Method for classification, classification device, classification system, classification program, and recording medium

Abstract

To reduce the processing amount when the base arrangements of nucleic acid molecules are classified.SOLUTION: The method for classification includes: a measurement step of measuring the base arrangement of nucleic acid molecules in a measurement sample as measurement arrangements; and a classification step of classifying the measurement arrangement into each group on the basis of the similarity between a representative arrangement set as a base arrangement representing each of the groups in which the base arrangements of the nucleic acid molecules are divided according to a specific rule and the measurement arrangement measured in the measurement step.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] In recent years, comprehensive quantitative analysis of nucleic acids, as represented by next-generation sequencers (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 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 occur.

[0004] Conventionally, in order to suppress such an increase in the amount of calculations, the amount of calculations has been reduced by reducing the number of molecules analyzed through the selection of important molecules using statistical analysis or machine learning (see Non-Patent Documents 1 and 2). [Prior art documents] [Non-patent literature]

[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 Summary of the Invention [Problem to be solved by the invention]

[0006] In the analysis 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 prediction

[0007] Here, for example, there are approximately 2600 types of known microRNAs in human blood, and several μL of blood as a sample contains several million microRNAs. When performing the above-mentioned step 1 on several million microRNAs, for example, the several million microRNAs have been classified by checking them against each of the approximately 2600 types of known microRNAs, resulting in a large amount of processing (specifically, a large amount of calculation).

[0008] In a method for selecting important molecules using statistical analysis and machine learning (see Non-Patent Documents 1 and 2), molecular species are comprehensively detected, and then the number of molecular species analyzed is reduced, thereby reducing the amount of processing (specifically, the amount of calculations) in the subsequent machine learning and statistical analysis, and thus making it possible to reduce the amount of processing in the aforementioned step 2.

[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. [Means for solving the problem]

[0011] A classification method according to one aspect of the present disclosure includes 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. Effect of the Invention

[0012] According to the present disclosure, it is possible to reduce the amount of processing required when classifying the base sequences of nucleic acid molecules. [Brief description of the drawings]

[0013] [Figure 1] FIG. 2 is a flow diagram showing each step of the classification method according to the present embodiment. [Diagram 2] FIG. 2 is a schematic block diagram illustrating an example of a computer that functions as a classification device according to the present embodiment. [Diagram 3] FIG. 2 is a block diagram showing a functional configuration of the classification device according to the present embodiment. [Figure 4] 1 is a list showing 56 types of microRNAs to be classified in this embodiment. [Diagram 5] 5 is a list showing the groups into which the 56 types of microRNAs shown in FIG. 4 are divided and the representative sequences set for each group. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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 operation, action, and function may be given the same reference numerals throughout all the drawings, and duplicated descriptions may be omitted as appropriate. Each drawing is merely a schematic illustration to the extent that the technology of the present disclosure can be fully understood. Therefore, the technology of the present disclosure is not limited to only the illustrated examples. In addition, in this embodiment, descriptions of configurations that are not directly related to the present disclosure or well-known configurations may be omitted.

[0015] <Classification method 10> First, a classification method 10 according to the present embodiment will be described. Fig. 1 is a schematic diagram showing each step of the classification method 10 according to the present embodiment.

[0016] Classification method 10 is a method for measuring the base sequence of a nucleic acid molecule in a measurement sample and classifying the base sequence into each group. Measurement samples include body fluids (e.g., blood, serum, urine, tears, saliva, sweat, semen, lymphatic fluid, tissue fluid, body cavity fluid (e.g., pleural fluid, ascites, etc.), cerebrospinal fluid, amniotic fluid, vaginal fluid, nasal mucus), tissues, and cells.

[0017] Examples of the nucleic acid molecule include microRNA. The nucleic acid molecule may be small RNA other than microRNA, other RNA, DNA, etc. Furthermore, the nucleic acid molecule may not be a nucleic acid molecule composed only of ATGCU bases. Specific examples include nucleic acids that have been modified by DNA / RNA methylation or edited by A-to-I RNA editing. In this way, various nucleic acid molecules can be the subject of 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 judgment step 16, it can also be called a counting method or a judgment method. If inspection or analysis is performed based on the judgment in judgment step 16, the classification method can also be called an inspection method or an analysis method. Each step of classification method 10 will be described below.

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

[0021] In the preparation step 11, the base sequences of a plurality of nucleic acid molecules are grouped 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. In the following, 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 a similar sequence.

[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 method 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, microRNAs present in large amounts in blood are determined. The first method is a method of using this to 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 a representative sequence in advance. Specifically, in the first method, for example, based on measurement data obtained by measuring a plurality of 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 set in advance 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. Also, in the second method, the preparation step 11 is performed after the measurement step 12.

[0027] Furthermore, the representative sequence may be an artificially defined sequence rather than a sequence that exists outside of nature, 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, it is possible for the specific rule to 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 may also include the similarity to the reverse complementary strand of the representative sequence. In other words, it is possible for the specific rule to be that the difference between the representative sequence or its reverse complementary strand is within n bases (n = a natural number).

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

[0031] The above-mentioned differences include, for example, substitution, increase, decrease, addition, and deletion of bases. As the similarity, for example, a similarity score used in a homology search of base sequences can be used.

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

[0033] In addition, the specific rule is not limited to the similarity of one or more similar sequences to the representative sequence, and the similarity between the representative sequence and one or more similar sequences.

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

[0035] Also, for example, the precursor base sequence and the isoform base sequence and the base sequence that is 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, it is possible to execute the classification step 13 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 execute 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 executed once every several times or every time the classification method is executed for the second or subsequent times.

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

[0040] <Classification process 13> The classification step 13 is a step of classifying the measured sequences into groups based on the similarity between the representative sequences set as base sequences 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 collated 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 scores 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 groups in which the similarity between the representative sequence and the measured sequence is equal to or greater than a threshold value. In this case, the measured sequence may be classified into a plurality of groups. In this manner, 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 collated and classified again with another group before being tabulated.

[0044] <Reclassification process 14> The reclassification step 14 is a step of reclassifying the measured sequences classified into each of the multiple groups into each of the 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 into a 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 having the highest similarity score used in a homology search of base sequences, or one or more similar sequences.

[0046] It should be noted that the execution of the reclassification step 14 is not essential, and the counting 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 (the representative sequence and one or more similar sequences).

[0048] It should be noted that if the counting step 15 is carried out without carrying out the reclassification step 14 after the classification step 13, 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] <Judgment step 16> The determination step 16 is a step of determining the specific nature of the measurement sample based on the number of molecules counted in the counting step 15.

[0050] The specific judgment includes prediction of disease of the donor who provided the sample. The prediction of disease can be performed, for example, based on the distribution of the number of molecules (i.e., expression level of nucleic acid) of the measured sequence counted for each base sequence (representative sequence and one or more similar sequences) in the counting step 15.

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

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

[0053] In addition, the expression level of microRNA may be used to determine a disease or the like by machine learning. Specifically, the expression level of each target microRNA and information on the presence or absence of a disease are machine-learned in advance as teacher data to construct a learning model. Then, the measurement sequence and its expression level tabulated in the tabulation step 15 are used as input factors, and the presence or absence of a 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, and may also include basic biological analysis, regression prediction, abnormality detection, and the like.

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

[0056] <Measuring device 21> The measurement device 21 is an example of a measurement unit, and is a device that executes 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. For example, an NGS is used as the measurement device 21.

[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 a plurality of groups into which the base sequences of a plurality of 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] The classification device 30 has a function as a computer, and as shown in Fig. 2, has 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. Each component is 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, and executes various programs and controls each part. That is, the CPU 31 reads a program from the ROM 32 or the storage 34, and executes the program using the RAM 33 as a working area. The CPU 31 controls each of the above components and performs various arithmetic processing according to the program stored in the ROM 32 or the storage 34. The CPU 31 is an example of a processor.

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

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

[0062] Furthermore, the storage 34 stores group information for each group obtained by the above-mentioned 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 and the storage 34 function 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 perform various input operations. The input unit 35 also receives information on the measurement sequence measured by the measuring device 21 as an input.

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

[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, a CPU 31 executes a classification program to function as a classification function unit 150, a re-classification 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 sequence into each of the groups based on the similarity between a representative sequence set as a base sequence representative of each of a plurality of groups into which base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, and the measured sequence 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 measurement 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 measurement sequences reclassified into either the representative sequence or one or more similar sequences by the reclassification unit 160 for each base sequence (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 performs a specific determination 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 one device. In this case, the one device functions as an example of the measurement unit and the classification unit.

[0072] The classification device 30 may be composed of a plurality of devices. For example, the classification device 30 may be composed of a plurality of devices (e.g., 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] <Example> Next, as an example, an example in which blood containing microRNA (an example of a nucleic acid molecule) is used as a measurement sample and each step of the above-mentioned classification method is performed will be described. In this example, for convenience, each step of the above-mentioned classification method is performed for 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 process 11> In this embodiment, 56 types of microRNAs were grouped under the specific rule that the difference between any of the base sequences serving as the representative sequence or its reverse complementary strand was within 3 bases. The representative sequence was set to the longest base sequence in the group. Therefore, a similar sequence is one that differs within 3 bases in the same sequence compared to the longest representative sequence. The difference includes, for example, substitution, increase, decrease, addition, or deletion of bases in the representative sequence.

[0076] In this example, 56 types of microRNAs (see FIG. 4) were grouped and representative sequences were set as shown in FIG. 5 based on the above-mentioned specific rules.

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

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

[0079] <Reclassification process 14> In this embodiment, after the classification step 13, all 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 embodiment, the number of molecules of the measured sequences that have been reclassified into either the representative sequence or one or more similar sequences in the reclassification step 14 is tallied for each base sequence (the representative sequence and one or more similar sequences).

[0081] <Judgment step 16> In this embodiment, in the tabulation step 15, disease discrimination and prediction are performed based on the distribution of the number of molecules (i.e., the expression level of nucleic acid) of the measured sequence 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 in group 1 + +Y 7 × Expression level of group 7 + X (Y is a coefficient that is predetermined for each group, and X is a predetermined constant)

[0084] In addition, the expression level of microRNA may be used to determine a disease or the like by machine learning. Specifically, the expression levels of each of the 56 types of target microRNA and information on the presence or absence of a disease are used as training data for machine learning in advance to construct a learning model. Then, the measurement sequence and its expression level collected in the collection step 15 are used as input factors, and the presence or absence of a disease can be determined as an output factor by the learning model.

[0085] <Effects of the embodiment> According to this embodiment, as described later, in the classification step 13, the amount of processing (specifically, the amount of calculation) required for classifying the base sequences of microRNAs 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 ways (1) From (1), the total computational effort = 560,000,000 × A (2)

[0088] On the other hand, in this embodiment, 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 ways (3) From (3), the total computational effort = 70,000,000 × A (4)

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

[0091] From (5), it can be seen that, according to this embodiment, the amount of calculation can be reduced by 0.125 times compared to the case where the measured sequence is classified into each of the 56 types of microRNA.

[0092] When the reclassification step 14 is executed, 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 complexity = 2,000,000 (molecules) × 34 (types) × A = 68,000,000 (11) Group 2: Total computational effort = 2,000,000 (molecules) × 6 (types) × A = 12,000,000 (12) Group 3: Total computational effort = 2,000,000 (molecules) × 5 (types) × A = 10,000,000 (13) Group 4: Total computational effort = 1,000,000 (molecules) × 4 (types) × A = 4,000,000 (14) Group 5: Total computational effort = 1,000,000 (molecules) × 3 (types) × A = 3,000,000 (15) Group 6: Total computational complexity = 1,000,000 (molecules) × 2 (types) × A = 2,000,000 (16) Group 7: Total computational complexity = 1,000,000 (molecules) × 2 (types) × A = 2,000,000 (17)

[0094] The total amount of calculations based on (11) to (17) is as follows: Total computational complexity = 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, according to this embodiment, the amount of calculation can be reduced by 0.323 times compared to the case where this embodiment is not used.

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

[0099] <Additional Notes> (Aspect 1) a measuring step of measuring a base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence; a classification step of classifying the 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 sequence measured in the measurement step and a representative sequence set as a representative base sequence for each of the plurality of groups; A classification method having the following structure. (Aspect 2) a counting step of counting the number of molecules of the measurement sequence classified into each of the plurality of groups in the classification step; 2. The method of claim 1, further comprising: (Aspect 3) The method further includes 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 after the classification step, The counting step counts the number of molecules of the measurement sequence reclassified into the base sequence for each base sequence. 3. The classification method according to embodiment 2. (Aspect 4) and a determination step of determining a specific property of the measurement sample based on the number of molecules counted in the counting step. The classification method according to embodiment 2 or embodiment 3. (Aspect 5) 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. The classification method according to any one of aspects 1 to 4. (Aspect 6) The representative sequence is set to the longest base sequence among the base sequences constituting the group. The classification method according to any one of aspects 1 to 5. (Aspect 7) The representative sequence is set to be the base sequence having the highest expression level among the base sequences constituting the group. The classification method according to any one of embodiments 1 to 6. (Aspect 8) 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. The classification method according to any one of embodiments 1 to 7. (Aspect 9) The measuring step measures the base sequence of the nucleic acid molecule in the measurement sample as a measurement sequence using a next-generation sequencer. The classification method according to any one of embodiments 1 to 8. (Aspect 10) A processor is provided. The processor classifies the 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. Classification device. (Aspect 11) a measurement unit that measures the base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence; a classification unit that classifies the measured sequence into each of a plurality of groups into which base sequences of a plurality of nucleic acid molecules are grouped according to a specific rule, based on the similarity between the measured sequence measured by the measurement unit and a representative sequence set as a representative base sequence for each of the plurality of groups; A classification system having the following structure: (Aspect 12) On the computer, 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. A classification program to execute the above. (Aspect 13) On the computer, 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. A non-transitory recording medium on which a classification program for executing the above is recorded. [Explanation of symbols]

[0100] 10 Classification method 11 Preparation process 12 Measurement process 13 Classification process 14 Reclassification process 15 Counting process 16 Judgment process 20 Classification Systems 21 Measuring device (example of measuring section) 30 Classification device (an example of a classification unit) 31 CPU (an example of a processor) 32 ROM 33 RAM 34 Storage (an example of a recording medium) 35 Input section 36 Display section 37 Communication Interface 39 Bus 150 Classification function section 160 Reclassification Department 170 Counting Department 180 Judgment section

Claims

1. a measuring step of measuring a base sequence of a nucleic acid molecule in a measurement sample as a measurement sequence; a classification step of classifying the 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 sequence measured in the measurement step and a representative sequence set as a representative base sequence for each of the plurality of groups; A classification method having the following structure.

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

3. The method further includes 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 after the classification step, The counting step counts the number of molecules of the measurement sequence reclassified into the base sequence for each base sequence. The classification method according to claim 2.

4. and a determination step of determining a specific property of the measurement sample based on the number of molecules counted in the counting step. The classification method according to claim 2.

5. 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. The classification method according to claim 1 .

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

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

8. 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. The classification method according to claim 1 .

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

10. A processor is provided. The processor classifies the 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. Classification device.

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

12. On the computer, 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. A classification program to execute the above.

13. On the computer, 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. A non-transitory recording medium on which a classification program for executing the above is recorded.

Images