Human leukocyte antigen typing method and device, computer device and storage medium

Abstract

This application provides a method, apparatus, computer equipment, and storage medium for human leukocyte antigen (HLA) typing, belonging to the field of biotechnology. The method includes: acquiring raw sequencing data; wherein the raw sequencing data is obtained by sequencing the HLA gene using third-generation sequencing technology; performing quality assessment on the raw sequencing data to obtain sequencing quality assessment data; selecting selected sequencing data from the raw sequencing data based on the sequencing quality assessment data; performing correction processing on the selected sequencing data to obtain target sequencing data; and searching for the target genotype in a pre-constructed HLA database based on the target sequencing data to obtain the target genotype. This application can improve the accuracy of HLA typing.

Description

Technical Field

[0001] This application relates to the field of biotechnology, and in particular to a method and apparatus for human leukocyte antigen typing, computer equipment, and storage medium. Background Technology

[0002] Human leukocyte antigen (HLA) typing is a crucial molecular biology technique used to identify subtypes of the human leukocyte antigen (HLA) gene in an individual's immune system. Traditional HLA typing methods primarily utilize next-generation sequencing (NGS) technology, sequencing the exons of the HLA gene and employing alignment algorithms for sequence analysis and subtype comparison. However, NGS typically relies on short-read data, requiring complex assembly and alignment algorithms to complete sequence assembly and alignment. The entire data analysis process is complex and prone to errors, thus affecting the accuracy of HLA typing. Therefore, improving the accuracy of HLA typing has become an urgent technical challenge. Summary of the Invention

[0003] The main objective of this application is to provide a method and apparatus for human leukocyte antigen typing, a computer device and a storage medium, which aim to improve the accuracy of human leukocyte antigen typing.

[0004] To achieve the above objectives, a first aspect of this application provides a method for human leukocyte antigen typing, the method comprising:

[0005] Obtain raw sequencing data; wherein, the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology;

[0006] The raw sequencing data were subjected to quality assessment to obtain sequencing quality assessment data;

[0007] Selected sequencing data are selected from the raw sequencing data based on the sequencing quality assessment data;

[0008] The selected sequencing data is corrected to obtain the target sequencing data;

[0009] The target genotype is obtained by searching the pre-constructed human leukocyte antigen database based on the target sequencing data.

[0010] In some embodiments, the human leukocyte antigen (HLA) database includes: reference sequencing data and a reference genotype of the reference sequencing data. The step of searching for cell subtypes in a preset HLA database based on the target sequencing data to obtain the target genotype includes:

[0011] A first genotype is selected from the reference genotype based on the target sequencing data and the reference sequencing data;

[0012] The first genotype is deduplicated to obtain the selected genotype;

[0013] Selected sequencing sequences are selected from the original sequencing data based on the selected genotype;

[0014] The selected sequencing sequences are aligned and assembled to obtain secondary alignment data;

[0015] A second genotype is selected from the reference genotype based on the secondary alignment data and the reference sequencing data;

[0016] The first genotype and the second genotype are spliced ​​together to obtain the target genotype.

[0017] In some embodiments, the step of screening for a first genotype from the reference genotype based on the target sequencing data and the reference sequencing data includes:

[0018] Obtain the sequence index information of the reference sequencing data;

[0019] The reference gene sequence is extracted from the reference sequencing data based on the sequence index information;

[0020] The sequence alignment information is obtained by comparing the preset matching rate, the target sequencing data, and the reference gene sequence.

[0021] The first genotype is selected from the reference genotypes based on the sequence alignment information.

[0022] In some embodiments, the quality assessment of the raw sequencing data to obtain sequencing quality assessment data includes:

[0023] Obtain sequencing parameters from the raw sequencing data; wherein, the sequencing parameters include: sequence length information and end base ratio;

[0024] The quality value of the raw sequencing data is set according to the end base ratio to obtain the sequence quality value;

[0025] The original sequencing data is evaluated based on the sequence quality value and the sequence length information to obtain the sequencing quality evaluation data.

[0026] In some embodiments, selecting selected sequencing data from the raw sequencing data based on the sequencing quality assessment data includes:

[0027] The raw sequencing data is filtered according to a preset quality threshold and the sequence quality value to obtain preliminary sequencing data;

[0028] The preliminary sequencing data is filtered based on a preset length threshold and the sequence length information to obtain the selected sequencing data.

[0029] In some embodiments, the step of correcting the selected sequencing data to obtain target sequencing data includes:

[0030] Primer identification is performed on the selected sequencing data according to the preset primer sequence to obtain primer identification information;

[0031] The selected sequencing data is split using primers based on a preset mismatch threshold and the primer identification information to obtain sequencing sequences.

[0032] The selected sequencing data is corrected based on the sequencing sequence to obtain the target sequencing data.

[0033] In some embodiments, after performing a genotype search in a preset human leukocyte antigen database based on the target sequencing data to obtain the target genotype, the method further includes:

[0034] Based on the preset verification genotype information and the target genotype, the typing quality is assessed to obtain typing quality assessment data;

[0035] The target genotype, the typing quality assessment data, and the sequence alignment information are used to generate a report, resulting in a human leukocyte antigen typing report.

[0036] To achieve the above objectives, a second aspect of this application provides a human leukocyte antigen typing device, the device comprising:

[0037] The data acquisition module is used to acquire raw sequencing data; wherein the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology.

[0038] The quality assessment module is used to assess the quality of the raw sequencing data to obtain sequencing quality assessment data;

[0039] A data filtering module is used to filter selected sequencing data from the raw sequencing data based on the sequencing quality assessment data;

[0040] The primer splitting module is used to split the selected sequencing data to obtain the target sequencing data;

[0041] The genotype lookup module is used to search for the target genotype in a pre-constructed human leukocyte antigen database based on the target sequencing data to obtain the target genotype.

[0042] To achieve the above objectives, a third aspect of the present application provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0043] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0044] The human leukocyte antigen (HLA) typing method, apparatus, computer equipment, and storage medium proposed in this application collect HLA genes using third-generation sequencing technology to obtain raw sequencing data, resulting in longer and more accurate raw sequencing data. To further improve the accuracy and reliability of typing, the raw sequencing data undergoes quality assessment and correction to select corrected sequencing data that meets the quality requirements. Simultaneously, during genotype lookup, a pre-defined HLA database is used, eliminating the need to retrieve data from public databases for each genotype lookup, thus improving the efficiency of sequence alignment and genotype lookup. Attached Figure Description

[0045] Figure 1 This is a flowchart of the human leukocyte antigen typing method provided in the embodiments of this application;

[0046] Figure 2 yes Figure 1 The flowchart of step S102 in the document;

[0047] Figure 3 yes Figure 1 The flowchart of step S103 in the process;

[0048] Figure 4 yes Figure 1 The flowchart of step S104 in the process;

[0049] Figure 5 yes Figure 1 The flowchart of step S105 in the process;

[0050] Figure 6 yes Figure 5 The flowchart of step S501 in the process;

[0051] Figure 7 This is a flowchart of a human leukocyte antigen typing method provided in another embodiment of this application;

[0052] Figure 8 This is an overall flowchart of the human leukocyte antigen typing method provided in the embodiments of this application;

[0053] Figure 9 This is a schematic diagram of the structure of the human leukocyte antigen typing device provided in the embodiments of this application;

[0054] Figure 10 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0055] 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.

[0056] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0058] First, let's analyze some of the terms used in this application:

[0059] Human leukocyte antigen (HLA) typing is a method for detecting individual differences in HLA genes, and it is of great significance in fields such as organ transplantation, disease association studies, and forensic medicine. The HLA system is located on chromosome 6 and includes class I, II, and III gene regions. Class I and II genes encode antigen-presenting molecules on the cell surface, while the class III region encodes other molecules that play a role in immune responses.

[0060] Genotypes are groups of highly similar protein members produced by a single gene or gene family, which vary due to genetic differences. Genotypes can be formed by a variety of mechanisms, including alternative splicing, alternative promoters, or other post-transcriptional modifications of a single gene. Alternative splicing is one of the main mechanisms for genotype formation, allowing mRNA to select different protein-coding regions (exons) from a gene, or even different portions of exons from RNA, to form different mRNA sequences, each unique mRNA sequence producing a unique protein.

[0061] BLAST (Basic Local Alignment Search Tool) is a widely used bioinformatics tool used to search for highly similar sequences in nucleic acid or protein databases. It helps scientists infer functional and evolutionary relationships between sequences, as well as identify members of gene families. The BLAST program can process any number of sequences, including protein and nucleic acid sequences, and can select multiple databases, but the databases must be of the same type—either all protein databases or all nucleic acid databases.

[0062] Primer splitting: refers to splitting the primer sequence in sequencing data into multiple subsequences in order to better analyze and process it.

[0063] Base pairs are the basic building blocks of DNA molecules. In a DNA molecule, a base pair consists of two complementary bases bonded together by hydrogen bonds. The DNA molecule consists of two intertwined strands, which are linked together by base pairing to form a double helix structure.

[0064] Exon regions are parts of eukaryotic genes that contain the information needed for protein synthesis. During gene expression, the DNA sequence of the exon region is transcribed into RNA, which is then spliced, introns are removed, and exons are spliced ​​together to form mature mRNA, which is then translated into protein.

[0065] Haplotype: is short for haploid genotype. In genetics, it refers to the combination of alleles on multiple loci that are inherited together on the same chromosome; in layman's terms, it is a genotype composed of several closely linked genes that determine the same trait.

[0066] Human leukocyte antigen (HLA) typing is a crucial molecular biology technique used to identify HLA gene subtypes in an individual's immune system. Traditional HLA typing methods are primarily based on next-generation sequencing (NGS) technology, which involves sequencing the exons of HLA genes and using alignment algorithms for sequence analysis and subtype comparison. However, NGS typically relies on short-read data, requiring complex assembly and alignment algorithms to complete sequence assembly and alignment. Therefore, the data analysis process is complex and prone to errors. Furthermore, NGS accuracy is limited when processing loaded HLA gene regions, especially in highly variable and repetitive sequence regions, leading to insufficient accuracy in HLA typing.

[0067] Based on this, embodiments of this application provide a method and apparatus for human leukocyte antigen (HLA) typing, a computer device, and a storage medium. The aim is to use third-generation sequencing technology to obtain raw sequencing data from human leukocyte genome sequencing, thus addressing the accuracy limitations of second-generation sequencing technology. Simultaneously, higher-quality sequencing data is selected based on sequencing quality assessment data from the raw sequencing data. This selected sequencing data is then corrected to obtain target sequencing data. By using the higher-quality and more accurate target sequencing data and the HLA database, the genotype of human leukocyte antigens is confirmed. This not only improves the accuracy of HLA typing but also simplifies its complexity, making the HLA typing operation easier and more efficient.

[0068] The human leukocyte antigen typing method, apparatus, computer equipment, and storage medium provided in this application are specifically described through the following embodiments. First, the human leukocyte antigen typing method in this application embodiment is described.

[0069] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0070] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0071] The human leukocyte antigen (HLA) typing method provided in this application relates to the field of biotechnology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the HLA typing method, but is not limited to the above forms.

[0072] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0073] Figure 1 This is an optional flowchart of the human leukocyte antigen typing method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0074] Step S101: Obtain raw sequencing data; wherein, the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology;

[0075] Step S102: Perform quality assessment on the raw sequencing data to obtain sequencing quality assessment data;

[0076] Step S103: Selected sequencing data are selected from the raw sequencing data based on sequencing quality assessment data;

[0077] Step S104: Correct the selected sequencing data to obtain the target sequencing data;

[0078] Step S105: Based on the target sequencing data, perform a genotype search in the pre-constructed human leukocyte antigen database to obtain the target genotype.

[0079] Steps S101 to S105, as illustrated in the embodiments of this application, involve sequencing the human leukocyte antigen (HLA) gene using third-generation sequencing technology to obtain high-quality, long-read raw sequencing data. Then, based on sequencing quality assessment data from the raw sequencing data, selected sequencing data meeting quality requirements are screened from the raw sequencing data. The selected sequencing data is then corrected to obtain the target sequencing data. Therefore, by using higher-quality and more accurate target sequencing data to locate the genotype of the HLA gene in the HLA database, not only can the accuracy of HLA typing be improved, but the data analysis for HLA typing is also simplified, increasing typing efficiency.

[0080] In step S101 of some embodiments, third-generation sequencing technology, also known as third-generation sequencing technology, can achieve longer read lengths compared to second-generation sequencing technology, and can better resolve complex regions in the human albumin antigen gene, such as repetitive sequences and structural variations. Furthermore, the error rate of third-generation sequencing technology is also lower than that of second-generation sequencing technology. Therefore, sequencing the human leukocyte antigen gene using third-generation sequencing technology yields longer and more accurate raw sequencing data.

[0081] It should be noted that human leukocyte antigen (HLA) typing can be applied to fields such as organ transplantation, disease-related research, and forensic medicine. In organ transplantation, for example, in bone marrow transplantation, the accuracy of HLA typing directly impacts the success rate and rejection rate. Therefore, improving HLA typing accuracy can increase the success rate of bone marrow transplantation and reduce rejection. When applied to bone marrow transplantation, multiple HLA third-generation sequencing data from bone marrow matching samples are obtained from an external sequencing platform as raw sequencing data. Therefore, multiple raw sequencing data sets exist, which are divided into a test dataset and a validation dataset. The raw sequencing data in the test dataset is used to test the accuracy of HLA typing, while the raw sequencing data in the validation dataset is used to re-validate the accuracy of HLA typing after adjustments based on the accuracy analysis of the test dataset.

[0082] Specifically, the HLA genotyping method provided in this application is applied to a locally running HLA genotyping platform. After obtaining raw sequencing data from an external platform, the raw sequencing data is compressed and stored in the local database of the HLA genotyping platform in fastq.gz format. It should be noted that fastq.gz format is a commonly used bioinformatics data file format used to store raw sequencing data generated by third-generation sequencing technology.

[0083] To further improve the accuracy of HLA genotyping, it is necessary to perform quality control and filtering on the raw sequencing data to retain selected sequencing data that meet the quality requirements. Therefore, it is necessary to first analyze the quality of each raw sequencing data set. The quality assessment of each raw sequencing data set is described in detail below.

[0084] As disclosed above, since the raw sequencing data is compressed and stored in the local database of the HLA typing platform in fastq.gz format, the compression state of the raw sequencing data in fastq.gz format is decompressed using the Gunzip tool during quality assessment.

[0085] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203:

[0086] Step S201: Obtain sequencing parameters from the raw sequencing data; wherein, sequencing parameters include: sequence length information and end base ratio;

[0087] Step S202: Set the quality value of the raw sequencing data according to the end base ratio to obtain the sequence quality value;

[0088] Step S203: The raw sequencing data is evaluated based on the sequence quality value and sequence length information to obtain sequencing quality evaluation data.

[0089] It should be noted that the screening of raw sequencing data mainly focuses on two aspects: quality and length. Therefore, the sequencing evaluation data is obtained by splicing sequence quality values ​​and sequence length information to select sequencing data that meet both length and quality requirements.

[0090] In step S201 of some embodiments, the sequencing parameters include: sequence length information and end base ratio, where the sequence length information is the total number of base pairs in the original sequencing data, and the end base ratio is the distribution ratio of base pairs at both ends of the original sequencing data.

[0091] In step S202 of some embodiments, the quality value setting of the raw sequencing data is used to evaluate the accuracy and reliability of the raw sequencing data. Typically, the quality value setting is mainly based on the fraction of each base in the raw sequencing data, which characterizes the quality of the raw sequencing data. In addition, the quality value can also be set based on sequence length distribution, repetitive sequences, adapter sequences, etc. This embodiment obtains the sequence quality value by setting the quality value through the ratio of bases in the head and tail of the raw sequencing data, thus characterizing the sequencing quality of the raw sequencing data through the sequence quality value.

[0092] In step S203 of some embodiments, the quality of the raw sequencing data is evaluated by combining sequence quality value and sequence length information to obtain sequencing quality evaluation data. Therefore, characterizing the quality of the raw sequencing data by both quality value and sequence length can more accurately characterize the sequencing quality of the raw sequencing data.

[0093] In steps S201 to S203 of this embodiment, the end base ratio is used as the sequence quality value, and the sequence quality value and sequence length information are combined as the sequencing quality assessment data of the original sequencing data. This allows the quality of the original sequencing data to be assessed from both the quality value and the sequence length, making it easier to select sequencing data that meet the requirements in both quality and length.

[0094] Please see Figure 3 In some embodiments, step S103 may include, but is not limited to, steps S301 to S302:

[0095] Step S301: The raw sequencing data is screened according to the preset quality threshold and sequence quality value to obtain preliminary sequencing data;

[0096] Step S302: The preliminary sequencing data is screened according to the preset length threshold and sequence length information to obtain selected sequencing data.

[0097] In steps S301 to S302 of some embodiments, in order to retain sequencing data that meets both quality and length requirements, preliminary sequencing data is first screened from the original sequencing data based on a quality threshold and sequence quality value, and then selected sequencing data is screened from the preliminary sequencing data based on a length threshold and sequence length information, so as to obtain selected sequencing data that meets both length and quality requirements.

[0098] Specifically, raw sequencing data with sequence quality values ​​greater than a quality threshold are used as preliminary sequencing data, and preliminary sequencing data with sequence length information greater than a length threshold are used as selected sequencing data. For example, if the quality threshold is 13 and the length threshold is 500, raw sequencing data with a sequence quality value greater than 13 and a total number of base pairs greater than 500 are used as selected sequencing data.

[0099] In steps S301 to S302 of this embodiment, preliminary sequencing data is selected from the original sequencing data according to the quality threshold and sequence quality value, and then selected sequencing data is selected from the preliminary sequencing data according to the length threshold and sequence length information, so as to select the selected sequencing data that meets the requirements in both length and quality.

[0100] Please see Figure 4 In some embodiments, step S104 may include, but is not limited to, steps S401 to S403:

[0101] Step S401: Primer identification is performed on the selected sequencing data according to the preset primer sequence to obtain primer identification information;

[0102] Step S402: Based on the preset mismatch threshold and primer identification information, the selected sequencing data is split using primers to obtain the sequencing sequence;

[0103] Step S403: Correct the selected sequencing data according to the sequencing sequence to obtain the target sequencing data.

[0104] It should be noted that after selecting the sequencing data, the selected sequencing data needs to be corrected by primer splitting to make subsequent sequence alignment and analysis more accurate.

[0105] In step S401 of some embodiments, Citrus software is used to perform primer splitting and sequence correction on the selected sequencing data. It should be noted that Citrus software is an automated testing framework for integration testing and end-to-end verification of Java applications, supporting multiple communication protocols, including HTTP, FTP, JMS, and WebSocket. Citrus software can be applied to various scenarios; in this embodiment, it is applied to primer splitting and sequence correction. Specifically, the selected sequencing data is input into the Citrus software. The Citrus software contains a text file with primer sequences and performs primer identification on the selected sequencing data based on the primer sequences to obtain primer identification information. Primers are short single-stranded DNA or RNA sequences used to initiate DNA synthesis in polymerase chain reaction (PCR). Therefore, identifying the primers in the selected sequencing data facilitates more accurate correction of the selected sequencing data.

[0106] In step S402 of some embodiments, the mismatch threshold is preset to control the quality of primer splitting. Specifically, the mismatch threshold also characterizes the allowable range of base mismatches or insertion / deletion mismatches between the primer sequence and selected sequencing data, equivalent to the maximum allowable error ratio or number. For example, if the error threshold is 1%, that is, it means that the allowable range of mismatches is within 1%, the accuracy of primer splitting can be guaranteed.

[0107] It should be noted that the selected sequencing data is split into primers based on the mismatch threshold and primer identification information. In other words, the range of base mismatches, insertions and deletions between the selected sequencing data and primer sequences is controlled below the mismatch threshold according to the primer identification information to obtain the sequencing sequence.

[0108] In step S403 of some embodiments, the sequencing sequence is also the HLA region sequence of the selected sequencing data. The selected sequencing data is sequence corrected according to the sequencing sequence to obtain the corrected sequencing sequence, and the corrected sequencing sequence is spliced ​​into the target sequencing data.

[0109] In steps S401 to S403 of this embodiment, the selected sequencing data is first split with primers and corrected before sequence alignment to obtain corrected target sequencing data. Then, sequence alignment and analysis are completed using the target sequencing data, which can improve the accuracy of sequence alignment and analysis, and thus improve the accuracy of HLA typing.

[0110] It's important to note that a Human Leukocyte Antigen (HLA) database needs to be constructed before HLA typing. This database is built using HLA gene sequences downloaded from a professional platform, and it is regularly updated based on HLA gene sequences from multiple connected professional platforms to ensure more accurate HLA typing. Specifically, the HLA database is also known as the G-domain HLA alignment library. The BLAST tool is used in the G-domain HLA alignment library to align the target sequencing data with the HLA sequences in the library. This not only optimizes and simplifies sequence alignment but also improves the accuracy of HLA typing.

[0111] In some embodiments, the human leukocyte antigen (HLA) database includes: reference sequencing data and a reference genotype for the reference sequencing data. Specifically, the reference sequencing data is the HLA gene sequence downloaded from a professional platform, and the reference genotype is the genotype of the HLA gene sequence, i.e., the HLA genotype. It should be noted that the HLA complex has 224 loci, which can be divided into three groups according to the structure, distribution, and function of the products: HLA-I, HLA-II, and HLA-III. Generally, HLA genotyping is mainly divided into serological typing and DNA typing. Serological typing focuses on analyzing the heterosomia of HLA antigens, while DNA typing focuses on analyzing the gene itself, and mainly includes two methods: nucleic acid sequence-based identification methods and sequence molecular conformation-based methods. This application embodiment uses a nucleic acid sequence-based identification method to complete HLA typing.

[0112] Please see Figure 5 In some embodiments, step S105 may include, but is not limited to, steps S501 to S506:

[0113] Step S501: Select the first genotype from the reference genotypes based on the target sequencing data and the reference sequencing data;

[0114] Step S502: Remove duplicates from the first genotype to obtain the selected genotype;

[0115] Step S503: Select the selected sequencing sequences from the original sequencing data based on the selected genotype;

[0116] Step S504: Align and assemble the selected sequencing sequences to obtain secondary alignment data;

[0117] Step S505: Select the second genotype from the reference genotype based on the secondary alignment data and the reference sequencing data;

[0118] Step S506: The first genotype and the second genotype are spliced ​​together to obtain the target genotype.

[0119] It should be noted that, in order to improve the accuracy of genotype identification, this embodiment uses a two-stage genotype screening method to complete HLA typing, thereby improving the accuracy of HLA typing.

[0120] In step S501 of some embodiments, the BLAST tool is used to align the target sequencing data and the reference sequencing data to obtain preliminary sequence alignment information, and the first genotype is screened from the reference genotypes based on the preliminary sequence alignment information. Therefore, using nucleic acid sequence alignment to complete HLA typing simplifies the HLA typing operation.

[0121] Specifically, during the initial alignment process, the target sequencing data and reference sequencing data are compared based on the G-domain HLA alignment library and the BLAST tool is used. The first genotype is then selected from the reference genotypes to identify the first haplotype, which is the HLA genotype found in the initial sequence alignment.

[0122] In step S502 of some embodiments, as disclosed above, after the initial alignment yields a first genotype, a second sequence alignment and genotype lookup are required to improve the accuracy of genotype identification. However, before performing sequence alignment and genotype lookup, the first genotype needs to be deduplicated to make the second sequence alignment and genotype lookup more accurate. Specifically, a sequence identifier is determined based on the first genotype, and the sequence identifier is represented by a query sequence ID. A CUT tool is used to sort and deduplicate the sequence identifiers to ensure the uniqueness of each sequence identifier. The selected genotype is then determined based on the unique sequence identifier, which also ensures the uniqueness of the selected genotype.

[0123] In steps S503 to S505 of some embodiments, during the second alignment process, the seqtk tool is used to extract sequences corresponding to the selected genotype from the original sequencing data as selected sequencing sequences. The seqtk tool is then used to perform a second alignment and assembly of the extracted selected sequencing sequences to obtain second alignment data (i.e., the sequence alignment file generated by the second alignment). After generating the second alignment data, it is compared with reference sequencing data in the G-domain HLA alignment library to identify the second genotype, i.e., the second haplotype. The alignment result of the identified second haplotype is then output to a file. It should be noted that the seqtk tool is a sequence data processing tool capable of filtering relevant sequences from the original sequencing data based on the selected genotype. Therefore, performing a second sequence alignment and genotype lookup using the second alignment data yields more accurate genotype information.

[0124] As previously disclosed, the selected sequencing sequences are aligned and assembled to obtain secondary alignment data. Based on this secondary alignment data and reference sequencing data, sequence alignment is performed to obtain target sequence alignment information. Then, based on the target sequence alignment information, the second genotype is selected from the reference genotypes, i.e., the second haplotype is identified. Therefore, by selecting secondary alignment data from the original sequencing data using deduplicated selected genotypes, and then performing a second sequence alignment using this secondary alignment data to find the second haplotype, a more accurate second genotype can be obtained.

[0125] In step S506 of some embodiments, the first genotype and the second genotype selected after two alignments are combined to form the target genotype. Specifically, by fusing the first haplotype identified in the initial alignment and the second haplotype confirmed in the second alignment to form the target genotype, the HLA subtype can be identified more accurately, improving the accuracy and reliability of HLA typing.

[0126] In steps S501 to S506 of this embodiment, by employing two sequence alignments and genotype screening methods, and by storing the reference sequencing data and reference genotype in a locally established G-domain HLA alignment library in advance, it is not necessary to search from a public database each time sequence alignment and genotype lookup is performed, which can avoid the delay of data retrieval from public databases and improve the alignment speed and efficiency.

[0127] Please see Figure 6 In some embodiments, step S501 includes, but is not limited to, steps S601 to S604:

[0128] Step S601: Obtain the sequence index information of the reference sequencing data;

[0129] Step S602: Extract the reference gene sequence from the reference sequencing data based on the sequence index information;

[0130] Step S603: Perform alignment processing based on the preset matching rate, target sequencing data, and reference gene sequence to obtain sequence alignment information;

[0131] Step S604: Select the first genotype from the reference genotypes based on the sequence alignment information.

[0132] It should be noted that before performing sequence alignment and genotype lookup, this embodiment employs diversity analysis and sequence verification to construct the human leukocyte antigen (HLA) database. Diversity analysis extracts exon region information from reference sequencing data and classifies various allelic variations, making genotype lookup more efficient and ensuring accuracy. Sequence verification involves self-aligning the extracted reference sequencing data with standard sequencing data, improving the sequence integrity and accuracy of the HLA database, thereby enhancing the accuracy of HLA typing.

[0133] In step S601 of some embodiments, as disclosed above, the human leukocyte antigen database performs gene variation classification analysis based on exon region information in advance. Therefore, during the initial and secondary genotype screening, it is also necessary to extract exon region information, name the reference sequencing data corresponding to the exon region information according to HLA genes and alleles, organize it into FASTA format, and construct sequence index information using tools such as BWA or Bowtie2, so that the corresponding reference gene sequence can be quickly found based on the sequence index information. It should be noted that exons are part of eukaryotic genes, and exons are the gene sequences that last appear in mature RNA, also known as expression sequences.

[0134] In step S602 of some embodiments, as disclosed above, a reference gene sequence is extracted from the reference sequencing data according to the sequence index information to enable rapid querying of the reference sequencing data so that the first genotype can be quickly found subsequently.

[0135] In step S603 of some embodiments, achieving perfect sequence consistency is difficult. To screen for the first genotype, a preset matching rate needs to be set in advance. This preset matching rate represents the allowable range of matching between the reference gene sequence and the target sequencing data; that is, sequences with high correlation are selected from the reference gene sequence for genotype classification. Specifically, the target sequencing data and the reference gene sequence are matched to obtain the sequence matching rate. Reference gene sequences with a sequence matching rate greater than the preset matching rate are used as target gene sequences. Multiple target gene sequences are used as sequence alignment information for genotype screening.

[0136] For example, if the preset matching rate is 90%, the reference gene sequence with a sequence matching rate of 90% or higher will be used as the target gene sequence.

[0137] In step S604 of some embodiments, the target sequence alignment information consists of multiple target gene sequences, and the reference genotype corresponding to each target gene sequence is used as the first genotype to achieve accurate screening of the first genotype.

[0138] It should be noted that in this embodiment, the sequence alignment and genotype screening of the first genotype are the same as those of the second genotype, and the screening of the second genotype will not be described again in this embodiment.

[0139] In steps S601 to S604 of this embodiment, reference gene sequences are selected by screening exon regions to reduce the number of sequences to be compared and improve the comparison efficiency. Simultaneously, during genotype screening, a preset matching rate is set, eliminating the need to screen for completely matching genotypes; instead, highly correlated genotypes are selected, achieving accurate screening of the first genotype.

[0140] Please see Figure 7 In some embodiments, after step S105, human leukocyte antigen typing may also include, but is not limited to, steps S701 to S702:

[0141] Step S701: Perform typing quality assessment based on preset verification genotype information and target genotype to obtain typing quality assessment data;

[0142] Step S702: Generate a report by combining the target genotype, typing quality assessment data, and sequence alignment information to obtain a human leukocyte antigen typing report.

[0143] In step S701 of some embodiments, the genotype information is verified as the genotype corresponding to the original sequencing data, used to verify the accuracy of the genotyping. Specifically, genotyping quality assessment data is obtained by performing loss calculations on the verified genotype information and the target genotype, and the genotyping quality assessment data characterizes the accuracy of HLA genotyping and is characterized by quality indicators.

[0144] In step S702 of some embodiments, the target genotype, genotyping quality assessment data, and sequence alignment information are integrated and presented in tabular form to obtain a human leukocyte antigen (HLA) genotyping report. Therefore, the HLA genotyping status and accuracy of the original sequencing data can be determined through the HLA genotyping report, facilitating appropriate actions in relevant scenarios.

[0145] For example, in this embodiment, HLA typing is applied to bone marrow transplantation. The HLA typing report can be used to determine bone marrow matching, so as to determine whether bone marrow transplantation should be performed based on the HLA typing report, thus playing a guiding role in bone marrow matching.

[0146] It should be noted that once the human leukocyte antigen (HLA) typing report is generated, a visualization tool can be used to display the report, allowing users to directly view the HLA typing status and accuracy on the HLA typing platform.

[0147] In steps S701 to S702 of this embodiment, by displaying the target genotype, genotyping quality assessment data, and sequence alignment information in the form of a report, the HLA genotyping status and accuracy can be viewed more comprehensively and intuitively.

[0148] As previously disclosed, multiple raw sequencing data were divided into test and validation datasets, and HLA genotyping accuracy was evaluated when using both the test and validation datasets. Therefore, the accuracy evaluations performed separately for the test and validation datasets are shown in Table 1.

[0149] Site accuracy (%) test set Validation set DRB1 75 91.67 DQB1 95 100 DPB1 85 100 A 100 91.67 B 80 100 C 100 91.67

[0150] Table 1

[0151] As shown in Table 1, in the test dataset, the initial genotyping accuracy of HLA loci DRB1, DQB1, DPB1, A, B, and C reached 75%, 95%, 85%, 100%, 80%, and 100%, respectively. In the validation dataset, the initial genotyping accuracy of HLA loci DRB1, DQB1, DPB1, A, B, and C reached 91.67%, 100%, 100%, 91.67%, 100%, and 91.67%, respectively. Therefore, the HLA genotyping performed using the embodiments of this application significantly improves the accuracy of HLA genotyping. Consequently, the accuracy of related applications based on the genotyping results can also be improved.

[0152] like Figure 8 As shown, taking bone marrow matching as an example, the embodiments of this application obtain raw sequencing data from an external sequencing platform. The raw sequencing data is obtained by the sequencing platform sequencing bone marrow matching samples based on third-generation sequencing technology. Then, the raw sequencing data is compressed and stored in the local database of the HLA typing platform in the fastq.gz format, and the raw sequencing data is decompressed locally for preprocessing.

[0153] Specifically, the ratio of bases at both ends of the raw sequencing data is first obtained as the end base ratio, and the total number of base pairs in the raw sequencing data is obtained as the sequence length information. The quality value of the raw sequencing data is set according to the end base ratio to obtain the sequence quality value. Then, the raw sequencing data with a sequence quality value greater than a preset quality threshold and a sequence length greater than a preset length threshold are selected as the selected sequencing data, so as to filter out the selected sequencing data that meet the requirements of both sequencing quality and length.

[0154] To improve the accuracy of sequence alignment and genotype screening, the selected sequencing data must first be primer identified using primer sequences in Citrus software. Then, the selected sequencing data is split according to the primer identification information, with the splitting process adhering to a preset error threshold to control the allowable range of base mismatches or insertion / deletion mismatches between the primer sequences and the selected sequencing data, resulting in sequencing sequences. The primer-split sequencing sequences are then corrected on the selected sequencing data to obtain the corrected target sequences, which are then assembled into the target sequencing data.

[0155] To improve the accuracy of HLA genotyping, a G-domain HLA alignment library was first constructed. HLA gene sequences and their genotypes were downloaded from a professional platform in advance to obtain reference sequencing data and reference genotypes. Then, the sequence index information of the reference sequencing data was extracted, and reference gene sequences were extracted from the reference sequencing data based on the sequence index information. Next, the reference gene sequences and target sequencing data were matched to obtain the sequence matching rate. Reference gene sequences with a sequence matching rate greater than a preset matching rate were selected as target gene sequences. Based on the target gene sequences, the first genotype was selected from the reference genotypes to identify the first haplotype.

[0156] To further improve the accuracy of HLA genotyping, a second genotype search is necessary. Therefore, duplicate genotype information is first removed from the first genotype, resulting in a deduplicated selected genotype. Then, selected sequencing sequences are selected from the original sequencing data based on the selected genotype. These selected sequences are aligned and assembled to generate a secondary-processed file (i.e., the aforementioned secondary alignment data). This secondary alignment data is compared with the reference sequencing data to obtain the target sequence alignment information. Based on this target sequence alignment information, a second genotype is extracted from the reference genotype to identify the second haplotype. The first and second haplotypes are then concatenated to form the target genotype, allowing for more accurate identification of the HLA subtype. Therefore, determining the final HLA genotype through the identification and alignment of two haplotypes improves the accuracy and reliability of HLA genotyping.

[0157] Please see Figure 9This application also provides a human leukocyte antigen typing device, which can implement the above-mentioned human leukocyte antigen typing method. The device includes:

[0158] The data acquisition module 901 is used to acquire raw sequencing data; wherein, the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology.

[0159] The quality assessment module 902 is used to assess the quality of the raw sequencing data and obtain sequencing quality assessment data.

[0160] The data filtering module 903 is used to filter selected sequencing data from the raw sequencing data based on sequencing quality assessment data;

[0161] Primer splitting module 904 is used to split selected sequencing data to obtain target sequencing data;

[0162] Genotype lookup module 905 is used to search for the target genotype in a pre-constructed human leukocyte antigen database based on the target sequencing data.

[0163] The specific implementation of this human leukocyte antigen typing device is basically the same as the specific implementation of the human leukocyte antigen typing method described above, and will not be repeated here.

[0164] This application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described human leukocyte antigen typing method. This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0165] Please see Figure 10 , Figure 10 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes:

[0166] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0167] The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the human leukocyte antigen typing method of the embodiments of this application.

[0168] Input / output interface 1003 is used to implement information input and output;

[0169] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0170] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0171] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0172] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described human leukocyte antigen typing method.

[0173] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0174] The human leukocyte antigen (HLA) typing method, apparatus, computer equipment, and storage medium provided in this application employ third-generation sequencing technology to sequence the HLA gene, obtaining longer reads and more accurate raw sequencing data. The raw sequencing data is then subjected to quality assessment to obtain sequencing quality assessment data. Selected sequencing data is then selected from the raw sequencing data based on the sequencing quality assessment data, and finally corrected to target sequencing data. Therefore, HLA typing using target sequencing data is more accurate and reliable. Simultaneously, genotype lookup is achieved through a pre-defined HLA database, eliminating the need to retrieve data from public databases, making the comparison and typing operations in the HLA typing process more efficient.

[0175] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0176] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0177] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0179] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0180] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0182] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0184] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0185] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for typing human leukocyte antigens, characterized in that, The method includes: Obtain raw sequencing data; wherein, the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology; The raw sequencing data were subjected to quality assessment to obtain sequencing quality assessment data; Selected sequencing data are selected from the raw sequencing data based on the sequencing quality assessment data; The selected sequencing data is corrected to obtain the target sequencing data; The target genotype is obtained by searching the pre-constructed human leukocyte antigen database based on the target sequencing data.

2. The method according to claim 1, characterized in that, The human leukocyte antigen (HLA) database includes: reference sequencing data and reference genotypes of the reference sequencing data. The step of searching for cell subtypes in the preset HLA database based on the target sequencing data to obtain the target genotype includes: A first genotype is selected from the reference genotype based on the target sequencing data and the reference sequencing data; The first genotype is deduplicated to obtain the selected genotype; Selected sequencing sequences are selected from the original sequencing data based on the selected genotype; The selected sequencing sequences are aligned and assembled to obtain secondary alignment data; A second genotype is selected from the reference genotype based on the secondary alignment data and the reference sequencing data; The target genotype is determined based on the first genotype and the second genotype.

3. The method according to claim 2, characterized in that, The step of selecting a first genotype from the reference genotype based on the target sequencing data and the reference sequencing data includes: Obtain the sequence index information of the reference sequencing data; The reference gene sequence is extracted from the reference sequencing data based on the sequence index information; The sequence alignment information is obtained by comparing the preset matching rate, the target sequencing data, and the reference gene sequence. The first genotype is selected from the reference genotypes based on the sequence alignment information.

4. The method according to any one of claims 1 to 3, characterized in that, The process of performing quality assessment on the raw sequencing data to obtain sequencing quality assessment data includes: Obtain sequencing parameters from the raw sequencing data; wherein, the sequencing parameters include: sequence length information and end base ratio; The quality value of the raw sequencing data is set according to the end base ratio to obtain the sequence quality value; The original sequencing data is evaluated based on the sequence quality value and the sequence length information to obtain the sequencing quality evaluation data.

5. The method according to claim 4, characterized in that, The step of selecting selected sequencing data from the raw sequencing data based on the sequencing quality assessment data includes: The raw sequencing data is filtered according to a preset quality threshold and the sequence quality value to obtain preliminary sequencing data; The preliminary sequencing data is filtered based on a preset length threshold and the sequence length information to obtain the selected sequencing data.

6. The method according to any one of claims 1 to 3, characterized in that, The step of correcting the selected sequencing data to obtain the target sequencing data includes: Primer identification is performed on the selected sequencing data according to the preset primer sequence to obtain primer identification information; The selected sequencing data is split using primers based on a preset mismatch threshold and the primer identification information to obtain sequencing sequences. The selected sequencing data is corrected based on the sequencing sequence to obtain the target sequencing data.

7. The method according to claim 3, characterized in that, After obtaining the target genotype by searching for the genotype in a preset human leukocyte antigen database based on the target sequencing data, the method further includes: Based on the preset verification genotype information and the target genotype, the typing quality is assessed to obtain typing quality assessment data; The target genotype, the typing quality assessment data, and the sequence alignment information are used to generate a report, resulting in a human leukocyte antigen typing report.

8. A human leukocyte antigen typing device, characterized in that, The device includes: The data acquisition module is used to acquire raw sequencing data; wherein the raw sequencing data is obtained by sequencing the human leukocyte antigen gene using third-generation sequencing technology. The quality assessment module is used to assess the quality of the raw sequencing data to obtain sequencing quality assessment data; A data filtering module is used to filter selected sequencing data from the raw sequencing data based on the sequencing quality assessment data; The primer splitting module is used to split the selected sequencing data to obtain the target sequencing data; The genotype lookup module is used to search for the target genotype in a pre-constructed human leukocyte antigen database based on the target sequencing data to obtain the target genotype.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the human leukocyte antigen typing method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the human leukocyte antigen typing method according to any one of claims 1 to 7.

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