binding, etc.; help identify mutations that may be associated with certain diseases, assist in disease diagnosis and personalized treatment; guide protein engineering, optimize protein performance or develop new drugs; clarify how certain mutations affect protein structure and thus affect its biological effects, providing clues for basic research.

[0127] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0128] Based on the same inventive concept, the present application also provides a viral SNP detection device for implementing the aforementioned viral SNP detection method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more viral SNP detection device embodiments provided below can be found in the above-mentioned limitations of the viral SNP detection method and will not be repeated here.

[0129] In an exemplary embodiment, Figure 4 As shown, a viral SNP detection device is provided, comprising: a data acquisition module 401, a sequence alignment module 402, a SNP identification module 403 and a SNP annotation module 404, wherein:

[0130] The data acquisition module 401 is used to obtain viral genome data of the viral genome to be detected, and obtain reference genome data preset for the viral genome;

[0131] A sequence alignment module 402 is used to perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file between the viral genome data and the reference genome data;

[0132] The SNP identification module 403 is used to identify SNP mutation sites in the genome data comparison file to obtain multiple SNP mutation sites;

[0133] The SNP annotation module 404 is used to perform mutation annotation on multiple SNP mutation sites and viral genome data based on the reference genome data to obtain mutation information of the multiple SNP mutation sites.

[0134] In one embodiment, when the viral genome data belongs to the read sequence of the second / third generation original offline data, the sequence alignment module 402 further includes: an index construction submodule, a search submodule, and a comparison file generation submodule, wherein:

[0135] The index construction submodule is used to construct a reference genome index based on the reference genome data.

[0136] The search submodule is used to search the reference genome index using each read sequence contained in the viral genome data to obtain the comparison position of each read sequence on the reference genome.

[0137] The comparison file generation submodule is used to sort the read sequences according to the comparison positions and use the sorted read sequences as genome data comparison files.

[0138] In one embodiment, the search submodule is further used to search the reference genome index using each read sequence to obtain multiple matching positions of each read sequence on the reference genome; from the multiple matching positions, a target matching position with the greatest matching degree with each read sequence is determined, and each target matching position is determined as a comparison position of each read sequence on the reference genome.

[0139] In an exemplary embodiment, the comparison file generation submodule is further used to determine the chromosome to which the comparison position belongs as the chromosome to which the read sequence belongs; sort each read sequence based on the attribution information of the read sequence and the chromosome information of the chromosome to obtain sorted read sequences; and use each sorted read sequence as a genome data comparison file.

[0140] In one embodiment, the chromosome information includes chromosome number information and the starting base position information of the reads sequence on the chromosome. The comparison file generation submodule is further used to arrange the reads sequences in ascending order based on the chromosome number information to obtain sorted reads sequences when the attribution information indicates that each read sequence belongs to a different chromosome; when the attribution information indicates that at least two target read sequences belong to the same chromosome, arrange the reads sequences in ascending order according to the chromosome number information to obtain initially sorted reads sequences, and determine the target reads sequence from the initially sorted reads sequences; sort the target reads sequence according to the starting base position information of the target reads sequence on the chromosome to which it belongs to obtain a sorted target reads sequence; and update the initially sorted reads sequences based on the sorted target reads sequence to obtain sorted reads sequences.

[0141] In one embodiment, the SNP identification module 403 is further used to identify SNP mutation sites in the genomic data comparison file to obtain multiple initial SNP mutation sites; based on pre-set screening parameters, the multiple initial SNP mutation sites are filtered to obtain multiple SNP mutation sites.

[0142] In an exemplary embodiment, when the viral genome data is spliced ​​genome sequence data, the sequence alignment module 402 is also used to compare and analyze the viral genome data with the reference genome data to obtain the alignment between the viral genome data and the reference genome data; and generate a genome data comparison file based on the alignment.

[0143] In one embodiment, the SNP annotation module 404 is also used to annotate multiple SNP mutation sites and the proteins encoded by the genes at their viral genome locations based on the reference genome data, to obtain the mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region and mutation type of the multiple SNP mutation sites; mutation types include insertion, deletion, transition and transversion; the mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region and mutation type are used as the mutation information of the multiple SNP mutation sites.

[0144] Each module in the viral SNP detection device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0145] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store viral genome data, reference genome data, genome data comparison files, SNP mutation sites, and corresponding mutation information data. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a viral SNP detection method is implemented.

[0146] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0147] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the viral SNP detection method of the above embodiment when executing the computer program.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the viral SNP detection method of the above embodiment is implemented.

[0149] In one embodiment, a computer program product is provided, comprising a computer program, which implements the viral SNP detection method of the above embodiment when executed by a processor.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0151] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0152] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0153] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for detecting viral SNPs, characterized in that: The method comprises: Obtaining viral genome data of a viral genome to be detected, and obtaining reference genome data preset for the viral genome; Performing sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data; Identifying SNP mutation sites on the genome data comparison file to obtain multiple SNP mutation sites; Based on the reference genome data, mutation annotation is performed on the multiple SNP mutation sites and the viral genome data to obtain mutation information of the multiple SNP mutation sites.

2. The method according to claim 1, characterized in that In the case where the viral genome data belongs to the read sequence of the second / third generation original offline data; The step of performing sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data includes: Constructing a reference genome index based on the reference genome data; Searching the reference genome index using each read sequence contained in the viral genome data to obtain a comparison position of each read sequence on the reference genome; According to the comparison position, each of the reads sequences is sorted, and the sorted reads sequences are used as the genome data comparison file.

3. The method according to claim 2, characterized in that The step of searching the reference genome index using each read sequence contained in the viral genome data to obtain a comparison position of each read sequence on the reference genome includes: Searching the reference genome index using each of the read sequences to obtain a plurality of matching positions of each of the read sequences on the reference genome; From the multiple matching positions, a target matching position with the greatest matching degree with each of the reads sequences is determined, and each of the target matching positions is determined as a comparison position of each of the reads sequences on the reference genome.

4. The method according to claim 3, characterized in that The step of sorting the read sequences according to the comparison positions and using the sorted read sequences as the genome data comparison file includes: Determine the chromosome to which the comparison position belongs as the chromosome to which the read sequence belongs; sorting the read sequences based on the attribution information of the read sequences and the chromosome information of the chromosome to obtain the sorted read sequences; The sorted read sequences are used as the genome data comparison files.

5. The method according to claim 4, characterized in that The chromosome information includes chromosome number information and starting base position information of the read sequence on the chromosome; The step of sorting the read sequences based on the attribution information of the read sequences and the chromosome information of the chromosome to obtain the sorted read sequences comprises: In a case where the attribution information indicates that each of the read sequences belongs to a different chromosome, the read sequences are arranged in ascending order based on the chromosome number information to obtain the sorted read sequences; When the attribution information indicates that at least two target read sequences belong to the same chromosome, the read sequences are arranged in ascending order according to the chromosome number information to obtain initially sorted read sequences, and the target read sequence is determined from the initially sorted read sequences; Sorting the target reads according to the starting base position information of the target reads on the chromosome to which they belong, to obtain a sorted target reads sequence; Based on the sorted target reads sequence, each read sequence after the initial sorting is updated to obtain each read sequence after the sorting.

6. The method according to claim 4, characterized in that The SNP mutation site identification is performed on the genome data comparison file to obtain multiple SNP mutation sites, including: Identifying SNP mutation sites on the genome data comparison file to obtain multiple initial SNP mutation sites; Based on pre-set screening parameters, the multiple initial SNP mutation sites are filtered to obtain the multiple SNP mutation sites.

7. The method according to claim 1, characterized in that In the case where the viral genome data is spliced ​​genome sequence data; The step of performing sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data includes: Comparing the viral genome data with the reference genome data to obtain an alignment between the viral genome data and the reference genome data; According to the alignment, the genome data comparison file is generated.

8. The method according to any one of claims 1 to 7, characterized in that The step of performing mutation annotation on the multiple SNP mutation sites and the viral genome data according to the reference genome data to obtain mutation information of the multiple SNP mutation sites includes: Based on the reference genome data, the multiple SNP mutation sites and the proteins encoded by the genes at their viral genome locations are annotated to obtain mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region, and mutation type of the multiple SNP mutation sites; the mutation types include insertion, deletion, transition, and transversion; The mutation position information, mutation nucleotide information, gene name, amino acid mutation position, functional region, and mutation type are used as mutation information of the multiple SNP mutation sites.

9. A viral SNP detection device, characterized in that: The device comprises: A data acquisition module, configured to acquire viral genome data of a viral genome to be detected, and to acquire reference genome data preset for the viral genome; A sequence alignment module, configured to perform sequence alignment on the viral genome data and the reference genome data to obtain a genome data comparison file of the viral genome data and the reference genome data; A SNP identification module is used to identify SNP mutation sites in the genome data comparison file to obtain multiple SNP mutation sites; The SNP annotation module is used to perform mutation annotation on the multiple SNP mutation sites and the viral genome data based on the reference genome data to obtain mutation information of the multiple SNP mutation sites.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images