WO2023136486A1 - Method and device for typing human leukocyte antigen - Google Patents

Abstract

Disclosed is a method for typing a human leukocyte antigen (HLA), performed by a computing device. The method may comprise the steps of: obtaining a result of nucleotide sequence analysis on DNA or RNA obtained from a subject-derived biological sample; and typing a HLA for the biological sample by using results and a mutually complementary score of a plurality of HLA typing tools using the nucleotide sequence analysis result as an input. A representative drawing may be figure 2.

Description

Methods and apparatus for determining the type of human leukocyte antigen

The present disclosure relates to genetic analysis, and more specifically to determining the type of human leukocyte antigen.

Human Leukocyte Antigen (HLA) is a glycoprotein molecule produced by the human Major Histocompatibility Complex (MHC) gene. HLA does not exist in mature erythrocytes, but is expressed in immature erythroblasts and is expressed on the surface of all tissue cells in the human body, including blood cells such as leukocytes and/or platelets. The MHC gene is present in all vertebrates, and the human MHC gene is referred to as the HLA gene, and the product expressed therefrom is referred to as HLA.

MHC genes are involved in self and non-self recognition, immune response to antigen stimulation, regulation of cellular and humoral immunity, and susceptibility to disease. HLA, a product of the MHC gene, is the second most important antigen next to the ABO blood type in survival of the transplanted organ in solid organ transplantation. HLA is known to play the most important role in transplantation success or failure in bone marrow transplantation. Therefore, recognizing the difference in HLA immunologically can be seen as the first step of the rejection action for the transplanted tissue. In addition, in transfusion therapy, HLA and antibodies play an important role in the occurrence of various side effects such as platelet transfusion refractory, febrile nonhemorrhagic transfusion side effects, acute lung injury, and graft-versus-host disease after transfusion.

HLA can be largely classified into Class I and Class II. Class I is classified into HLA-A, HLA-B, and HLA-C, and is expressed in most nucleated cells and platelets, and antigen recognition when cytotoxic T cells recognize and remove virus-infected cells or tumor cells. ) is essential for HLA Class II is classified into HLA-DR, HLA-DQ, and HLA-DP, and is expressed in B cells, monocytes, dendritic cells, and activated T cells. and induces a humoral immune response, and is known to be essential when recognizing antigens expressed in antigen-presenting cells. HLA is a gene that shows the largest polymorphism among genes possessed by humans, and there is a frequency difference between races and ethnic groups.

HLA typing for determining the exact HLA type is required in various fields such as organ transplantation, immunotherapy, disease-related research, paternity tests such as paternity, forensic use, and genetic research. Until now, various HLA types have been analyzed based on PCR-SSP, PCR-SSOP, Polymerase Chain Reaction-Sequencing Based Typing (PCR-SBT) technologies and Next Generation Sequencing (NGS) methods. Methods have been suggested.

The NGS method is based on exon 2, 3, and 4 for Class I (HLA-A, -B, -C) and exon 2 (or exon 2) for Class II (DRB1, DQB1, DPB1), which has been mainly performed in the PCR-SBT test method. , 3), more genes and more exons can be analyzed at the same time, which eliminates the need for additional primers and group PCR, which have been trying to reduce the ambiguity rate. It has the advantage of pooling multiple target genes or multiple samples at once.

However, since it is based on the HLA alleles provided by the IMGT-HLA database, the prediction accuracy is relatively high for commonly found HLA alleles, but the accuracy is low for infrequent alleles. Therefore, it is not only necessary to build a highly reliable method for more accurately predicting the HLA type, but also, depending on the individual, accurate typing is required by additionally performing the PCR-SBT test method even after NGS.

In addition, with the recent development of medical technology, anticancer immunotherapy based on neoantigens generated from tumors caused by somatic mutations has been developed. After a neoantigen is discovered, a tumor vaccine can be produced from it, or T cells with a T cell receptor (TCR) capable of attacking tumors in response to the neoantigen are selected, and the selected T cells are selected. A therapeutic agent may be prepared by culturing cells in vitro or by manufacturing a TCR engineered T cell (TCR engineered T cell) having a T cell receptor engineered. Even in patients with the same mutation, the mutant protein may be combined with HLA and presented as a neoantigen according to the type of HLA of each patient. In addition, even in patients with the same mutation, neoantigens cannot be produced when the corresponding mutant protein cannot bind to HLA depending on the type of HLA of each patient. Therefore, it is very important to accurately identify or determine the type of HLA to determine whether or not it is a neoantigen in the development of anti-cancer immunotherapy based thereon. As a related document, Korean Patent Registration No. 10-2215286 has been devised.

The present disclosure has been made in response to the above background art, and is intended to more accurately and efficiently determine or predict the type of human leukocyte antigen (HLA).

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

According to one embodiment of the present disclosure, a method for determining a type of Human Leukocyte Antigen (HLA) performed by a computing device is disclosed. The method may include obtaining a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from a subject; and determining the HLA type of the biological sample by using a plurality of HLA typing tools that take the result of the sequencing as an input.

In one embodiment, the step of determining the HLA type may include, based on HLA type candidates included in calls obtained from the plurality of HLA typing tools and complementary scores between the plurality of HLA typing tools, the It may include determining the HLA type for the biological sample.

In one embodiment, the determining of the HLA type may include determining the number of tools presenting calls among the plurality of typing tools; and determining the HLA type based on the determined number of tools.

In one embodiment, the determining of the HLA type may include determining the number of tools presenting calls among the plurality of typing tools; Calculating a degree of agreement between HLA type candidates included in the presented calls; and determining the HLA type based on the determined number of tools and the calculated degree of agreement.

In an embodiment, the determining of the HLA type may include calculating a complementary score between any two typing tools among the plurality of HLA typing tools; and determining the HLA type of the biological sample based on the complementary score.

In one embodiment, the complementarity score may include a value quantitatively indicating the possibility that any two HLA typing tools among the plurality of HLA typing tools can be used together to determine the HLA type.

In one embodiment, the complementary score may include a value quantitatively indicating the accuracy of the determined HLA type when any two HLA typing tools among the plurality of HLA typing tools are used together.

In one embodiment, the complementary score is a correction value to be applied to HLA type candidates included in calls obtained from two HLA typing tools when any two of the plurality of HLA typing tools are used together. It may include a value that quantitatively represents.

In one embodiment, a complementary score between a first HLA typing tool and a second HLA typing tool among the plurality of HLA typing tools is a result of a first call obtained from the first HLA typing tool for the biological sample. When wrong, a value quantitatively indicative of the accuracy of the result for the second call obtained from the second HLA typing tool for the biological sample.

In one embodiment, the complementary score between a first HLA typing tool and a second HLA typing tool among the plurality of HLA typing tools is the determined score when the first HLA typing tool is used together with the second HLA typing tool. The accuracy value for the HLA type may include a value quantitatively representing a result of comparison with a predetermined first accuracy value for the first HLA typing tool.

In one embodiment, the determining of the HLA type may include determining whether to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools. determining based on a first complementary score between the first HLA typing tool and the second HLA typing tool; and determining the HLA type based on a decision on whether to combine the first call and the second call.

In one embodiment, the determining of the HLA type may include whether to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools, to a first complementary score between the first HLA typing tool and the second HLA typing tool, a first predetermined accuracy value for the first HLA typing tool and a second predetermined accuracy value for the second HLA typing tool determining based on; and determining the HLA type based on a determination as to whether to combine the first call and the second call.

In one embodiment, the determining of the HLA type may include determining a correction value to be applied to a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools. determining based on a first complementary score between the first HLA typing tool and the second HLA typing tool; and determining the HLA type based on the correction value.

In one embodiment, the determining of the HLA type comprises determining accuracy of the HLA type when using the plurality of HLA typing tools based at least in part on a complementary score associated with the plurality of HLA typing tools. It may include predicting.

In one embodiment, the complementary score is determined by determining whether a first result from a first HLA typing tool among the plurality of HLA typing tools does not match an actual result, and a second result from a second HLA typing tool being the actual result. A first sub-score indicating the case of coincidence with; a second sub-score indicating a case in which a first result from a first HLA typing tool matches an actual result when a second result from a second HLA typing tool among the plurality of HLA typing tools does not match an actual result; a first predetermined accuracy value for the first HLA typing tool; and a predetermined second accuracy value for the second HLA typing tool.

In an embodiment, the method may further include determining a degree of similarity between algorithms included in the plurality of HLA typing tools based on the complementarity score.

According to one embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program, when executed by one or more processors, may perform a method for determining the type of Human Leukocyte Antigen (HLA). The method includes: acquiring a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from a subject; and determining the HLA type of the biological sample by using a plurality of HLA typing tools that take the sequencing result as an input.

According to one embodiment of the present disclosure, a computing device for determining the type of Human Leukocyte Antigen (HLA) is disclosed. The computing device may include a memory; and a processor, wherein the processor: obtains a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from the subject; In addition, the HLA type of the biological sample may be determined using a plurality of HLA typing tools that take the sequencing result as an input.

The method and apparatus according to one embodiment of the present disclosure are intended to more accurately and efficiently determine or predict the type of human leukocyte antigen (HLA).

A method and apparatus according to an embodiment of the present disclosure may determine similarity between a plurality of tools for determining the type of human leukocyte antigen (HLA).

1 schematically illustrates a block configuration diagram of a computing device according to an embodiment of the present disclosure.

2 illustrates an exemplary method for determining HLA type according to one embodiment of the present disclosure.

3 illustrates an example algorithm for determining a complementarity score, according to one embodiment of the present disclosure.

4 illustrates an exemplary algorithm for determining a complementarity score, according to one embodiment of the present disclosure.

5 depicts an exemplary algorithm for determining a complementarity score according to one embodiment of the present disclosure.

6 is a schematic diagram of a computing environment according to one embodiment of the present disclosure.

Various embodiments are described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. Prior to describing specific details for the implementation of the present disclosure, it should be noted that configurations not directly related to the technical gist of the present disclosure have been omitted within the scope of not distracting from the technical gist of the present invention. In addition, the terms or words used in this specification and claims have meanings consistent with the technical idea of the present invention based on the principle that the inventor can define the concept of appropriate terms in order to best describe his/her invention. concept should be interpreted.

As used herein, the terms "component", "module", "system", "unit", and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software, and are interchangeable. can possibly be used. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term "at least one of A or B" or "at least one of A and B" means "includes only A", "includes only B", "includes A and B in combination" should be interpreted as meaning

Those skilled in the art will further understand that the various illustrative logical components, blocks, modules, circuits, means, logics, and algorithms described in connection with the embodiments disclosed herein may be electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

In the present disclosure, terms expressed as Nth, such as first, second, or third, are used to distinguish at least one entity. For example, entities represented as first and second may be the same as or different from each other.

As the term used in the present disclosure, sequencing may be performed by any type of technique capable of sequencing, for example, whole genome sequencing, whole exome base It may include, but is not limited to, whole exome sequencing or whole transcriptome sequencing.

As used in the present disclosure, a subject may refer to a subject or individual for determining an HLA type.

As used in the present disclosure, the term, sample, can be used without limitation as long as it is obtained from an individual or subject whose HLA type is to be determined, for example, cells or tissues obtained by biopsy, blood, whole blood, serum, plasma, saliva, It may be cerebrospinal fluid, various secretions, urine and/or feces, and the like. Preferably, the sample may be selected from the group consisting of blood, plasma, serum, saliva, nasal fluid, sputum, ascites, vaginal secretion and/or urine, and more preferably blood, plasma or serum. The sample may be pretreated prior to use for detection or diagnosis. For example, pretreatment methods may include homogenization, filtration, distillation, extraction, concentration, inactivation of interfering components, and/or addition of reagents, and the like.

In the present disclosure, a biological sample may be tissue, cell, whole blood, and/or blood, but is not limited thereto.

As used in the present disclosure, the term “Human Leukocyte Antigen (HLA)” is a glycoprotein molecule produced by the major histocompatibility complex (MHC) gene of humans, and is the most abundant among the genes possessed by humans. It is a gene with a large polymorphism. HLA typing, which determines the HLA type, can be used very actively in various fields such as organ transplantation, immunotherapy, disease-related research, paternity tests such as paternity, forensic use, and genetic research.

An HLA type in the present disclosure may include, for example, an HLA-A type, an HLA-B type, and/or an HLA-C type.

In one embodiment of the present disclosure, a plurality of tools for HLA typing are exemplarily disclosed. Tools in the present disclosure may include algorithms for genetic analysis. For example, the tool may include an algorithm for HLA typing. HLA typing methods and tools in the present disclosure may be used interchangeably with each other.

Exemplary HLA typing methods or tools are briefly described below.

OptiType is an HLA genotype determination algorithm based on integer linear programming announced in 2014. It selects all major or minor HLA class I alleles and accurately predicts 4-digit HLA genotype from NGS data. . However, OptiType is known to have problems in terms of accuracy and usability (see Szolek et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatic, Vol. 30 no. 23 2014, pages 3310-3316).

HLA-HD is a method that can accurately determine HLA alleles with 6-digit precision from NGS data published in Japan in 2017. For details, see Hum Mutat. 2017 Jul;38(7):788-797. doi: 10.1002/humu.23230. (Kawaguchi, Shuji, et al. Human mutation 38.7 (2017): 788-797.) can refer

PHLAT is a method designed for high-resolution (six-digit) typing of major class I and class II HLA genes using RNAseq or exome sequencing data as input. For details, reference may be made to the literature (Bai, Yu, David Wang, and Wen Fury. HLA Typing . Humana Press, New York, NY, 2018. 193-201.).

HLAscan is a method for performing read alignment on HLA sequences from the International ImMunoGeneTics Project/Human Leukocyte Antigen (IMGT/HLA) database. For details, refer to the literature (Ka, Sojeong, et al. BMC bioinformatics 18.1 (2017): 1-11.).

HLA*LA is a model that infers HLA types based on projecting linear alignment onto a variance graph, and is a method capable of accurately predicting HLA genotypes from whole-exome and whole-genome Illumina data. For details, see Dilthey, Alexander T., et al. "HLA* LA-HLA typing from linearly projected graph alignments." Bioinformatics 35.21 (2019): 4394-4396.

Availability and/or accuracy of HLA typing results according to each of the above HLA typing methods may be different. Accordingly, a method for determining an optimal HLA type with higher accuracy is required.

1 schematically illustrates a block configuration diagram of a computing device 100 according to one embodiment of the present disclosure.

Computing device 100 according to an embodiment of the present disclosure may include a processor 110 and a memory 130 .

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 in the present disclosure may refer to a node constituting a system for implementing embodiments of the present disclosure. The computing device 100 may refer to any type of user terminal or any type of server. Components of the aforementioned computing device 100 are exemplary and some may be excluded or additional components may be included. For example, when the aforementioned computing device 100 includes a terminal, an output unit (not shown) and an input unit (not shown) may be included within its scope.

The computing device 100 in the present disclosure may perform technical features according to embodiments of the present disclosure to be described later. For example, the computing device 100 may determine the HLA type of the biological sample based at least in part on typing results from a plurality of HLA typing tools that take as input a result of performing sequencing of the biological sample. .

In one embodiment, the processor 110 may include at least one core, and may include a central processing unit (CPU) or a general purpose graphics processing unit (GPGPU) of the computing device 100 . , a processor for data analysis and/or processing, such as a tensor processing unit (TPU). The processor 110 reads the computer program stored in the memory 130 and, according to an embodiment of the present disclosure, typing results from a plurality of HLA typing tools that take as input a result of base sequence analysis on a biological sample. The HLA type for the biological sample may be determined based, at least in part, on. The processor 110 reads the computer program stored in the memory 130 and, according to an embodiment of the present disclosure, HLA type candidates included in calls obtained from a plurality of HLA typing tools and the plurality of HLAs An HLA type of the biological sample may be determined based on a complementary score between typing tools.

According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in one embodiment of the present disclosure, learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

Additionally, the processor 110 may typically handle overall operations of the computing device 100 . For example, the processor 110 processes data, information, signals, etc. input or output through components included in the computing device 100 or drives an application program stored in a storage unit, thereby providing appropriate information or information to the user. A function can be provided or processed.

According to one embodiment of the present disclosure, memory 130 may store any type of information generated or determined by processor 110 and any type of information received by computing device 100 . According to one embodiment of the present disclosure, the memory 130 may be a storage medium that stores computer software that causes the processor 110 to perform operations according to embodiments of the present disclosure. Accordingly, the memory 130 may refer to computer readable media for storing software codes necessary for performing embodiments of the present disclosure, data subject to execution of the codes, and results of execution of the codes.

According to an embodiment of the present disclosure, the memory 130 may refer to any type of storage medium. For example, the memory 130 may be a flash memory type, a hard disk type ), multimedia card micro type, card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only memory, ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above memory is only an example, and the memory 130 used in the present disclosure is not limited to the above example.

The communication unit (not shown) in the present disclosure may be configured regardless of its communication mode, such as wired and wireless, and may be configured in various communication networks such as a personal area network (PAN) and a wide area network (WAN). can be configured. In addition, the network unit 150 may operate based on the known World Wide Web (WWW), and a wireless transmission technology used for short-range communication such as Infrared Data Association (IrDA) or Bluetooth. can also be used.

The computing device 100 in the present disclosure may include any type of user terminal and/or any type of server. Accordingly, embodiments of the present disclosure may be performed by a server and/or a user terminal.

A user terminal may include any type of terminal capable of interacting with a server or other computing device. User terminals include, for example, mobile phones, smart phones, laptop computers, personal digital assistants (PDAs), slate PCs, tablet PCs, and ultrabooks. can include

A server may include any type of computing system or computing device, such as, for example, microprocessors, mainframe computers, digital processors, portable devices and device controllers, and the like.

In an additional embodiment, the server may refer to an entity that stores and manages nucleotide sequence information or gene information. The server may include a storage unit (not shown) for storing nucleotide sequence information, gene information, and/or index information, and the storage unit may be included in the server or exist under the management of the server. As another example, the storage unit may exist outside the server and may be implemented in a form capable of communicating with the server. In this case, the storage unit may be managed and controlled by another external server different from the server.

2 illustrates an exemplary method for determining HLA type according to one embodiment of the present disclosure.

In one embodiment, the steps shown in FIG. 2 may be performed by computing device 100 . In a further embodiment, the steps in FIG. 2 may be implemented by a plurality of entities, such that some of the steps shown in FIG. 2 are performed in a user terminal and others are performed in a server.

As shown in FIG. 2 , the computing device 100 may obtain a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from a subject (210).

In one embodiment of the present disclosure, the computing device 100 may obtain a result of performing nucleotide sequence analysis (eg, NGS) from a server or an external entity. In another embodiment, the computing device 100 may perform nucleotide sequence analysis on genetic data (eg, DNA or RNA) obtained from a biological sample derived from a subject.

The computing device 100 may determine the HLA type of the biological sample based on the HLA type candidates included in the calls obtained from the plurality of HLA typing tools and the complementary score between the plurality of HLA typing tools (220).

In one embodiment of the present disclosure, the computing device 100 may use three or more tools selected from among a plurality of HLA typing tools to determine the HLA type from the result of sequencing. For example, the computing device may determine the final HLA type from the HLA type (or HLA type candidates) obtained from a combination of three or four selected from among OptiType, HLA-HD, PHLAT, HLAscan, and HLA*LA. The names of the aforementioned HLA typing tools are used for illustrative purposes for convenience of explanation, and additional HLA typing tools may be used or only some of the aforementioned HLA typing tools may be used according to implementation aspects.

In the present disclosure, a call from an HLA typing tool may mean an HLA typing result output from the HLA typing tool. For example, HLA type candidates output from each of a plurality of HLA typing tools may correspond to calls from the HLA typing tool.

Accuracy and/or usability may be different for each of the plurality of HLA typing tools. In the technique according to an embodiment of the present disclosure, HLA typing with high accuracy and/or usability can be achieved by combining results of a plurality of tools through a novel algorithm.

The inventors of the present disclosure, as a result of intensive research on a method for more accurately determining a person's Human Leukocyte Antigen (HLA) type, more accurately use base sequence data such as next-generation sequencing (NGS). An optimal method for predicting the HLA type was developed, and based on this, the present invention was completed.

Through this, it is possible to accurately predict the HLA type by combining a plurality of tools and by using the mutual security score between the plurality of tools, and through such HLA type prediction, an effect that individualized medical intervention can be determined more efficiently can be derived. there is.

The inventors of the present disclosure have confirmed that the accuracy of HLA Class I typing is significantly improved when all or at least some of a plurality of HLA typing tools are used and when a mutual complement score between a plurality of HLA typing tools is used, in particular, the present disclosure It was found that the embodiments of are the optimal HLA type determination method with improved reliability. Therefore, when using the HLA type determination method according to an embodiment of the present invention, it is expected that personalization of medical care, such as determining whether to additionally perform PCR-SBT as well as histocompatibility testing to increase the transplant success rate, is expected.

In one embodiment of the present disclosure, the computing device 100 may determine the number of tools that presented calls among a plurality of HLA typing tools and determine the HLA type based on the determined number of tools. Each HLA typing tool may output a call including an HLA typing result through its own unique algorithm. For example, if there are a total of 5 tools presenting calls, and all HLA types included in the calls presented by the 5 tools match or all 4 tools match, the matching HLA type is selected as the final HLA type. can be determined by The computing device 100 determines the number of tools that present calls from among a plurality of HLA typing tools, determines the degree of agreement between HLA type candidates included in the presented calls, and based on the determined number of tools and the determined degree of agreement , It is possible to determine the HLA type.

For example, if the number of tools presenting a call is too small (eg, only two tools present a call) and/or there are two HLA type candidates among the HLA type candidates included in the call, a plurality of It may be determined that the reliability of the HLA type candidate obtained from the tools is low. In the opposite case, when it is determined that the number of calls is greater than a specific threshold and/or the degree of matching of HLA type candidates included in the call is higher than a specific threshold, it may be determined that the reliability of the finally determined HLA type is high. When it is determined that the reliability is high, the determined HLA type may be determined and output as the final HLA type.

Here, the degree of coincidence may indicate the number of calls in which the HLA type included in the call presented by the tool performing HLA typing matches. For example, OptiType, HLA-HD, PHLAT, HLAscan, and HLA*LA all presented calls, and if the HLA types included in three of these calls are the same, the degree of concordance can be expressed as 3/5, If the HLA types included in four of these calls are the same, the degree of concordance may be expressed as 4/5. The expression method and calculation method for the degree of concordance are not limited to the above examples, and any form of method indicating the identity of the HLA type included in the call may be included within the scope of the degree of concordance as long as it does not depart from the scope of the present disclosure. .

In one embodiment of the present disclosure, a computing device determines the number of available calls among calls presented by a plurality of tools, and based on the number of available calls, generates a matching criterion for determining the HLA type. And, based on the generated matching criterion, the HLA type may be determined. Here, the match criterion may indicate a match threshold for determining the HLA type. Also, the usable call may mean a call usable for determining an HLA type among calls presented by the HLA typing tool. In another embodiment, available calls may mean all calls presented by a plurality of HLA typing tools. In this example, calls presented by the remaining tools, except for the tool that did not present the call, may be considered usable calls.

The consistency criterion may vary according to the number of available calls. As another example, the consistency criterion may vary depending on the number of available calls and HLA type candidates included in the calls.

In one embodiment of the present disclosure, the computing device 100 determines, when a specific type is presented only in five methods among a plurality of HLA typing tools, an HLA type candidate identically presented in all five methods as the final HLA type. Alternatively, the HLA type candidate presented identically in three or four methods may be determined as the final HLA type, but the scope of the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the computing device 100, during the process of analyzing the HLA type, when multiple calls are presented by one or more tools among a plurality of HLA typing tools, the HLA typing tool presenting the multiple calls The result from may be determined to be excluded in determining the final HLA type. That is, the computing device 100 may determine that it is not usable for multiple calls in this case, but is not limited thereto.

In one embodiment of the present disclosure, the computing device 100 calculates a complementary score between any two typing tools among a plurality of HLA typing tools in determining the HLA type, and based on the complementary score, the biological The HLA type for the sample can be determined.

In one embodiment, the complementarity score may include a value quantitatively indicating the possibility that any two HLA typing tools among a plurality of HLA typing tools can be used together to determine the HLA type. For example, a complementary score may be generated corresponding to a combination of two HLA typing tools. For example, assuming that there are four HLA typing tools including A, B, C, and D, A and B, A and C, A and D, B and A, B and C, and B and D , C and A, C and B, C and D, D and A, D and B, and D and C, the number of complementary scores corresponding to each other may be generated. In another example, assuming that there are four HLA typing tools including A, B, C, and D, A and B, A and C, A and D, B and C, B and D, and C and A number of complementary scores corresponding to D may be generated.

As described above, the mutual complementation score may indicate the possibility of complementation when one of the two HLA typing tools is used together, compared to when either one of the two HLA typing tools is used. In a further embodiment, the complementary score may represent a score for a combination of three or more HLA typing tools rather than two.

In one embodiment, the complementary score may include a value quantitatively indicating the accuracy of the determined HLA type when any two HLA typing tools among a plurality of HLA typing tools are used together. For example, it is assumed that the accuracy of the comparison result between the HLA typing result in one HLA typing tool and the actual HLA type is 70%. Under this assumption, if the accuracy of the final HLA typing result determined by referring to the results output from the one HLA typing tool and the other HLA typing tool, which is different from the one HLA typing tool, is 85%, the mutual complement between these two HLA typing tools The score may be set to have a relatively high value. Conversely, when the accuracy is not higher than that of one HLA typing tool even when two HLA typing tools are combined, a relatively low complementary score may be set.

In one embodiment, the complementary score is a quantitative correction value to be applied to HLA type candidates included in calls obtained from a plurality of HLA typing tools when any two HLA typing tools are used together. It may contain a value represented by .

For example, when a first HLA typing tool and a second HLA typing tool are used together, a first accuracy numerical value for the first HLA typing tool is combined with a second HLA typing tool for the first HLA typing tool. A first complementary scoring value may be added as a correction value. In this example, the first HLA typing tool, when used in conjunction with the second HLA typing tool, generates an accuracy value obtained by adding a first accuracy numerical value inherent in the first HLA typing tool and a correction value corresponding to the first complementary scoring. can have

In another example, when the second HLA typing tool and the first HLA typing tool are used together, according to the combination of the first HLA typing tool with respect to the second HLA typing tool to the second accuracy numerical value for the second HLA typing tool. A second complementary scoring value may be added as a correction value. In this example, the second HLA typing tool, when used in conjunction with the first HLA typing tool, generates an accuracy value that is the sum of a second accuracy numerical value inherent in the second HLA typing tool and a correction value corresponding to the second complementary scoring. can have

In one embodiment of the present disclosure, a complementary score between a first HLA typing tool and a second HLA typing tool among a plurality of HLA typing tools is a result of a first call obtained from the first HLA typing tool for a biological sample. may include a value quantitatively indicating the accuracy of the result for the second call obtained from the second HLA typing tool for the biological sample when is wrong. For example, if the result of the first call is incorrect and the probability of the result of the second call being correct is high, the complementary score may be set to a high value.

In one embodiment of the present disclosure, a complementary score between a first HLA typing tool and a second HLA typing tool among a plurality of HLA typing tools is obtained when the first HLA typing tool is used together with the second HLA typing tool. The determined accuracy value for the HLA type may include a value quantitatively indicating a result of comparison with a predetermined first accuracy value for the first HLA typing tool. For example, a difference between a first accuracy value inherent in the first HLA typing tool and an accuracy value when the first HLA typing tool and the second HLA typing tool are used together may represent a complementary score.

In one embodiment of the present disclosure, the computing device 100 may determine whether to combine a plurality of HLA typing tools based on the value of the complementarity score. For example, the computing device 100 determines whether to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among a plurality of HLA typing tools. based on a first complementary score between the tool and the second HLA typing tool. Determining whether or not to combine such a combination may be performed according to a comparison between a predetermined threshold and a mutual complement score. Alternatively, when the first HLA typing tool and the second HLA typing tool are combined, it is determined through the complementary score that the first HLA typing tool has an accuracy that exceeds the accuracy inherent in the first HLA typing tool or the accuracy inherent in the second HLA typing tool. If so, the computing device 100 may determine to combine the two HLA typing tools. Conversely, when the first HLA typing tool and the second HLA typing tool are combined, the accuracy inherent in the first HLA typing tool or the accuracy inherent in the second HLA typing tool is determined through the complementary score. In this case, the computing device 100 may determine to combine the two HLA typing tools.

In one embodiment of the present disclosure, the computing device 100 determines whether to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among a plurality of HLA typing tools, the based on a first complementarity score between a first HLA typing tool and a second HLA typing tool, a first predetermined accuracy value for the first HLA typing tool and a second predetermined accuracy value for the second HLA typing tool can be determined by For example, a complementary score when a first HLA typing tool is combined with a second HLA typing tool together with a first accuracy value inherent in the first HLA typing tool and a second accuracy value inherent in the second HLA typing tool. Using as factors, the computing device 100 may determine whether to output an HLA typing result by combining the first HLA typing tool and the second HLA typing tool.

In one embodiment of the present disclosure, the computing device 100 determines a correction value to be applied to a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools. It may be determined based on a first complementary score between the first HLA typing tool and the second HLA typing tool. The computing device 100 may determine the HLA type corresponding to the biological sample based on the correction value. For example, when calls obtained from two HLA typing tools match each other, and when the complementary score between the two tools has a relatively high value (eg, when it has a value greater than or equal to a predetermined threshold value), computing The device 100 may determine the final HLA type as the HLA type corresponding to the two matching calls. In this example, the computing device 100 may consider calls from HLA typing tools other than the two HLA typing tools together with their complementary scores to determine which tool results to use as the HLA typing result. there is.

In one embodiment of the present disclosure, the computing device 100 may determine a degree of similarity between algorithms included in a plurality of HLA typing tools based on the complementary score. The complementary score of the second HLA typing tool with respect to the first HLA typing tool may be used as an index indicating the possibility that the result of the second HLA typing tool is correct when the result of the first HLA typing tool is incorrect. This can also be used as an index indicating a difference between the typing algorithm of the first HLA typing tool and the algorithm of the second HLA typing tool. Accordingly, according to an embodiment of the present disclosure, the computing device 100 may calculate similarities or differences between typing methods or typing algorithms for each HLA typing tool based on the calculated complementarity score.

3 illustrates an example algorithm for determining a complementarity score, according to one embodiment of the present disclosure.

The HLA typing tools described in FIGS. 3-5 are used for illustrative purposes, and depending on the implementation, additional HLA typing tools or only some of them may be used. As an example, additional HLA typing tools may include any type of tools for HLA typing, such as Polysolver, HLAreporter, HLAforest, and HLAminer.

Among the HLA typing tools illustratively described in FIGS. 3 to 5, Opt represents OptiType, HD represents HLA-HD (HLA typing from High-quality Dictionary), and Phlat, SeqLA, and kourami are PHLAT and seq2HLA, respectively, by themselves. and kourami, arcas denotes arcasHLA, Scan denotes HLAscan, and *LA was used to denote HLA*LA.

In one embodiment, FIG. 3 shows an exemplary data structure for expressing a correction score, which is one of factors for determining a complementary score.

The data structure 300 shown by reference numeral 300 in FIG. 3 shows the result of the maintype HLA typing tool when each of the subtype HLA typing tools is combined for each of the maintype HLA typing tools among eight HLA typing tools. Indicates the score that the subtype's HLA typing tool can correct. The data structure 300 is a result obtained by combining a plurality of HLA typing tools for a large amount of samples.

As shown in FIG. 3, the correction score of the second HLA typing tool of a specific subtype for the first HLA typing tool of a specific maintype is, in an experiment targeting a large number of samples, the first HLA typing tool It may be determined based on information on the number of times the result for is wrong and information on the number of times the result for the second HLA typing tool is correct when the result for the first HLA typing tool is wrong. The correction score may have a higher value as a value for the number of times that the result of the second HLA typing tool is correct when the result of the first HLA typing tool is incorrect. As such, the correction score quantitatively indicates the possibility of correcting the result of using a specific HLA typing tool through another HLA typing tool.

As the value of the correction score shown in FIG. 3 increases, it indicates that the HLA typing tool of the subtype is more likely to correct the HLA typing tool of the maintype. As the value of the correction score increases, the value of the mutual complementation score may increase.

In the example shown in FIG. 3 , the value of the correction score may be included in the range of 0 to 1. In this example, the closer the value of the correction score is to 1, the higher the possibility that the subtype corrects the result of the maintype and outputs an accurate value.

4 illustrates an exemplary algorithm for determining a complementarity score, according to one embodiment of the present disclosure.

In one embodiment, FIG. 4 shows an exemplary data structure for expressing a complementary ratio, which is one of the factors for determining a complementary score. As an example, FIG. 5 may include a table of complementarity ratios for a plurality of HLA typing tools.

In one embodiment of the present disclosure, the complementarity ratio of HLA typing tool B to HLA typing tool A may be determined based on the correction score of B to A and the accuracy value inherent in B. For example, the complementarity ratio of an HLA typing tool B to an HLA typing tool A is 1 minus B's accuracy value as the denominator and 1 minus B's correction score for A as the numerator. can be determined by

For example, the accuracy value may be included in the range of 0 to 1.

In a further embodiment of the present disclosure, the complementarity ratio of HLA typing tool B to HLA typing tool A may be determined based on the correction score of B to A and the implicit accuracy value of A. For example, the complementarity ratio of an HLA typing tool B to an HLA typing tool A is 1 minus A's accuracy value as the denominator and 1 minus B's correction score for A as the numerator. can be determined by

In one embodiment, two complementarity ratios may be calculated for two HLA type tools. In one example, the complementarity ratio of B to A and the complementarity ratio of A to B can be calculated. As shown in FIG. 4 , two complementary ratios may be calculated according to the relationship between maintype and subtype for two corresponding HLA typing tools.

In one example, the higher the value of the complementarity ratio, the higher the probability of correcting the result for the maintype compared to the accuracy of the HLA typing result inherent in the subtype.

In another example, the higher the value of the complementarity ratio, the higher the accuracy corresponding to the corrected result according to the result from the subtype compared to the HLA typing accuracy inherent in the maintype.

In one embodiment, the value of the complementarity ratio greater than 1 may mean that the accuracy inherent in the corresponding HLA typing tool is outperformed when combined with.

5 depicts an exemplary algorithm for determining a complementarity score according to one embodiment of the present disclosure.

In one embodiment, FIG. 5 illustrates an example data structure for representing a complementary score. As an example, FIG. 5 may include a table of complementary scores for a plurality of HLA typing tools.

According to an embodiment of the present disclosure, the computing device 100 may use the complementary score shown in FIG. 5 to determine whether to combine the results of a plurality of HLA typing tools. Additionally, in order to predict the accuracy of an HLA typing result when a plurality of HLA typing tools are combined according to an embodiment of the present disclosure, the computing device 100 may use the complementary score shown in FIG. 5 .

In a further embodiment of the present disclosure, computing device 100 may use a complementarity score to determine similarity of HLA typing algorithms among a plurality of HLA typing tools. For example, it indicates that a plurality of HLA typing tools having a relatively high value of complementarity score are highly likely to have different algorithms. In addition, a plurality of HLA typing tools having a relatively small complementarity score indicate that the algorithms between them are highly likely to be the same or similar.

In one embodiment of the present disclosure, the complementary score is determined by determining whether a first result from a first HLA typing tool among a plurality of HLA typing tools does not match an actual result, and a second result from a second HLA typing tool being the actual result. A first sub-score indicating a case in which the result matches the actual result, and a first result from the first HLA typing tool in case the second result from the second HLA typing tool among the plurality of HLA typing tools does not match the actual result. It may be calculated based on the second sub-score indicating the case of coincidence, the first accuracy value predetermined for the first HLA typing tool, and the second accuracy value predetermined for the second HLA typing tool. Here, the first sub-score includes a complementarity ratio or correction score of the second HLA typing tool to the first HLA typing tool, and the second sub-score includes the complementary score of the first HLA typing tool to the second HLA typing tool. It can include ratios or correction scores.

In one embodiment, as shown in FIG. 5 , a value of one complementary score may be calculated for two corresponding HLA typing tools.

For example, the complementarity scores of HLA typing tool A and HLA typing tool B are the complementarity ratio of HLA typing tool B to HLA typing tool A and HLA typing tool A to HLA typing tool B. It can be determined based on the mutual complement ratio of

For example, the complementary score of A and B may be determined based on the sum of the complementary ratio of B to A and the complementary ratio of A to B.

For example, the complementarity score of A and B may mean the average of the complementarity ratio of B to A and the complementarity of A to B.

In one embodiment of the present disclosure, it is assumed that a total of four HLA typing tools including HLA typing tool A, HLA typing tool B, HLA typing tool C, and HLA typing tool D are combined to derive an HLA typing result. do. In addition, a unique accuracy value is mapped to each of these four HLA typing tools. In this case, combinations for a total of five HLA typing tools including AB, AC, AD, BC, and CD may be determined. In addition, a predetermined complementary score may be assigned to each of these five combinations. That is, a total of five complementary scores may be determined for the five combinations.

Under these assumptions, the computing device 100 obtains data obtained from the four HLA typing tools based on the accuracy values assigned to each of the four HLA typing tools and the complementarity score assigned to each of the five combinations of the HLA typing tools. It is possible to determine which of the results from the calls made is selected as the final HLA typing result. Alternatively, a total of 5 mutually complementary scores may be determined.

Under these assumptions, the computing device 100 obtains data obtained from the four HLA typing tools based on the accuracy values assigned to each of the four HLA typing tools and the complementarity score assigned to each of the five combinations of the HLA typing tools. You can decide whether and/or what to do with the combined calls.

In one embodiment, the complementarity score assigned to each of the five combinations of HLA typing tools can be obtained from the exemplary table shown in FIG. 5 .

In one example, the computing device 100 includes a first factor including accuracy values assigned to each of A, B, C, and D, and an average of complementary scores assigned to each of AB, AC, AD, BC, and CD. Based on the second factor, it is possible to determine whether to select the results of the calls obtained from the four HLA typing tools as the final HLA typing result.

In one example, the computing device 100 calculates a first factor including accuracy values assigned to A, B, C, and D, respectively, and an average and a total of complementary scores assigned to each of AB, AC, AD, BC, and CD. Based on the second factor obtained by calculating K (for example, 4), the number of HLA typing tools, it may be determined whether to select the results of calls obtained from the four HLA typing tools as the final HLA typing result.

As another example, the computing device 100 divides the total sum of the complementarity scores assigned to each of AB, AC, AD, BC, and CD by the number of combinations K times, which is the total number of HLA typing tools (eg, , 4) and the first factor including the accuracy values assigned to A, B, C, and D, respectively, the results in the calls obtained from the four HLA typing tools are finalized. You can decide whether to select based on the HLA typing result.

Equation 1 above shows an exemplary calculation formula for determining whether to combine results of a plurality of HLA typing tools according to an embodiment of the present disclosure. Additionally, Equation 1 may be used as an exemplary calculation formula for determining the accuracy of an HLA typing result when a plurality of HLA typing tools are combined according to an embodiment of the present disclosure. Equation 1 is used for illustrative purposes, and various types of equations or calculation algorithms using the accuracy information of each of the plurality of HLA typing tools and the mutual complement scores between the plurality of HLA typing tools as factors according to implementation aspects can be used

In Equation 1, k represents the number of HLA typing tools used, n represents the number of combinations between HLA typing tools, Acc represents an accuracy value assigned to each of the HLA typing tools, and Value _cor represents HLA typing Indicates a complementary score corresponding to a combination of tools.

Equation 1 described above can be used to calculate a final accuracy value for a combination of HLA typing tools used to determine an HLA type, using accuracy information of each of the HLA typing tools and complementary score information of a combination of the HLA typing tools. there is. For example, the computing device 100 compares the final accuracy value according to Equation 1 and a predetermined threshold value (eg, a value such as 99.8%) to determine whether to combine a plurality of HLA typing tools and / or Reliability of HLA typing results obtained from a plurality of HLA typing tools may be determined.

As another example, the computing device 100 may determine whether to combine calls obtained from a plurality of HLA typing tools based on the algorithm according to Equation 1 described above. As another example, the computing device 100 may determine a final HLA type result by determining weights for calls obtained from a plurality of HLA typing tools based on the algorithm according to Equation 1 described above.

As another example, the computing device 100 determines an HLA type candidate to be determined as the final HLA type among HLA type candidate values included in calls obtained from a plurality of HLA typing tools based on the algorithm according to Equation 1 described above. can decide

As described above, the HLA typing method according to an embodiment of the present disclosure uses a plurality of HLA typing tools, but obtains a higher score from calls obtained from a plurality of HLA typing tools based on a complementary score between the plurality of HLA typing tools. Accurate HLA typing results can be obtained.

In one embodiment, the value of the complementarity score greater than 1 may mean that it outperforms its inherent accuracy when combined with.

In a further embodiment of the present disclosure, when the HLA type is not determined by the above-described HLA typing technique, the computing device 100 extracts DNA or RNA from a biological sample derived from a subject and uses PCR-SBT (Polymerase Chain Reaction -Sequencing Based Typing) may be performed. As another example, the computing device may obtain a corresponding result by acquiring an execution result of PCR-SBT executed by another entity. In an embodiment of the present disclosure, when the HLA type is not determined by the above-described algorithm, the degree of concordance of the HLA type included in the call is low or the degree of certainty of the HLA typing result is high even considering the mutual complement score. It may correspond to a situation below the determined threshold. In this case, it may mean that the accuracy of HLA typing output by the corresponding algorithm is reduced. In this case, it may be desirable to increase the accuracy of HLA type determination by performing additional PCR-SBT.

In the present disclosure, the PCR-SBT can be used as an auxiliary test in situations where additional HLA genotype information is required or the determination of PCR-SSP test results is ambiguous. A disadvantage of PCR-SSO or RFLP is that it can detect single-base differences in the DNA sequence between two alleles, but it is less likely to detect a new, undefined allele unless the mutation occurs at a specific site detected by the probe. It is. PCR-SBT was originally developed as a manual sequencing method, and the introduction of dye-labeled primers and fluorescence automated sequencing enabled automation and greatly improved accuracy. PCR-SBT is a technology that directly detects the nucleotide sequence of an allele, and has excellent accuracy, but it takes a long time, is expensive, and has a low sample throughput.

In the present disclosure, PCR may be real-time PCR, and more specifically may be single PCR or multiplex PCR, but is not limited thereto. In the present invention, the single or multiplex real-time PCR reaction conditions may take conventional conditions. In addition, single real-time PCR and multiplex real-time PCR reactions can be performed under the same conditions. For example, initial denaturation is performed at 95 ° C for 1 minute, followed by denaturation (95 ° C for 25 seconds), annealing (annealing, 45 seconds at 65 ° C.) and extension (extension, 30 seconds at 72 ° C.) may be performed a total of 40 times. Real-time PCR devices usable in the present disclosure include Real-time PCR devices 7900, 7500, and 7300 from AB, LightCycler 80 from Roche, Mx3000p from Stratagene, and Chromo 4 devices from BioRad, which are described for illustrative purposes. However, the scope of the present disclosure is not limited to specific descriptions of the characteristics of the PCR device and the PCR reaction.

In the present disclosure, the PCR may be PCR-SSOP (PCR sequence-specific oligonucleotide probes) or PCR-SBT (PCR sequencing-based typing), but is not limited thereto.

6 is a schematic diagram of a computing environment according to one embodiment of the present disclosure.

A component, module or unit in this disclosure includes routines, procedures, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods presented in this disclosure can be used in single-processor or multiprocessor computing devices, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like ( It will be fully appreciated that each of these may be implemented with other computer system configurations, including those that may be operative in connection with one or more associated devices.

Embodiments described in this disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

A computing device typically includes a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media.

Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 2000 implementing various aspects of the present invention is shown comprising a computer 2002, which includes a processing unit 2004, a system memory 2006 and a system bus 2008. do. Computer 200 herein may be used interchangeably with a computing device. System bus 2008 couples system components, including but not limited to system memory 2006, to processing unit 2004. Processing unit 2004 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as the processing unit 2004.

System bus 2008 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 2006 includes read only memory (ROM) 2010 and random access memory (RAM) 2012 . The basic input/output system (BIOS) is stored in non-volatile memory 2010, such as ROM, EPROM, EEPROM, etc. BIOS is a basic set of information that helps transfer information between components within the computer 2002, such as during startup. contains routines. RAM 2012 may also include high-speed RAM, such as static RAM, for caching data.

The computer 2002 may also read from an internal hard disk drive (HDD) 2014 (eg EIDE, SATA), a magnetic floppy disk drive (FDD) 2016 (eg a removable diskette 2018), or for writing to them), SSDs and optical disk drives 2020 (for example, for reading CD-ROM disks 2022 or reading from or writing to other high capacity optical media such as DVDs) include The hard disk drive 2014, magnetic disk drive 2016, and optical disk drive 2020 are connected to the system bus 2008 by the hard disk drive interface 2024, magnetic disk drive interface 2026, and optical drive interface 2028, respectively. ) can be connected to The interface 2024 for external drive implementation includes, for example, at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 2002, drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable storage media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, cartridges, It will be appreciated that other types of computer-readable storage media, such as those of other types, may also be used in the exemplary operating environment and that any such media may contain computer-executable instructions for performing the methods of the present invention. .

A number of program modules may be stored on the drive and RAM 2012, including an operating system 2030, one or more application programs 2032, other program modules 2034, and program data 2036. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 2012. It will be appreciated that the present invention may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 2002 through one or more wired/wireless input devices, such as a keyboard 2038 and a pointing device such as a mouse 2040. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 2004 through an input device interface 2042 that is connected to the system bus 2008, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 2044 or other type of display device is also connected to the system bus 2008 through an interface such as a video adapter 2046. In addition to the monitor 2044, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 2002 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 2048 via wired and/or wireless communications. Remote computer(s) 2048 may be workstations, server computers, routers, personal computers, handheld computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and generally relate to computer 2002. Although many or all of the described components are included, for brevity, only memory storage device 2050 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 2052 and/or a larger network, such as a wide area network (WAN) 2054 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 2002 is connected to local network 2052 through wired and/or wireless communication network interfaces or adapters 2056. Adapter 2056 may facilitate wired or wireless communications to LAN 2052, which also includes a wireless access point installed therein to communicate with wireless adapter 2056. When used in a WAN networking environment, computer 2002 may include a modem 2058, be connected to a communications server on WAN 2054, or other means of establishing communications over WAN 2054, such as over the Internet. have Modem 2058, which can be internal or external and wired or wireless, is connected to system bus 2008 through serial port interface 2042. In a networked environment, program modules described for computer 2002, or portions thereof, may be stored in remote memory/storage device 2050. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1602 is any wireless device or entity that is deployed and operating in wireless communication, such as printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, and associated with wireless detectable tags. It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of example approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The method claims of this disclosure present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

As described above, the related contents have been described in the best mode for carrying out the invention.

devices, systems, etc., for determining the type of human leukocyte antigen.

Claims (18)

1. A method for determining the type of Human Leukocyte Antigen (HLA) performed by a computing device, comprising:

   Obtaining a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from a subject; and

   determining an HLA type for the biological sample using a plurality of HLA typing tools that take the result of the sequencing as an input;

   including,

   method.
2. According to claim 1,

   Determining the HLA type,

   determining the HLA type of the biological sample based on HLA type candidates included in calls obtained from the plurality of HLA typing tools and complementary scores between the plurality of HLA typing tools;

   including,

   method.
3. According to claim 1,

   Determining the HLA type,

   determining the number of tools presenting calls among the plurality of typing tools; and

   determining the HLA type based on the determined number of tools;

   including,

   method.
4. According to claim 1,

   Determining the HLA type,

   determining the number of tools presenting calls among the plurality of typing tools;

   Calculating a degree of agreement between HLA type candidates included in the presented calls; and

   determining the HLA type based on the determined number of tools and the calculated degree of agreement;

   including,

   method.
5. According to claim 1,

   Determining the HLA type,

   calculating a complementary score between any two or more of the plurality of HLA typing tools; and

   Based on the complementarity score, determining the HLA type of the biological sample;

   including,

   method.
6. According to claim 5,

   The mutual complement score,

   Including a value quantitatively indicating the possibility that any two HLA typing tools of the plurality of HLA typing tools can be used together to determine the HLA type,

   method.
7. According to claim 5,

   The mutual complement score,

   Including a value quantitatively indicating the accuracy of the determined HLA type when any two HLA typing tools among the plurality of HLA typing tools are used together,

   method.
8. According to claim 5,

   The mutual complement score,

   A value indicating quantitatively a correction value to be applied to HLA type candidates included in calls obtained from the two HLA typing tools when any two of the plurality of HLA typing tools are used together,

   method.
9. According to claim 5,

   The complementary score between the first HLA typing tool and the second HLA typing tool among the plurality of HLA typing tools,

   Quantitatively indicating the accuracy of the result of the second call obtained from the second HLA typing tool for the biological sample when the result of the first call obtained from the first HLA typing tool for the biological sample is incorrect containing the value

   method.
10. According to claim 5,

    The complementary score between the first HLA typing tool and the second HLA typing tool among the plurality of HLA typing tools,

    Quantitatively comparing a result of comparing an accuracy value for the determined HLA type when the first HLA typing tool is used together with the second HLA typing tool with a predetermined first accuracy value for the first HLA typing tool containing a value representing

    method.
11. According to claim 1,

    Determining the HLA type,

    A decision between the first HLA typing tool and the second HLA typing tool determines whether to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools. 1 determining based on the complementary score; and

    determining the HLA type based on a decision whether to combine the first call with the second call;

    including,

    method.
12. According to claim 11,

    Determining the HLA type,

    Whether or not to combine a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools is determined between the first HLA typing tool and the second HLA typing tool. determining based on a first complementary score, a first predetermined accuracy value for the first HLA typing tool and a second predetermined accuracy value for the second HLA typing tool; and

    determining the HLA type based on a decision whether to combine the first call with the second call;

    including,
13. According to claim 1,

    Determining the HLA type,

    A correction value to be applied to a first call obtained from a first HLA typing tool and a second call obtained from a second HLA typing tool among the plurality of HLA typing tools is determined by determining a difference between the first HLA typing tool and the second HLA typing tool. 1 determining based on the complementary score; and

    determining the HLA type based on the correction value;

    including,

    method.
14. According to claim 5,

    Determining the HLA type,

    predicting accuracy of the determined HLA type when using the plurality of HLA typing tools based at least in part on a complementarity score associated with the plurality of HLA typing tools;

    including,

    method.
15. According to claim 5,

    The mutual complement score,

    a first sub-score indicating a case in which a first result from a first HLA typing tool among the plurality of HLA typing tools does not match an actual result and a second result from a second HLA typing tool matches the actual result;

    a second sub-score indicating a case in which a first result from a first HLA typing tool matches an actual result when a second result from a second HLA typing tool among the plurality of HLA typing tools does not match an actual result;

    a first predetermined accuracy value for the first HLA typing tool; and

    a second pre-determined accuracy value for the second HLA typing tool;

    calculated based on

    method.
16. According to claim 2,

    determining a degree of similarity between algorithms included in the plurality of HLA typing tools based on the complementary score;

    Including more,

    method.
17. A computer program stored on a computer readable storage medium that, when executed by one or more processors, performs a method for determining a type of Human Leukocyte Antigen (HLA), the method comprising:

    Obtaining a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from a subject; and

    determining the HLA type of the biological sample using a plurality of HLA typing tools that take the result of the sequencing as an input;

    including,

    A computer program stored on a computer readable storage medium.
18. A computing device for determining the type of Human Leukocyte Antigen (HLA), comprising:

    Memory; and

    contains a processor;

    The processor:

    Obtaining a result of performing nucleotide sequence analysis on DNA or RNA obtained from a biological sample derived from the subject; and

    Determining the HLA type for the biological sample using a plurality of HLA typing tools that take the sequencing result as an input,

    computing device.

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