Determination method, determination device, determination system, determination program, and recording medium

Abstract

This determination method is for determining the addition amount of a PEG reagent used in ligation with respect to a plurality of small RNA molecules in a biological sample collected from a subject by employing small RNA data that indicate the results of the measurement of expression levels of the plurality of small RNA molecules. The determination method includes an addition amount determination step for determining the above-mentioned addition amount from the above-mentioned small RNA data by using determination criteria based on the relationship between measurement data that indicate the results of the measurement of expression levels of a plurality of small RNA molecules in biological samples collected from a plurality of subjects and addition amount data that indicate addition amounts of a PEG reagent used in ligation with respect to the plurality of small RNA molecules in the biological samples.

Description

Determination method, determination device, determination system, determination program, and recording medium

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

[0002] Japanese Patent No. 7021097 discloses a disease prevalence assessment device including a sample data acquisition unit and a prevalence assessment unit. The sample data acquisition unit acquires sample data including expression levels of multiple types of miRNA in a biological sample. The prevalence assessment unit uses a trained model to output prevalence assessment results for the multiple diseases in the multiple body parts for the acquired sample data. The trained model is a trained model that is capable of determining the prevalence of each of the multiple diseases, including multiple malignant diseases or multiple benign diseases, including cases where the subject is affected by multiple diseases, obtained in advance by machine learning using training data including multiple sample data having items for identifying the presence or absence of the multiple diseases in the multiple body parts.

[0003] WO 2021 / 132547 discloses a testing method for testing for a disease using a disease marker, the testing method including a specimen data acquisition step and a discrimination step. The specimen data acquisition step acquires marker data indicating the results of measuring a disease marker in a body fluid sample collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample. The discrimination step determines the presence or absence of a disease in the subject by inputting the marker data and preparation data acquired in the specimen data acquisition step into a trained model that has been machine-learned to determine the correlation between a set of marker data indicating the results of measuring a disease marker in a body fluid sample and preparation data indicating the preparation conditions of the body fluid sample, and the presence or absence of a disease in the subject from whom the body fluid sample was collected.

[0004] Non-Patent Document 1 discloses that, using 20 types of fluorescently labeled synthetic miRNAs, the reaction efficiency of each miRNA in a 3' ligation enzymatic reaction, which adds a nucleic acid sequence to the 3' end of the miRNA, was investigated under multiple conditions, varying the type of ligation enzyme, the amount of PEG reagent added, and the reaction time. The results show that the efficiency of the miRNA ligation reaction may vary depending on the PEG concentration. Non-Patent Document 1: Song Y, Liu KJ, Wang TH. Elimination of ligation-dependent artifacts in T4 RNA ligase to achieve high efficiency and low bias microRNA capture. PLoS One. 2014 Apr 10

[0005] Here, when diagnosing a disease based on the expression levels of multiple small RNAs in a biological sample from a subject, if ligation is performed at the 5' or 3' ends of multiple small RNAs using an adapter and a PEG reagent, the expression levels of the small RNAs that are ultimately measured may change depending on the amount of PEG reagent added. Therefore, the amount of PEG reagent added affects the diagnosis of the disease.

[0006] In particular, PEG reagents have a relatively high viscosity, which tends to lead to significant differences in technique depending on the person who adds the PEG reagent, and therefore the amount of PEG reagent added tends to vary.

[0007] An object of the present disclosure is to provide a determination method, a determination device, a determination system, a determination program, and a recording medium that are capable of determining with high accuracy the amount of PEG reagent added when ligating an adapter and a PEG reagent to the 5' or 3' ends of multiple small RNAs.

[0008] A determination method according to one aspect of the present disclosure is a method for determining the amount of PEG reagent added when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, and includes an addition amount determination step for determining the amount added from the small RNA data using determination criteria based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological sample.

[0009] According to the present disclosure, when ligating an adaptor and a PEG reagent to the 5' or 3' ends of a plurality of small RNAs, the amount of PEG reagent to be added can be determined with high accuracy.

[0010] FIG. 1 is a flow diagram showing each step of a determination method according to the present embodiment. FIG. 2 is a schematic block diagram of an example of a computer that functions as a determination device according to the present embodiment. FIG. 3 is a block diagram showing the functional configuration of the determination device according to the present embodiment. FIG. 4 is a diagram showing criteria for binary determination in addition amount learned models 1 to 4 according to the present embodiment. FIG. 5 is a table showing determination results in addition amount learned models 1 to 4 according to the present embodiment. FIG. 6 is an image diagram showing a continuous value regression model according to the present embodiment. FIG. 7 is a graph showing determination results in the continuous value regression model according to the present embodiment.

[0011] 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.

[0012] <Determination Method 10> First, a description will be given of a determination method 10 according to this embodiment. Fig. 1 is a schematic diagram showing each step of the determination method 10 according to this embodiment.

[0013] Determination method 10 is a determination method that determines the amount of PEG reagent added (hereinafter sometimes referred to as the PEG addition amount) used when performing ligation on multiple small RNAs in a biological sample collected from a subject, using small RNA data that shows the results of measuring the expression levels of the multiple small RNAs.

[0014] Examples of biological samples collected from subjects include body fluids such as blood, serum, urine, tears, saliva, sweat, semen, lymph, tissue fluid, body cavity fluid (e.g., pleural effusion, ascites, etc.), cerebrospinal fluid, amniotic fluid, vaginal fluid, and nasal discharge. A biological sample may be any sample that can be collected from a living body and from which the expression levels of multiple small RNAs can be measured. A subject is a concept that refers to the subject from whom a biological sample is collected. The subject from whom a biological sample is collected may be a human or a non-human animal. Examples of non-human animals include non-human mammals (monkeys, dogs, cats, mice, rats, rabbits, cows, horses, pigs, sheep, etc.), birds (chickens, quails, etc.), and the like.

[0015] Examples of small RNAs include microRNAs. The small RNAs may also be small RNAs other than microRNAs (e.g., piRNAs and tsRNAs). Ligation and PEG reagents will be described later.

[0016] 1, the determination method 10 specifically includes a collection step 11, a ligation step 13, a measurement step 12, an addition amount determination step 14, and a disease determination step 16. In this embodiment, the collection step 11, the ligation step 13, the measurement step 12, the addition amount determination step 14, and the disease determination step 16 are performed in this order, for example.

[0017] Since the determination method 10 is a method including a collection step 11, a measurement step 12, and a disease determination step 16, it can also be called a collection method, a measurement method, and a disease determination method. Furthermore, if analysis, testing, etc. is performed based on the determination in the disease determination step 16, it can also be called an analysis method, a testing method, etc. Each step of the determination method 10 will be described below.

[0018] <Collecting step 11> The collecting step 11 is a step of collecting a biological sample from a subject. The collecting step 11 is performed before the measurement step 12. In the collecting step 11, when serum is collected as the biological sample, the collecting process is performed, for example, as follows.

[0019] First, blood is collected using a vacuum blood collection tube containing a serum separator (blood collection process). Next, immediately after the blood collection process, the blood is mixed by inversion. Next, the blood is left at room temperature for 30 minutes or more to allow coagulation. Next, the blood is centrifuged and the serum is separated (centrifugation process). The serum is then separated and stored at -80°C (storage process).

[0020] In the collection step 11, the steps from collection of a biological sample from a subject to completion of a centrifugation operation (specifically, the steps from the blood collection process to the preservation process) are performed within a predetermined time (e.g., 2 hours). Note that the centrifugation operation is, for example, the steps from the centrifugation process to the preservation process.

[0021] <Ligation Step 13> Ligation is an enzymatic reaction in which a base sequence called an adapter is attached to the 5' or 3' end of a nucleic acid such as DNA or RNA, including small RNA. Ligation Step 13 is a process in which a base sequence called an adapter is attached to the 5' or 3' end of a small RNA. In Ligation Step 13, a PEG reagent is used for ligation. Specifically, Ligation Step 13 is performed, for example, in the preparation of a library for NGS.

[0022] The PEG reagent is a reagent containing PEG (PolyEthylene Glycol). The PEG reagent may contain at least PEG, and other components may also be used. PEG is a polymer of the monomer Ethylene Glycol, and there are several types of PEGs, each with different degrees of polymerization and average molecular weights (e.g., PEG 200, PEG 300, PEG 400, PEG 600, PEG 1000, PEG 2000, PEG 4000, PEG 6000, PEG 8000, PEG 10000, PEG 20000, PEG 500000, PEG 2000000, PEG 4000000, etc.). However, since all PEGs share a common chemical structure (i.e., ethylene glycol, a monomer), their chemical properties are similar, and therefore any type of PEG may be used. PEG functions as a catalyst to promote the enzymatic reaction in ligation, thereby mitigating bias in reactivity among small RNAs.

[0023] As an example of a PEG reagent, a reagent containing the following components can be used: Nuclease-free water, T4 RNA Ligase Reaction Buffer (NEB / M0242), 100% DMSO (13445-74 / Nacalai Tesque), 50% PEG (NEB / B1004), and total RNA.

[0024] <Measurement Step 12> Measurement step 12 is a step of measuring the expression levels of multiple small RNAs in a biological sample from a subject. Specifically, in measurement step 12, a next-generation sequencer (NGS) is used as a measurement device to measure multiple small RNAs contained in the biological sample from the subject (e.g., serum) and identify the base sequence of each small RNA. Next, the number of identified small RNAs is counted for each base sequence to determine the number of small RNA reads in the NGS. This number of small RNA reads corresponds to the expression level (specifically, the absolute expression level) of the small RNA. In other words, this number of small RNA reads represents small RNA data indicating the results of measuring the expression levels of multiple small RNAs in the biological sample from the subject. However, although the expression levels of multiple small RNAs (e.g., microRNAs) in a biological sample (e.g., serum) are absolute values ​​derived from the living organism, it is difficult to quantify the expression levels in a biological sample as absolute values ​​because the expression levels must be quantified through a process using a measurement device or reagent. Therefore, the expression levels of small RNAs may be expressed as relative values ​​by processing the measurement results of NGS, and such relative values ​​also represent small RNA data.

[0025] Furthermore, to measure absolute expression levels, the number of reads of small RNAs output by NGS may be normalized to obtain relative expression levels (i.e., relative expression levels). As a normalization method, for example, RPM (Read Per Million) normalization or normalization using an internal standard small RNA may be used. In this way, small RNA data may indicate normalized relative expression levels. Small RNA data may also be absolute values ​​quantified as absolute values.

[0026] Furthermore, NGS allows for simultaneous measurement of the expression levels of multiple small RNAs in multiple biological samples (e.g., serum samples collected from multiple subjects). That is, in measurement step 12, the expression levels of multiple small RNAs are measured for multiple biological samples in the same process.

[0027] In addition to next-generation sequencers, other measuring devices such as DNA chips, quantitative PCR, and flow cytometers can also be used as long as they can measure the expression levels of multiple small RNAs. As described above, if the expression levels of multiple small RNAs can be measured and small RNAs that represent the measurement results can be obtained, various methods, including known methods, can be used to measure the expression levels of multiple small RNAs.

[0028] <Addition amount determination step 14> Addition amount determination step 14 is a step of determining the amount of PEG reagent to be added using small RNA data indicating the results of measuring the expression levels of the plurality of small RNAs. Note that the determination of the amount of PEG reagent to be added is a concept that includes the determination of whether the amount to be added is appropriate or not and the determination of the amount to be added itself.

[0029] In this embodiment, in the addition amount determination step 14, the small RNA data obtained in the measurement step 12 is input into the addition amount trained model to determine whether the amount of PEG reagent added is appropriate.

[0030] The criterion for determining whether the amount of PEG reagent added is appropriate is set, for example, based on whether a correct determination can be made in the disease assessment step 16. If a correct determination can be made in the disease assessment step 16 even if the amount of PEG reagent added is low, the criterion becomes one that allows a low amount to be added.

[0031] The allowable amount to be added varies depending on the disease to be determined in the disease determination step 16, and the determination criteria in the addition amount determination step 14 also vary correspondingly. Furthermore, the determination step is not limited to disease determination, and various determinations can be applied, and the allowable amount to be added varies depending on the disease to be determined, and the determination criteria in the addition amount determination step 14 also vary correspondingly.

[0032] <Method for generating additive amount trained model> Here, the additive amount trained model used in the additive amount determination step 14 is generated, for example, as follows: That is, the additive amount trained model is generated by machine learning the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and additive amount data indicating the amounts of PEG reagent added when ligating the multiple small RNAs in the biological samples collected from the multiple subjects.

[0033] Specifically, the measurement data is the result of measuring a plurality of small RNAs in biological samples collected from a plurality of subjects. The plurality of subjects may include subjects with a disease and subjects without a disease (i.e., healthy subjects), or may include only subjects with a disease and subjects without a disease (i.e., healthy subjects).

[0034] The measurement data is obtained by the same measurement method as the small RNA data described above. Specifically, a next-generation sequencer (NGS) is used as a measurement device to measure multiple small RNAs contained in a biological sample from a subject, and identify the base sequence of each small RNA. Next, the number of identified small RNAs is counted for each base sequence to determine the number of small RNA reads in the NGS. This number of small RNA reads corresponds to the expression level (specifically, absolute expression level) of the small RNA. In other words, this number of small RNA reads becomes the measurement data.

[0035] The addition amount data is specifically data indicating whether the amount of PEG added related to the measurement data is appropriate. As an example, the addition amount data may be data indicating that the amount of PEG added that allows correct determination in the disease determination step 16 is appropriate (good), and that the amount of PEG added that does not allow correct determination in the disease determination step 16 is inappropriate (bad).

[0036] Specifically, as the measurement data to be subjected to machine learning (hereinafter sometimes referred to as training data), a trained model of the amount of PEG added can be generated using measurement data obtained in biological samples collected from multiple subjects under conditions where the amount of PEG added is appropriate, and measurement data obtained in biological samples collected from multiple subjects under conditions where the amount of PEG added is inappropriate.

[0037] The disease of the subject that is machine-learned by the additive amount trained model used in the additive amount determination step 14 may include the disease determined in the prevalence determination step 16. That is, if the disease determined in the prevalence determination step 16 is a cancer, the measurement data of a sample from a subject suffering from the cancer is included. Note that the type of cancer determined in the prevalence determination step 16 and the type of cancer in the measurement data may be the same or different. Therefore, for example, if the disease determined in the prevalence determination step 16 is pancreatic cancer, the measurement data of a sample from a subject suffering from lung cancer may be used as training data.

[0038] Furthermore, in the addition amount determination step 14, measurement data from the top 100 small RNAs with the highest expression levels in biological samples from multiple subjects is used as training data.

[0039] Thus, in this embodiment, measurement data is used that excludes small RNAs with relatively low expression levels. Small RNAs with relatively low expression levels have the advantage of functioning easily as explanatory variables because even small fluctuations in expression level result in large fold changes. However, they are susceptible to fluctuations due to factors other than the element of interest (specifically, the amount of PEG added) and fluctuations due to measurement errors, and therefore have the disadvantage of being less robust. Therefore, using only small RNAs with relatively high expression levels as training data can enhance robustness.

[0040] Furthermore, measurement data excluding small RNAs whose expression level was zero in any of the subject's biological samples is used as training data in the addition amount determination step 14. Since fluctuations from zero result in an infinite fold change, making it difficult to quantify and particularly susceptible to fluctuations due to factors other than the amount of PEG added and fluctuations due to measurement errors, small RNAs whose expression level was zero in any of the samples used as training data are excluded from the training data.

[0041] As described above, in this embodiment, the learning model is made to qualitatively learn whether the amount of PEG added is appropriate, thereby generating an added amount trained model. The added amount trained model then determines whether the amount of PEG added is appropriate and outputs the appropriateness, thereby determining whether the amount of PEG added is appropriate in the added amount determination step 14. Therefore, the added amount determination step 14 displays a determination result that the amount of PEG added is appropriate or inappropriate.

[0042] In this embodiment, the training model for determining the amount of PEG added is trained to determine whether the amount is appropriate, thereby generating a training model for determining the amount of PEG added that performs a binary judgment. However, the present invention is not limited to this. For example, the training model for determining the amount of PEG added may be generated so that the amount of PEG added is judged as one of three values: appropriate (good), warning, and inappropriate (bad).

[0043] As described above, in this embodiment, the training model is generated by qualitatively training the learning model on the appropriateness of the PEG addition amount, but this is not limited thereto. For example, the training model may be generated by quantitatively training the PEG addition amount. In this case, the training model determines the PEG addition amount, and the determination result of the PEG addition amount is output, for example, as a judgment value. The judgment value here is not a binary value of 0 or 1, but a value having a predetermined numerical range. Then, in the addition amount determination step 14, the appropriateness of the PEG addition amount is determined based on the judgment result of the determination unit, which determines the appropriateness of the PEG addition amount based on a comparison between the output judgment value and a threshold value. That is, in the addition amount determination step 14, if the determination unit determines that the output judgment value is equal to or greater than the threshold value, the determination result indicates that the PEG addition amount is inappropriate. If the determination unit determines that the output judgment value is less than the threshold value, the determination result indicates that the PEG addition amount is appropriate.

[0044] <Modification of addition amount determination process 14> As described above, in addition amount determination process 14, the amount of PEG addition is determined using an addition amount trained model that has been machine-learned to determine the correlation between measurement data and addition amount data, but this is not limited to this.

[0045] For example, the amount of PEG to be added may be determined using a regression model that determines the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when performing ligation on the multiple small RNAs in the biological samples collected from the multiple subjects.

[0046] In this modification, the amount of PEG to be added is determined by inputting small RNA data into a regression model in the amount-to-addition determination step 14. Specifically, in the amount-to-addition determination step 14, for example, the small RNA data is input into a regression model, and a value for the amount of PEG to be added is output.

[0047] In this case, the amount of PEG to be added is determined using a regression model, and the determination result of the amount of PEG to be added is output, for example, as a determination value. The determination value here is not a binary value of 0 or 1, but a value having a predetermined numerical range. In the amount-of-addition determination step 14, the appropriateness of the amount of PEG to be added may be determined based on the determination result of a determination unit that determines whether the amount of PEG to be added is appropriate based on a comparison between the output determination value and a threshold value. That is, in the amount-of-addition determination step 14, if the determination unit determines that the output determination value is equal to or greater than the threshold value, the determination result indicates that the amount of PEG to be added is inappropriate, and if the determination unit determines that the output determination value is less than the threshold value, the determination result indicates that the amount of PEG to be added is appropriate.

[0048] Note that the addition amount determination step 14 is not limited to the use of the above-mentioned addition amount trained model and the above-mentioned regression model, and it is possible to use determination criteria based on various algorithms. The determination criteria used in the addition amount determination step 14 are based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological samples, and any criteria can be used as long as the addition amount can be determined from the small RNA data.

[0049] <Presence determination step 16> Presence determination step 16 is a step of determining the presence or absence of a disease. Specifically, in the prescribing step 16, the small RNA data obtained in the measurement step 12 is input into a disease-trained model that has been machine-learned to determine the correlation between measurement data indicating the results of measuring a plurality of small RNAs in a biological sample and prescribing data indicating the presence or absence of a disease in the subject from whom the biological sample was collected, thereby determining the presence or absence of a disease.

[0050] In the disease determination step 16, the presence or absence of a disease is determined if the determination result in the addition amount determination step 14 is that the amount of PEG added is appropriate. In other words, if the determination result in the addition amount determination step 14 is that the amount of PEG added is inappropriate, the disease determination step 16 is not executed. In this case, for example, the determination result that the amount of PEG added is inappropriate may be presented to the user (i.e., the person executing the determination method).

[0051] It is desirable that the disease determined by the disease discrimination trained model is the same disease as the disease used for training in the additive amount trained model. For example, if the disease used for training in the additive amount trained model is a cancer disease, the disease determined by the disease discrimination trained model is also a cancer disease. Specifically, it is desirable that the disease type of the measurement data used as training data in the disease discrimination trained model is the same as the disease type of the measurement data used as training data in the additive amount trained model.

[0052] As described above, if the addition amount determination step 14 determined that the amount of PEG added was inappropriate, the disease determination step 16 was not executed; however, this is not limited to this. For example, even if the addition amount determination step 14 determined that the amount of PEG added was inappropriate, the disease determination step 16 may be executed if the determination result is obtained as reference data, for example. In this case, for example, after the determination result that the amount of PEG added was inappropriate is obtained, the fact that the determination was made may be presented to the user. Furthermore, although the disease determination step 16 is executed after the addition amount determination step 14 in this embodiment, it may also be executed before the addition amount determination step 14. In this case, if the addition amount determination step 14 determined that the amount of PEG added was inappropriate, the determination result of the disease determination step 16 is treated as reference data, for example.

[0053] <Determination System 20> Next, a description will be given of the determination system 20 that executes the above-described determination method. The determination system 20 includes a measurement device 21 and a determination device 30, as shown in FIG.

[0054] <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 expression levels of multiple small RNAs contained in each of multiple samples. As the measurement device 21, for example, an NGS is used.

[0055] <Determination device 30> The determination device 30 is an example of a determination unit. The determination device 30 is a device that executes the aforementioned addition amount determination step 14. That is, the determination device 30 acquires small RNA data indicating the results of measuring multiple small RNAs in a biological sample from a subject, and inputs the acquired small RNA data into an addition amount trained model, thereby determining the amount of PEG added to the biological sample collected from the subject.

[0056] Furthermore, the determination device 30 executes the aforementioned disease determination step 16. That is, the determination device 30 determines the presence or absence of a disease by inputting the small RNA data obtained in the measurement step 12 into a disease-trained model that has been machine-learned to determine the correlation between measurement data indicating the results of measuring a plurality of small RNAs in a biological sample and disease prevalence data indicating the presence or absence of a disease in the subject from whom the biological sample was collected.

[0057] 2, the determination 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.

[0058] The CPU 31 (an example of a processor) is a central processing unit that 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 work area. The CPU 31 controls each of the above components and performs various arithmetic processing in accordance with the program stored in the ROM 32 or the storage 34. The CPU 31 is an example of a processor.

[0059] 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.

[0060] In this embodiment, for example, a determination program for executing a determination process that performs the above-described determination method is recorded in the storage 34. The determination program may be a single program, or a group of programs configured by multiple programs or modules. The determination program may be recorded in the ROM 32. The ROM 32 and the storage 34 function as an example of a non-transitory recording medium.

[0061] An example of a processor is not limited to the above-mentioned CPU, which is a general-purpose processor, but may be, for example, a dedicated processor configured with a circuit designed specifically for executing a specific process. Also, an example of a processor is not limited to a single processor, but may be a processor configured by multiple processors located in physically separate locations working together.

[0062] The input unit 35 includes a pointing device such as a mouse and a keyboard, and is used to input various information. The input unit 35 also receives information on the expression levels of multiple small RNAs measured by the measurement device 21 as input.

[0063] The display unit 36 ​​is, for example, a liquid crystal display, and displays various information. The determination device 30 can present the determination result of the amount of PEG added and the determination result of the presence or absence of a disease to the user through the display unit 36. The display unit 36 ​​may be a touch panel type and function as the input unit 35.

[0064] 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).

[0065] As shown in FIG. 3 , in the determination device 30 , the CPU 31 executes a determination program to function as a determination function unit 160 and an morbidity determination unit 170 .

[0066] The determination function unit 160 executes the aforementioned addition amount determination step 14. That is, in the addition amount determination step 14, the determination function unit 160 inputs the small RNA data obtained by the measurement device 21 into the addition amount trained model to determine whether the amount of PEG added to the biological sample collected from the subject is appropriate (see the aforementioned addition amount determination step 14).

[0067] The morbidity determination unit 170 executes the morbidity determination step 16. That is, the morbidity determination unit 170 determines the presence or absence of a disease by inputting the small RNA data obtained by the measurement device 21 into a disease-trained model that has been machine-learned to determine the correlation between measurement data indicating the results of measuring multiple small RNAs in a biological sample and morbidity data indicating the presence or absence of a disease in the subject from whom the biological sample was collected (see the morbidity determination step 16 described above).

[0068] In the determination device 30, the CPU 31 may perform a process of outputting the determination result of the amount of PEG reagent added and the determination result of the presence or absence of a disease. This process is performed, for example, by displaying on the display unit 36 ​​and transmitting to an external device (e.g., the cloud).

[0069] In this embodiment, the determination system 20 includes the measurement device 21 and the determination device 30, but the determination 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 determination unit.

[0070] Furthermore, the determination device 30 may be configured with a plurality of devices. For example, the determination device 30 may be configured with a plurality of devices (for example, two devices) that share the functions of the addition amount determination step 14 and the disease determination step 16.

[0071] <Effects of this embodiment> In this embodiment, as described above, in the addition amount determination step 14, the amount of PEG reagent to be added is determined by inputting the small RNA data obtained in the measurement step 12 into an addition amount trained model that has been machine-learned to determine the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data indicating the amount of PEG reagent added when ligating the multiple small RNAs in the biological samples.

[0072] In this way, the amount of addition is determined by inputting small RNA data into an addition amount trained model that has been machine-learned to determine the correlation between measurement data and addition amount data, so the amount of PEG reagent added can be determined with high accuracy compared to when the determination is made by visual inspection or measurement using a measurement kit, for example.

[0073] In this way, in this embodiment, the amount of PEG reagent added can be determined with high accuracy, so that highly accurate information on the amount of PEG reagent added can be obtained, and for example, in the disease determination process 16, there is no need to obtain information on the amount of PEG reagent added by input by the user.

[0074] <Examples> Next, examples will be described. Note that the examples are intended to illustrate 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] <Acquisition of training data and evaluation data> In this example, serum was obtained as a biological sample from 24 healthy individuals by performing the collection step 11 within 2 hours. The serum was subjected to the ligation step 13 using five PEG reagent formulations in which the amount of PEG was increased or decreased as follows based on the basic formulation below, thereby obtaining a total of 120 specimens (24 individuals x 5 formulations).

[0076] For the serum samples (i.e., 120 samples) that underwent ligation step 13, the expression levels of multiple small RNAs were measured using NGS, and small RNA data showing the measurement results were obtained. Of the obtained small RNA data, small RNAs with zero expression levels were excluded, and small RNA data for the top 100 small RNAs with the highest expression levels was used. Of the small RNA data for the 120 samples, the small RNA data for 100 samples (20 individuals x 5 formulations) was used as training data, and the small RNA data for 20 samples (4 individuals x 5 formulations) was used as evaluation data.

[0077] [Basic formulation] Nuclease-free water 1.0 μL T4 RNA Ligase Reaction Buffer (NEB / M0242) 1.0 μL 100% DMSO (13445-74 / Nacalai Tesque) 1.0 μL 50% PEG (NEB / B1004) 1.0 μL Total RNA 4.0 μL

[0078] [Increase or decrease in PEG amount] Formula 1: PEG amount reduced by 50% in the basic formula Formula 2: PEG amount reduced by 25% in the basic formula Formula 3: No increase or decrease in PEG amount in the basic formula Formula 4: PEG amount increased by 25% in the basic formula Formula 5: PEG amount increased by 50% in the basic formula

[0079] <Generation of Addition Amount Trained Models> The following four types of binary decision models, addition amount trained models 1 to 4, were generated (see FIG. 4).

[0080] [Addition Amount Trained Model 1] The training data obtained from prescription 1 was set as a bad data group, and the training data obtained from prescriptions 2 to 5 was set as a good data group, and the addition amount trained model 1 was generated using training data for 100 samples. In other words, the addition amount trained model 1 is a model that makes a binary judgment that prescription 1 is bad and prescriptions 2 to 5 are good.

[0081] [Addition amount trained model 2] The training data obtained from prescriptions 1 and 2 were set as a bad data group, and the training data obtained from prescriptions 3 to 5 were set as a good data group, and the addition amount trained model 2 was generated using training data for 100 samples. In other words, the addition amount trained model 2 is a model that makes a binary judgment that prescriptions 1 and 2 are bad and prescriptions 3 to 5 are good.

[0082] [Addition Amount Trained Model 3] The training data obtained from prescriptions 1 to 3 was set as a bad data group, and the training data obtained from prescriptions 4 and 5 was set as a good data group, and the addition amount trained model 3 was generated using training data for 100 samples. In other words, the addition amount trained model 3 is a model that makes a binary judgment that prescriptions 1 to 3 are bad and prescriptions 4 and 5 are good.

[0083] [Addition Amount Trained Model 4] The training data obtained from prescriptions 1 to 4 was set as a bad data group, and the training data obtained from prescription 5 was set as a good data group, and the training data for 100 samples was used to generate addition amount trained model 4. In other words, addition amount trained model 4 is a model that makes a binary judgment that prescriptions 1 to 4 are bad and prescription 5 is good.

[0084] <Examples of learning models> Various linear and non-linear algorithms known as machine learning algorithms can be used, or multiple algorithms can be combined. For example, the following algorithms can be used:

[0085] Random forest Gradient boosted trees Extreme gradient boosted trees Light-gradient boosted machine Neural networks Regularized regression Elastic-net K-Nearest neighbors Support vector machine Generalized additive model

[0086] <Performance verification of additive amount trained model> Evaluation data for 20 samples was input into each of additive amount trained models 1 to 4, and a binary judgment of good or bad was performed. Addition amount trained models 1 to 4 were able to make a good judgment with an AUC exceeding 0.96 (see Figure 5).

[0087] By combining additive amount trained models 1 to 4, it is possible to estimate the amount of PEG added within a range that can be specified for prescriptions 1 to 5. For example, if additive amount trained model 2 judges the product as good and additive amount trained model 3 judges the product as bad, the amount of PEG added is estimated to be in the range of -25% to +25% of the basic prescription (1.0 μL).

[0088] <Generation of continuous regression model> A continuous regression model was generated to estimate the percentage of PEG added relative to the basic formula (1.0 μL) using all of the training data obtained from formulas 1 to 5. The continuous regression model estimates whether the amount is -50%, -25%, 0%, +25%, or +50% relative to the basic formula (1.0 μL) (see Figure 6).

[0089] <Performance Verification of Continuous Value Regression Model> The evaluation data for 20 samples were input into the continuous value regression model to estimate the amount of PEG added (see Figure 7). In Figure 7, the horizontal axis represents the actual amount of PEG added, and the vertical axis represents the estimated amount of PEG added. As shown in Figure 7, it was demonstrated that the amount of PEG added can be estimated using the continuous value regression model.

[0090] The present invention is not limited to the above-described embodiment, and various modifications, changes, and improvements are possible without departing from the spirit and scope of the present invention. The above-described modifications may be appropriately combined to form a configuration.

[0091] In the determination method 10, as described above, the small RNA data obtained in the measurement step 12 is input into a disease-trained model that has been machine-learned to learn the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in a biological sample and incidence data showing the presence or absence of a disease in the subject from whom the biological sample was collected, thereby indicating the presence or absence of a disease. Therefore, the measured expression levels of the small RNAs can be said to indicate the presence or absence of a disease based on the judgment criteria in the disease-trained model. Therefore, the determination method 10 can also be said to be a determination method that determines the amount of PEG reagent used when ligating multiple small RNAs in a biological sample collected from a subject using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, and the expression levels of the small RNAs indicate the presence or absence of a disease based on the judgment criteria in the disease-trained model that has been machine-learned to learn the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in a biological sample and incidence data showing the presence or absence of a disease in the subject from whom the biological sample was collected.

[0092] <Additional Notes> (Aspect 1) A method for determining the amount of PEG reagent used in ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data indicating the results of measuring the expression levels of the multiple small RNAs, the method comprising: an addition amount determination step of determining the amount to be added from the small RNA data using a determination criterion based on the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in the biological samples collected from the multiple subjects and addition amount data indicating the amount of PEG reagent added in ligating the multiple small RNAs in the biological samples. (Aspect 2) The method of Aspect 1, wherein the addition amount determination step determines the amount to be added by inputting the small RNA data into an addition amount trained model that has been trained to learn the correlation between the measurement data and the addition amount data through machine learning. (Aspect 3) The method of Aspect 2, wherein the addition amount determination step determines the appropriateness of the addition amount by inputting the small RNA data into the addition amount trained model. (Aspect 4) The determination method according to Aspect 2, wherein the addition amount determination step outputs the addition amount by inputting the small RNA data into the addition amount trained model. (Aspect 5) The determination method according to Aspect 4, wherein the addition amount determination step determines whether the addition amount is appropriate based on a determination result from a determination unit that determines whether the addition amount is appropriate based on a comparison between the output addition amount and a threshold. (Aspect 6) The determination method according to Aspect 1, wherein the addition amount determination step determines the addition amount by inputting the small RNA data into a regression model that determines a correlation between the measurement data and the addition amount data. (Aspect 7) The determination method according to Aspect 6, wherein the addition amount determination step determines whether the addition amount is appropriate by inputting the small RNA data into the regression model. (Aspect 8) The determination method according to Aspect 6, wherein the addition amount determination step outputs the addition amount by inputting the small RNA data into the regression model.(Aspect 9) The method of Aspect 8, wherein the addition amount determining step determines whether the addition amount is appropriate based on a determination result of a determination unit that determines whether the addition amount is appropriate based on a comparison between the output addition amount and a threshold. (Aspect 10) The method of Aspect 1 to 9, further comprising a disease determination step of determining whether or not the subject has the disease by inputting the small RNA data into a disease-trained model that has been machine-learned to determine a correlation between measurement data indicating the results of measuring the expression amounts of multiple small RNAs in a biological sample and disease prevalence data indicating the presence or absence of a disease in the subject from whom the biological sample was collected. (Aspect 10-2) The method of Aspect 1 to 9, wherein the expression level of the small RNA indicates the presence or absence of the disease based on a determination criterion in a disease-trained model that has been machine-learned to determine a correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in a biological sample and disease prevalence data indicating the presence or absence of a disease in the subject from whom the biological sample was collected. (Aspect 11) The method of determining according to Aspect 10, wherein the addition amount determining step determines whether the addition amount is appropriate, and the presence or absence of the disease is determined in the disease assessment step if the addition amount determining step determines that the addition amount is appropriate. (Aspect 12) A determination device that determines the addition amount of a PEG reagent used in ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data indicating results of measuring expression levels of the multiple small RNAs, the device comprising a processor, wherein the processor determines the addition amount from the small RNA data using determination criteria based on a correlation between measurement data indicating results of measuring expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data indicating the addition amount of the PEG reagent used in ligating the multiple small RNAs in the biological sample.(Aspect 13) A determination system for determining the amount of PEG reagent added when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, comprising: a measurement unit that acquires small RNA data showing the results of measuring the multiple small RNAs in the biological sample from the subject; and a determination unit that determines the amount added from the small RNA data using determination criteria based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological sample. (Aspect 14) A determination program for causing a computer to execute a determination process for determining the amount of PEG reagent added when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, wherein the determination process determines the amount of addition from the small RNA data using a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological sample. (Aspect 15) A non-transitory recording medium having recorded thereon a determination program for causing a computer to execute a determination process for determining the amount of PEG reagent added when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, wherein the determination process determines the amount of addition from the small RNA data using determination criteria based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added when ligating the multiple small RNAs in the biological sample.

[0093] The disclosure of Japanese Patent Application No. 2023-182778, filed on October 24, 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 method for determining the amount of PEG reagent added used when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, comprising an addition amount determination step of determining the amount added from the small RNA data using a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent added used when ligating multiple small RNAs in the biological sample.

2. The method of claim 1, wherein the addition amount determination step determines the addition amount by inputting the small RNA data into an addition amount trained model that has been machine-learned to determine the correlation between the measurement data and the addition amount data.

3. The method according to claim 2, wherein in the addition amount determination step, the appropriateness of the addition amount is determined by inputting the small RNA data into the addition amount trained model.

4. The method according to claim 2, wherein in the addition amount determination step, the addition amount is output by inputting the small RNA data into the addition amount trained model.

5. The method according to claim 4, wherein in the addition amount determination step, the appropriateness of the addition amount is determined based on a determination result of a determination unit that determines the appropriateness of the addition amount based on a comparison between the output addition amount and a threshold value.

6. The method according to claim 1, wherein the step of determining the amount to be added determines the amount to be added by inputting the small RNA data into a regression model that determines the correlation between the measurement data and the amount to be added.

7. The method according to claim 6, wherein in the step of determining the amount to be added, the appropriateness of the amount to be added is determined by inputting the small RNA data into the regression model.

8. The method according to claim 6, wherein in the step of determining the amount to be added, the small RNA data is input into the regression model to output the amount to be added.

9. The method according to claim 8, wherein in the addition amount determination step, the appropriateness of the addition amount is determined based on a determination result of a determination unit that determines the appropriateness of the addition amount based on a comparison between the output addition amount and a threshold value.

10. The method of claim 1, further comprising a disease determination step of determining the presence or absence of the disease by inputting the small RNA data into a disease-trained model that has been machine-learned to determine the correlation between measurement data indicating the results of measuring multiple small RNAs in a biological sample and disease presence data indicating the presence or absence of the disease in a subject from whom the biological sample was taken.

11. The method of claim 10, wherein in the addition amount determination step, the appropriateness of the addition amount is determined, and in the disease determination step, the presence or absence of the disease is determined if the addition amount determination step determines that the addition amount is appropriate.

12. A determination device that determines the amount of PEG reagent used to add when ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data indicating the results of measuring the expression levels of the multiple small RNAs, the device having a processor, wherein the processor determines the amount to be added from the small RNA data using a determination criterion based on the correlation between measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data indicating the amount of PEG reagent added when ligating multiple small RNAs in the biological sample.

13. A determination system for determining the amount of PEG reagent used to ligate multiple small RNAs in a biological sample collected from a subject, using small RNA data indicating the results of measuring the expression levels of the multiple small RNAs, comprising: a measurement unit that acquires small RNA data indicating the results of measuring multiple small RNAs in the biological sample from the subject; and a determination unit that determines the amount to be added from the small RNA data using a determination criterion based on the correlation between the measurement data indicating the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data indicating the amount of PEG reagent used to ligate the multiple small RNAs in the biological sample.

14. A determination program for causing a computer to execute a determination process for determining the amount of PEG reagent used to ligate multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, the determination process determining the amount to be added from the small RNA data based on a determination criterion based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent used to ligate multiple small RNAs in the biological sample.

15. A non-temporary recording medium having recorded thereon a judgment program for causing a computer to execute a judgment process for judging the amount of PEG reagent used in ligating multiple small RNAs in a biological sample collected from a subject, using small RNA data showing the results of measuring the expression levels of the multiple small RNAs, the judgment process determining the amount of addition from the small RNA data based on judgment criteria based on the correlation between measurement data showing the results of measuring the expression levels of multiple small RNAs in biological samples collected from multiple subjects and addition amount data showing the amount of PEG reagent used in ligating multiple small RNAs in the biological sample.

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