KR20180060764A - Methods for detecting nucleic acid sequence variations and a device for detecting nucleic acid sequence variations using the same - Google Patents

Abstract

The present invention provides a method for detecting a base sequence variation. The method comprises the steps of obtaining a base sequence of a target sample by using a next generation base sequence analysis method; matching the base sequence of a reference sample with the base sequence of the target sample; selecting a locus inconsistent with the base sequence of the reference sample among the base sequences of the target sample; and determining a candidate of a base sequence variation among the inconsistent loci with an error-corrected mutation probability calculation method according to a type of base sequence variation. Therefore, the method can improve accuracy to reduce an analysis error and to detect a base sequence variation in low frequency.

Description

TECHNICAL FIELD [0001] The present invention relates to a method for detecting a mutation of a nucleotide sequence, and a device for detecting a mutation of a nucleotide sequence using the same. BACKGROUND ART < RTI ID = 0.0 >

The present invention relates to a method for detecting a mutation in a base sequence and a device for detecting a base sequence variation using the same, and more particularly, to a method for detecting a mutation in a base sequence by matching a base sequence of a target sample and a reference sample, , And a device for detecting a mutation of a base sequence using the same.

An allele may refer to a gene having a different DNA sequence for one locus in a pair of chromosomes. These alleles may be caused by a number of factors, including mutations in the individual. Specifically, the locus in which mutation occurs in one individual may represent a different base sequence than the normal locus, resulting in an allele. Thus, a variety of tests have been developed that can detect mutations in an individual by searching for alleles. However, in the case of mutations present at low frequencies, existing methods of detection have limitations in detecting them.

For example, somatic mutation is a genetic mutation in somatic cells that is known to cause various problems in the medical field. Especially somatic mutations may be associated with cancer. Thus, somatic mutation profiling can be used to quickly detect changes in mechanisms that cause cancer cell proliferation, and profiling mutation profiling can be useful as a research tool for oncology. However, unlike germline mutation, somatic mutation often has less than 1% expression ratio, which makes it difficult to detect mutations.

Thus, more effective low frequency mutation detection methods are required.

BACKGROUND OF THE INVENTION [0002] Techniques as a background of the invention have been made in order to facilitate understanding of the present invention. And should not be construed as an admission that the matters described in the technical background of the invention are present in the prior art.

Next Generation Sequencing (NGS) is a high-throughput sequencing method that allows the generation of multiple nucleotide sequencing results. This high-density parallel sequencing can be effective for low-rate mutation detection. However, due to the biased phenomenon that occurs during PCR for library production, a mutant gene with a low percentage of 1% or less may be difficult to detect because it is buried in false positives that appear in more than 99% of normal genes. Thus, the minimum detectable ratio of mutations using next-generation sequencing technology is only 3%.

To address these problems, researchers have attempted to increase the limit of low frequency mutation detection by increasing the depth, which reads the same locus multiple times. However, this has also increased the false positive rate, that is, the error of analysis for detection, and this phenomenon remains a problem to be solved for accurate low frequency mutation detection.

Accordingly, the inventors of the present invention have recognized that by correcting the mutation probability value, the false positive rate can be reduced, and a method of detecting the mutation of the base sequence capable of detecting the mutation at a low frequency with high precision can be provided.

Accordingly, it is an object of the present invention to provide a mutation probability value corrected for errors according to the type of mutation of a nucleotide sequence, thereby providing a mutation probability value which can reduce an analysis error and improve the accuracy of detecting a mutation of a low frequency nucleotide sequence. A mutation detection method and a device for detecting a mutation of a base sequence using the same.

The inventors of the present invention have recognized that, depending on the type of analysis platform, the analysis error of the type of mutation of the nucleotide sequence that appears is different.

Another problem to be solved by the present invention is to provide an error-corrected mutation probability value considering an analysis platform type and a mutation type of a nucleotide sequence so as to detect a mutation of a low frequency nucleotide sequence with high accuracy according to an analysis platform type A method for detecting a mutation of a base sequence, and a device for detecting a base sequence variation using the same.

The problems of the present invention are not limited to the above-mentioned problems, and other problems not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a method for detecting a mutation in a nucleotide sequence, comprising: obtaining a nucleotide sequence of a target sample using a next generation sequencing (NGS) method; , Matching the base sequence of the reference sample with the base sequence of the target sample, selecting the locus of the base sequence of the target sample that is inconsistent with the base sequence of the reference sample, And determining a nucleotide sequence variation candidate among the gene segments that are inconsistent with the error-corrected mutation probability calculation method.

According to another aspect of the present invention, the step of determining the nucleotide sequence variation candidate further includes the step of determining the inconsistent gene locus as a nucleotide sequence variation candidate when the mutation probability value of the inconsistent locus of the gene locus is a predetermined level or more can do.

According to another feature of the present invention, the type of mutation of the base sequence is a mutation of C to A, a mutation of G to A, a mutation of T to A, mutation of A to C, mutation of G to C, mutation of T to C A variant of at least one base sequence selected from the group consisting of mutations, G to T mutations, C to T mutations, A to T mutations, T to G mutations, C to G mutations, and A to G mutations .

According to another aspect of the present invention, the mutation probability value is determined by determining an analysis error profile including an analysis error of a base sequence variation type and a base call quality score of an analysis error according to an analysis platform type, May be a corrected mutation probability value for the type of mutation of at least one or more base sequences.

According to another aspect of the present invention, the analysis error profile may further include information of a base sequence existing before or after the inconsistent gene spot in the base sequence of the target sample.

According to another aspect of the present invention, when the analysis platform type is Illumina hybrid-capture, the probability of analysis error for the type of mutation from C to A and from G to T among the analysis errors of the base sequence variation type is determined by the remaining base sequence May be higher than the probability of the analysis error of the type of variation of.

According to another feature of the present invention, when the analysis platform type is Illumina Amplicon, the mutation type of the nucleotide sequence is G to A mutation, C to T mutation, T to A mutation, A to T mutation, T The probability of an analysis error for a variant of C to C and a variant of A to G may be higher than the probability of an analysis error of the variant type of the remaining base sequence.

According to another feature of the present invention, when the analysis platform type is IonTorrent Amplicon, the mutation type of the base sequence is G to A mutation, C to T mutation, A to C mutation, T to G mutation, T The probability of an analysis error for a variant of C to C and a variant of A to G may be higher than the probability of an analysis error of the variant type of the remaining base sequence.

According to another aspect of the present invention, the acquiring step may further include acquiring a nucleotide sequence using Illumina hybrid-capture having a depth set to 50 to 5,000.

According to still another aspect of the present invention, the acquiring step may further include acquiring a nucleotide sequence using Illumina Amplicon whose depth is set to 100 to 20,000.

According to another aspect of the present invention, the acquiring step may further include acquiring a nucleotide sequence using an IonTorrent Amplicon whose depth is set to 100 to 20,000.

According to another aspect of the present invention, a reference sample can be obtained using a next-generation sequencing method.

According to another aspect of the present invention, the mutation of the base sequence may include at least one selected from the group consisting of a low frequency somatic mutation, a mutation of a base sequence due to contamination of a sample, and a mutation of a base sequence due to a genetic disease.

According to an aspect of the present invention, there is provided a device for detecting a variation in a base sequence, the device comprising: a processor operatively connected to a communication unit, A base sequence analyzed by a sequencing method is obtained, the base sequence of the reference sample is matched with the base sequence of the target sample, the locus of the base sequence of the target sample, which does not coincide with the base sequence of the reference sample, Based on the mutation probability value of inconsistent locus calculated by the error-corrected calculation method according to the type of mutation of the base sequence, the inconsistent gene locus is determined as the nucleotide sequence variation candidate.

According to another aspect of the present invention, the processor may further be configured to determine the base sequence variation candidate as a base sequence variation when the mutation probability value of the base sequence variation candidate is equal to or higher than a certain level.

According to another aspect of the present invention, the mutation probability value is determined by determining an analysis error profile including an analysis error of a base sequence variation type and a base call quality score of an analysis error according to an analysis platform type, May be a corrected mutation probability value for the type of mutation of at least one or more base sequences.

According to another aspect of the present invention, the analysis error profile may further include nucleotide sequence information existing before or after the inconsistent locus of the nucleotide sequence of the target sample.

The present invention provides an error-corrected mutation probability value according to the type of mutation of the base sequence, thereby reducing the analysis error and detecting the mutation of the low-frequency base sequence. Accordingly, the present invention has an effect of providing a method for detecting a variation of a base sequence with improved precision and a device for detecting a base sequence variation using the same.

In addition, the present invention provides mutation probability values that are error-corrected considering the type of assay platform and the mutation type of the nucleotide sequence, so that mutation of the nucleotide sequence capable of detecting the mutation of the low frequency nucleotide sequence with high accuracy according to the analysis platform type A detection method and a device for detecting a mutation of a base sequence can be provided.

Specifically, the present invention can provide an analysis error profile including a type of mutation of a base sequence according to an analysis platform type and a base call quality score thereof, and has an effect of providing an error-corrected mutation probability value based on the analysis error profile have.

In addition, the present invention has an effect of determining an inconsistent gene locus as a nucleotide sequence variation candidate when an error-corrected probability value for a locus of a target sample inconsistent with a reference sample is equal to or higher than a predetermined level.

Furthermore, the present invention can be applied not only to low-frequency mutations but also to a method for detecting the presence of a small amount of a sample to be sequenced in other samples due to contamination or difficulty in harvesting, It is effective.

The effects according to the present invention are not limited by the contents exemplified above, and more various effects are included in the specification.

1 is a block diagram showing a schematic configuration of a base sequence variation detecting device according to an embodiment of the present invention.
2 is a flowchart illustrating a method of detecting a variation of a base sequence according to an embodiment of the present invention.
FIG. 3A is a flowchart for explaining correction of a mutation probability value in a method of detecting a mutation of a base sequence according to an embodiment of the present invention. FIG.
FIG. 3B is a graph showing the variation of the base sequence according to the analysis platform type for the correction of the mutation probability value provided in the method of detecting the base sequence variation according to an embodiment of the present invention. FIG.
FIG. 3C is a graph showing an analysis error profile of the Illumina hybrid-capture analysis platform provided in the method of detecting a mutation of a base sequence according to an embodiment of the present invention.
FIG. 3D is a graph showing an analysis error profile of the Illumina Amplicon analysis platform provided in the method for detecting a mutation of a base sequence according to an embodiment of the present invention.
FIG. 3E is a graph showing an analysis error profile of the IonTorrent Amplicon analysis platform provided in the method of detecting a mutation of a base sequence according to an embodiment of the present invention.
FIG. 4A is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to Illumina hybrid- Lt; / RTI >
FIG. 4B is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to Illumina Amplicon Fig.
FIG. 4c is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to IonTorrent Amplicon Fig.

BRIEF DESCRIPTION OF THE DRAWINGS The features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: FIG. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims.

The shapes, sizes, ratios, angles, numbers, and the like disclosed in the drawings for describing the embodiments of the present invention are illustrative, and thus the present invention is not limited thereto. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. Where the terms 'comprises', 'having', 'done', and the like are used herein, other parts may be added as long as '~ only' is not used. Unless the context clearly dictates otherwise, including the plural unless the context clearly dictates otherwise.

In interpreting the constituent elements, it is construed to include the error range even if there is no separate description.

It is to be understood that each of the features of the various embodiments of the present invention may be combined or combined with each other partially or entirely and technically various interlocking and driving is possible as will be appreciated by those skilled in the art, It may be possible to cooperate with each other in association.

For clarity of interpretation of the present specification, the terms used in this specification will be defined below.

As used herein, the term "next generation nucleotide sequence analysis" is one of DNA sequencing techniques for genomic DNA. It can analyze nucleotide sequences at high speed by treating DNA fragments in parallel. With this feature, next-generation sequencing can be called high-throughput sequencing, massive parallel sequencing, or second-generation sequencing. In addition, the next-generation sequencing can be used as a variety of analysis platforms depending on the purpose. For example, the platform for the next generation sequencing analysis is the Roche 454, GS FLX Titanium, Illumina MiSeq, Illumina HiSeq, Illumina Genome Analyzer IIX, Life Technologies SOLiD4, Life Technologies Ion Proton, Life Technologies Ion Proton, Complete Genomics, Helicos Biosciences Heliscope , And Pacific Biosciences SMRT. Furthermore, next-generation sequencing techniques can be used to detect mutations in the nucleotide sequence. Preferred assay platforms for detection of mutations in the base sequence may be, but are not limited to, Illumina hybrid-capture, Illumina Amplicon and IonTorrent Amplicon.

As used herein, the term "nucleotide sequence variation" may refer to a variation of a nucleotide sequence that occurs due to various factors. For example, the mutation of the base sequence may be a mutation in the base sequence due to the mutation of the base sequence due to the contamination of the sample, and the mutation of the base sequence due to the genetic disease, and further, the base sequence mutation may occur in the blood of the mother, , A mutation in the nucleotide sequence by an allele that may occur due to the fetal DNA present in the brain cell, and a mutation that exists in a small amount in the brain cell. However, the nucleotide sequence variants are not limited to those described above.

 A somatic mutation means a genetic mutation in a somatic cell, and this mutation can cause various problems in the medical field. Especially somatic mutations may be associated with cancer. Accordingly, the mutation of the nucleotide sequence to be detected can be a low-frequency somatic mutation of 1% or less.

As used herein, the term "locus" may refer to a nucleotide sequence at a specific position in the nucleotide sequence of the analyzed genome. The nucleotide sequence of the gene locus may be a single nucleotide sequence, but is not limited thereto and may be two or more consecutive nucleotide sequences.

As used herein, the term "type of mutation in a base sequence" refers to a single nucleotide sequence (A, T, or B) that can be analyzed in a nucleotide sequence analysis of a genome differently from a genomic sequence having a genetic sequence due to mutation or analysis errors. C, and G). However, it should not be construed to be limited thereto. The sequence of the nucleotide sequence is C to A mutation, G to A mutation, T to A mutation, A to C mutation, G to C mutation, T to C mutation, G to T mutation, C T mutation, A to T mutation, T to G mutation, C to G mutation, and A to G mutation. For example, a mutation from C to A may be of the mutation type that is sequenced to A due to errors or mutations in the sequence analysis for the C base.

As used herein, the term "depth" may refer to the extent to which one gene locus is analyzed in the next generation sequencing. At this time, as the depth increases, the leads that are subjected to sequencing of the gene locus may increase. By increasing the depth, mutations that are present at low frequencies can be detected, but this can lead to false positives, that is, errors in analysis for detection. In contrast, the method of detecting a base sequence variation of the present invention can maintain a low false positive rate even when the depth is increased. Accordingly, when the assay platform is Illumina hybrid-capture, the depth for detecting low-frequency mutations according to the method of detecting the sequence variation of the present invention may be 50 to 5,000. Preferably, the depth in Illumina hybrid-capture may be from 100 to 1,000, but is not limited thereto. Further, when the analysis platform is Illumina Amplicon, the depth for mutation detection at low frequency according to the detection method of nucleotide sequence variation of the present invention may be 100 to 20,000. Preferably, the depth at Illumina Amplicon may be from 100 to 10,000, but is not limited thereto. Furthermore, when the assay platform is IonTorrent Amplicon, the depth for detecting mutations present at low frequencies according to the method of detecting the nucleotide sequence variation of the present invention may be 100 to 20,000. Preferably, the depth at Illumina Amplicon may be from 100 to 10,000, but is not limited thereto.

As used herein, the term "subject sample" may be a biological sample obtained from a patient who wishes to ascertain a variation in the base sequence, and the term "reference sample " It may be a normal biological sample with no mutations. A preferred subject sample may be a tumor cell associated with a somatic mutation, and the preferred reference sample may be, but is not limited to, data previously sequenced for normal cells. For example, the reference sample may be variously selected according to the target sample, and the base sequence thereof may be analyzed together with the base sequence of the target sample.

The variation of the base sequence in the target sample can be detected by comparing the base sequence analyzed in the reference sample with the base sequence of the target sample. For example, the base sequence of the target sample is analyzed and then matched with the reference sample sequence. Then, the gene locus of the target sample inconsistent with the nucleotide sequence of the reference sample can be selected, and the mutation candidate of the nucleotide sequence in the target sample can be determined based on the mutation probability value for the inconsistent locus of the gene.

Here, the "mutation probability value" can be an index for determining whether the locus of the target sample inconsistent with the reference sample is an error of the base sequence analysis or a true base sequence variation. For example, a determination as a candidate for a mutation of a nucleotide sequence to a locus of an unmatched target sample can be performed based on a mutation probability value for a locus of an inconsistent target sample. For example, if the mutation probability value for an inconsistent spot is greater than or equal to a predetermined level, the inconsistent locus of the gene may be determined as a candidate for the nucleotide sequence variation of the target sample. However, it is not limited thereto. Here, the mutation probability value of the inconsistent place may be the mutation probability value corrected by considering the mutation type of various base sequences as described above. Specifically, the mutation probability value corrected in consideration of the mutation type of the base sequence can be corrected on the basis of the analysis error profile including the analysis error of the base sequence variation type and the base call quality score thereof according to the analysis platform type. Here, the "base call quality score" may be the confidence score of a calling base for a nucleotide sequence analyzing device for a particular locus. More specifically, a base call quality score may be associated with an analysis error in the sequencing step. For example, base-call quality low inconsistent gene loci can be determined by analysis errors, and inconsistent gene loci with high base call quality scores can be determined as mutations. As a result, the mutation probability value of the locus having a high base call quality score may be higher than the locus of the inconsistent locus having a low base call quality score. However, the analysis error generated in the library production stage before the sequencing may not be dependent on the base call quality score. Accordingly, the mutation probability value considering the mutation type of the base sequence selects the mutation type of the base sequence of the analysis error having a high base call quality score according to the analysis platform, and determines the analysis error profile including the information on the mutation type. The mutation probability value for the mutation type of the base sequence can then be corrected based on the determined assay error profile.

As used herein, the term " error-corrected calculation method according to mutation type of base sequence "may be a mutation probability value calculation method that is corrected based on the above-described analysis error profile. For example, if the base call quality score is a locus inconsistent with a reference sample that may have a variation type of base sequence of analysis error higher than that of another base sequence, the analysis error probability value Can be corrected higher than when the mutation type of the remaining base sequence is present. Accordingly, the mutation probability value for the inconsistent locus of the gene can be corrected to be lower than the case of having the mutation type of another base sequence. In contrast, in the case of a locus inconsistent with a reference sample in which the base call quality score has a variant type of base sequence of analysis error that is lower than that of another base sequence, the analysis error probability value Can be corrected lower than when the variant types of other base sequences are used. As a result, mutation probability values for inconsistent gene loci can be highly corrected. Accordingly, the mutation detection method of the present invention can provide a mutation probability value that is error-corrected according to the mutation type of the base sequence, which can consider the base sequence for the gene locus. Thus, the present invention can provide an accurate discrimination of analysis errors and mutations for inconsistent gene loci.

Hereinafter, with reference to Fig. 1, a device for detecting a base sequence variation according to an embodiment of the present invention will be described.

1 is a block diagram showing a schematic configuration of a base sequence variation detecting device according to an embodiment of the present invention. Referring to FIG. 1, a base sequence variation detection device 100 includes a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a processor 150.

Through the communication unit 110, the base sequence variation detection device 100 can acquire the base sequence analyzed using the next generation base sequence analysis method for the base sequence of the target sample.

The input unit 120 is not limited to a keyboard, a mouse, a touch screen panel, and the like. The user can set the base sequence variation detection device 100 through the input unit 120 and direct the operation thereof.

The display unit 130 may display menus that allow the user to easily set the base sequence variation detecting device 100 from the user. Furthermore, the display unit 130 can display the base sequence variation candidate of the target sample determined based on the mutation probability value for the unmatched gene place, so that the user can easily recognize the base sequence variation candidate. At this time, the display unit 130 may be a display device including a liquid crystal display device, an organic light emitting display device, and the like, and menus may be displayed to a user. In addition, the display unit 130 may be implemented in various forms or methods within the scope of achieving the objects of the present invention, in addition to those described above.

The storage unit 140 may store the base sequence of the target sample acquired through the communication unit 110. It is also possible to store a nucleotide sequence variation candidate of a target sample determined on the basis of a mutation probability value for an inconsistent locus of a gene.

The processor 150 performs various instructions for operating the base sequence variation detection device 100 according to an embodiment of the present invention. The processor 150 is connected to the communication unit 110 to acquire a nucleotide sequence analyzing the nucleotide sequence of the target sample by the next generation nucleotide sequence analysis method through the communication unit 110, The nucleotide sequence of the target sample is matched and the locus of the nucleotide sequence of the target sample which does not coincide with the nucleotide sequence of the reference sample is selected and an error is corrected according to the type of mutation of the nucleotide sequence, Based on the mutation probability value, an inconsistent locus of the gene is determined as a nucleotide sequence variation candidate.

Hereinafter, with reference to FIG. 2, a method of detecting a variation of a base sequence according to an embodiment of the present invention will be described in detail.

2 is a flowchart illustrating a method of detecting a variation of a base sequence according to an embodiment of the present invention.

First, the nucleotide sequence of the target sample is obtained using a next-generation sequencing method (S210). In this case, in the obtaining step S210, when there is no base sequence for the reference sample, the base sequence analysis for the reference sample can be performed together with the base sequence analysis of the sample to be tested. For example, each of the reference sample and the target sample may include a plurality of leads, and the plurality of leads are mapped with a reference sequence and finally analyzed in step S210 of acquiring The base sequence of the reference sample and the base sequence of the target sample can be collected.

Then, the base sequence of the reference sample is matched with the base sequence of the target sample (S220). Alternatively, in step S220, the base sequence of the reference sample is matched with the base sequence of one target sample, and as a result, the base sequence of the target sample can be matched to the base sequence of the reference sample. For example, in the matching step S220, the nucleotide sequence of the reference sample and the nucleotide sequence of the target sample collected in step S210 may be compared by gene locus.

Next, a gene spot which does not match the base sequence of the reference sample among the base sequence of the target sample is selected (S230). At this time, the locus of the gene that is inconsistent with the nucleotide sequence of the reference sample may be a nucleotide sequence variation or an error in sequence analysis.

Finally, the inconsistent gene locus is determined as a nucleotide sequence variation candidate based on the mutation probability value for the inconsistent locus of the gene, which is calculated by an error-corrected calculation method according to the type of mutation of the nucleotide sequence (S240). According to another embodiment, in the determining step S240, if the mutation probability value for the inconsistent locus of the gene is equal to or higher than the predetermined level, the locus of the inconsistent gene may be determined as the nucleotide sequence variation candidate. According to various embodiments, in step S240, the inconsistent locus of the gene may be determined as an analysis error regardless of the mutation probability value. For example, if the mapping quality of the reference sample and the target sample is below a predetermined level, the majority of the bases in the analyzed sample are below the predetermined level of base call quality score, Indicates a predetermined level or more, and the unequal locus of each of the plurality of leads indicates a predetermined level or more, when the mutation appears in the matching control data, the gene locus of these target samples is determined regardless of the mutation probability value , And can be determined as an analysis error. However, the determination of the analysis error for the locus of the gene is not limited to the above.

The method for detecting a base sequence variation according to an embodiment of the present invention and the device using the same can detect a base sequence variation with high precision and provide the base sequence variation candidate to a user .

Hereinafter, with reference to FIG. 3A, a method for correcting a mutation probability value for an inconsistent locus provided by a method for detecting a nucleotide sequence variation according to an embodiment of the present invention will be described in detail.

FIG. 3A is a flowchart for explaining correction of a mutation probability value provided by the method for detecting a mutation of a base sequence according to an embodiment of the present invention. FIG.

First, the analysis error probability value is provided as a corrected value considering the type of mutation of the base sequence. Specifically, an analysis error profile including an analysis error of the base sequence variation type and a base call quality score thereof is determined according to the analysis platform type (S310). More specifically, the probability of mutation of one locus can be increased as the base call quality score increases. Accordingly, in the step of determining the analysis error profile (S310), the type of mutation of the base sequence of the analysis error having a high base call quality score according to the analysis platform can be selected in order to correct the analysis error considering the mutation type of the base sequence have. For example, if the analysis platform is Illumina hybrid-capture, the base call quality score of the C-to-A variant and G-to-T variant of analysis errors for each base sequence variant may be higher than for the other variants. That is, in Illumina hybrid-capture, the C to A mutation and the G to T variant types are actually analytical errors, but they can lead to errors in the detection of mutations. In addition, when the analysis platform is Illumina Amplicon, the mutation type of the nucleotide sequence is G to A, C to T, T to A, A to T, T to C, and A to G The base call quality score of the variant type may be higher than the variance of the remaining types. Further, when the analysis platform is IonTorrent Amplicon, the type of mutation of the base sequence is G to A, C to T, A to C, T to G, T to C, and A to G The base call quality score of the variant type may be higher than the variance of the remaining types. As a result, the step of determining the analysis error profile (S310) can determine the analysis error profile including the base sequence quality score and the analysis error according to the variation type of the base sequence according to the above-described analysis platform type. In addition, the analysis error profile may further include information of the base sequence existing before or after the inconsistent locus of the base sequence of the sample of interest.

Then, the probability value of the analysis error for the variation type of the base sequence is corrected according to the analysis platform type based on the analysis error profile determined in step S310 of determining the analysis error profile (S320). For example, for Illumina hybrid-capture, the probability of an analysis error for a mutation type C to A and a mutation type G to T in the analysis error of each base sequence variation type is higher than the analysis error probability value of the mutation type of the remaining base sequence Can be corrected. Also, for Illumina Amplicon, the type of mutation of the base sequence is G to A mutation, C to T mutation, T to A mutation, A to T mutation, T to C mutation, and A to G mutation type It is possible to correct the probability value of the analysis error to be higher than the probability value of the analysis error of the mutation type of the remaining base sequence. Furthermore, for the IonTorrent Amplicon, mutation types from base sequence to G to A, from C to T, from A to C, from T to G, from T to C, and from A to G Can be corrected to be higher than the probability value of the analysis error of the mutation type of the remaining base sequence. As a result, the analysis error probability value for the inconsistent locus of the locus is calculated as the analysis error probability value corrected through the analysis error probability value correction step (S320). Accordingly, the base sequence variation candidates of the target sample can be determined based on the analysis error probability value and the mutation probability value, which are calculated in consideration of the base sequence for the inconsistent locus of the gene.

Hereinafter, with reference to Figs. 3B, 3C, 3d and 3E, a method for determining an analysis error profile, which is provided in the method for detecting a variation of base sequence according to an embodiment of the present invention, will be described in detail.

FIG. 3B is a graph showing the variation of the base sequence according to the analysis platform type for the correction of the mutation probability value provided in the method of detecting the base sequence variation according to an embodiment of the present invention. FIG. FIG. 3C is a graph showing an analysis error profile of the Illumina hybrid-capture analysis platform provided in the method of detecting a mutation of a base sequence according to an embodiment of the present invention. FIG. 3D is a graph showing an analysis error profile of the Illumina Amplicon analysis platform provided in the method for detecting a mutation of a base sequence according to an embodiment of the present invention. FIG. 3E is a graph showing an analysis error profile of the IonTorrent Amplicon analysis platform provided in the method of detecting a mutation of a base sequence according to an embodiment of the present invention.

Referring to FIG. 3B, the x-axis of the graph represents the type of the mutation of the base sequence, and the y-axis represents the ratio of the total analysis error to the analysis error generated in the library production step. At this time, the type of mutation of the base sequence may further include a mutation type of the base sequence having a complementary relationship therebetween.

Specifically, in the case of Illumina hybrid-capture, the analysis error of the type C to A (mutation from G to T) type has a higher base call quality score than the analysis error of the mutation type of the remaining base sequence. That is, the analysis platform of the Illumina hybrid-capture may show high analysis errors for C to A variants (G to T variants) types. Thus, the Illumina hybrid-capture analysis platform can be calibrated to have high analytical error probability values for C to A variants (G to T variants) types. As a result of the analysis error probability correction, the Illumina hybrid-capture analysis platform can be calibrated to have a low mutation probability value from C to A mutation (G to T variant). In addition, the A to C variant (T to G variant) types with lower base call quality than other types can be calibrated to have higher mutation probability values.

For the Illumina Amplicon, the type of G to A mutation (C to T variant), the type of T to A mutation (A to T mutation), and the type of T to C mutation (mutation A to G) the base call quality score is high. In particular, the base call quality score of the analysis error of type A to G (mutation from T to C) type appears higher than that of the remaining base sequences. That is, the analysis platform of Illumina Amplicon may show the highest analytical error for A to G variants (T to C variants) types. Illumina Amplicon's analytical platform is based on the assumption that A to G mutation (T to C mutation) types, furthermore T mutation (A mutation to T mutation) type and C mutation to T mutation ) ≪ / RTI > type. ≪ RTI ID = 0.0 > As a result of the analysis error probability correction, the analysis platform of Illumina Amplicon has a variation from A to G (from T to C) type, further from T to A (mutation from A to T) and from C to T To A) variant of the present invention can be corrected to have a low mutation probability value. In addition, the C to A variant (G to T variant) type with a lower base call quality than other types can be calibrated to have a higher mutation probability value.

 In the case of IonTorrent Amplicon, it can be seen that analysis errors of all types of base sequences have a high base call quality score. That is, IonTorrent's analysis platform can exhibit high analysis errors over all variant types compared to other analysis platforms. Accordingly, the analysis platform of IonTorrent Amplicon can be calibrated to have a high analytical error probability value for all variant types of base sequences, and as a result, to have a low mutation probability value.

By correcting for analysis errors that are highly frequent in the above three analysis platforms, the method for detecting mutation of the base sequence of the present invention and the device for detecting base sequence using the same are capable of calculating a mutation probability value with high reliability . Thus, the invention can provide detection of mutation of highly precise base sequence. Furthermore, the present invention can detect low-frequency somatic mutation with high precision.

Referring to FIGS. 3c, 3d and 3e, the six graphs are empirical cumulative distribution functions (ECDFs) according to the type of mutation of the base sequence, and the x-axis shows BAF frequency, and the y-axis represents the cumulative ratio of these BAF values. Here, the BAF value may mean the frequency of a base (B allele) that is inconsistent with the base sequence of the reference sample compared to the total number of bases analyzed for the locus. Each graph shows the distribution of BAF values for the loci showing the variant types of nucleotide sequences according to the analysis platform, and the analysis error distribution for each analysis platform can be confirmed. The dotted line graph is an empirical cumulative distribution function constructed using known data, and the solid line graph is a cumulative distribution function of the exponential distribution estimated using the data of the analysis error profile provided by the present invention.

Specifically, the analysis platform of the Illumina hybrid-capture in FIG. 3c shows that the BAF value of most of the genetic loci representing these types is close to zero with respect to the overall nucleotide sequence variation. It can be seen that the BAF value of the genetic loci representing the C to A mutation (G to T variant) type has a large number of loci with a BAF value of 0 to 0.05 as compared to the mutation types of the remaining base sequences. As a result, the BAF values of the locus with a C to A mutation (G to T variant) type are distributed in a variety of different values. The results are shown in FIG. 3C. In the Illumina hybrid-capture assay platform, the C-to-A variant (G to T variant) type was associated with a higher base call quality score than the variant type of the remaining base sequences .

In Figure 3D, Illumina Amplicon's analysis platform is A to G mutation (T to C mutation) type furthermore mutation from T to A mutation (A to T mutation) type and C mutation to T mutation ), It can be seen that the BAF values of the genetic loci representing these types are distributed in various values in comparison with the mutation types of the other base sequences. In particular, it can be seen that the BAF value of the gene spot showing the type of A to G mutation (T to C) has a large number of gene sites having a BAF value of 0 to 0.10 as compared with the mutation type of the remaining base sequence. As a result, the A to G mutation (T to C mutation) type and furthermore, the T to A mutation (A to T mutation) type and the C to T mutation (G to A mutation) BAF values are distributed in various values. The results are shown in Figure 3c, in the case of the Illumina Amplicon analysis platform, the type of mutation from A to G (mutation from T to C), mutation from T to A (mutation from A to T), and mutation from C to T G-to-A variants) were associated with higher base call quality scores than variants of the remaining base sequences.

In Figure 3E, the analysis platform of IonTorrent Amplicon reveals that the BAF values of most of the genetic loci representing these types are close to zero for the overall nucleotide sequence variants. Compared with the Illumina hybrid-capture analysis platform of FIG. 3c, the IonTorrent Amplicon analysis platform shows that all nucleotide sequence variants have more diverse BAF values. This result, in FIG. 3c, can be related to the result that the base call quality score for the variant type of all base sequences was high in the case of the IonTorrent Amplicon analysis platform.

As a result, the empirical cumulative distribution function (dotted line) of the analytical error profile and the cumulative distribution function (solid line) of the estimated exponential distribution provided by the present invention in the analysis platforms of both Illumina Amplicon, IonTorrent Amplicon and IonTorrent Amplicon fitting. Further, it is desirable that the three assay platforms be calibrated to have a high assay error probability value for variant types of base sequences with BAF values varying relatively and with values greater than zero. As a result of the correction of the analysis error, the three assay platforms can be calibrated to have a low mutation probability value for the mutation types of the base sequences with relatively large BAF values and values greater than zero. The method for detecting mutation of the base sequence of the present invention and the device for detecting base sequence using the same provide a mutation probability value calculated by a calibrated calculation method for an inconsistent locus, Accurate distinction to errors or mutations may be possible. Furthermore, the present invention can detect low-frequency somatic mutation with high precision.

Evaluation of the mutation detection method of the base sequence according to one embodiment of the present invention

Hereinafter, with reference to Figs. 4A, 4B, and 4C, the method of detecting mutation of the base sequence of the present invention is evaluated by comparing with the method of detecting mutation of the conventional base sequence.

FIG. 4A is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to Illumina hybrid- Lt; / RTI > FIG. 4B is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to Illumina Amplicon Fig. FIG. 4c is a graph showing the accuracy, sensitivity, F-score, and false positive rate of detection of mutations in a base sequence, which were measured by applying the method of detecting mutation of a base sequence according to an embodiment of the present invention and a conventional detection method to IonTorrent Amplicon Fig.

Specifically, MuTect was used as a conventional method for detecting a somatic mutation.

For evaluation, A blood samples and B blood samples containing different dielectrics are prepared.

A B blood sample was then serially diluted in A blood sample to obtain a blood sample A containing 0.5% B blood sample, a blood sample containing 1% B blood sample, and a sample containing 5% B blood sample A blood sample containing a blood sample and a 10% B blood sample is prepared to prepare an artificial somatic mutation sample. That is, for the A blood sample, the B blood sample can be a somatic mutation, and its four concentrations can mean the frequency of the somatic mutation.

Next, the detection method of the present invention and MuTect were applied to the analysis platform of Illumina hybrid-capture, Illumina Amplicon, and IonTorrent Amplicon for four prepared artificial somatic mutation samples.

Hereinafter, the results of detecting A blood sample containing 0.5% B blood sample by the detection method according to one embodiment of the present invention are shown in Example 1, by serially diluting the B blood sample with the A blood sample. Subsequently, the A blood sample containing 1% of the B blood sample was detected by the detection method of the present invention. In Example 2, the A blood sample containing 5% of the B blood sample was detected by the detection method of the present invention The results are shown in Example 3. Finally, the result of detection of the A blood sample containing 10% of the B blood sample by the detection method of the present invention is shown in the fourth example.

A B blood sample was serially diluted in a blood sample and the result of detection of A blood sample containing 0.5% B blood sample with MuTect is shown in Comparative Example 1. Subsequently, the A blood sample containing 1% B blood sample was detected with MuTect as Comparative Example 2, and the A blood sample containing 5% B blood sample was detected with MuTect as Comparative Example 3 . Finally, the result of detection of the A blood sample containing 10% of the B blood sample with MuTect is shown in Comparative Example 4.

Referring to the comparative example in FIG. 4A, it can be seen that the detection accuracy is lowered as the B blood sample is present at a low concentration with respect to the A blood sample. In particular, it can be seen that the accuracy of the 0.5% concentration of Comparative Example 1 dropped to about a quarter of the accuracy of the 5% concentration of Comparative Example 3 and the 10% concentration of Comparative Example 4. Furthermore, it can be seen that as the depth increases, the false positive rate increases significantly in all four comparative examples.

In contrast, referring to the results of applying the detection method of the present invention to the Illumina hybrid-capture, accuracy, sensitivity, and F-score were higher than those of the comparative example obtained by applying MuTect to Illumina hybrid-capture. In particular, the accuracy of detection at 0.5% concentration in Example 1 was about 3.5 times higher than that in Comparative Example 1. [ Furthermore, even if the false positive rate is increased, the detection method of the present invention maintains a value close to 0 in all four embodiments, so that the detection of a somatic mutation that exists at low frequency without error in analysis can be possible.

 In Figure 4b, when applying MuTect to the Illumina Amplicon, it can be seen that the four comparative examples show a low level of precision and F-score in all four concentrations. Furthermore, it can be seen that as the depth increases, the false positive rate increases significantly in all four comparative examples.

In contrast, referring to the example of applying the detection method of the present invention to the Illumina Amplicon, it can be seen that the accuracy and F-score of the llumina Amplicon are higher than those of the comparative example which is the result of applying MuTect to the llumina Amplicon. In particular, it can be seen that the precision of 1% concentration in Example 2 is about 70 times higher than that in Comparative Example 2. Furthermore, even if the false positive rate is increased, the detection method of the present invention maintains a value close to 0 in all four embodiments, so that the detection of a somatic mutation that exists at low frequency without error in analysis can be possible.

In FIG. 4C, referring to the comparative example, which is the result of applying MuTect to the IonTorrent Amplicon, it can be seen that the detection accuracy and the F-score are lowered as the B blood sample is present at a low concentration for the A blood sample. Furthermore, it can be seen that as the depth increases, the false positive rate increases significantly in all four comparative examples.

In contrast, referring to the embodiment which is the result of applying the detection method of the present invention to the IonTorrent Amplicon, the accuracy is higher in all four embodiments than the result of the comparative example in which MuTect is applied to the IonTorrent Amplicon. In particular, the accuracy of detection of the concentration of 1% in Example 2 was about three times higher than that of Comparative Example 2. Furthermore, even if the false positive rate is increased, the detection method of the present invention maintains a value close to 0 in all four embodiments, so that the detection of a somatic mutation that exists at low frequency without error in analysis can be possible.

As a result of the above examples and comparative examples, the method of detecting mutation of the base sequence according to one embodiment of the present invention has a low false positive rate as the depth increases in both the Illumina hybrid-capture, Illumina Amplicon and IonTorrent Amplicon analysis platforms So that the detection error can be reduced. Accordingly, the present invention is capable of providing a low frequency somatic mutation capable of highly accurate analysis of base sequence variation detection, and further, a method of detecting a base sequence variation. In addition, when the mutation detection method of the present invention is applied to Illumina Amplicon, the detection accuracy of the nucleotide sequence can be enhanced when the mutation present at a frequency of 1% is detected. Furthermore, in all of the analysis platforms using the existing somatic mutation detection method, the detection accuracy may be low for the detection of low frequency somatic mutation, and the false positive rate increases as the depth increases, . This is in contrast to the results obtained by applying the mutation detection method of the nucleotide sequence of the present invention to the same analysis platform.

Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, it is to be understood that the present invention is not limited to those embodiments and various changes and modifications may be made without departing from the scope of the present invention. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

100: Mutation detection device of base sequence
110:
120: Input unit
130:
140:
150: Processor
S210: acquiring step
S220: matching step
S230: Selection step
S240: determining step
S310: Determining the analysis error profile
S320: correcting the analysis error probability value

Claims (17)

Obtaining a base sequence of the target sample using a next generation sequencing (NGS) method;
Matching the base sequence of the reference sample with the base sequence of the subject sample;
Selecting a locus of the base sequence of the target sample that is inconsistent with the base sequence of the reference sample; And
And determining the inconsistent gene locus as a nucleotide sequence variation candidate based on the mutual probability value of the inconsistent locus of the locus calculated by an error-corrected calculation method according to the mutation type of the nucleotide sequence. A method for detecting a mutation in a sequence. The method according to claim 1,
The step of determining the nucleotide sequence variation candidate comprises:
If the mutation probability value of the inconsistent locus of the locus is above a predetermined level,
Further comprising the step of determining the inconsistent gene locus as a nucleotide sequence variation candidate. The method according to claim 1,
The type of mutation of the base sequence is a mutation from A to C, a mutation from A to G, mutation from A to T, mutation from A to C, mutation from C to G, mutation from C to T, mutation from G to T, A mutation type of at least one base sequence selected from the group consisting of a mutation from C to T, a mutation from A to T, a mutation from T to G, a mutation from C to G, and a mutation from A to G . The method according to claim 1,
The mutation probability value,
Determining an analysis error profile including an analysis error for each type of mutation of the base sequence and a base call quality score of the analysis error according to the analysis platform type,
Wherein the mutation probability value is a corrected mutation probability value for a mutation type of at least one or more base sequences based on the analysis error profile. 5. The method of claim 4,
Wherein the analysis error profile comprises:
Further comprising information of a nucleotide sequence existing before or after the inconsistent gene spot in the nucleotide sequence of the target sample. 5. The method of claim 4,
If the analysis platform type is Illumina hybrid-capture,
Wherein the probability of an analysis error for the type of mutation from C to A and from G to T in the analysis error for each type of mutation of the base sequence is higher than the probability of the analysis error of the mutation type of the remaining base sequence. 5. The method of claim 4,
If the analysis platform type is Illumina Amplicon,
The probability of the analysis error for the type of mutation from G to A, from C to T, from T to A, from A to T, from T to C, Is higher than the probability of the analysis error of the type of mutation of the remaining base sequence. 5. The method of claim 4,
If the analysis platform type is IonTorrent Amplicon,
The probability of the analysis error for the type of mutation of the above base sequence is G to A, C to T, A to C, T to G, T to C, and A to G Is higher than the probability of the analysis error of the type of mutation of the remaining base sequence. The method according to claim 1,
Wherein the acquiring comprises:
Further comprising the step of acquiring a nucleotide sequence using Illumina hybrid-capture having a depth set to 50 to 5,000. The method according to claim 1,
Wherein the acquiring comprises:
Further comprising the step of obtaining a nucleotide sequence using Illumina Amplicon having a depth of 100 to 20,000. The method according to claim 1,
Wherein the acquiring comprises:
And obtaining a nucleotide sequence using an IonTorrent Amplicon having a depth of 100 to 20,000. The method according to claim 1,
Wherein the reference sample is obtained using a next-generation sequencing method. The method according to claim 1,
The mutation of the above base sequence is,
At least one selected from the group consisting of low-frequency somatic mutation, mutation of the base sequence due to contamination of the sample, and mutation of the base sequence due to genetic disease. And a processor operatively coupled to the communication unit,
The processor comprising:
A nucleotide sequence analyzing a nucleotide sequence of a target sample using a next generation nucleotide sequence analysis method is obtained through the communication unit,
The base sequence of the reference sample and the base sequence of the target sample are matched,
Selecting a locus of the nucleotide sequence of the target sample that does not match the nucleotide sequence of the reference sample,
A mutation of the base sequence, which is constructed to determine the inconsistent gene locus as a base sequence mutation candidate, based on the mutual probability value of the inconsistent locus of the locus calculated by an error-corrected calculation method according to the mutation type of the base sequence Detection device. 15. The method of claim 14,
The processor comprising:
When the mutation probability value of the base sequence mutation candidate is equal to or higher than a certain level,
Wherein the base sequence variation candidate is further determined to be a base sequence variation. 15. The method of claim 14,
The mutation probability value,
Determining an analysis error profile including an analysis error for each type of mutation of the base sequence and a base call quality score of the analysis error according to the analysis platform type,
And a mutation probability value corrected for the type of mutation of at least one or more base sequences based on the analysis error profile. 17. The method of claim 16,
Wherein the analysis error profile comprises:
Further comprising nucleotide sequence information existing before and after the inconsistent gene locus in the nucleotide sequence of the subject sample.

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