Method, device, equipment, medium and product for identifying variations of a tuberculosis sample to which genomic sequencing data belongs

Abstract

The application relates to a method, device, equipment, medium and product for identifying variations of a tuberculosis sample to which genomic sequencing data belongs. The method comprises the following steps: screening to-be-identified sequencing data from the genomic sequencing data according to sample quality characteristics of the tuberculosis sample to which the genomic sequencing data belongs; generating a sequencing reference file corresponding to multiple target identification objects in common according to reference genomic sequences corresponding to the multiple target identification objects respectively; obtaining a potential variation sequence file between the to-be-identified sequencing data and all the reference genomic sequences by comparing the to-be-identified sequencing data with the sequencing reference file; and identifying variations of the to-be-identified sequencing data according to polymorphic characteristic information of potential variation genomic sequences in the potential variation sequence file. The method improves the variation identification accuracy of the tuberculosis sample to which the genomic sequencing data belongs.

Description

Technical Field

[0001] This application relates to the field of bioinformatics technology, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for identifying variations in tuberculosis samples to which genome sequencing data belongs. Background Technology

[0002] With the continuous development of bioinformatics and sequencing technology, the technology for identifying variants in genome sequencing data is also constantly iterating. Accurate variant identification can play a role in many aspects such as genetic disease diagnosis, disease risk assessment, and drug development.

[0003] Currently, in the process of identifying variants in tuberculosis samples from which genome sequencing data belongs, personalized bioinformatics databases are typically constructed to perform bioinformatics analysis on the genome sequencing data, thereby detecting genomic variations in individuals or groups. However, due to the complexity of the causes of genomic sequence variations, the pre-set screening conditions in bioinformatics analysis are difficult to capture the actual variation locations in the genome sequencing data, which easily leads to errors in variant identification. Therefore, the current accuracy of variant identification in tuberculosis samples from which genome sequencing data belongs is low. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for identifying variants in tuberculosis samples to which genome sequencing data belongs, in order to improve the accuracy of variant identification in tuberculosis samples to which genome sequencing data belongs.

[0005] In a first aspect, this application provides a method for identifying variants in tuberculosis samples to which genome sequencing data belongs, including:

[0006] Based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs, sequencing data to be identified is screened from the genome sequencing data;

[0007] Based on the reference genome sequences corresponding to the multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to the multiple target identification objects is generated.

[0008] By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained;

[0009] Based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file, the sequencing data to be identified is subjected to variant identification, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genomic sequences and all reference genomic sequences.

[0010] In one embodiment, the step of screening sequencing data to be identified from the genome sequencing data based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs includes:

[0011] Based on the sample quality characteristics, query the data screening conditions for the tuberculosis sample;

[0012] The genome sequencing data is filtered according to the data filtering criteria to obtain the sequencing data to be identified.

[0013] In one embodiment, the data filtering criteria include at least one of the following:

[0014] The base quality value of the selected genomic sliding sequence in the genome sequencing data is less than or equal to a preset base quality threshold, wherein the genomic sliding sequence is a genomic sequence located within a preset sequence sliding window;

[0015] In the genome sequencing data, the base overlap of multiple genomic adapter sequences is greater than or equal to a preset base overlap threshold.

[0016] The sequence length of the genomic sequence fragment in the genome sequencing data is less than or equal to a preset sequence length threshold.

[0017] In one embodiment, the polymorphic feature information includes base variation sites; the step of identifying variations in the sequencing data to be identified based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file includes:

[0018] Determine a first mutation type and a second mutation type for the base mutation site, wherein the first mutation type is a mutation type with a mutation frequency greater than a preset mutation frequency threshold, and the second mutation type is a mutation type with a mutation frequency less than or equal to the preset mutation frequency threshold.

[0019] The first mutation type and the second mutation type are combined to form a set of mutation types for the base mutation site;

[0020] Based on the base variation frequencies of multiple variation types in the variation type set, the sequencing data to be identified is subjected to variation identification.

[0021] In one embodiment, after determining the first variant type and the second variant type of the base variant site, the method further includes:

[0022] Based on the first variant type and the second variant type, the potential variant genome sequence is translated into a first amino acid sequence and a second amino acid sequence, respectively;

[0023] Amino acid variation identification is performed on the sequencing data to be identified based on the first amino acid sequence and the second amino acid sequence.

[0024] In one embodiment, the method further includes:

[0025] Construct a common object reference file for the multiple target recognition objects;

[0026] By comparing the sequencing data to be identified with the object reference file, a sequence alignment file of the sequencing data to be identified relative to all target objects is obtained;

[0027] Based on the sequence alignment file, a sequence coverage result of the sequencing data to be identified is generated, wherein the sequence coverage result is used to characterize the sequence coverage of the sequencing data to be identified on each of the target identification objects;

[0028] Based on the sequence coverage results, the variant identification quality of the sequencing data to be identified is detected.

[0029] Secondly, this application also provides a variant identification device for tuberculosis samples to which genome sequencing data belongs, comprising:

[0030] The screening module is used to screen sequencing data to be identified from the genome sequencing data based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs;

[0031] The generation module is used to generate a sequencing reference file that corresponds to the multiple target identification objects in the tuberculosis sample, based on the reference genome sequences corresponding to each of the multiple target identification objects.

[0032] The alignment module is used to obtain a potential variant sequence file between the sequencing data to be identified and all reference genome sequences by aligning the sequencing data to be identified with the sequencing reference file.

[0033] The variant identification module is used to identify variants in the sequencing data to be identified based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genomic sequences and all reference genomic sequences.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] Based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs, sequencing data to be identified is screened from the genome sequencing data;

[0036] Based on the reference genome sequences corresponding to the multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to the multiple target identification objects is generated. By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained. Based on the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file, variant identification is performed on the sequencing data to be identified, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genome sequences and all reference genome sequences.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0038] Based on the reference genome sequences corresponding to the multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to the multiple target identification objects is generated. By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained. Based on the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file, variant identification is performed on the sequencing data to be identified, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genome sequences and all reference genome sequences.

[0039] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0040] Based on the reference genome sequences corresponding to the multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to the multiple target identification objects is generated. By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained. Based on the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file, variant identification is performed on the sequencing data to be identified, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genome sequences and all reference genome sequences.

[0041] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for variant identification of tuberculosis samples to which the genome sequencing data belongs firstly performs quality control on the genome sequencing data using sample quality characteristics of the tuberculosis samples to which the genome sequencing data belongs, obtaining sequencing data to be identified. Since sample quality characteristics can objectively reflect the quality requirements of the tuberculosis samples to which the genome sequencing data belongs, the screening of genome sequencing data based on sample quality characteristics can obtain high-quality sequencing data to be identified. Then, based on the reference genome sequences corresponding to multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to multiple target identification objects is generated. That is, by combining the reference genome sequences corresponding to multiple target identification objects, a sequencing reference file serving as an alignment reference for the sequencing data to be identified is constructed. Then, by comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained. Finally, variant identification is performed on the sequencing data to be identified using the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file. The polymorphic feature information is used to... This method characterizes the sequence features that differentiate between potential variant genome sequences and all reference genome sequences. Since the sequencing reference file is generated based on the reference genome sequences corresponding to multiple target identification objects, by comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file reflecting the potential genome sequence situation in the sequencing data to be identified relative to all target identification objects can be obtained. The polymorphic feature information in the potential variant sequence file can characterize the sequence features that differentiate between potential variant genome sequences and reference genome sequences. Thus, the polymorphic feature information can reflect the polymorphic variation in the genome sequencing data. Therefore, it can achieve the purpose of variant identification of tuberculosis samples to which the genome sequencing data belongs by using the actual polymorphic variation of the genome sequencing data relative to all target identification objects. This overcomes the technical defect that the pre-set screening conditions in bioinformatics analysis are difficult to capture the actual variant locations in the genome sequencing data due to the complexity of the mutation causes of genome sequences, which makes it easy to make variant identification errors. Therefore, it improves the identification accuracy of variants in tuberculosis samples to which the genome sequencing data belongs. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1This is a flowchart illustrating a method for identifying variants in a tuberculosis sample to which genome sequencing data belongs, as illustrated in one embodiment.

[0044] Figure 2 This is a flowchart illustrating a method for identifying variations in tuberculosis samples to which genome sequencing data belongs, as described in another embodiment.

[0045] Figure 3 This is a structural block diagram of a variant identification device for a tuberculosis sample to which genome sequencing data belongs, as shown in one embodiment.

[0046] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] Firstly, in the field of bioinformatics, identifying variants in tuberculosis samples based on sequencing technology is essential. Taking its role in disease risk assessment as an example, it has significant guiding implications for the precise prevention and control of tuberculosis. Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, affecting many parts of the body, especially the lungs, and seriously threatening human health. Traditional techniques typically involve constructing a personalized bioinformatics database by combining multiple tuberculosis drug resistance genes, followed by bioinformatics analysis of second-generation or third-generation sequencing data to ultimately derive the results of second-generation sequencing data analysis. While traditional sequencing data or third-generation sequencing data can identify variants, the combinatoriality of tuberculosis drug resistance genes is relatively limited. More importantly, it does not analyze the complex causes of variations in the genome sequence, thus failing to detect multidirectional variations of point mutations. Consequently, it may not identify low-frequency nucleotide and amino acid mutations, ultimately affecting the accuracy of variant identification in tuberculosis samples from which the genome sequencing data belongs. This hinders the effective control and prevention of tuberculosis transmission. Therefore, for effective prevention and control, there is an urgent need for a variant identification method for tuberculosis samples from which the genome sequencing data belongs, which can improve the accuracy of variant identification.

[0049] In one embodiment, such as Figure 1As shown, a method for identifying variants in tuberculosis samples to which genome sequencing data belongs is provided. This embodiment uses the application of this method to a variant identification system for tuberculosis samples to which genome sequencing data belongs as an example. The variant identification system for tuberculosis samples to which genome sequencing data belongs is deployed on a terminal, which includes, but is not limited to, personal computers and laptops. The variant identification system for tuberculosis samples to which genome sequencing data belongs includes a screening module, a generation module, a comparison module, and a variant identification module. The screening module is used to screen sequencing data to be identified from the genome sequencing data based on the sample quality characteristics of the tuberculosis samples to which the genome sequencing data belongs. The generation module is used to generate a sequencing reference file corresponding to multiple target identification objects based on the reference genome sequences corresponding to each of the multiple target identification objects of the tuberculosis sample. The comparison module is used to obtain a potential variant sequence file between the sequencing data to be identified and all reference genome sequences by comparing the sequencing data to be identified with the sequencing reference file. The variant identification module is used to identify variants in the sequencing data to be identified based on the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file. The polymorphic feature information is used to characterize the sequence features that differ between the potential variant genome sequences and all reference genome sequences. Then, through the screening module, generation module, and comparison module, the variant identification system is further improved. The information interaction between the block and the variant identification module can screen high-quality sequencing data to be identified based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs, and then obtain potential variant sequence files of the sequencing data to be identified relative to all reference genome sequences by comparing the sequencing data to be identified with the sequencing reference file. At the same time, since the polymorphic feature information in the potential variant sequence file can characterize the sequence features that differ between the potential variant genome sequence and the reference genome sequence, the polymorphic feature information can reflect the polymorphic variation in the genome sequencing data. Therefore, it is possible to realize the actual polymorphic variation of the genome sequencing data relative to all target identification objects. That is, in the process of variant identification of the tuberculosis sample to which the genome sequencing data belongs, both the specific combination of target identification objects are considered, and the multi-dimensional variation of the sequencing data to be identified can be completed through the polymorphic feature information of the potential variant genome sequence. Therefore, it can improve the accuracy of variant identification of the tuberculosis sample to which the genome sequencing data belongs. It is understood that the variant identification system of the tuberculosis sample to which the genome sequencing data belongs can also be deployed on a server, or it can be deployed on a system including terminals and servers, and implemented through the interaction between terminals and servers. In this embodiment, the method includes the following steps:

[0050] Step 102: Based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs, screen the sequencing data to be identified from the genome sequencing data;

[0051] It should be noted that the identification of variants in tuberculosis samples to which genome sequencing data belongs has significant guiding significance for disease prevention and control. Based on the different tuberculosis samples to which the genome sequencing data belongs, variant identification and analysis can be performed in different directions. It can be understood that variant identification in tuberculosis samples to which genome sequencing data belongs involves comparing the actual sequenced genome data with a known reference genome sequence, and then identifying the differences between the two to obtain the variant identification results. Specifically, variant identification results can include single nucleotide polymorphisms, insertions or deletions, and structural variations. Tuberculosis samples to which genome sequencing data belongs can specifically include sputum tuberculosis samples, cerebrospinal fluid tuberculosis samples, and pleural biopsy tissue tuberculosis samples. Meanwhile, based on the different dimensions of gene mutation analysis... Similarly, in addition to tuberculosis samples indicating chronic infectious diseases being used as tuberculosis samples for genomic sequencing data, tuberculosis samples obtained for considerations such as tumor-related gene mutation analysis, hereditary disease gene mutation analysis, and infectious disease pathogen gene mutation analysis can also be used as tuberculosis samples for genomic sequencing data. Genomic sequencing data is used to characterize information about an organism's genome obtained through genome sequencing technology. Specifically, it can be raw second-generation genome sequencing data or raw third-generation genome sequencing data. In terms of the long fragment coverage, it avoids to some extent the problems of information loss and base mismatch caused by PCR enriched sequences in second-generation sequencing. That is, using third-generation genome sequencing data for variant identification can avoid the problems of information loss and base mismatch caused by PCR enriched sequences.

[0052] It should be noted that the sequencing data to be identified refers to the genome sequencing data awaiting variant identification. Specifically, this can be high-quality genome sequencing data after screening. Understandably, to avoid the impact of genomic sequence fragments in the genome sequencing data on the accuracy of bioinformatics analysis results, quality control processing of the genome sequencing data is necessary before data alignment to remove low-quality sequence fragments. Specifically, this can be done using quality control software (such as Trim). In addition to the quality control processing of genomic sequencing data, due to the differences in quality control of tuberculosis samples during the variant identification process of different genomic sequencing data, the sample quality characteristics of the tuberculosis sample to which the genomic sequencing data belongs can be used as a benchmark during the screening process. The sample quality characteristics are used to characterize the sequencing quality requirements of the genomic sequencing data. For example, in one feasible approach, genomic sequencing data A needs to remove the base sequence fragments with an average base quality of less than a in the base reading frame of the quality control software, and genomic sequencing data B needs to remove the base sequence fragments with a greater than b number of overlapping bases in the first and last adapters (genomic adapter sequences). a and b are different. It can be seen that different genomic sequencing data can rely on different genomic sequence screening procedures when screening.

[0053] As an example, step 102 includes: using the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs as an index to perform a screening strategy for matching genome sequencing data; and based on the screening strategy, screening the genome sequencing data to obtain sequencing data to be identified.

[0054] Step 104: Generate a sequencing reference file that corresponds to multiple target identification objects based on their respective reference genome sequences.

[0055] It should be noted that the target identification object refers to the gene panel used as a reference for variant identification. For example, in one feasible approach, assuming the genomic sequencing data belongs to a tuberculosis sample, the multiple target identification objects of the tuberculosis sample can specifically be 'gyrb', 'gyrA', 'fgd1', 'HSP65', 'rpoB', 'Rv0678', 'rpsL', 'rplC', 'fbiC', 'atpE', 'rrs', 'rrL', etc. ', 'fabG1', 'inhA', 'rpsA', 'tlyA', 'katG', 'Rv1979c', 'erm', 'pncA', 'eis', 'ahpC', 'folC', 'pepQ' , 'ribD', 'thyA', 'ald', 'Rv2983', 'whiB7', 'fbiA', 'fbiB', 'alr', 'ddn', 'embA', 'embB', 'ubiA', 'et Thirty-eight selected tuberculosis drug resistance genes, such as 'hA' and 'gidB', can also be referred to as the detection and analysis targets for variant identification. It can be understood that, in order to identify polymorphic variations in the sequencing data to be identified, after completing the combination of multiple target identification targets, the reference genome sequence corresponding to each target identification target is further obtained. For example, the base sequence fragments of each detection and analysis target in a high-quality reference genome are obtained. Then, a personalized strategy is used to construct a sequencing reference file that corresponds to multiple target identification targets. For example, in one feasible approach, the reference genome sequence of each drug resistance gene can be used as a separate unit in the FASTA format. For example, the character content described after each delimiter ">" (up to the next ">" constitutes a unit, thereby constructing a FASTA format file with a total of 38 units. An index file is then constructed using a genome sequence alignment tool (such as BWA), ultimately generating a sequencing reference file (e.g., the database file TB_aro_amplicon_DB.fasta).

[0056] As an example, step 104 includes: generating a sequencing reference sub-file corresponding to each target identification object based on the reference genome sequence corresponding to each target identification object in the tuberculosis sample; and combining multiple sequencing reference sub-files into a sequencing reference file corresponding to multiple target identification objects based on a preset field separator.

[0057] Step 106: By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained.

[0058] It should be noted that by comparing the sequencing data to be identified with the sequencing reference files, the differences between the sequencing data to be identified and all reference genomes can be determined. It can be understood that the potential variant sequence files carry information about gene sequence variations in the genomic sequence data, specifically variant type, variant location, variant information, and other annotation information. The alignment between the sequencing data to be identified and the sequencing reference files can be performed using a genome sequence alignment tool (e.g., bwa). For example, in one feasible approach, assuming the sequencing reference file (e.g., TB_aro_amplicon_DB.fasta) is used, the sequencing data to be identified is aligned using a genome sequence alignment tool (e.g., bwa), and the intermediate files are sorted using a data processing tool (e.g., samtools) to obtain the intermediate file. The intermediate file (e.g., the file amplicon_align.sort.bam) is further analyzed and extracted for single nucleotide polymorphisms (SNPs), insertions, and deletions (INDELs) using file processing commands (e.g., mpileup) in data processing tools (e.g., samtools). This yields a variant depth file in VCF format. The variant detection command (e.g., mpileup2cns) in a variant detection tool (e.g., varscan2) is then used to perform variant statistics on the VCF variant depth file to obtain a potential variant sequence file for a single tuberculosis sample. In other words, a variant (variant or variant) statistical result file (e.g., the file variants.txt) is obtained.

[0059] As an example, step 106 includes: aligning the sequencing data to be identified with all reference genome sequences in the sequencing reference file one by one to obtain the data alignment results; after sorting the data alignment results, extracting the potential variant genome sequences in the sorted data alignment results; and constructing a potential variant sequence file between the sequencing data to be identified and all reference genome sequences.

[0060] Step 108: Based on the polymorphic feature information of the potential variant genome sequence in the potential variant sequence file, perform variant identification on the sequencing data to be identified. The polymorphic feature information is used to characterize the sequence features that differ between the potential variant genome sequence and all reference genome sequences.

[0061] It should be noted that polymorphic feature information is used to characterize the sequence features that differ between potential variant genome sequences and all reference genome sequences. Polymorphic feature information can reflect the variation polymorphism of the tuberculosis sample to which the genome sequencing data belongs in its genome. For example, in one feasible approach, polymorphic feature information can be used to analyze the variation polymorphism of each variant (variant or variant) in its genome, thereby detecting the multidirectional variation of point mutations and discovering features such as frequent nucleotide mutations and amino acid mutations.

[0062] As an example, step 108 includes: obtaining the variant identification result of the sequencing data to be identified by parsing the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file.

[0063] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for variant identification of tuberculosis samples to which the genome sequencing data belongs firstly performs quality control on the genome sequencing data using sample quality characteristics of the tuberculosis samples to which the genome sequencing data belongs, obtaining sequencing data to be identified. Since sample quality characteristics can objectively reflect the quality requirements of the tuberculosis samples to which the genome sequencing data belongs, the screening of genome sequencing data based on sample quality characteristics can obtain high-quality sequencing data to be identified. Then, based on the reference genome sequences corresponding to multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to multiple target identification objects is generated. That is, by combining the reference genome sequences corresponding to multiple target identification objects, a sequencing reference file serving as an alignment reference for the sequencing data to be identified is constructed. Then, by comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained. Finally, variant identification is performed on the sequencing data to be identified using the polymorphic feature information of the potential variant genome sequences in the potential variant sequence file. The polymorphic feature information is used to... This method characterizes the sequence features that differentiate between potential variant genome sequences and all reference genome sequences. Since the sequencing reference file is generated based on the reference genome sequences corresponding to multiple target identification objects, by comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file reflecting the potential genome sequence situation in the sequencing data to be identified relative to all target identification objects can be obtained. The polymorphic feature information in the potential variant sequence file can characterize the sequence features that differentiate between potential variant genome sequences and reference genome sequences. Thus, the polymorphic feature information can reflect the polymorphic variation in the genome sequencing data. Therefore, it can achieve the purpose of variant identification of tuberculosis samples to which the genome sequencing data belongs by using the actual polymorphic variation of the genome sequencing data relative to all target identification objects. This overcomes the technical defect that the pre-set screening conditions in bioinformatics analysis are difficult to capture the actual variant locations in the genome sequencing data due to the complexity of the mutation causes of genome sequences, which makes it easy to make variant identification errors. Therefore, it improves the identification accuracy of variants in tuberculosis samples to which the genome sequencing data belongs.

[0064] In one embodiment, such as Figure 2 As shown, based on the sample quality characteristics of the tuberculosis samples to which the genome sequencing data belongs, sequencing data to be identified is screened from the genome sequencing data, including:

[0065] Step 202: Based on the sample quality characteristics, query the data screening criteria for tuberculosis samples;

[0066] It should be noted that, in order to improve the processing efficiency of screening, a mapping relationship between sample quality characteristics and data screening conditions of different tuberculosis samples can be constructed in advance. The data screening conditions are used to screen low-quality genomic sequences in the genomic sequencing data. For example, in one feasible approach, sample quality characteristic x1 corresponds to data screening condition 1, and sample quality characteristic x2 corresponds to data screening condition 2.

[0067] As an example, step 202 includes: querying the data screening criteria for tuberculosis samples using sample quality characteristics as an index.

[0068] Step 204: Based on the data screening criteria, the genome sequencing data is screened to obtain the sequencing data to be identified.

[0069] As an example, step 204 includes: removing genomic sequences that meet the data screening criteria from the genome sequencing data to obtain sequencing data to be identified.

[0070] In this embodiment, during the screening of genome sequencing data, the data screening conditions for tuberculosis samples are first obtained based on sample quality characteristics. Then, low-quality genome sequences in the genome sequencing data are removed based on the data screening conditions to obtain the sequencing data to be identified. Since the data screening conditions are set before the variant identification of the tuberculosis samples to which the genome sequencing data belongs, there is no need to generate a screening strategy in real time when screening the genome sequencing data. Therefore, the time for matching data screening conditions to the tuberculosis samples to which the genome sequencing data belongs can be shortened. Thus, while improving the accuracy of variant identification of tuberculosis samples to which the genome sequencing data belongs, it also lays the foundation for improving the efficiency of variant identification of tuberculosis samples to which the genome sequencing data belongs.

[0071] In one embodiment, the data filtering criteria include at least one of the following:

[0072] The base quality value of the selected genomic sliding sequence in the genome sequencing data is less than or equal to a preset base quality threshold, wherein the genomic sliding sequence is a genomic sequence located within a preset sequence sliding window; the base overlap of multiple genomic adapter sequences in the genome sequencing data is greater than or equal to a preset base overlap threshold; and the sequence length value of the genomic sequence fragment in the genome sequencing data is less than or equal to a preset sequence length threshold.

[0073] It should be noted that the pre-set data screening conditions vary depending on the tuberculosis sample to which the genome sequencing data belongs. For example, in one feasible approach, quality control is required in three aspects during the screening of genome sequencing data of tuberculosis samples: genome sliding sequence, genome adapter sequence, and genome sequence fragment. Genome sliding sequence refers to the genome sequence located within the preset sequence sliding window. The length of the genome sliding sequence can be determined by the relevant parameters set on the quality control software. Genome adapter sequence refers to the known genome sequence that connects the unknown genome sequence in the genome sequencing data. Specifically, the genome adapter sequence can be the genome head adapter sequence or the genome tail adapter sequence. Genome sequence fragment refers to any sequence fragment in the genome sequencing data. Specifically, the genome sequence fragment can be a spliced ​​genome sequence or an unspliced ​​genome sequence.

[0074] As an example, a genomic sliding sequence is defined in the genomic sequencing data, and the base quality value of the genomic sliding sequence is detected to be less than or equal to a preset base quality threshold; multiple genomic adapter sequences are identified in the genomic sequencing data, and the base overlap of the multiple genomic adapter sequences is detected to be greater than or equal to a preset base overlap threshold; any genomic sequence fragment is obtained from the genomic sequencing data, and the sequence length value of the genomic sequence fragment is less than or equal to a preset sequence length threshold.

[0075] In one feasible approach, assuming the preset base quality threshold for the genomic sliding sequence of a tuberculosis sample can be 25, the preset base overlap threshold can be 3, and the preset sequence length threshold can be 30 bp, it can be understood that the base quality value of the genomic sliding sequence represents the average base quality of the genomic sliding sequence. Therefore, the specific data screening conditions are: 1) The base quality value of the genomic sliding sequence within the base reading frame of the quality control software is less than or equal to 25; 2) The base overlap of the first and last adapters (genomic adapter sequences) is greater than or equal to 3; and the sequence length of the cleaved or uncleaved genomic sequence fragment is less than 30 bp.

[0076] In this embodiment, specific data screening conditions are set for the tuberculosis sample to which the genome sequencing data belongs, from three aspects: genome sliding sequence, genome adapter sequence, and genome sequence fragment. This ensures that the sequencing data to be identified obtained through the above data screening conditions excludes low-quality genome sequences to a large extent, thereby providing extremely high-quality genome sequencing data for the subsequent data alignment process. Therefore, this lays the foundation for improving the accuracy of variant identification of the tuberculosis sample to which the genome sequencing data belongs.

[0077] In one embodiment, polymorphic feature information includes base variation sites; based on the polymorphic feature information of potential variant genomic sequences in the potential variant sequence file, variant identification is performed on the sequencing data to be identified, including:

[0078] The first and second variant types of the base variation sites are determined, wherein the first variant type is a variant type with a variant frequency greater than a preset variant frequency threshold, and the second variant type is a variant type with a variant frequency less than or equal to the preset variant frequency threshold; the first and second variant types are combined into a variant type set of the base variation sites; and variant identification is performed on the sequencing data to be identified based on the base variation frequencies of multiple variant types in the variant type set.

[0079] It should be noted that, in the process of variant identification of sequencing data to be identified, certain personalized strategies can be adopted to further improve the identification accuracy of variants and expand the identification of genome sequencing data. For example, in one feasible approach, the polymorphic feature information of potential variant genome sequences in the potential variant sequence file can be used to identify the variant status of other bases different from vairants (variants or variants). Specifically, each output variant site in the potential variant sequence file (e.g., variants.txt) is recorded. Through the specific base positions in the VCF variant depth file, and using certain regular expression rules in a programming language (e.g., Python 3), the variant status of other bases different from vairants (variants or variants) can be obtained. For example, if the variant frequency of AT is 50%, the variant frequencies of AC and AG are calculated accordingly. The above statistics can describe the polymorphism statistics of a single SNP. That is, through the base variant frequency analysis of multiple variant types, different mutation patterns of a single mutated base can be detected, thereby detecting low-frequency mutations.

[0080] It should be noted that the first variant type is the variant type with a variant frequency greater than the preset variant frequency threshold, and the second variant type is the variant type with a variant frequency less than or equal to the preset variant frequency threshold. Specifically, the first variant type is a high-frequency variant type, and the second variant type is a low-frequency variant type. Based on the base variant frequency of different variant types, variant identification is performed on the data to be identified, which can identify the all-round variation of point mutations in the genome sequencing data. Among them, the base variant site is used to identify the specific location where the potential variant genome sequence has a variant.

[0081] As an example, variant types with a mutation frequency greater than a preset mutation frequency threshold in the potential variant genome sequence are identified as the first variant type, and variant types with a mutation frequency less than or equal to the preset mutation frequency threshold in the potential variant genome sequence are identified as the second variant type. Based on the first and second variant types, a variant type set of base variant sites is constructed. The base mutation frequencies of multiple variant types in the variant type set are obtained respectively, and the multiple base mutation frequencies are used together as the variant identification result of the sequencing data to be identified.

[0082] In this embodiment, different mutation types are determined based on base variation sites, thereby constructing a set of mutation types for base variation sites in potential mutated genomic sequences. Finally, the mutation identification of the sequencing data to be identified is performed by using the base variation frequencies of multiple mutation types in the mutation type set. Thus, by using multiple base variation frequencies, a comprehensive identification of polymorphic variations in genomic sequencing data is achieved, thereby further improving the accuracy of mutation identification in genomic sequencing data.

[0083] In one embodiment, after determining the first and second variant types of the base variation site, the method further includes:

[0084] Based on the first and second variant types, the potential variant genome sequences are translated into the first and second amino acid sequences, respectively; based on the first and second amino acid sequences, amino acid variants are identified in the sequencing data to be identified.

[0085] It should be noted that different variations in genome sequencing data have varying degrees of impact. For example, some variations in the genome sequence in genome sequencing data do not change the encoded amino acid sequence; these variations are called synonymous variations. However, if some variations in the genome sequence in genome sequencing data cause changes in the encoded amino acid sequence, they are called non-synonymous variations. Therefore, to improve the variation identification effect of tuberculosis samples to which genome sequencing data belongs, certain personalized strategies can be adopted to identify amino acid variations in the sequencing data to be identified. For example, in one feasible approach, each SNP and its polymorphic SNP obtained above can be translated into the corresponding amino acid to detect amino acid variations.

[0086] As an example, based on the first variant type, the potential variant genome sequence is translated into the first amino acid sequence, and based on the second variant type, the potential variant genome sequence is translated into the second amino acid sequence; by analyzing the first and second amino acid sequences, the identification results of amino acid variant identification of the sequencing data to be identified are obtained.

[0087] In this embodiment, by integrating different mutation types, the first amino acid sequence of a single SNP and the second amino acid sequence of a polymorphic SNP are translated. Then, by comprehensively analyzing the first and second amino acid sequences, the amino acid mutation identification results of the sequencing data to be identified are obtained. This allows it to detect whether the mutations appearing in the genome sequencing data will change the amino acid sequence, thus improving the identification effect of mutations in the tuberculosis sample to which the genome sequencing data belongs.

[0088] In one embodiment, the method further includes:

[0089] A common object reference file is constructed for multiple target identification objects; by comparing the sequencing data to be identified with the object reference file, a sequence alignment file of the sequencing data to be identified relative to all target identification objects is obtained; based on the sequence alignment file, a sequence coverage result of the sequencing data to be identified is generated, wherein the sequence coverage result is used to characterize the sequence coverage of the sequencing data to be identified on each target identification object; based on the sequence coverage result, the variant identification quality of the sequencing data to be identified is detected.

[0090] It should be noted that, in the process of targeted gene panels for identification and variant recognition, in addition to providing feedback on variant recognition from the perspective of polymorphic variations in genome sequencing data, it is also possible to analyze the variant associations between genome sequencing data and multiple target objects. This allows for overall control of the variant recognition quality of genome sequencing data during the variant recognition process. For example, in one feasible approach, the object reference files can be constructed simultaneously during the construction of the sequencing reference file (e.g., file TB_ARO_amplicon_DB.fasta) database file. Specifically, assuming multiple target objects are 'gyrb', 'gy rA', 'fgd1', 'HSP65', 'rpoB', 'Rv0678', 'rpsL', 'rplC', 'fbiC', 'atpE', 'rrs', 'rrL', 'fabG1', 'inhA', 'rpsA', 'tlyA', 'katG', 'Rv1979c', 'erm', 'pncA', 'eis', 'ahpC', 'folC', 'pepQ', 'ribD', 'thyA', 'ald', 'Rv2983', 'whiB7', 'fbiA', 'fbiB', 'alr', 'ddn', 'embA', 'embB', 'ubi Thirty-eight tuberculosis drug resistance genes, such as 'A', 'ethA', and 'gidB', can be linked together in pairs using 100 consecutive N characters to construct a FASTA format file. The FASTA format is a single long unit, and an index file is built using a genome sequence alignment tool (e.g., BWA) to obtain an object reference file (e.g., the database file TB_refernece.fasta). Further, using the object reference file as the object, the genome sequence alignment tool (e.g., BWA) is used to align the sequencing data to be identified, and data processing tools (e.g., SAMTools) are used to sort the intermediate files to obtain the sequence. The sequence alignment file (e.g., reference_align.sort.bam) is then used by genomic data analysis tools (e.g., the deep statistical tool bedtools) to perform deep statistical analysis on the sequence alignment file, resulting in the sequence coverage results of the sequencing data to be identified. These sequence coverage results characterize the sequence coverage of the sequencing data on each target object. The sequence coverage results can be textual information reflecting the sequence coverage or a visually displayed coverage depth map. Specifically, the steps for generating the sequence coverage results of the sequencing data to be identified based on the sequence alignment file can be: performing deep statistical analysis on the sequence alignment file to obtain a deep statistical file (e.g., cov).(bed), and then, through certain personalized strategies, plot the coverage depth map of a single binding tuberculosis sample on an object reference file (e.g., the database file TB_refernece.fasta). This view determines the depth coverage of each drug resistance gene, and ultimately, users can judge the quality of the experiment by observing the coverage depth map.

[0091] As an example, multiple target identification objects are combined by pre-defined transition fields to obtain a common object reference file for multiple target identification objects; the sequencing data to be identified is compared with each target identification object in the object reference file to obtain a sequence alignment file of the sequencing data to be identified relative to all target identification objects; the sequence alignment file is statistically analyzed to obtain a sequence coverage intermediate file; based on the sequence coverage intermediate file, a sequence coverage depth map of the sequencing results to be identified is generated, wherein the sequence coverage intermediate file can specifically be a cov.bed file; the variation identification quality of the sequencing data to be identified is detected by the sequence coverage depth map.

[0092] In this embodiment, by constructing an object reference file that corresponds to multiple target identification objects, and during the process of identifying variants in tuberculosis samples to which the genome sequencing data belongs, the quality of variant identification in tuberculosis samples to which the genome sequencing data belongs is detected simultaneously by comparing the generated sequence coverage results. Thus, the sequence coverage results can reflect the variant association between the genome sequencing data and multiple target identification objects. Therefore, while improving the accuracy of variant identification in tuberculosis samples to which the genome sequencing data belongs, the analytical capability for identifying variants in tuberculosis samples to which the genome sequencing data belongs is also improved.

[0093] In one feasible approach, during the variant identification process of the tuberculosis sample to which the genome sequencing data belongs, the data screening criteria for the tuberculosis sample are first queried based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs. Specifically, the data screening criteria can be set as follows: 1) The base quality value of the selected genomic sliding sequence in the genome sequencing data is less than or equal to a preset base quality threshold, wherein the genomic sliding sequence is a genomic sequence located within a preset sequence sliding window; 2) The base overlap of multiple genomic adapter sequences in the genome sequencing data is greater than or equal to a preset base overlap threshold; 3) The sequence length value of the genomic sequence fragment in the genome sequencing data is less than or equal to a preset sequence length threshold.

[0094] Furthermore, using data screening criteria, the genome sequencing data is screened to obtain high-quality sequencing data to be identified. Then, by comparing the sequencing data to be identified with the sequencing reference files, a potential variant sequence file is obtained between the sequencing data to be identified and all reference genome sequences. Specifically, the polymorphic characteristics of the potential variant sequences can be base variation sites. Therefore, based on these base variation sites, the polymorphism of the genome sequencing data can be detected. That is, firstly, the first and second variant types of the base variation sites are determined. The first variant type is the variant type with a frequency greater than a preset variant frequency threshold, and the second variant type is the variant type with a frequency less than or equal to the preset variant frequency threshold. The first and second variant types are combined to form a variant type set of base variation sites. Based on the base variation frequencies of multiple variant types in the variant type set, variant identification is performed on the sequencing data to be identified.

[0095] Furthermore, after determining the first and second variant types, amino acid variant identification can be performed simultaneously on the genome sequencing data. Specifically, based on the first and second variant types, the potential variant genome sequences are translated into the first and second amino acid sequences, respectively; and based on the first and second amino acid sequences, amino acid variant identification is performed on the sequencing data to be identified.

[0096] Furthermore, while performing variant identification, the variant identification quality between the genome sequencing data and multiple target identification objects can also be detected. Specifically, a common object reference file is constructed for multiple target identification objects; by comparing the sequencing data to be identified with the object reference file, a sequence alignment file of the sequencing data to be identified relative to all target identification objects is obtained; based on the sequence alignment file, a sequence coverage result of the sequencing data to be identified is generated, wherein the sequence coverage result is used to characterize the sequence coverage of the sequencing data to be identified on each target identification object; based on the sequence coverage result, the variant identification quality of the sequencing data to be identified is detected.

[0097] Since the sequencing reference file is generated based on the reference genome sequences corresponding to multiple target objects, by comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file reflecting the potential genome sequence situation in the sequencing data to be identified relative to all target objects can be obtained. The polymorphic feature information in the potential variant sequence file can characterize the sequence features that differ between the potential variant genome sequence and the reference genome sequence. Therefore, the polymorphic feature information can reflect the polymorphic variations in the genome sequencing data. Thus, the goal of variant identification of the tuberculosis sample to which the genome sequencing data belongs can be achieved based on the actual polymorphic variations of the genome sequencing data relative to all target objects. This overcomes the limitations of pre-set screening criteria in bioinformatics analysis due to the complexity of genome sequence variations. The existing technology has limitations in capturing the actual mutation locations in genome sequencing data, leading to potential errors in mutation identification. Therefore, this method improves the accuracy of mutation identification in tuberculosis samples associated with genome sequencing data. Simultaneously, by constructing a reference file corresponding to multiple target identification objects and comparing the generated sequence coverage results during mutation identification in tuberculosis samples associated with genome sequencing data, the method simultaneously detects the quality of mutation identification in these samples. This allows the sequence coverage results to reflect the mutation associations between genome sequencing data and multiple target identification objects. Therefore, while improving the accuracy of mutation identification in tuberculosis samples associated with genome sequencing data, this method also enhances the analytical capabilities for analyzing the quality of mutation identification in these samples.

[0098] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0099] Based on the same inventive concept, this application also provides a device for identifying variants in tuberculosis samples to which genomic sequencing data belongs, for implementing the variant identification method for tuberculosis samples to which genomic sequencing data belongs as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for identifying variants in tuberculosis samples to which genomic sequencing data belongs provided below can be found in the limitations of the variant identification method for tuberculosis samples to which genomic sequencing data belongs above, and will not be repeated here.

[0100] In one exemplary embodiment, such as Figure 3 As shown, a variant identification device for tuberculosis samples to which genome sequencing data belongs is provided, comprising: a screening module 301, a generation module 302, an alignment module 303, and a variant identification module 304, wherein:

[0101] The screening module 301 is used to screen sequencing data to be identified from the genome sequencing data based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs;

[0102] The generation module 302 is used to generate a sequencing reference file that corresponds to the multiple target identification objects in the tuberculosis sample based on the reference genome sequences corresponding to each of the multiple target identification objects.

[0103] The alignment module 303 is used to obtain a potential variant sequence file between the sequencing data to be identified and all reference genome sequences by aligning the sequencing data to be identified with the sequencing reference file.

[0104] The variant identification module 304 is used to identify variants in the sequencing data to be identified based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genomic sequences and all reference genomic sequences.

[0105] In one embodiment, the filtering module 301 is further configured to:

[0106] Based on the sample quality characteristics, the data screening conditions for the tuberculosis sample are queried; based on the data screening conditions, the genome sequencing data is screened to obtain the sequencing data to be identified.

[0107] In one embodiment, the data filtering criteria include at least one of the following:

[0108] The base quality value of the selected genomic sliding sequence in the genome sequencing data is less than or equal to a preset base quality threshold, wherein the genomic sliding sequence is a genomic sequence located within a preset sequence sliding window; the base overlap of multiple genomic adapter sequences in the genome sequencing data is greater than or equal to a preset base overlap threshold; and the sequence length value of the genomic sequence fragment in the genome sequencing data is less than or equal to a preset sequence length threshold.

[0109] In one embodiment, the polymorphic feature information includes base variation sites; the variation recognition module 304 is further configured to:

[0110] A first variant type and a second variant type are determined for the base variant site, wherein the first variant type is a variant type with a variant frequency greater than a preset variant frequency threshold, and the second variant type is a variant type with a variant frequency less than or equal to the preset variant frequency threshold; the first variant type and the second variant type are combined to form a variant type set for the base variant site; and variant identification is performed on the sequencing data to be identified based on the base variant frequencies of multiple variant types in the variant type set.

[0111] In one embodiment, the variant identification device for the tuberculosis sample to which the genome sequencing data belongs is further used for:

[0112] Based on the first variant type and the second variant type, the potential variant genome sequence is translated into a first amino acid sequence and a second amino acid sequence, respectively; based on the first amino acid sequence and the second amino acid sequence, amino acid variant identification is performed on the sequencing data to be identified.

[0113] In one embodiment, the variant identification device for the tuberculosis sample to which the genome sequencing data belongs is further used for:

[0114] A common object reference file is constructed for the multiple target identification objects; by comparing the sequencing data to be identified with the object reference file, a sequence alignment file of the sequencing data to be identified relative to all target identification objects is obtained; based on the sequence alignment file, a sequence coverage result of the sequencing data to be identified is generated, wherein the sequence coverage result is used to characterize the sequence coverage of the sequencing data to be identified on each target identification object; based on the sequence coverage result, the variant identification quality of the sequencing data to be identified is detected.

[0115] Each module in the variant identification device for the tuberculosis sample to which the aforementioned genome sequencing data belongs can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0116] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for identifying variations in tuberculosis samples to which genome sequencing data belongs. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0117] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0118] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0119] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0120] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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 many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0121] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0122] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for identifying variants in tuberculosis samples to which genome sequencing data belongs, characterized in that, The method includes: Based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs, sequencing data to be identified is screened from the genome sequencing data; Based on the reference genome sequences corresponding to the multiple target identification objects of the tuberculosis sample, a sequencing reference file corresponding to the multiple target identification objects is generated. By comparing the sequencing data to be identified with the sequencing reference file, a potential variant sequence file between the sequencing data to be identified and all reference genome sequences is obtained; Based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file, the sequencing data to be identified is subjected to variant identification, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genomic sequences and all reference genomic sequences.

2. The method according to claim 1, characterized in that, The step of screening sequencing data to be identified from the genome sequencing data includes: Based on the sample quality characteristics, query the data screening conditions for the tuberculosis sample; The genome sequencing data is filtered according to the data filtering criteria to obtain the sequencing data to be identified.

3. The method according to claim 2, characterized in that, The data filtering criteria include at least one of the following: The base quality value of the genomic sliding sequence in the genome sequencing data is less than or equal to a preset base quality threshold, wherein the genomic sliding sequence is a genomic sequence located within a preset sequence sliding window; In the genome sequencing data, the base overlap of multiple genomic adapter sequences is greater than or equal to a preset base overlap threshold. The sequence length of the genomic sequence fragment in the genome sequencing data is less than or equal to a preset sequence length threshold.

4. The method according to claim 1, characterized in that, The polymorphic feature information includes base variation sites; the step of identifying variations in the sequencing data to be identified based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file includes: Determine a first mutation type and a second mutation type for the base mutation site, wherein the first mutation type is a mutation type with a mutation frequency greater than a preset mutation frequency threshold, and the second mutation type is a mutation type with a mutation frequency less than or equal to the preset mutation frequency threshold. The first mutation type and the second mutation type are combined to form a set of mutation types for the base mutation site; Based on the base variation frequencies of multiple variation types in the variation type set, the sequencing data to be identified is subjected to variation identification.

5. The method according to claim 4, characterized in that, After determining the first and second variant types of the base variation sites, the method further includes: Based on the first variant type and the second variant type, the potential variant genome sequence is translated into a first amino acid sequence and a second amino acid sequence, respectively; Amino acid variation identification is performed on the sequencing data to be identified based on the first amino acid sequence and the second amino acid sequence.

6. The method according to claim 1, characterized in that, The method further includes: Construct a common object reference file for the multiple target recognition objects; By comparing the sequencing data to be identified with the object reference file, a sequence alignment file of the sequencing data to be identified relative to all target objects is obtained; Based on the sequence alignment file, a sequence coverage result of the sequencing data to be identified is generated, wherein the sequence coverage result is used to characterize the sequence coverage of the sequencing data to be identified on each of the target identification objects; Based on the sequence coverage results, the variant identification quality of the sequencing data to be identified is detected.

7. A variant identification device for tuberculosis samples to which genome sequencing data belongs, characterized in that, The device includes: The screening module is used to screen sequencing data to be identified from the genome sequencing data based on the sample quality characteristics of the tuberculosis sample to which the genome sequencing data belongs; The generation module is used to generate a sequencing reference file that corresponds to the multiple target identification objects in the tuberculosis sample, based on the reference genome sequences corresponding to each of the multiple target identification objects. The alignment module is used to obtain a potential variant sequence file between the sequencing data to be identified and all reference genome sequences by aligning the sequencing data to be identified with the sequencing reference file. The variant identification module is used to identify variants in the sequencing data to be identified based on the polymorphic feature information of the potential variant genomic sequences in the potential variant sequence file, wherein the polymorphic feature information is used to characterize the sequence features that differ between the potential variant genomic sequences and all reference genomic sequences.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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