Classification method, classification device, classification system, classification program, and recording medium

Abstract

This classification method includes: a measurement step in which the base sequences of nucleic acid molecules in a measurement sample are measured as measurement sequences; and a classification step in which the similarity between the measurement sequences measured in the measurement step and representative sequences set as base sequences representing each of a plurality of groups in which the base sequences of a plurality of nucleic acid molecules are grouped by specific rules is used as a basis to classify the measurement sequences into the individual groups.

Description

Classification method, classification device, classification system, classification program, and recording medium

[0001] The present disclosure relates to a classification method, a classification device, a classification system, a classification program, and a recording medium.

[0002] In recent years, comprehensive quantitative analysis of nucleic acids, typified by next-generation sequencing (hereafter sometimes abbreviated as NGS), has been applied to the identification of diseases including cancer.

[0003] With the development of comprehensive analytical techniques, the number of molecular species that can be analyzed has increased dramatically. However, as the number of molecular species to be analyzed increases, the amount of calculation required for analysis has also increased explosively. As a result, serious problems such as increased equipment costs and reduced throughput due to the need for high-performance computing equipment and long analysis times often arise.

[0004] Conventionally, in order to suppress such an increase in the amount of calculation, the number of molecules to be analyzed has been reduced by selecting important molecules using statistical analysis or machine learning (see Non-Patent Documents 1 and 2).

[0005] Non-patent document 1: Jin_et_al, 2017, Clinical Cancer Research, Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non-Small Cell Lung Cancer Using Next-Generation Sequencing Non-patent document 2: Asakura_et_al, 2020, Communications Biology, A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy

[0006] In the analytical process using comprehensive molecular species analysis, the following two steps are considered to be processes requiring a large amount of processing (specifically, a large amount of calculation): Step 1: Classification of target nucleic acid molecules; Step 2: Disease discrimination and prediction.

[0007] For example, there are approximately 2,600 types of known microRNAs in human blood, and several million microRNAs are present in a few μL of blood sample. When performing the above-described step 1 on several million microRNAs, for example, the processing volume (specifically, the amount of calculation) has been enormous because the millions of microRNAs have been classified by comparing them with each of the approximately 2,600 known types of microRNAs.

[0008] In a method for selecting important molecules using statistical analysis or machine learning (see Non-Patent Documents 1 and 2), molecular species are comprehensively detected, and then the number of molecular species analyzed is reduced. This reduces the amount of processing (specifically, the amount of calculation) required for subsequent machine learning and statistical analysis, thereby making it possible to reduce the amount of processing required in Step 2 described above.

[0009] However, the methods of Non-Patent Documents 1 and 2 do not achieve a reduction in the amount of processing in step 1 described above, and problems remain, such as increased equipment costs due to the need for high-performance computing devices and long-term analysis, and reduced throughput.

[0010] An object of the present disclosure is to provide a classification method, a classification device, a classification system, a classification program, and a recording medium that can reduce the amount of processing required when classifying the base sequences of nucleic acid molecules.

[0011] A classification method according to one aspect of the present disclosure includes a measurement step of measuring the base sequences of nucleic acid molecules in a measurement sample as measurement sequences, and a classification step of classifying the measurement sequences into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measurement sequence measured in the measurement step and a representative sequence set as a base sequence representative of each of the plurality of groups.

[0012] According to the present disclosure, the amount of processing required when classifying the base sequences of nucleic acid molecules can be reduced.

[0013] Fig. 1 is a flow chart showing an example of each step of the classification method according to the present embodiment. Fig. 2 is a block diagram showing an example of a computer that functions as the classification device of the present embodiment. Fig. 3 is a block diagram showing an example of the functional configuration of the classification device of the present embodiment. Fig. 4 is a list showing 56 types of microRNAs to be classified in this example. Fig. 5 is a list showing each group into which the 56 types of microRNAs shown in Fig. 4 are grouped, and the representative sequences set for each group.

[0014] An example of an embodiment of the technology of the present disclosure will be described below with reference to the drawings. Note that components and processes that perform the same operations, actions, and functions are given the same reference numerals throughout the drawings, and redundant explanations may be omitted as appropriate. Each drawing is merely a schematic illustration to allow a sufficient understanding of the technology of the present disclosure. Therefore, the technology of the present disclosure is not limited to the illustrated examples. Furthermore, in this embodiment, explanations of configurations that are not directly related to the present disclosure or well-known configurations may be omitted.

[0015] <Classification Method 10> First, a description will be given of the classification method 10 according to this embodiment. Fig. 1 is a schematic diagram showing an example of each step of the classification method 10 according to this embodiment.

[0016] Classification method 10 is a method of measuring the base sequences of nucleic acid molecules in a measurement sample and classifying the base sequences into each group. Measurement samples include body fluids (e.g., blood, serum, urine, tears, saliva, sweat, semen, lymph, interstitial fluid, body cavity fluid (e.g., pleural effusion, ascites, etc.), cerebrospinal fluid, amniotic fluid, vaginal fluid, nasal mucus, etc.), tissues, and cells. Measurement samples may be collected from humans or non-human animals. Non-human animals include non-human mammals (monkeys, dogs, cats, mice, rats, rabbits, cows, horses, pigs, sheep, etc.), birds (chickens, quail, etc.), etc.

[0017] Examples of nucleic acid molecules include microRNAs. Nucleic acid molecules may also include small RNAs other than microRNAs, other RNAs, DNA, and the like. Furthermore, nucleic acid molecules do not necessarily have to be composed solely of ATGCU bases. Specific examples include nucleic acids that have undergone modifications such as DNA / RNA methylation and editing such as A-to-I RNA editing. Thus, various nucleic acid molecules can be subject to this classification method.

[0018] 1, the classification method 10 specifically includes a preparation step 11, a measurement step 12, a classification step 13, a reclassification step 14, a counting step 15, and a determination step 16. In this embodiment, the preparation step 11, the measurement step 12, the classification step 13, the reclassification step 14, the counting step 15, and the determination step 16 are performed in this order, for example.

[0019] Since classification method 10 is a method including counting step 15 and determination step 16, it can also be called a counting method or a determination method. Furthermore, if testing or analysis is performed based on the determination in determination step 16, the classification method can also be called an testing method or an analysis method. Each step of classification method 10 will be described below.

[0020] <Preparation Step 11> The preparation step 11 is a step that is performed in preparation for performing the classification step 13. In this embodiment, the preparation step 11 is performed, for example, before performing the measurement step 12 and the classification step 13. Note that the preparation step 11 only needs to be performed before performing at least the classification step 13, and may also be performed after the measurement step 12.

[0021] In the preparation step 11, the base sequences of a plurality of nucleic acid molecules are divided into a plurality of groups according to a specific rule, and a representative sequence is set as a base sequence that represents each of the plurality of groups. Note that, hereinafter, among the base sequences that constitute each of the plurality of groups, base sequences other than the representative sequence may be referred to as similar sequences.

[0022] Each of the multiple groups divided in the preparation step 11 may be composed of a representative sequence and one or more similar sequences. That is, each of the multiple groups includes a representative sequence and one or more similar sequences. Note that a group may be composed of only a representative sequence. That is, a group may not include any similar sequences.

[0023] For example, there are approximately 2600 types of known microRNAs in human blood. When the base sequences of microRNAs in human blood are to be classified, the base sequences of these approximately 2600 types of microRNAs are divided into groups.

[0024] The representative sequence may be, for example, the longest base sequence among the base sequences constituting the group. Note that the representative sequence is not limited to the longest base sequence among the base sequences constituting the group, and may be, for example, the base sequence with the highest expression level among the base sequences constituting the group. The base sequence with the highest expression level can be set, for example, by the following first or second method.

[0025] Here, the base sequence of the nucleic acid molecule present in a large amount in the measurement sample is determined for each measurement sample. That is, for example, in the case of human blood, the microRNA present in a large amount in the blood is determined. The first method utilizes this to pre-set the base sequence of the nucleic acid molecule present in the largest amount in the measurement sample among the base sequences constituting a group as the base sequence with the highest expression level as the representative sequence. Specifically, in the first method, for example, based on measurement data obtained by measuring multiple measurement samples as setting samples or statistical data, the base sequence of the nucleic acid molecule present in the largest amount in the measurement sample among the base sequences constituting a group can be pre-set as the representative sequence as the base sequence with the highest expression level.

[0026] The second method is a method in which, based on the measurement results measured in the measurement step 12, the base sequence with the highest expression level among the base sequences constituting a group is set as the representative sequence. In the second method, for example, in the measurement step 12, the base sequence with the highest measured amount among the base sequences constituting a group can be set as the representative sequence as the base sequence with the highest expression level. Therefore, in the second method, the representative sequence may be changed depending on the measurement results of the measurement step 12. Furthermore, in the second method, the preparation step 11 is performed after the measurement step 12.

[0027] Furthermore, the representative sequence may not be a sequence that exists in nature, but may be an artificially defined sequence, such as a sequence partially amplified by polymerase chain reaction (PCR).

[0028] The specific rule is based on the similarity of the base sequences grouped into multiple groups (specifically, one or more similar sequences) to the representative sequence, or the similarity between the base sequences grouped into multiple groups (specifically, the representative sequence and one or more similar sequences).

[0029] For example, when the specific rule is the similarity of one or more similar sequences to a representative sequence, the specific rule can be that the difference between the similar sequences and the representative sequence is within n bases (n = a natural number). In this case, the specific rule can also include the similarity to the reverse complementary strand of the representative sequence. In other words, the specific rule can be that the difference is within n bases (n = a natural number) from the representative sequence or its reverse complementary strand.

[0030] Furthermore, when the specific rule is the mutual similarity of base sequences grouped into multiple groups (specifically, a representative sequence and one or more similar sequences), the specific rule can be that the difference between the base sequences is within n bases (n = a natural number). In this case, the specific rule can also include the similarity to the reverse complementary strand of the base sequence. In other words, the specific rule can be that the difference is within n bases (n = a natural number) from the base sequence or its reverse complementary strand.

[0031] The differences include, for example, substitution, addition, deletion, etc. of bases. The similarity can be determined, for example, by using a similarity score used in a homology search of base sequences.

[0032] When determining the similarity, attention may be paid to the 5' and 3' sequences of the base sequences (i.e., terminal sequences). That is, for example, the base sequences may be grouped based on the similarity (specifically, the difference) between the 5' and 3' sequences of the base sequences (i.e., terminal sequences).

[0033] Note that the specific rules are not limited to those based on the similarity of one or more similar sequences to a representative sequence, and those based on the mutual similarity of base sequences grouped into multiple groups (specifically, a representative sequence and one or more similar sequences).

[0034] For example, base sequences may be grouped based on the specific rule that they have a specific motif (e.g., a specific base sequence, a consensus sequence). Specifically, for example, it is possible to group sequences that have a specific base sequence (see the sequence below) as one group. AT**A*C*A**************** (* can be any of ATGCU.)

[0035] Furthermore, for example, the precursor base sequence and the isoform base sequence and the base sequence that forms the basis thereof may be grouped together.

[0036] Furthermore, grouping may be performed by combining a plurality of the specific grouping rules described above.

[0037] In this embodiment, the preparation step 11 is performed, for example, when the classification method is executed for the first time, and when the classification method is executed for the second or subsequent times, the classification step 13 can be performed using the groups that were created when the classification method was executed for the first time and the representative sequences that were set.

[0038] Therefore, it is not necessary to perform the preparation step 11 every time the classification method is executed. However, in cases where the grouping of groups and the settings of the representative sequences are changed, the preparation step 11 may be performed once every several times or every time the classification method is executed from the second time onwards.

[0039] <Measurement step 12> Measurement step 12 is a step of measuring the base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence. Specifically, in measurement step 12, the base sequence of a nucleic acid molecule in a measurement sample is measured as a measurement sequence using a next-generation sequencer (NGS) as a measurement device.

[0040] <Classification step 13> The classification step 13 is a step of classifying the measured sequences into groups based on the similarity between a representative sequence set as a base sequence representative of each of the multiple groups and the measured sequences measured in the measurement step 12. That is, in the classification step 13, the measured sequences are compared with the representative sequences based on the similarity, and the measured sequences are classified into groups.

[0041] In the classification step 13, for example, the measured sequences are classified into groups having the highest similarity between the representative sequence and the measured sequences. Specifically, in the classification step 13, for example, the measured sequences are classified into groups having the highest similarity score used in a homology search of base sequences. In this case, the measured sequences are classified into one group. In the classification step 13, all the measured sequences measured in the measurement step 12 are classified into groups.

[0042] In the classification step 13, the representative sequence may be classified into a group in which the similarity between the representative sequence and the measured sequence is equal to or greater than a threshold. In this case, the measured sequence may be classified into a plurality of groups. In this way, in the classification step 13, the measured sequence may be classified into a plurality of groups. In other words, there is no need for a one-to-one correspondence between the measured sequence and the group.

[0043] The measured sequence does not have to be the same as any of the grouped base sequences. Furthermore, the measured sequences classified into groups may be compared with and classified into other groups again before being tabulated.

[0044] <Reclassification step 14> The reclassification step 14 is a step of reclassifying the measured sequences classified into each of the multiple groups into the respective base sequences constituting the group after the classification step 13. That is, the measured sequences classified into a group are further classified into either a representative sequence belonging to the group or one or more similar sequences.

[0045] In the reclassification step 14, the measured sequence is classified into, for example, either the representative sequence or one or more similar sequences, and the sequence having the highest similarity to the measured sequence. Specifically, in the reclassification step 14, for example, the measured sequence is reclassified into either the representative sequence or one or more similar sequences that shows the highest similarity score used in a homology search of base sequences.

[0046] It should be noted that the execution of the reclassification step 14 is not essential, and the tallying step 15 may be executed after the classification step 13 without executing the reclassification step 14 .

[0047] <Counting Step 15> The counting step 15 is a step of counting the number of molecules of the measured sequences classified into each of the multiple groups in the classification step 13. Specifically, in the counting step 15, the number of molecules of the measured sequences reclassified into either the representative sequence or one or more similar sequences in the reclassification step 14 is counted for each base sequence (i.e., the representative sequence and one or more similar sequences).

[0048] If the counting step 15 is performed after the classification step 13 without performing the reclassification step 14, the number of molecules of the measurement sequence classified into each of the multiple groups is counted for each group in the counting step 15.

[0049] <Determination Step 16> The determination step 16 is a step of determining the specific characteristics of the measurement sample based on the number of molecules counted in the counting step 15.

[0050] The specific determination may include disease discrimination prediction of the donor who provided the sample. Disease discrimination prediction can be performed, for example, based on the distribution of the number of molecules (i.e., expression levels of nucleic acids) of the measured sequences compiled for each base sequence (i.e., representative sequence and one or more similar sequences) in the compilation step 15.

[0051] Specifically, for example, a disease or the like is determined using the expression level of microRNA using an arbitrary determination formula in which the number of molecules in each group is used as a variable. For example, the following determination formula can be used.

[0052] Judgment formula = Y １ × Expression level of Group 1 + ... + Y ｎ × expression level of group n + X (Y is a coefficient predetermined for each group, and X is a predetermined constant)

[0053] Note that the diagnosis of disease, etc., may be performed using the expression levels of microRNAs by machine learning. Specifically, a learning model is constructed in advance by machine learning using the expression levels of each target microRNA and information on the presence or absence of disease as training data. Then, the measurement sequences and their expression levels compiled in the compilation step 15 are used as input factors, and the presence or absence of disease can be determined as an output factor by the learning model.

[0054] Furthermore, the determination step 16 is not limited to disease discrimination and prediction, but may also include basic biological analysis, regression prediction, abnormality detection, and the like.

[0055] <Classification System 20> Next, a description will be given of the classification system 20, which is a system for executing the above-described classification method. The classification system 20 includes a measurement device 21 and a classification device 30, as shown in FIG.

[0056] <Measurement device 21> The measurement device 21 is an example of a measurement unit, and is a device that performs the above-mentioned measurement step 12. That is, the measurement device 21 measures the base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence. As the measurement device 21, for example, an NGS is used.

[0057] <Classification device 30> The classification device 30 is an example of a classification unit, and is a device that executes the above-mentioned classification step 13. That is, the classification device 30 classifies the measured sequences into each of the groups based on the similarity between a representative sequence set as a base sequence representative of each of multiple groups into which the base sequences of multiple nucleic acid molecules are grouped according to a specific rule, and the measured sequence measured by the measurement device 21. Furthermore, the classification device 30 executes the above-mentioned reclassification step 14, counting step 15, and determination step 16.

[0058] 2, the classification device 30 has a function as a computer, and includes a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage 34, an input unit 35, a display unit 36, and a communication interface (I / F) 37. The components are connected to each other via a bus 39 so as to be able to communicate with each other.

[0059] The CPU 31 is a central processing unit that executes various programs and controls each part. That is, the CPU 31 reads programs from the ROM 32 or the storage 34 and executes the programs using the RAM 33 as a work area. The CPU 31 controls each of the above components and performs various arithmetic processing in accordance with the programs stored in the ROM 32 or the storage 34. The CPU 31 is an example of a processor.

[0060] The ROM 32 stores various programs and various data. The RAM 33 temporarily stores programs or data as a working area. The storage 34 is configured by an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including the operating system and various data.

[0061] In this embodiment, for example, a classification program for executing a classification process that performs the above-described classification method is recorded in the storage 34. The classification program may be a single program, or may be a group of programs configured by multiple programs or modules.

[0062] The storage 34 also stores group information for each group obtained by the preparation step 11, and sequence information for representative sequences set as base sequences representative of each of the groups. The classification program, group information, and sequence information may be stored in the ROM 32. The ROM 32 or the storage 34 functions as an example of a non-transitory recording medium.

[0063] The input unit 35 includes a pointing device such as a mouse and a keyboard, and is used to input various data. The input unit 35 also receives information on the measurement sequence measured by the measuring device 21 as input.

[0064] The display unit 36 ​​is, for example, a liquid crystal display, and displays various information. The display unit 36 ​​may be a touch panel type and function as the input unit 35.

[0065] The communication interface 37 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI (Fiber Distributed Data Interface), and Wi-Fi (registered trademark).

[0066] As shown in FIG. 3, in the classification device 30, the CPU 31 executes a classification program to function as a classification function unit 150, a reclassification unit 160, a counting unit 170, and a determination unit 180.

[0067] The classification function unit 150 executes the above-mentioned classification step 13. That is, the classification function unit 150 classifies the measured sequences into each of the groups based on the similarity between the representative sequences set as base sequences representative of each of the multiple groups into which the base sequences of multiple nucleic acid molecules are grouped according to a specific rule and the measured sequences measured by the measurement device 21 (see the above-mentioned classification step 13).

[0068] The reclassification unit 160 executes the above-mentioned reclassification step 14. That is, the measurement sequences classified into each of the multiple groups by the classification function unit 150 are reclassified into each of the base sequences constituting the group (see the above-mentioned reclassification step 14).

[0069] The counting unit 170 executes the counting step 15. That is, the counting unit 170 counts the number of molecules of the measured sequences classified into each of the multiple groups by the classification function unit 150. Specifically, the counting unit 170 counts the number of molecules of the measured sequences reclassified into either the representative sequence or one or more similar sequences by the reclassification unit 160 for each base sequence (i.e., the representative sequence and one or more similar sequences) (see the above-mentioned counting step 15).

[0070] The determination unit 180 executes the above-mentioned determination step 16. That is, the determination unit 180 determines the specificity of the measurement sample based on the number of molecules counted by the counting unit 170 (see the above-mentioned determination step 16).

[0071] In this embodiment, the classification system 20 includes the measurement device 21 and the classification device 30, but the classification system 20 may be configured with a single device. In this case, the single device functions as an example of a measurement unit and a classification unit.

[0072] The classification device 30 may also be configured with a plurality of devices. For example, the classification device 30 may be configured with a plurality of devices (for example, four devices) that share the tasks of the classification step 13, the reclassification step 14, the counting step 15, and the determination step 16.

[0073] Next, as an example, an example will be described in which blood containing microRNA (an example of a nucleic acid molecule) is used as a measurement sample and each step of the classification method described above is performed. In this example, for convenience, each step of the classification method described above is performed using 56 types of microRNA (see FIG. 4).

[0074] It should be noted that the examples are merely examples of the technology of the present disclosure, and the technology of the present disclosure is not limited to the contents of the examples.

[0075] <Preparation Step 11> In this example, 56 types of microRNAs were grouped according to a specific rule that they differ by no more than three bases from any base sequence serving as a representative sequence or its reverse complementary strand. The representative sequence was set to the longest base sequence in the group. Therefore, similar sequences were sequences that differed by no more than three bases within the same sequence compared to the longest representative sequence. Such differences include, for example, base substitutions, additions, deletions, or additions of bases relative to the representative sequence.

[0076] In this example, 56 types of microRNAs (see FIG. 4) were grouped and representative sequences were set based on the above-mentioned specific rules, as shown in FIG. 5. The representative sequences set were as follows: CAAAAACCGCAAUUACUUUUGCA (SEQ ID NO: 1) (hsa-miR-548h-3p) AAAAGCUGGGUUGAGAGGGCGA (SEQ ID NO: 2) (hsa-miR-320a-3p) CAAAGUGCUUACAGUGCAGGUAG (SEQ ID NO: 3) (hsa-miR-17-5p) UGAGGUAGUAGUUUGUGCUGUU (SEQ ID NO: 4) (hsa-let-7i-5p) UAUUGCACUCGUCCCGGCCUCC (SEQ ID NO: 5) (hsa-miR-92b-3p) CUAUACAACUUACUACUUUCCC (SEQ ID NO: 6) (hsa-miR-98-3p) UAGCAGCACGUAAAUAUUGGCG (SEQ ID NO: 7) (hsa-miR-16-5p)

[0077] <Measurement Step 12> In this example, the base sequences of microRNAs in blood, which is a measurement sample, are measured as measurement sequences by an NGS, which is a measurement device, and the number of measurement sequences is quantified.

[0078] <Classification step 13> In this example, all of the measured sequences measured in the measurement step 12 are classified into the group showing the highest similarity score with each representative sequence of the multiple groups. Note that microRNAs other than the 56 types of microRNAs were excluded from the classification targets in advance.

[0079] <Reclassification step 14> In this example, after the classification step 13, all of the measured sequences classified into each of the multiple groups are reclassified into a representative sequence of the group and one or more similar sequences. At this time, the reclassification is performed into either the representative sequence or one or more similar sequences that shows the highest similarity score with the measured sequence.

[0080] <Counting Step 15> In this example, the number of molecules of the measured sequences reclassified into either the representative sequence or one or more similar sequences in the reclassification step 14 is counted for each base sequence (representative sequence and one or more similar sequences).

[0081] <Determination step 16> In this example, in the tabulation step 15, disease discrimination prediction is performed based on the distribution of the number of molecules (i.e., the expression level of nucleic acid) of the measured sequences tabulated for each base sequence (representative sequence and one or more similar sequences).

[0082] Specifically, for example, the expression level of microRNA is used to determine a disease or the like using the following determination formula, in which the number of molecules in each group is used as a variable.

[0083] Judgment formula = Y １ × Expression level of Group 1 + ... + Y ７ × Expression level of group 7 + X (Y is a coefficient predetermined for each group, and X is a predetermined constant)

[0084] Note that the expression levels of microRNAs may be used to determine diseases, etc., by machine learning. Specifically, a learning model is constructed in advance by machine learning using the expression levels of each of the 56 target microRNAs and information on the presence or absence of disease as training data. Then, the measurement sequences and their expression levels tallied in the aggregation step 15 are used as input factors, and the presence or absence of disease can be determined as output factors by the learning model.

[0085] <Effects of the Example> According to the example, as will be described later, the processing amount (specifically, the amount of calculation) required to classify the base sequences of microRNAs in the classification step 13 can be reduced.

[0086] For example, if the number of measured sequences is 10,000,000 and the amount of calculation required to match one measured sequence with one base sequence is A, when classifying the measured sequences into each of the 56 types of microRNA, the amount of calculation is as follows:

[0087] 10,000,000 (molecules) x 56 (types) = 560,000,000 combinations... (1) From (1), the total computational effort = 560,000,000 x A... (2)

[0088] On the other hand, in this example, 56 types of microRNAs are divided into 7 groups, and the measured sequences are classified based on the similarity to the representative sequence, resulting in the following amount of calculation.

[0089] 10,000,000 (molecules) x 7 (types) = 70,000,000 combinations... (3) From (3), the total computational effort = 70,000,000 x A... (4)

[0090] From (2) and (4), 70,000,000 × A / 560,000,000 × A = 0.125... (5)

[0091] (5) shows that, according to this example, the amount of calculation can be reduced to 0.125 times compared to the case where the measured sequences are classified into each of the 56 types of microRNAs.

[0092] When the reclassification step 14 is performed, the following calculation amount is required. Here, it is assumed that 2,000,000 (molecules) are classified into each of groups 1, 2, and 3, and 1,000,000 (molecules) are classified into each of groups 4, 5, 6, and 7.

[0093] Group 1: Total computational effort = 2,000,000 (molecules) x 34 (types) x A = 68,000,000 (11) Group 2: Total computational effort = 2,000,000 (molecules) x 6 (types) x A = 12,000,000 (12) Group 3: Total computational effort = 2,000,000 (molecules) x 5 (types) x A = 10,000,000 (13) Group 4: Total computational effort = 1,000,000 (molecules) x 4 (types) x A = 4,000,000 (14) Group 5: Total computational effort = 1,000,000 (molecules) x 3 (types) x A = 3,000,000 (15) Group 6: Total computational effort = 1,000,000 (molecules) × 2 (types) × A = 2,000,000 (16) Group 7: Total computational effort = 1,000,000 (molecules) × 2 (types) × A = 2,000,000 (17)

[0094] The total amount of calculations (11) to (17) is as follows: Total amount of calculation = 111,000,000 × A... (18)

[0095] The sum of (4) and (18) (see (19) below)) is the amount of calculation in the classification step 13 and the reclassification step 14. 70,000,000 × A + 111,000,000 × A = 181,000,000 × A ... (19)

[0096] From (2) and (19), 181,000,000 × A / 560,000,000 × A = 0.323... (20)

[0097] From (20), it can be seen that the amount of calculation can be reduced by 0.323 times in this embodiment compared to when this embodiment is not used.

[0098] As described above, the technology disclosed herein utilizes the knowledge that similar base sequences exist among nucleic acids, and has the technical idea of ​​reducing the amount of processing (specifically, the amount of calculation) by aggregating and grouping similar base sequences.

[0099] <Additional Notes> (Aspect 1) A classification method comprising: a measurement step of measuring the base sequences of nucleic acid molecules in a measurement sample as measurement sequences; and a classification step of classifying the measurement sequences into each of a plurality of groups obtained by grouping the base sequences of a plurality of nucleic acid molecules according to a specific rule, based on the similarity between the measurement sequences measured in the measurement step and a representative sequence set as a base sequence representative of each of the plurality of groups. (Aspect 2) The classification method according to Aspect 1, comprising: a counting step of counting the number of molecules of the measurement sequences classified into each of the plurality of groups in the classification step. (Aspect 3) The classification method according to Aspect 2, further comprising, after the classification step, a reclassification step of reclassifying the measurement sequences classified into each of the plurality of groups into each of the base sequences constituting the group, wherein the counting step counts the number of molecules of the measurement sequences reclassified into each of the base sequences for each of the base sequences. (Aspect 4) The classification method according to Aspect 2 or Aspect 3, further comprising a determination step of determining a specific identity of the measurement sample based on the number of molecules counted in the counting step. (Aspect 5) The classification method according to any one of Aspects 1 to 4, wherein the specific rule is based on the similarity of the base sequences to be divided into the plurality of groups to the representative sequence, or the mutual similarity of the base sequences to be divided into the plurality of groups. (Aspect 6) The classification method according to any one of Aspects 1 to 5, wherein the representative sequence is set to be the longest base sequence among the base sequences constituting the group. (Aspect 7) The classification method according to any one of Aspects 1 to 6, wherein the representative sequence is set to be the base sequence with the highest expression level among the base sequences constituting the group. (Aspect 8) The classification method according to any one of Aspects 1 to 7, wherein the classifying step classifies the measured sequence into a group in which the similarity between the representative sequence and the measured sequence is equal to or greater than a threshold, or into a group in which the similarity between the representative sequence and the measured sequence is highest. (Aspect 9) The classification method according to any one of Aspects 1 to 8, wherein the measuring step measures the base sequence of a nucleic acid molecule in the measurement sample as the measured sequence using a next-generation sequencer.(Aspect 10) A classification device comprising a processor, wherein the processor classifies a measured measurement sequence into each of a plurality of groups obtained by grouping the base sequences of a plurality of nucleic acid molecules according to a specific rule, based on the similarity between the measured measurement sequence and a representative sequence set as a representative base sequence for each of the plurality of groups. (Aspect 11) A classification system comprising: a measurement unit that measures the base sequences of nucleic acid molecules in a measurement sample as the measured sequence, and a classification unit that classifies the measured sequence into each of the groups based on the similarity between the representative sequence set as a representative base sequence for each of a plurality of groups obtained by grouping the base sequences of a plurality of nucleic acid molecules according to a specific rule, and the measured measurement sequence measured by the measurement unit. (Aspect 12) A classification program that causes a computer to execute a classification process that classifies the measured measurement sequence into each of the groups based on the similarity between the measured measurement sequence and a representative sequence set as a representative base sequence for each of a plurality of groups obtained by grouping the base sequences of a plurality of nucleic acid molecules according to a specific rule. (Aspect 13) A non-transitory recording medium having recorded thereon a classification program for causing a computer to execute a classification process for classifying a measured sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measured measured sequence and a representative sequence set as a base sequence representative of each of the plurality of groups.

[0100] The disclosure of Japanese Patent Application No. 2023-108941, filed on June 30, 2023, is incorporated herein by reference in its entirety. All documents, patent applications, and technical standards mentioned herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

Claims

1. A classification method comprising: a measurement step of measuring the base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence; and a classification step of classifying the measurement sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measurement sequence measured in the measurement step and a representative sequence set as a base sequence representative of each of the plurality of groups.

2. The classification method according to claim 1, further comprising a counting step of counting the number of molecules of said measurement sequence classified into each of said plurality of groups in said classification step.

3. The classification method according to claim 2, further comprising a reclassification step, after the classification step, of reclassifying the measurement sequences classified into each of the plurality of groups into each of the base sequences constituting the group, and the counting step counts the number of molecules of the measurement sequences reclassified into each of the base sequences for each of the base sequences.

4. The classification method according to claim 2, further comprising a determination step of making a specific determination of the measurement sample based on the number of molecules counted in the counting step.

5. The classification method according to claim 1, wherein the specific rule is based on the similarity of the base sequences grouped into the plurality of groups to the representative sequence, or on the mutual similarity of the base sequences grouped into the plurality of groups.

6. The classification method according to claim 1, wherein the representative sequence is set to the longest base sequence among the base sequences constituting the group.

7. The classification method according to claim 1, wherein the representative sequence is set to be the base sequence having the highest expression level among the base sequences constituting the group.

8. The classification method according to claim 1, wherein in the classification step, the measured sequence is classified into a group in which the similarity between the representative sequence and the measured sequence is equal to or greater than a threshold value, or into a group in which the similarity between the representative sequence and the measured sequence is the highest.

9. The classification method according to claim 1, wherein the measuring step uses a next-generation sequencer to measure the base sequence of the nucleic acid molecule in the measurement sample as the measurement sequence.

10. A classification device comprising a processor, the processor classifying a measured sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the degree of similarity between the measured measured sequence and a representative sequence set as a representative base sequence for each of the plurality of groups.

11. A classification system having a measurement unit that measures the base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence, and a classification unit that classifies the measurement sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the representative sequence and the measurement sequence measured by the measurement unit.

12. A classification program for causing a computer to execute a classification process for classifying a measured sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measured measured sequence and a representative sequence set as a representative base sequence for each of the plurality of groups.

13. A non-transitory recording medium having recorded thereon a classification program for causing a computer to execute a classification process for classifying a measured sequence into each of a plurality of groups into which the base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measured measured sequence and a representative sequence set as a base sequence representative of each of the plurality of groups.

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