KR20230140370A - Apparatus and method for determining reliability of immunopeptidome information - Google Patents

Abstract

In accordance with one embodiment of the present disclosure, a method performed by a computing device is disclosed. The method includes: obtaining a first set of peptide sequences corresponding to a first Major Histocompatibility Complex (MHC) type, obtaining a second set of peptide sequences corresponding to a second MHC type. calculating a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences, and based on the first degree of similarity, reliability of immunopeptidome information stored in a database. It may include a step of determining (reliability).

Description

Method and apparatus for determining reliability of immune peptidome information stored in a database {APPARATUS AND METHOD FOR DETERMINING RELIABILITY OF IMMUNOPEPTIDOME INFORMATION}

This disclosure relates to immunopeptidome, and more specifically to techniques for determining the reliability of immunopeptidome information stored in a database.

The major histocompatibility complex is a locus that encodes ‘MHC molecules’ that function in the immune system. MHC molecules can be of type 1 (class I) and type 2 (class II).

The immunopeptidome refers to a set of peptides expressed on the surface of a cell. For example, the immunopeptidome may refer to a combination of peptides associated with MHC.

Human Leukocyte Antigen (HLA) is a glycoprotein molecule produced by the human Major Histocompatibility Complex (MHC) gene. HLA is not present in mature red blood cells, 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 white blood cells and/or platelets. MHC genes exist in all vertebrates, and the human MHC gene is called an HLA gene, and the product expressed therefrom is called HLA.

MHC genes are involved in recognition of self and non-self, 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 after the ABO blood group in the survival of the transplanted organ in solid organ transplantation. HLA is known to play the most important role in the success or failure of bone marrow transplantation. Therefore, immunological recognition of HLA differences can be considered the first step in rejection action against transplanted tissue. Additionally, in transfusion therapy, HLA and antibodies play an important role in the occurrence of various side effects such as platelet transfusion refractoriness, febrile non-hemolytic transfusion side effects, acute lung injury, and post-transfusion graft-versus-host disease.

HLA, like MHC, can be broadly classified into Class I and Class II. Class I is classified into HLA-A, HLA-B, and HLA-C and is expressed on most nucleated cells and platelets. When cytotoxic T cells recognize and eliminate virus-infected cells or tumor cells, they recognize antigens. ) is essential. 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. It interacts with the antigen receptor of helper T cells to induce cellular It is known to be essential for inducing humoral immune responses and recognizing antigens expressed on antigen-presenting cells. HLA is a gene that shows the greatest polymorphism among genes possessed by humans, and differences in frequency also exist among races and ethnicities.

When a peptide derived from an infectious microorganism-derived protein or a cancer cell-specific protein binds to MHC and is presented on the cell surface, T cells recognize it and trigger an immune response to eliminate the infected cell or cancer cell. In this way, T cells are key regulators (players) that determine specific immune responses to foreign substances that do not exist in the normal human body. Therefore, prediction of peptides that bind to MHC can be used in the development of peptide vaccines to prevent infectious diseases or cancer.

US Patent Publication US2016-0025726A1

The present disclosure has been made in response to the above-described background technology, and is intended to determine the reliability of immune peptidome information stored in a database.

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.

The method and device according to an embodiment of the present disclosure can efficiently determine the reliability of immune peptidome information stored in a database.

In accordance with one embodiment of the present disclosure, a method performed by a computing device is disclosed. The method includes: obtaining a first set of peptide sequences corresponding to a first Major Histocompatibility Complex (MHC) type; Obtaining a second set of peptide sequences corresponding to a second MHC type; calculating a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences; And based on the first similarity, it may include determining the reliability of the immunopeptidome information stored in the database.

In one embodiment, the reliability of the immune peptidome information stored in the database is determined by the first set of peptide sequences corresponding to the first MHC type and the first set of peptide sequences corresponding to the second MHC type stored in the database. Contamination scores of two sets of peptide sequences may be included.

In one embodiment, the method may further include generating alert information for the database based on the quantitative value of the contamination level.

In one embodiment, the method may further include determining a common peptide sequence occurring in the first set of peptide sequences and the second set of peptide sequences.

In one embodiment, computing a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences is performed when the common peptide sequence is determined and the first set of peptide sequences may not be performed if there is no common peptide sequence between the peptide sequences and the second set of peptide sequences.

In one embodiment, the common peptide sequence may be a peptide sequence in which the sequential combination of a plurality of amino acids in the first set of peptide sequences and the second set of peptide sequences is the same.

In one embodiment, calculating a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences comprises representing the first set of peptide sequences corresponding to the first MHC type: and comparing a first matrix for representing the second set of peptide sequences corresponding to the second MHC type with a second matrix for representing the second set of peptide sequences corresponding to the second MHC type.

In one embodiment, the step of comparing the first matrix and the second matrix includes, for each of the elements corresponding to each other in the first matrix and the second matrix, the first value and the first value of the first matrix. 2 may include comparing the second value of the matrix.

In one embodiment, calculating a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences comprises:

The first similarity is calculated using , where i represents a position identifier or element identifier in the matrix, and Ni represents the first matrix corresponding to the first set of peptide sequences and the second set of peptides. represents the smaller value of the two values at the i position in the second matrix corresponding to the sequences, Mi represents the larger value of the two values at the i position, Lp represents the length of the peptide, and a It may correspond to a variable.

In one embodiment, calculating the first degree of similarity between the first set of peptide sequences and the second set of peptide sequences may be based on the length of the peptide and the value for each position in the matrix.

In one embodiment, calculating the first degree of similarity between the first set of peptide sequences and the second set of peptide sequences comprises calculating a value representing the probability of presence of a type of amino acid at each of a plurality of predetermined positions. Using the matrix including, the first similarity to quantitatively express the degree of peptide sequence similarity between the first set of peptide sequences and the second set of peptide sequences can be calculated.

In one embodiment, based on the first similarity, determining the reliability of the immune peptidome information stored in the database includes: a quantitative measurement value of peptides whose sequences are identical between the first MHC type and the second MHC type A step of calculating; and determining the reliability of the immune peptidome information stored in the database based on the quantitative measurement value and the first similarity.

In one embodiment, calculating the quantitative measurement value includes,

or

Based on, where Ns represents the number of peptides whose sequences are identical between the first MHC type and the second MHC type, N _A represents the number of peptides corresponding to the first MHC type, and N _B is It can indicate the number of peptides corresponding to the second MHC type.

In one embodiment, based on the quantitative measurement value and the first similarity, determining the reliability of immune peptidome information stored in a database includes: correlation to correlate the first similarity with the quantitative measurement value. converting the first similarity based on an algorithm; and determining the reliability of the immune peptidome information stored in the database by comparing the converted first similarity with the quantitative measurement value.

In one embodiment, determining the reliability of immune peptidome information stored in the database by comparing the converted first similarity and the quantitative measurement value may include: the quantitative measurement value is greater than the converted first similarity; If large, calculating an error rate according to the comparison; and determining the reliability of the immune peptidome information stored in the database based on the error rate.

In one embodiment, the method comprises mapping the common peptide sequence and the error rate occurring in the first set of peptide sequences and the second set of peptide sequences to each other. It may further include storing in the database.

In one embodiment, the error rate may be a value obtained by dividing the converted first similarity by the quantitative measurement value.

In one embodiment, the method includes: obtaining a third set of peptide sequences corresponding to a third MHC type; and calculating a second similarity between the first set of peptide sequences and the third set of peptide sequences, and a third similarity between the second set of peptide sequences and the third set of peptide sequences. Further comprising, determining the reliability of the immune peptidome information stored in the database, based on the first similarity, the second similarity, and the third similarity, of the immune peptidome information stored in the database. It may include the step of determining reliability.

In one embodiment, the step of calculating the second similarity and the third similarity is: a common peptide sequence appearing in the first set of peptide sequences and the second set of peptide sequences is obtained from the obtained third set. It can be performed if it appears in the peptide sequences.

In one embodiment, the step of determining the reliability of the immune peptidome information stored in the database based on the first similarity, the second similarity, and the third similarity includes: an error rate corresponding to the second similarity; generating an updated error rate based on an operation of multiplying an average value between error rates corresponding to the third similarity by the error rate corresponding to the first similarity; and determining the updated error rate based on the reliability of the immune peptidome information stored in the database.

In one embodiment, the method may further include determining whether to generate warning information for the database based on an operation comparing the updated error rate to a predetermined contamination threshold.

In one embodiment, a computer program stored on a computer-readable storage medium is disclosed. The computer program, when executed by a computing device, causes the computing device to perform the following method, the method comprising: obtaining a first set of peptide sequences corresponding to a first MHC type; Obtaining a second set of peptide sequences corresponding to a second MHC type; calculating a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences; And based on the first similarity, it may include determining the reliability of the immune peptidome information stored in the database.

In one embodiment, a computing device is disclosed. The computing device includes at least one processor; and memory. The at least one processor is configured to: obtain a first set of peptide sequences corresponding to a first MHC type; Obtaining a second set of peptide sequences corresponding to a second MHC type; calculate a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences; And, based on the first similarity, it may be configured to determine the reliability of the immune peptidome information stored in the database.

The method and device according to an embodiment of the present disclosure can efficiently determine the reliability of immune peptidome information stored in a database.

1 schematically shows a block diagram of a computing device according to an embodiment of the present disclosure.
Figure 2 illustrates an exemplary method for determining the degree of contamination of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an embodiment of the present disclosure.
FIG. 3 illustrates an example method for determining the reliability of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an embodiment of the present disclosure.
Figure 4 illustrates an example method for determining the reliability of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an embodiment of the present disclosure.
Figure 5 exemplarily shows general operations for analyzing immune peptidome information according to an embodiment of the present disclosure.
Figure 6 exemplarily shows a result matrix indicating the distance or similarity between matrices according to the Distance Equation.
Figure 7 exemplarily shows a result matrix indicating the distance or similarity between matrices according to the Concordance Equation.
8 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 disclosure. Before describing specific details for implementing the present disclosure, it should be noted that configurations that are not directly related to the technical gist of the present disclosure have been omitted to the extent that they do not distract from the technical gist of the present invention. In addition, the terms or words used in this specification and claims have meanings that are 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 explain his or her invention in the best way. It should be interpreted as a concept.

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

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” or “at least one of A and B” means “if it contains only A,” “if it contains only B,” or “if it is a combination of A and B.” It should be interpreted to mean.

Those skilled in the art will additionally recognize that the various illustrative logical components, blocks, modules, circuits, means, logics, and algorithms described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, means, logic, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone 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. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

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

As used in this disclosure, the term “immunopeptidome” may be used to include the MHC peptidome or the HLA peptidome. For example, the HLA peptidome may refer to peptides bound to HLA, and the MHC peptidome may refer to peptides bound to MHC.

In this disclosure, for convenience of explanation, human leukocyte antigen (HLA) is used as an example of MHC. Therefore, the description of HLA used below is an example for expressing the description of MHC, and the scope of rights of the present disclosure will be determined based on the content stated in the claims, and the scope of rights will be determined through examples of HLA. The interpretation should not be limited to HLA.

HLA and MHC in the present disclosure may be used interchangeably.

The term “Human Leukocyte Antigen (HLA)” used in this disclosure is a glycoprotein molecule produced by the human MHC gene, and is a gene that shows the greatest polymorphism among genes possessed by humans. HLA typing, which determines HLA type, can be actively used in various fields such as organ transplantation, immunotherapy, disease-related research, paternity tests such as paternity determination, forensic use, and genetic research.

MHC types in the present disclosure may include HLA types. These HLA types may include, for example, HLA-A type, HLA-B type, and/or HLA-C type.

1 schematically shows a block diagram of a computing device 100 according to an 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 different configurations for performing the computing environment of the computing device 100, and only some of the disclosed configurations may configure 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. Computing device 100 may refer to any type of user terminal or any type of server. The components of the computing device 100 described above are exemplary and some may be excluded or additional components may be included. For example, when the above-described 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, which will be described later. For example, the computing device 100 acquires peptide sequences corresponding to each of a plurality of HLA types and compares these peptide sequences to determine peptide sequences of a specific HLA type and peptide sequences of other HLA types. The similarity between the livers can be calculated, and the reliability of the immune peptidome information stored in the database can be determined based on the calculated similarity.

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

In one embodiment, the processor 110 may consist of at least one core, including a central processing unit (CPU) and a general purpose graphics processing unit (GPGPU) of the computing device 100. , may include a processor for data analysis and/or processing, such as a tensor processing unit (TPU).

The processor 110 may read the computer program stored in the memory 130 to determine the reliability of the immune peptidome information, according to an embodiment of the present disclosure. In one embodiment, the processor 110 may read a computer program stored in the memory 130 to determine the degree of contamination of the immune peptidome information and generate an alarm therefor, according to an embodiment of the present disclosure.

According to an additional embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network 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, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, processors of a plurality of computing devices may be used together to process learning of a network function and data classification using a network function. Additionally, 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, processor 110 may typically handle overall operations of computing device 100. For example, the processor 110 processes data, information, or signals input or output through components included in the computing device 100 or runs an application program stored in the storage to provide information or information appropriate to the user. Functions can be provided or processed.

According to one embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the computing device 100. According to an embodiment of the present disclosure, the memory 130 may be a storage medium that stores computer software that allows 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 required to perform embodiments of the present disclosure, data to be executed by the codes, and execution results of the codes.

According to one 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 or a hard disk type. ), multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only) Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is only an example, and the memory 130 used in the present disclosure is not limited to the examples described above.

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

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. It can be included.

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

In an additional embodiment, the server may refer to an entity that stores and manages immune peptidome information, peptide sequence information, base sequence information, or genetic information. The server has a storage unit (not shown) for storing immune peptidome information, information related to the reliability and/or contamination level of the immune peptidome, analysis information about the immune peptidome, peptide sequence information, nucleotide sequence information, or genetic information. It may be included, and the storage unit may be included in the server or may exist under the management of the server. As another example, the storage unit may be implemented in a form that exists outside the server and can communicate with the server. In this case, the storage may be managed and controlled by an external server that is different from the server.

Figure 2 illustrates an exemplary method for determining the degree of contamination of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an 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 multiple entities, such that some of the steps shown in FIG. 2 are performed at a user terminal and others are performed at a server.

In one embodiment, the computing device 100 may obtain a list of peptides corresponding to each of the MHC types (210). For example, computing device 100 may obtain a list of peptide sequences for each of a plurality of HLA types. In one embodiment, the computing device 100 may obtain a list of peptide sequences corresponding to each of the HLA types from a public DB or from experimental results. For example, the computing device 100 may obtain HLA type identification information and list information on peptide sequences likely to be combined with it from a public database or experimental results.

In one embodiment, the computing device 100 may check whether a common peptide exists for a plurality of MHC types (220). For example, when a peptide that commonly appears in a plurality of HLA types exists, the computing device 100 may compare lists of peptide sequences corresponding to each of the HLA types in which the common peptide exists. As described above, the computing device 100 selects a common peptide sequence among the first set of peptide sequences corresponding to the first type and the second set of peptide sequences corresponding to the second type among the plurality of HLA types. If it is determined that peptide sequences exist, the degree of similarity between the first set of peptide sequences and the second set of peptide sequences can be calculated. Here, the common peptide sequence may mean a peptide sequence in which the sequential combination of a plurality of amino acids in the first set of peptide sequences and the second set of peptide sequences is the same.

In one embodiment, the computing device 100 may determine the degree of contamination of peptide lists corresponding to MHC types (230). For example, the degree of contamination of peptide lists may refer to a quantitative value indicating whether the reliability of peptide sequences for each of a plurality of MHC types stored in a database is high. In this example, contamination and reliability may be inversely related to each other. For example, if the value of the degree of contamination is large, the reliability may be determined to be low. The computing device 100 may generate alert information for a database based on the quantitative value of the degree of contamination or the quantitative value of the reliability. Warning information about the database may include information about peptide sequences for each HLA type stored in the database that may be contaminated.

According to one embodiment of the present disclosure, the computing device 100, when acquiring peptide sequences corresponding to a new HLA type and/or acquiring new peptide sequences corresponding to an existing stored HLA type, stores the corresponding information in the database. Contents can be saved. In this way, the computing device 100 checks the presence of a common peptide sequence in the data previously stored in the database and the newly stored data, and then determines the degree of contamination of the data stored in the database based on analyzing the similarity of the peptide sequences for each HLA type. and/or reliability may be determined.

3 to 5, a method for determining the degree of contamination and/or reliability of data stored in a database will be explained in more detail.

FIG. 3 illustrates an example method for determining the reliability of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an embodiment of the present disclosure.

In one embodiment, the steps shown in FIG. 3 may be performed by computing device 100. In a further embodiment, the steps shown in FIG. 3 may be implemented by multiple entities, such that some of the steps shown in FIG. 3 are performed at a user terminal and others are performed at a server.

In one embodiment, computing device 100 may obtain a first set of peptide sequences corresponding to a first MHC type (310). Computing device 100 may obtain a second set of peptide sequences corresponding to a second MHC type (320).

For example, peptide sequences with high binding potential or high affinity for a specific type of HLA (eg, HLA A 0201) can be determined. Peptide sequences corresponding to each HLA type can be stored in a database. In Figure 3, two types including the first type and the second type are exemplarily described, but it is understood that peptide sequences corresponding to three or more MHC types will also be included within the scope of the present disclosure. It will be clear to those skilled in the art.

In one embodiment, computing device 100 may calculate a first degree of similarity between the first set of peptide sequences and the second set of peptide sequences (330).

In one embodiment, this similarity comparison may be performed when there is a common peptide sequence occurring in the first set of peptide sequences and the second set of peptide sequences. The common peptide sequence in the present disclosure may refer to a peptide sequence in which the sequential combination of a plurality of amino acids is the same, for example, in the first set of peptide sequences and the second set of peptide sequences.

For example, assume that there is a common peptide sequence named APKEKPEEA in the first set of peptide sequences corresponding to HLA A 0201 and the second set of peptide sequences corresponding to HLA A 0301. In this case, the computing device 100 may calculate the similarity between the first set of peptide sequences corresponding to HLA A 0201 and the second set of peptide sequences corresponding to HLA A 0301.

In one embodiment of the present disclosure, calculating similarity comprises: a first matrix representing the first set of peptide sequences corresponding to a first MHC type and the second set of peptide sequences corresponding to a second MHC type It may include comparing a second matrix for representing the data. For example, when the same peptide sequence is found in HLS B 3501 and HLA B 5101, the computing device 100 can obtain a PWM (Position Weight Matrix) corresponding to HLA B 3501 and a PWM corresponding to HLA B 5101, respectively. You can. Computing device 100 may calculate similarity through comparison between these PWMs.

In one embodiment, when comparing matrices, the computing device 100 compares the first value of the first matrix and the second value of the second matrix for each of the corresponding elements in the first matrix and the second matrix. Values can be compared.

In one embodiment, the computing device 100 quantitatively determines the degree of peptide sequence matching between two or more peptide sequences using a matrix containing values indicating the possibility of existence of a type of amino acid at each of a plurality of predetermined positions. Similarity for expression can be calculated.

In one embodiment, the computing device 100 may calculate the similarity between matrices based on the length of the peptide and the value for each position within the matrix. For example, when the computing device 100 performs a similarity calculation,

can be used. where i represents a position identifier or element identifier within a matrix, and Ni represents i in the first matrix corresponding to the first set of peptide sequences and the second matrix corresponding to the second set of peptide sequences. represents the smaller of the two values at the position, Mi represents the larger of the two values at the i position, Lp represents the length of the peptide, and a may correspond to a rational number. In other words, the operation result according to Equation 1 may include quantitative values for the similarity between matrices.

For example, the similarity between these matrices may correspond to the distance between the matrices. A close distance between matrices may mean that the values of elements between matrices are similar. A short distance between these matrices may mean that the peptide sequences included in the matrices have a high degree of similarity.

In one embodiment of the present disclosure, the computing device 100 may determine the reliability of the immune peptidome information stored in the database based on the similarity calculation result (340). For example, the immune peptidome information stored in the database may include a specific MHC type and a list of peptide sequences corresponding to that type. The reliability of immune peptidome information can be determined based on the quantitative value of the degree of contamination of this stored information.

In one embodiment, in determining the reliability of the immune peptidome information stored in the database, the computing device 100 uses quantitative information about peptides whose sequences match between MHC types for which similarity has been calculated to provide quantitative measurement values. It can be calculated. These measurements can be obtained by a process of comparing peptide sequences corresponding to each MHC type to determine whether there are matching sequences.

In one embodiment, the computing device 100 may determine the reliability of immunopeptidome information stored in the database based on quantitative measurement values and similarity.

In one embodiment, computing device 100 calculates a quantitative measurement value,

or

can be used. That is, the quantitative measurement value can be calculated using one of the two equations described in Equation 2 or Equation 3. Here, Ns represents the number of peptides whose sequences match between the first MHC type and the second MHC type, N _A represents the number of peptides corresponding to the first MHC type, and N _B corresponds to the second MHC type. It can represent the number of peptides. That is, the computing device 100 obtains the above-described measurement value based on the number of peptides in common among the peptides corresponding to each of the two MHC types and the number of peptides corresponding to each of the two MHC types. You can. These measurements, when used in conjunction with similarity, can determine the confidence or contamination of the immune peptidome information. As such, the technique according to an embodiment of the present disclosure can efficiently calculate the reliability of the immune peptidome information stored in the database by comparing the identity information of the actually measured peptides and the identity information of the peptides calculated through a matrix. .

In one embodiment, the computing device 100 may determine the reliability of immune peptidome information stored in a database based on the quantitative measurement value and similarity, and may use an algorithm for associating the similarity with the measurement value. For example, the computing device 100 converts the similarity based on an association algorithm for correlating the similarity and the quantitative measurement value, and compares the transformed similarity with the quantitative measurement value to store the immune system information stored in the database. The reliability of peptidome information can be determined. For example, an association algorithm may have the equation y = k / x. Here, y corresponds to Equation 1 for calculating the similarity between matrices, x corresponds to Equation 2 or Equation 3 for calculating quantitative measurements, and k refers to a value that varies depending on the length of the peptide. do. Therefore, transformation of the similarity value can be performed based on the correlation between the quantitative measurement value and the similarity value according to the similarity calculation. Through comparison of the converted similarity value and the quantitative measurement value, an error rate, which will be explained below, can be determined.

In this way, the computing device 100 can determine the reliability of the immune peptidome information by comparing the similarity between the measurements and the matrices. For example, the computing device 100 compares the converted similarity and the quantitative measurement value, and when the quantitative measurement value is greater than the converted similarity, calculates an error rate according to the comparison, and calculates the error rate. Based on the rate, the reliability of the immune peptidome information stored in the database can be determined.

For example, error rate may refer to the ratio between converted similarity (i.e., predicted value) and quantitative measured value (i.e., actual value). The error rate can be calculated when the quantitative measure is greater than the transformed similarity. The error rate may mean the converted similarity divided by a quantitative measurement value.

When the error rate is calculated, the computing device 100 stores the common peptide sequences and error rates in a database by mapping the common peptide sequences and error rates appearing in the first set of peptide sequences and the second set of peptide sequences to each other. It can be saved in . The value of this error rate and the common peptide sequence among the peptide sequences corresponding to the two types can be stored in the database. For example, the common peptide sequence APKEKPEEA and the error rate 0.832 can be stored together.

In a further embodiment of the present disclosure, computing device 100 may determine the reliability of immunopeptidome information stored in a database based on an error rate. The confidence here may include, for example, the degree of contamination of the first set of peptide sequences corresponding to the first MHC type and the second set of peptide sequences corresponding to the second MHC type stored in a database. there is.

In one embodiment, the computing device 100 may generate alert information for the database based on the quantitative value of the contamination level. For example, this degree of contamination can be determined based on the error rate. In one example, the degree of contamination may be correlated with the error rate. If there are a plurality of error rates according to a plurality of operations for each of the plurality of HLA types, the final error rate can be generated by multiplying the values of these error rates. This final error rate can correspond to the degree of contamination of the immunopeptide information in the database. For example, warning information about the database may be variably generated based on the value of the contamination level or final error rate. In this example, warning information corresponding to “Caution” may be generated if the final error rate is in the range of 10 ^-3 , and warning information corresponding to “Caution” may be generated if the final error rate is in the range of 10 ^-5 . can be created.

Figure 4 illustrates an example method for determining the reliability of immune peptidome information based on peptide lists corresponding to each of the two MHC types according to an embodiment of the present disclosure.

In one embodiment, the steps shown in FIG. 4 may be performed by computing device 100. In a further embodiment, the steps in FIG. 4 may be implemented by multiple entities, such that some of the steps shown in FIG. 4 are performed at a user terminal and others are performed at a server.

In Figure 4, in addition to the embodiment described in Figure 3, a detailed algorithm for determining the degree of contamination and/or reliability in a database storing information on peptide sequences corresponding to three or more MHC types is disclosed.

In one embodiment, computing device 100 may obtain a third set of peptide sequences corresponding to a third MHC type (410). The third MHC type here may include an MHC type different from the previously mentioned first MHC type and second MHC type. The method of obtaining the third set of peptide sequences corresponding to the third MHC type may correspond to the method of obtaining the peptide sequences corresponding to the first MHC type and/or the second MHC type mentioned above. For example, peptide sequences corresponding to the third MHC type can be obtained by clustering techniques from public databases, experimental data, and/or lists of peptide sequences.

In one embodiment, computing device 100 may determine whether a common peptide sequence that occurs in the first set of peptide sequences and the second set of peptide sequences also occurs in the third set of peptide sequences. For example, if APKEKPEEA, which commonly appears in the first set of peptide sequences and the second set of peptide sequences, is present in the third set of peptide sequences, the similarity calculation for the third set of peptide sequences is It can be done.

In one embodiment, computing device 100 is configured to determine a second degree of similarity between the first set of peptide sequences and the third set of peptide sequences, and a third degree of similarity between the second set of peptide sequences and the third set of peptide sequences. Similarity can be calculated (420). For example, the calculation for similarity may include a similarity calculation for PWM. Computing device 100 may perform a similarity operation between a first matrix representing a first set of peptide sequences and a third matrix representing a third set of peptide sequences and a second matrix representing a second set of peptide sequences. A similarity operation may be performed between the third matrices representing the third set of peptide sequences.

As described above, in one embodiment, when the computing device 100 acquires a plurality of peptide sequences corresponding to a new MHC type, each set of peptide sequences corresponding to each of the previously acquired MHC types and a new By comparing sets of peptide sequences of MHC types, a similarity calculation can be performed on peptide sequences corresponding to a new MHC type. For the specific calculation method for similarity, refer to what was explained above.

In one embodiment, the computing device 100 may determine the reliability of the immune peptidome information stored in the database based on the first similarity, second similarity, and third similarity (430).

For example, the computing device 100 may determine an error rate corresponding to a second similarity between the peptide sequences corresponding to the first MHC type and the peptide sequences corresponding to the third MHC type and the peptide sequence corresponding to the second MHC type. The average value between the error rates corresponding to the third degree of similarity between the peptide sequences corresponding to the third MHC type and the third MHC type can be obtained. As described above, when peptide sequences corresponding to new MHC types are entered into the database, the computing device 100 performs a similarity calculation with each of the peptide sequences corresponding to existing stored MHC types, and then As mentioned, the error rate corresponding to each similarity calculation can be generated by comparing the similarity information of the actually measured peptides with the similarity information of the peptides according to the similarity calculation. The computing device 100 may update the existing error rate using the average value of the error rates generated in this way.

In one embodiment, the computing device 100 may generate an updated error rate by performing a multiplication operation on the average of these error rates and the error rate corresponding to the first similarity obtained in advance. Computing device 100 may determine this updated error rate based on the reliability of the immune peptidome information stored in the database.

In one embodiment, computing device 100 may determine whether to generate warning information for the database based on an operation comparing the updated error rate to a predetermined contamination threshold. For example, warning information may have multiple levels, and in this case, there may be contamination thresholds corresponding to each of the multiple levels. If the updated error rate exceeds this contamination threshold, warning information appropriate to the level corresponding to the exceeded contamination threshold may be generated.

As described above, in one embodiment of the present disclosure, when a common peptide sequence exists among the peptide sequences corresponding to the two MHC types, the immune peptidome information for the two MHC types may be stored in the database. . For example, if the same peptide sequence is found in HLA B 3501 and HLA B 5101, the computing device 100 can acquire PWMs corresponding to HLA B 3501 and HLA B 5101.

In one embodiment, the computing device 100 may predict the degree of correspondence between peptides (i.e., degree of similarity between PWMs) by performing an operation according to Equation 1 described above. That is, the computing device 100 can generate a predicted value for the degree of identity of peptides for two MHC types. The computing device 100 can calculate the peptide identity of the results from the actual experiment. That is, the computing device 100 can calculate the degree of identity (i.e., measurement value) of the peptides of the two MHC types based on the results from the actual experiment. Calculations on these measured values can be performed according to Equation 2 or Equation 3 described above.

In one embodiment, the computing device 100 compares the measured value and the predicted value, and when it is determined that the measured value is greater than the predicted value, the computing device 100 may calculate an error rate representing the predicted value/measured value. Based on this error rate, the degree of contamination or reliability of immunofemtidom information for the two MHC types can be determined. That is, when the predicted value < the measured value, the computing device 100 can store the error rate, which is the predicted value/measured value, in the database along with the corresponding common peptide.

Whenever data for a new allele is discovered, the computing device 100 may calculate error rates corresponding to all combinations between the new allele and the pre-stored allele and calculate an average of the error rates thus calculated. Computing device 100 may calculate the updated final error rate by multiplying the average of these error rates by the previously stored error rate, and this error rate may correspond to the reliability or contamination level.

For example, if the same peptide (e.g., APKEPEEA) is obtained in HLA B 5701 after analysis of the peptide sequences corresponding to HLA B 3501 and HLA B 5101, computing device 100 determines HLA B 3501 and HLA B Obtaining a first error rate (e.g., 0.534) from predicted values and measurements obtained between 5701 and obtaining a second error rate (e.g., 0.898) from predicted values and measurements obtained between HLA B 5101 and HLA B 5701. can do. The computing device 100 calculates an average value (e.g., 0.716) of the first error rate and the second error rate and calculates the average value to the error rate (e.g., 0.832) calculated between the existing HLA B 3501 and HLA B 5101. By multiplying, an updated error rate (eg, 0.5957) can be obtained. Computing device 100 may store the updated error rate (0.5957) and the common peptide sequence (APKEPEEA) together in a database.

In one embodiment, computing device 100 may remove relevant data and/or generate a warning signal if the updated error rate exceeds or is greater than a predetermined corruption threshold.

Figure 5 exemplarily shows general operations for analyzing immune peptidome information according to an embodiment of the present disclosure.

Example operations shown in FIG. 5 may be performed by computing device 100.

A similarity calculation may be performed on the peptide sequences corresponding to HLA type A (510a) and the peptide sequences corresponding to HLA type B (510b). This similarity calculation may include an operation to calculate the distance between a matrix (e.g., PWM) representing peptide sequences corresponding to HLA type A (510a) and a matrix representing peptide sequences corresponding to HLA type B (510b). You can. In one embodiment, the similarity operation may be used interchangeably with Distance Equation 520b. For example, Distance Equation 520b may correspond to Equation 1. Through the Distance Equation (520b), a predicted value for the similarity or consistency of peptides between HLA type A (510b) and HLA type B (510b) can be obtained.

In one embodiment, computing device 100 determines that a common peptide with binding potential or binding affinity exists for HLA type A (510b) and HLA type B (510b), HLA type B (510b) from the database (505). A PWM corresponding to type A (510b) and a PWM corresponding to HLA type B (510b) can be obtained. The computing device 100 may calculate the distance 530 between PWMs based on the Distance Equation 520b corresponding to Equation 1. This Distance Equation 520b may be based on comparing the values of corresponding elements in two matrices. The results of similarity comparison according to the Distance Equation 520b can be illustrated in FIG. 6. Figure 6 exemplarily shows a result matrix indicating the distance or similarity between matrices according to the Distance Equation 520b.

As shown in FIG. 6, the result of similarity comparison according to the Distance Equation 520b can be exemplarily displayed as a single matrix. Information for identifying the HLA type (i.e., HLA type name) is described in each row and column of the result matrix shown in FIG. 6. The values of elements in the matrix quantitatively represent the distance or similarity between HLA types in the row and column corresponding to the element. The larger the value of an element, the higher the similarity or the closer the distance. In the example in Figure 6, elements with dark colors mean that the similarity between two HLA types is relatively high compared to elements with light colors. A relatively high degree of similarity indicates that the peptides corresponding to each of the two HLA types are likely to be similar to each other. That is, the result matrix illustrated in FIG. 6 may represent the results of comparing a plurality of HLA types in pairs and calculating the similarity or distance between them.

In one embodiment, the computing device 100 uses the Distance-Concordance Correlation Equation 540 to adjust the calculated distance 530 so that the calculated distance 530 corresponds to the concordance rate associated with the measured value. It can be converted. For example, the Distance-Concordance Correlation Equation (540) is y = k / x, where y means the Distance Equation (520b), x means the Concordance Equation (520a), and k is the length of the peptide. It means a value that changes based on For example, in the case of a 9-mer peptide length, k=0.006644. The computing device 100 uses the Distance Equation (520b) and the Distance-Concordance Correlation Equation (540) for the two PWMs to predict the degree of identity or similarity between the peptide sequences of the two HLA types (510a and 510b). You can obtain (550b, PC).

In the above-described manner, the computing device 100 can obtain a predicted value (550b, PC) for the degree of identity or similarity between the peptide sequences of the two HLA types (510a and 510b).

In one embodiment, the computing device 100 determines the measured value 550a based on actual measured data for the peptide sequences corresponding to HLA type A 510a and the peptide sequences corresponding to HLA type B 510b. can be obtained. This measurement value 550a may be obtained using, for example, Concordance Equation 520a. Here, Concordance Equation 520a may correspond to Equation 2 or Equation 3. Concordance Equation (520a) can be used to calculate the degree of agreement between peptides based on results from actual experiments. The degree of concordance between peptides based on experimental results for a plurality of matrices calculated using the Concordance Equation 520a is exemplarily shown in FIG. 7 .

Figure 7 exemplarily shows a result matrix indicating the distance or similarity between matrices according to the Concordance Equation.

Information for identifying the HLA type (i.e., HLA type name) is described in each row and column of the result matrix shown in FIG. 7. The values of elements in the matrix are values calculated based on the Concordance Equation (520a), and quantitatively represent the distance or similarity between HLA types in the row and column corresponding to the element. The larger the value of an element, the higher the similarity or the closer the distance. A relatively high degree of similarity indicates that the peptides corresponding to each of the two HLA types are likely to be similar to each other. That is, the result matrix illustrated in FIG. 7 can express the results of comparing a plurality of HLA types in pairs based on actual measurement values and calculating the similarity or distance between them.

In one embodiment, the computing device 100 uses the measured value 550a calculated based on the Concordance Equation 520a and the predicted value 550b calculated by the Distance Equation 520b and the Distance-Concordance Correlation Equation 540. You can compare (560). As a result of this comparison 560, if the measured value 550a is greater than the predicted value 550b, computing device 100 can calculate an error rate 580, which is the ratio between the predicted value 550b and the measured value 550a. there is. For example, the error rate 580 may mean the predicted value 550b divided by the measured value 550a. Based on this error rate 580, the stored information in the contamination database 590 may be updated, and whether to generate warning information on the contamination database 590 may be determined according to this update.

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

A component, module, or unit in the present disclosure includes routines, procedures, programs, components, data structures, etc. that perform a specific task or implement a specific abstract data type. Additionally, one of ordinary skill in the art will understand that the methods presented in this disclosure can be used in uni-processor or multiprocessor computing devices, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. ( It will be fully appreciated that each of these may be implemented with other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

Embodiments described in this disclosure can 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.

Computing devices typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile 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 refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, 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, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed 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 example environment 2000 is shown that implements various aspects of the invention, including 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 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 processing units 2004.

System bus 2008 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and 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, and EEPROM, and is a basic input/output system (BIOS) 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.

Computer 2002 may also read from or use an internal hard disk drive (HDD) 2014 (e.g., EIDE, SATA), magnetic floppy disk drive (FDD) 2016 (e.g., removable diskette 2018). (for writing to), SSDs, and optical disk drives (2020) (e.g., for reading CD-ROM disks (2022) or for reading from or writing to other high-capacity optical media, such as DVDs). Includes. The hard disk drive 2014, magnetic disk drive 2016, and optical disk drive 2020 are connected to a system bus 2008 by a hard disk drive interface 2024, magnetic disk drive interface 2026, and optical drive interface 2028, respectively. ) can be connected to. The interface 2024 for implementing an external drive 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. For the computer 2002, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable storage media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also understand removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable storage media 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 invention. .

A number of program modules may be stored in 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 invention may be implemented on various 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 pointing device such as a keyboard 2038 and a mouse 2040. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 2004 through an input device interface 2042, which is often connected to the system bus 2008, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 2044 or other type of display device is also connected to 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, etc.

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 a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and generally refers to computer 2002. For simplicity, only memory storage device 2050 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 2052 and/or a larger network, such as a wide area network (WAN) 2054. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 2002 is connected to local network 2052 through wired and/or wireless communications network interfaces or adapters 2056. Adapter 2056 may facilitate wired or wireless communication to LAN 2052, which also includes a wireless access point installed thereon for communicating with wireless adapter 2056. When used in a WAN networking environment, the computer 2002 may include a modem 2058, connected to a communication server on the WAN 2054, or other means of establishing communication over the WAN 2054, such as via the Internet. has Modem 2058, which may be internal or external and a wired or wireless device, is coupled to system bus 2008 via 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 that other means of establishing a communications link between computers may be used.

Computer 1602 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be 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 illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The method claims of this disclosure provide elements of the various steps in a sample order but are not meant to be limited to the specific order or hierarchy presented.

Claims (1)

A method performed by a computing device, comprising:
Analyzing data on peptide sequences for each Major Histocompatibility Complex (MHC) type stored in a database, determining whether a common peptide sequence that commonly appears in a plurality of MHC types exists;
When it is determined that a common peptide sequence that commonly appears in the plurality of MHC types exists, obtaining peptide sequences corresponding to each of the MHC types in which the common peptide exists from the database;
Through a similarity determination process, the similarity between peptide sequences corresponding to each of the MHC types in which the common peptide exists is determined, and through an actual measurement process, a measured value between the peptide sequences corresponding to each of the MHC types in which the common peptide exists determining;
Based on the similarity and the measured value, determining a contamination level indicating the possibility that information on peptide sequences for each MHC type stored in the database is contaminated;
Including,
method.