Patent Publication Number: US-11640662-B2

Title: Somatic mutation detection apparatus and method with reduced sequencing platform-specific error

Description:
TECHNICAL FIELD 
     The present disclosure relates to a method and apparatus for detecting a mutation, and more particularly, to a method and apparatus for detecting a mutation using a neural network trained to decrease a sequencing platform-specific error. 
     BACKGROUND ART 
     Next-generation sequencing (NGS) may refer to a method of decomposing deoxyribonucleic acid into a plurality of fragments and performing sequencing in parallel. Unlike conventional Sanger sequencing, NGS can analyze multiple DNA fragments at the same time, and thus can be advantageous in terms of analysis time, analysis cost, and analysis accuracy. 
     Referring to  FIG.  1   , a graph  100  for comparing NGS  110  and Sanger sequencing  120  is shown. As shown in the graph  100 , the NGS  110  may have higher performance than the Sanger sequencing  120 . Meanwhile, as can be seen from the horizontal axis of the graph  100 , the NGS  110  may have various read lengths. 
     The NGS may be used to sequence a cancer patient&#39;s DNA to detect mutations. Mutations may be detected in cancer tissue through various software applications that sequence DNA through the NGS. 
     When a mutation is detected by conventional software, and particularly, when DNA is sequenced with a specific sequencing platform such as short-read sequencing, there may occur a false positive in which, although there are no mutations, the mutation is falsely detected due to the nature of the sequencing platform. Such a sequencing platform-specific false positive may degrade the accuracy of mutation detection. 
     Therefore, in order to prevent the accuracy of mutation detection from being degraded by the sequencing platform-specific false positive, there may be a need to improve the mutation detection method. 
     DISCLOSURE 
     Technical Problem 
     A technical object to be achieved by the present disclosure is to improve mutation detection performance by solving a problem which is caused by conventional software and in which the accuracy of mutation detection is degraded due to a sequencing platform-specific false positive. 
     Technical Solution 
     According to an aspect of the present disclosure, there is provided a mutation detection apparatus including a memory configured to store software for implementing a neural network and a processor configured to detect a mutation by executing the software, wherein the processor is configured to generate first genome data extracted from a target tissue and second genome data extracted from a normal tissue, extract image data by preprocessing the first genome data and the second genome data, and detect a mutation of the target tissue on the basis of the image data through the neural network trained to correct a sequencing platform-specific false positive. 
     According to another aspect of the present disclosure, there is provided a method of detecting a mutation by executing software for implementing a neural network, the method including generating first genome data extracted from a target tissue and second genome data extracted from a normal tissue, extracting image data by preprocessing the first genome data and the second genome data; and detecting a mutation of the target tissue on the basis of the image data through the neural network trained to correct a sequencing platform-specific false positive. 
     Advantageous Effects 
     With the apparatus and method according to the present disclosure, the neural network may be used during a process of detecting mutations and may be trained in advance to correct a sequencing platform-specific false positive. Thus, it is possible to prevent the accuracy of mutation detection from being degraded due to the sequencing platform-specific false positive. In particular, unlike conventional statistical methods, a neural network may be used to detect mutations, and thus it is possible to detect mutations with high performance compared to the conventional methods. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG.  1    is a diagram showing a graph for comparing a next-generation sequencing method and a conventional sequencing method. 
         FIG.  2    is a diagram illustrating a neural network according to an embodiment. 
         FIG.  3    is a diagram illustrating a process of detecting a mutation according to an embodiment. 
         FIG.  4    is a block diagram showing elements constituting a mutation detection apparatus according to an embodiment. 
         FIG.  5    is a diagram illustrating a structure of and a training method for a neural network according to an embodiment. 
         FIG.  6    is a diagram illustrating a process of generating data for training a neural network according to an embodiment. 
         FIG.  7    is a flowchart illustrating operations constituting a mutation detection method according to an embodiment. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The following description is only for specifying the embodiments and is not intended to limit or restrict the scope of the present disclosure. What those skilled in the art can easily infer from the detailed description and embodiments of the present disclosure should be construed as falling within the scope of the present disclosure. 
     The terms used herein are described as general terms widely used in the technical field related to the present disclosure, but the meanings of the terms may be altered according to the intent of a technician in this field, the emergence of new technology, examination criteria, precedents, or the like. Some of the terms may be arbitrarily selected by the applicant, and in this case, the meanings of the arbitrarily selected terms will be described in detail. The terms used herein should not be interpreted as being limited to dictionary definitions, but should be interpreted as having meanings reflecting the overall context of the specification. 
     The term “comprising” or “including” used herein should be construed as not necessarily including all of the elements or operations disclosed herein and should be construed as including the exclusion or addition of some elements or operations. 
     Although terms including ordinal numbers such as “first” and “second” may be used herein to describe various elements or operations, these elements or operations should not be limited by these terms. Terms including ordinal numbers should be construed only for the purpose of distinguishing one element or operation from other elements or operations. 
     Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Detailed descriptions of matters well known to those skilled in the art will be omitted. 
       FIG.  2    is a diagram illustrating a neural network according to an embodiment. 
     Referring to  FIG.  2   , elements constituting a neural network  200  are shown. The neural network  200  is an artificially implemented neural network and has a hidden layer in addition to an input layer and an output layer to efficiently perform various nonlinear functions. The neural network  200  may include a plurality of hidden layers and correspond to a deep neural network. In addition to the structure illustrated in  FIG.  2   , the neural network  200  may be implemented with various architectures such as a recurrent neural network (RNN) or a convolutional neural network (CNN). 
     The neural network  200  may be trained by adjusting values of various parameters constituting the neural network  200 . When the neural network  200  is properly trained according to various machine learning and deep learning methods, the neural network  200  may perform a function corresponding to the training purpose with high performance. Accordingly, the neural network  200  may be widely used in various fields in addition to fields such as speech recognition, natural language processing, and image analysis. In particular, as in the present disclosure, the neural network  200  may be utilized to solve conventional problems, such as mutation detection, in the bio field. 
       FIG.  3    is a diagram illustrating a process of detecting a mutation according to an embodiment. 
     Referring to  FIG.  3   , a series of processing processes on first genome data  310  and second genome data  320  may be performed in a mutation detection apparatus  300  to generate a mutation detection result  350 . As described below, the mutation detection apparatus  300  may be implemented as an apparatus  400  of  FIG.  4   . 
     The series of processing processes in the apparatus  300  may be implemented in the form of software or programs. Each operation of the series of processing processes in the apparatus  300  may be implemented with a module for performing a specific function, such as an image generation module  330  or a mutation detection module  340 . For example, the software for implementing the series of processing processes may be implemented with a Python script and may be executed in an environment such as LINUX CentOS release 7.6. 
     The first genome data  310  may refer to genome data extracted from a target tissue. The target tissue is a tissue from which a mutation is to be detected and may refer to a cancer tissue. The second genome data  320  may refer to genome data extracted from a normal tissue. 
     In order to accurately determine a gene with a mutation from among genes of the target tissue, the second genome data  320  may be considered in addition to the first genome data  310 . Meanwhile, although not shown in  FIG.  3   , a process of extracting the first genome data  310  from the target tissue and a process of extracting the second genome data  320  from the normal tissue may be implemented with a separate software module in the apparatus  300 . 
     In the apparatus  300 , a mutation is not detected in a statistical manner solely on the basis of genome data of a cancer patient. The first genome data  310  and the second genome data  320  may be extracted from a tissue where a cancer actually originates and a normal tissue which is subject to comparison, and a mutation may be detected. Thus, individual characteristics that may differ for each cancer patient and cancer tissue may be reflected in the mutation detection process. Therefore, it is possible to more accurately detect a gene with a mutation from among genes of a cancer tissue. 
     The image generation module  330  may extract image data from the first genome data  310  and the second genome data  320 . The image data may refer to data obtained by visualizing the first genome data  310  and the second genome data  320  such that the image data can be provided to the neural network  200  trained to detect mutations. 
     The mutation detection module  340  may detect a mutation of a target tissue on the basis of the image data. To this end, the neural network  200  may be implemented in the mutation detection module  340 , and the neural network  200  may be trained to detect a gene with a mutation among from genes of the target tissue. For example, as will be described below with reference to  FIGS.  5  and  6   , the neural network  200  may be implemented with a convolutional neural network (CNN) that is trained to extract a feature from an image and perform a specific function on the basis of the feature. 
     The mutation detection module  340  may perform additional processing on an output of the neural network  200  to generate the mutation detection result  350 . The mutation detection result  350  may be generated in a standard format (e.g., the Variant Call Format (VCF)) that displays information of a gene determined as having a mutation through a comparison to a reference gene. 
     With the apparatus  300 , the neural network  200  trained for a specific purpose may be utilized to detect mutations, and thus it is possible to improve the accuracy of mutation detection. As will be described below, the neural network  200  may be trained to correct a sequencing platform-specific false positive, and thus it is possible to prevent a decrease in accuracy due to a false positive which has been pointed out as a problem in conventional mutation detection software. 
     Meanwhile, the mutation detected from the target tissue by the apparatus  300  may be a somatic single nucleotide variant (sSNV). The sSNV is a somatic mutation and may mean that a mutation has occurred in only a single nucleotide among nucleotides constituting a nucleotide sequence. The sSNV may be suitable to be detected by the NGS and, in particular, may be suitable to be detected by the neural network  200  trained to correct a sequencing platform-specific false positive. However, the present disclosure is not limited thereto, and other types of mutations may be detected by the apparatus  300  in addition to the sSNV. 
       FIG.  4    is a block diagram showing elements constituting a mutation detection apparatus according to an embodiment. 
     Referring to  FIG.  4   , a mutation detection apparatus  400  may include a memory  410  and a processor  420 . However, the present disclosure is not limited thereto, and the apparatus  400  may further include other general-purpose elements in addition to the elements shown in  FIG.  4   . Meanwhile, the apparatus  400  of  FIG.  4    may be an example of implementing the apparatus  300  of  FIG.  3   . 
     The apparatus  400  may correspond to various devices configured to detect mutations. For example, the apparatus  400  may be various kinds of computing devices such as personal computers (PCs), server devices, smartphones, tablet PCs, and other mobile devices. 
     The memory  410  may store software for implementing the neural network  200 . For example, data on layers and nodes constituting the neural network  200 , computations performed by the nodes, and parameters applied to computation processes may be stored in the memory  410  in at least one instruction, program, or software. 
     The memory  410  may be implemented with a non-volatile memory such as read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), flash memory, phase-change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), and ferroelectric RAM (FRAM) or may be implemented with a volatile memory such as dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), phase-change RAM (PRAM), resistive RAM (RRAM), and ferroelectric RAM (FeRAM). Also, the memory  410  may be implemented with a hard disk drive (HDD), solid-state drive (SSD), secure digital (SD), or micro secure digital (Micro-SD). 
     The processor  420  may detect mutations by executing the software stored in the memory  410 . The processor  420  may detect a mutation of a target tissue by performing a series of processing processes for mutation detection. The processor  420  may perform an overall function for controlling the apparatus  400  and may process various kinds of computations in the apparatus  400 . 
     The processor  420  may be implemented with an array of multiple logic gates or a general-purpose microprocessor. The processor  420  may include a single processor or a plurality of processors. The processor  420  may be formed integrally with the memory  410  for storing software rather than separately from the memory  410 . The processor  420  may be at least one of a central processing unit (CPU), a graphics processing unit (GPU), and an application processor (AP) included in the apparatus  400 . However, this is only an example, and the processor  420  may be implemented in various other forms. 
     The processor  420  may generate first genome data extracted from a target tissue and generate second genome data extracted from a normal tissue. The processor  420  may embed a result dataset obtained by sequencing the target tissue into genome data to extract the first genome data and may embed a result dataset obtained by sequencing the normal tissue into genome data to extract the second genome data. 
     For example, the processor  420  may generate the first genome data and the second genome data through the HCC1143 cell line or the like. Meanwhile, the first genome data and the second genome data may be whole-genome data. 
     The processor  420  may extract image data by preprocessing the first genome data and the second genome data. The processor  420  may perform the preprocessing such that the first genome data and the second genome data have a form suitable to be processed by the neural network  200 . 
     As an example, the first genome data and the second genome data may be converted into an image form such as image data. However, the conversion into the image form is only an example, and the first genome data and the second genome data may be converted into various forms other than an image depending on how the neural network  200  is implemented. 
     The processor  420  may perform the preprocessing by correcting the first genome data and the second genome data on the basis of mapping quality and depth. The processor  420  may remove reads with low quality with respect to the mapping quality and may adjust the depth of the first genome data and the second genome data. Through such a preprocessing process, the processor  420  may generate image data having a format suitable to be processed in the neural network  200 . 
     The processor  420  may detect a mutation of the target tissue on the basis of the image data through the neural network  200  trained to correct a sequencing platform-specific false positive. By utilizing the trained neural network  200 , the processor  420  may detect which gene of the target tissue has a mutation from the image data. 
     The sequencing platform may refer to a detailed method for sequencing the target tissue. The sequencing method may vary depending on what sequencing platform is applied. In the case of the NGS, the type of the sequencing platform may be determined according to the size of the DNA fragments, that is, according to the read length of the DNA fragments processed in parallel. For example, the sequencing platform may include long-read sequencing and short-read sequencing. However, the present disclosure is not limited to such classification based on the read length, and the sequencing platform may refer to various analysis methods for performing sequencing. 
     The neural network  200  may be trained in advance to receive image data and output a mutation of a target tissue. The trained neural network  200  may be stored in the memory  410  in the form of software, and the processor  420  may detect the mutation of the target tissue from the image data by executing the software for implementing the trained neural network  200 . 
     The training of the neural network  200  may be performed by the apparatus  400 . The apparatus  400  or the processor  420  may train the neural network  200  by repeatedly updating the values of the parameters constituting the neural network  200 . Alternatively, the neural network  200  may be implemented with software after being trained outside the apparatus  400 . 
     The neural network  200  may be trained to correct a sequencing platform-specific false positive. For example, the neural network  200  may be trained to a short-read sequencing-specific false positive, and the read length of the short-read sequencing may be 100 or less. However, the present disclosure is not limited to such a specific value, and the short-read sequencing may refer to a sequencing method having a read length shorter than that of the long-read sequencing. 
     The sequencing platform-specific false positive may refer to a case in which a mutation is detected in a specific gene according to a specific sequencing platform even though no mutation occurs in the gene. That is, the false positive may refer to a case in which a mutation is determined to have occurred according to a specific sequencing platform but the mutation is determined not to have occurred according to other sequencing platforms. 
     For example, a false positive specific to a specific sequencing platform may be a short-read sequencing-specific false positive. The short-read sequencing-specific false positive may refer to an error indicating that a mutation is detected according to the short-read sequencing but no mutation is detected according to the long-read sequencing. When a short-read sequencing-specific false positive is present, it may be falsely determined that a mutation has occurred in a gene that actually has no mutations, and thus the accuracy of mutation detection may be degraded. 
     Since the neural network  200  may be trained to correct a sequencing platform-specific false positive, it is possible to improve the accuracy of mutation detection when a mutation of a target tissue is detected using the neural network  200 . The details about the training of the neural network  200  will be described below with reference to  FIGS.  5  and  6   . 
       FIG.  5    is a diagram illustrating a structure of and a training method for a neural network according to an embodiment. 
     Referring to  FIG.  5   , the structure of a neural network  530  and a process of training the neural network  530  on the basis of first training image data  510  and second training image data  520  are illustrated. The neural network  530  of  FIG.  5    may be an example in which the neural network  200  described with reference to  FIGS.  2  to  4    is implemented. 
     As described above, the neural network  530  may be a convolutional neural network that extracts features from the image data and computes a probability that genes of the target tissue correspond to mutations on the basis of the features. 
     The neural network  530  may be implemented with a convolutional neural network (CNN) including a first network  531  and a second network  532 . The first network  531  may include a convolutional layer and a pooling layer, and the second network  532  may include a fully connected network. When the training of the neural network  530  is completed, the first network  531  may extract a feature indicating the characteristic of input data from the input data, and the second network  532  may perform a function corresponding to the purpose of the neural network on the basis of the feature. 
     As described above, the training of the neural network  530  may be performed by the apparatus  400 . Alternatively, after the training of the neural network  530  is completed outside the apparatus  400 , only inference of the neural network  530  may be performed in the apparatus  400 . 
     The neural network  530  may be trained using the first training image data  510  and the second training image data  520  as training data. Specifically, the neural network  530  may be trained to distinguish actual mutations from misdetected mutations on the basis of the first training image data  510  indicating training data on the actual mutations and the second training image data  520  indicating training data on the misdetected mutations due to a false positive. 
     The first training image data  510  may indicate training data on actual mutations. An actual mutation may refer to a case in which what is determined as a mutation according to one sequencing platform is also determined as a mutation according to other sequencing platforms. For example, an actual mutation may refer to a case in which what is determined as a mutation by the short-read sequencing is also determined as a mutation by the long-read sequencing. 
     The second training image data  520  may indicate training data on misdetected mutations due to a false positive. As described above, what is not actually a mutation may be falsely detected as a mutation according to a specific sequencing platform, and thus the neural network  530  may be trained to correct a false positive by using the misdetected mutations due to the false positive. For example, a misdetected mutation may refer to a case in which it is determined according to the long-read sequencing that there is no mutation but it is determined according to the short-read sequencing that there is a mutation. 
     In order to train the neural network  530 , the first training image data  510  and the second training image data  520  may be used as train data, and thus as a result of the training, the neural network  530  may be configured to correct a sequencing platform-specific false positive. Since both of the first training image data  510  and the second training image data  520  are set as training data, it is possible to improve the accuracy of the neural network  530  in detecting mutations. 
       FIG.  6    is a diagram illustrating a process of generating data for training a neural network according to an embodiment. 
     Referring to  FIG.  6   , long-read sequencing  610  and short-read sequencing  620  are illustrated as examples of different sequencing platforms for generating the first training image data  510  and the second training image data  520 . 
     The first training image data  510  and the second training image data  520  may be generated based on results of performing the long-read sequencing  610  and the short-read sequencing  620  on the same training tissue. In order to secure training data for training the neural network  530 , the long-read sequencing  610  and the short-read sequencing  620  may be performed on the same cancer tissue containing a gene with a mutation, and the results of the performance may be compared. 
     For example, PacBio sequencing may be performed as the long-read sequencing  610 , and Illumina sequencing may be performed as the short-read sequencing  620 . However, the present disclosure is not limited thereto, and other sequencing methods having appropriate lead lengths for short reads and long reads may be performed. 
     The results of performing the long-read sequencing  610  and the short-read sequencing  620  are illustrated in  FIG.  6   . For the same reference, there may be some differences between the mapping result by the long-read sequencing  610  and the mapping result by the short-read sequencing  620 . For example, a comparison result  630  shows that both the long-read sequencing  610  and the short-read sequencing  620  determine that a mutation has occurred. In this case, a nucleotide corresponding to the comparison result  630  may be set as a mutation. 
     However, a comparison result  640  shows that the long-read sequencing  610  determines that no mutation occurs but the short-read sequencing  620  determines that a mutation has occurred. In this case, a nucleotide corresponding to the comparison result  640  may be set as a misdetected mutation due to a short-read sequencing-specific false positive. 
     Data on the actual mutation corresponding to the comparison result  630  may be labeled with the first training image data  510 , and data on the misdetected mutation corresponding to the comparison result  640  may be labeled with the second training image data  520 . The neural network  530  may be trained using the first training image data  510  and the second training image data  520  generated in the above-described way and thus may be trained to correct a false positive as in the comparison result  640 . 
     Meanwhile, the data on the actual mutation corresponding to the comparison result  630  and the data on the misdetected mutation corresponding to the comparison result  640  may be implemented as virtual cancer tissue genome data through the HCC1143 cell line or the like. Through a process of obtaining information such as gene sequence, insertion/deletion (indel), and mapping quality from the virtual cancer tissue genome data, the first training image data  510  and second training image data  520  may be generated for the actual mutation and the misdetected mutation, respectively. That is, the first training image data  510  and the second training image data  520  may include at least one of the gene sequence, the indel, and the mapping quality. 
       FIG.  7    is a flowchart illustrating operations constituting a method of generating a mutation according to an embodiment. 
     Referring to  FIG.  7   , the mutation detection method may include operations  710  to  730 . However, the present disclosure is not limited thereto, and the mutation detection method may further include other general-purpose operations in addition to the operations shown in  FIG.  7   . 
     The mutation detection method of  FIG.  7    may include operations performed in time series in the apparatus  300  or the apparatus  400  which has been described with reference to  FIGS.  3  to  6   . Therefore, the above description of the apparatus  300  or the apparatus  400  with reference to  FIGS.  3  to  6    is equally applicable to the mutation detection method of  FIG.  7    even when the description is omitted in the following description of the mutation detection method of  FIG.  7   . 
     In operation  710 , the apparatus  400  may generate first genome data extracted from a target tissue and generate second genome data extracted from a normal tissue. 
     The apparatus  400  may perform the preprocessing by correcting the first genome data and the second genome data on the basis of mapping quality and depth. 
     In operation  720 , the apparatus  400  may extract image data by preprocessing the first genome data and the second genome data. 
     In operation  730 , the apparatus  400  may detect a mutation of the target tissue on the basis of the image data through a neural network trained to correct a sequencing platform-specific false positive. 
     The neural network may be trained to distinguish actual mutations from misdetected mutations on the basis of first training image data indicating training data on the actual mutations and second training image data indicating training data on the misdetected mutations due to a false positive. 
     The first training image data and the second training image data may be generated based on results of performing long-read sequencing and short-read sequencing on the same training tissue. 
     The first training image data and the second training image data may include at least one of gene sequence, indel, and mapping quality. 
     The neural network may be a convolutional neural network (CNN) that extracts features from the image data and computes a probability that genes of the target tissue correspond to mutations on the basis of the features. 
     The mutation detected from the target tissue may be a somatic single nucleotide variant (sSNV). 
     The mutation detection method of  FIG.  7    may be recorded on a computer-readable recording medium on which at least one program or software including instruction for executing the method is recorded. 
     Examples of the computer-readable recording medium may include a magnetic medium, such as a hard disk, a floppy disk, and a magnetic tape, an optical medium, such as a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), etc., a magneto-optical medium such as a floptical disk, and a hardware device specially configured to store and perform program instructions, for example, a read-only memory (ROM), a random access memory (RAM), a flash memory, etc. Examples of the program instructions may include high-level language codes that can be executed by a computer using an interpreter as well as machine language codes such as those produced by a compiler. 
     Although the embodiments of the present disclosure have been described in detail, the scope of the present disclosure is not limited thereto, and several variations and modifications made by those skilled in the art using the basic concept of the present disclosure defined in the appended claims should be construed as falling within the scope of the present disclosure.