KR20170000744A - Method and apparatus for analyzing gene - Google Patents

Abstract

The present invention relates to a method and a device for analyzing a gene. The present invention generates a reference data set by performing deep sequencing for reference genes, analyzes the depths of the genes by performing deep sequencing for the genes, and compares the analyzed depths with the depths of the reference genes included in the reference data set to determine whether there is a copy number variation (CNV) gene among the genes.

Description

[0001] METHOD AND APPARATUS FOR ANALYZING GENE [0002]

And more particularly, to a method and apparatus for analyzing genes of copy number variation (CNV).

A genome is any genetic information that a creature has. Several techniques have been developed for DNA sequencing of a single individual, such as DNA chip and next generation sequencing technology, and next generation sequencing technology. Genetic information such as nucleic acid sequences and proteins are widely used to find genes expressing diseases such as diabetes and cancer or to correlate genetic diversity and expression characteristics of individuals. In particular, genetic data collected from individuals is important in identifying genetic characteristics of individuals with different symptoms or progression of disease. Thus, genetic data such as individual nucleic acid sequences, proteins, and the like are crucial data that can identify current and future disease-related information to prevent disease or select optimal treatment methods at an early stage of disease. Techniques for accurately diagnosing individuals' genetic data and diagnosing individual diseases using genome detection devices that detect SNP (Single Nucleotide Polymorphism) and CNV (Copy Number Variation) as biological genetic information are under study.

And a method and apparatus for analyzing genes. The technical problem to be solved by this embodiment is not limited to the above-mentioned technical problems, and other technical problems can be deduced from the following embodiments.

According to one aspect, a method of analyzing a gene comprises generating a set of reference data for the depths of leads aligned with each of the reference genes by performing a deep sequencing on the reference genes; Analyzing the depths of the leads aligned with each of the tested genes by performing the deep sequencing on the tested genes; And comparing the analyzed depths with depths of the reference genes included in the reference data set to determine whether a copy number variation (CNV) gene exists among the tested genes.

In addition, the analyzing step analyzes the depth of the leads aligned with the exon regions of the test genes.

In addition, the determining step determines the presence of the copy number mutation (CNV) gene by comparing the depths of the reference genes and the test genes for the same exon region.

Also, in the determining step, the exon regions of the exon regions of the test genes are not statistically significant in the difference between the depths of the exon regions corresponding to each other among the reference genes and the test genes , It is determined that the copy number variation (CNV) gene is present.

Also, the generating may include obtaining the lead-depths corresponding to the reference genes for each of the people through the deep sequencing of gene data of a plurality of people; Clustering the people into different groups according to the distribution of the obtained lead-depths; And obtaining standard depths of each of the reference genes representing each of the groups by standardizing the lead-depths obtained for each of the reference genes for each group, For each of the reference genes, data representative of the standard depths of each of the reference genes.

The determining may include determining a group having a smallest statistical difference between the distribution of the analyzed depths and the distribution of the standard depths among the groups; And comparing the analyzed depths with standard depths corresponding to the determined group to determine whether the copy number variation (CNV) gene is present.

The method further includes obtaining the gene data of the people from public genomic data or public map (HapMap) data.

In addition, the reference genes or the test genes may be obtained from biopsy tissue, formalin-fixed paraffin-embedded (FFPE) tissue.

In addition, when it is determined that the copy number mutation (CNV) gene is present among the genes to be tested, an annotation is performed to identify a drug corresponding to the copy number mutation (CNV) gene.

According to another aspect, there is provided a computer-readable recording medium storing a program for causing a computer to execute the method.

According to another aspect, an apparatus for analyzing a gene includes: a reference data generator for generating a reference data set for the depths of leads aligned to each of the reference genes by performing a deep sequencing on the reference genes; An analyzer for analyzing the depths of the leads aligned to each of the tested genes by performing the deep sequencing on the tested genes; And a determination unit for determining whether a copy number variation (CNV) gene exists among the test genes by comparing the analyzed depths with depths of the reference genes included in the reference data set.

In addition, the analyzing unit analyzes the depth of the leads aligned on the exon regions of the test genes.

In addition, the determination unit determines the existence of the copy number variation (CNV) gene by comparing the depths between the reference genes and the test genes for the same exon region.

In addition, when the exon regions of the exon regions of the test genes are not statistically significant in the difference between the depths of the exon regions corresponding to each other among the reference genes and the test genes, , And the copy number variation (CNV) gene is present.

In addition, the reference data generator may acquire lead-depths corresponding to the reference genes for each of the people through the deep sequencing of gene data of a plurality of people, The standard depths of each of the reference genes representing each of the groups are obtained by clustering the people into different groups and standardizing the lead-depths obtained for each of the reference genes for each group, The reference data set includes, for each of the groups, data representing the standard depths of each of the reference genes.

The determining unit may determine a group having a smallest statistical difference between the distribution of the analyzed depths and the distribution of the standard depths among the groups, and compare the analyzed depths with the standard depths corresponding to the determined group Thereby determining whether the copy number variation (CNV) gene is present.

Also, the reference data generator obtains the genetic data of the people from public genome data or public map (HapMap) data.

In addition, the reference genes or the test genes may be obtained from biopsy tissue, formalin-fixed paraffin-embedded (FFPE) tissue.

In addition, when the determination unit determines that the copy number mutation (CNV) gene exists, the determination unit annotates the drug corresponding to the copy number mutation (CNV) gene.

According to the above, it is possible to more accurately analyze whether the copy number mutation (CNV) gene is present from the test gene of the test subject.

1 is a diagram for explaining a gene analysis apparatus according to an embodiment.
2 is a block diagram illustrating hardware configurations of a gene analysis apparatus according to an exemplary embodiment of the present invention.
3 is a flow diagram of a method for generating a set of reference data in accordance with an embodiment.
FIG. 4 is a diagram for explaining obtaining lead-depths corresponding to reference genes for each of a plurality of people (for example, normal persons) according to an embodiment.
5 is a diagram for explaining deep sequencing for exon regions according to an embodiment.
FIG. 6 is a diagram illustrating clustering of people into different groups according to the distribution of lead-depths obtained from the normal population 400 according to an embodiment.
FIG. 7 is a diagram for explaining the standard depths of reference genes, which represent a group according to an embodiment.
FIG. 8 is a diagram for explaining performing deep sequencing on the test genes obtained from a biological sample of a subject according to an embodiment. FIG.
9 is a flowchart of a method for determining whether a copy number variation (CNV) gene is present according to an embodiment.
FIG. 10 is a diagram for explaining whether or not a copy number variation (CNV) gene is present according to an embodiment.
11 is a flowchart of a method for analyzing a gene according to an embodiment.
12 is a block diagram illustrating hardware configurations of a computing device according to one embodiment.

Although the terms used in the present embodiments have been selected in consideration of the functions in the present embodiments and are currently available in common terms, they may vary depending on the intention or the precedent of the technician working in the art, the emergence of new technology . Also, in certain cases, there are arbitrarily selected terms, and in this case, the meaning will be described in detail in the description part of the embodiment. Therefore, the terms used in the embodiments should be defined based on the meaning of the terms, not on the names of simple terms, and on the contents of the embodiments throughout.

In the descriptions of the embodiments, when a part is connected to another part, it includes not only a case where the part is directly connected but also a case where the part is electrically connected with another part in between . Also, when a component includes an element, it is understood that the element may include other elements, not the exclusion of any other element unless specifically stated otherwise. The term " ... ", " module ", as used in the embodiments, means a unit for processing at least one function or operation, and may be implemented in hardware or software, or a combination of hardware and software Can be implemented.

It should be noted that the terms such as " comprising " or " comprising ", as used in these embodiments, should not be construed as necessarily including the various components or stages described in the specification, Some steps may not be included, or may be interpreted to include additional components or steps.

The following description of the embodiments should not be construed as limiting the scope of the present invention and should be construed as being within the scope of the embodiments of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Exemplary embodiments will now be described in detail with reference to the accompanying drawings.

1 is a diagram for explaining a gene analysis apparatus according to an embodiment.

1, the gene analyzer 10 uses the gene data 20 obtained from the normal population and the gene data 30 obtained from the subject to determine the copy number variation (CNV) It is possible to identify whether or not the gene exists.

The gene data 20 and the gene data 30 received by the gene analysis apparatus 10 may correspond to the gene data of the FASTQ file format obtained by the next generation sequencing (NGS). The FASTQ format is a text-based format that stores biological sequences, such as nucleotide sequences, and their corresponding quality scores. However, the gene analysis apparatus 10 according to the present embodiment is not limited to the FASTQ format, and gene data 20 and 30 of different formats can be analyzed.

The genomic data 20 of the normal population can be obtained from a database (DB) already known in the art such as National Center for Biotechnology Information (NCBI), Gene Expression Omnibus (GEO), or the like, Lt; RTI ID = 0.0 > a < / RTI > That is, the genetic data 20 may be obtained from public genomic data or public map (HapMap) data. On the other hand, the reference genes contained in the gene data 20 or the test genes contained in the gene data 30 may be obtained from biopsy tissue, formalin-fixed tissue, or formalin-fixed (paraffin-embedded) tissue Lt; / RTI >

The copy number variation (CNV) is known to mean a variation in a gene that is relatively repeated as compared with a reference genome, in which a relatively large region of a specific chromosome is missing or amplified. That is, the gene analysis apparatus 10 can determine whether or not an abnormally deficient or amplified gene exists in the gene data 30 obtained from the subject relative to the gene data 20 obtained from the normal population. Here, the gene analyzed by the gene analysis apparatus 10 may mean a nucleic acid such as DNA (deoxyribonucleic acid), RNA (ribonucleic acid) and the like.

In the present embodiments, the normal population refers to a group of general people who have not found a specific disease, such as cancer or tumor, and the subject may refer to a patient who has found a specific disease such as cancer, have. On the other hand, in the present embodiments, the normal group, the subject may be other animals than humans.

The gene analysis apparatus 10 may be implemented with at least one processor having a function of data processing for performing various algorithms for analyzing the gene data 20 and 30 to identify the copy number variation (CNV) gene, various algorithms .

2 is a block diagram illustrating hardware configurations of a gene analysis apparatus according to an exemplary embodiment of the present invention.

Referring to FIG. 2, the gene analysis apparatus 10 may include a reference data generator 110, an analyzer 120, and a determiner 130. 2, only the components related to the present embodiment are shown in order to prevent the characteristics of the present embodiment from being blurred. Therefore, the gene analysis apparatus 10 is shown in FIG. 2 Other general components may be further included.

The reference data generating unit 110 receives the gene data 20 obtained from the normal population described above with reference to FIG. 1 and generates the reference data set using the received gene data 20.

More specifically, the reference data generator 110 performs deep sequencing on the reference genes included in the gene data 20, thereby extracting the depths of readings sorted on each of the reference genes (depths) of the reference data set. Deep sequencing is a technique for sequencing nucleic acids such as DNA fragments and RNA fragments by repeatedly aligning leads to nucleic acids such as DNA fragments and RNA fragments. As a result of the deep sequencing, data on the depths corresponding to the number of leads complementarily bound to nucleic acids such as DNA fragments, RNA fragments and the like can be obtained. In the present embodiments, the term " depth " may be used interchangeably with the term " read-depth ".

The reference data generating unit 110 firstly generates the reference data for each of the people through deep sequencing of gene data (20 in FIG. 1) of a plurality of people (for example, normal persons) ≪ / RTI > Then, the reference data generator 110 clusters the users into different groups according to the distribution of the obtained lead-depths. The reference data generator 110 normalizes the lead-depths obtained for each of the reference genes for each group, thereby obtaining the standard depths of the respective reference genes representing each of the groups. As a result, the reference data set generated by the reference data generator 110 may include, for each of the groups, data representing the standard depths of each of the reference genes.

The analyzer 120 receives the gene data 30 obtained from the subject described above with reference to FIG. 1 and performs deep sequencing on the test genes included in the gene data 30, Analyze the depths of the aligned leads.

Meanwhile, the deep sequencing performed by the reference data generator 110 and the analyzer 120 may be performed on reference genes or exon regions in the test gene. In other words, the reference data set generated by the reference data generating unit 110 or the data of the depths analyzed by the analyzing unit 120 corresponding to the result of the deep sequencing includes the depths of the exon regions Only data is included, and data about the depths of leads aligned to intron sites may not be included. However, the present embodiments are not limited thereto, and depth data about intron portions may be included.

The determination unit 130 compares the depths analyzed by the analysis unit 120 with the depths of the reference genes included in the reference data set generated by the reference data generation unit 110. [ Then, the determination unit 130 determines whether a copy number variation (CNV) gene exists among the tested genes. At this time, the determination unit 130 can determine the presence of the copy number variation (CNV) gene by comparing the depths of the reference genes and the test genes by the same exon region.

The determination unit 130 determines whether there is an exon site in which the difference in the depth between the exon regions corresponding to each other among the exon regions of the test genes is not statistically significant , It can be judged that the copy number mutation (CNV) gene exists.

The determination unit 130 detects or identifies that the gene corresponding to the exon region in which the difference in depth in the corresponding exon regions is not statistically significant corresponds to the copy number variation (CNV) gene. In addition, when it is determined that the copy number mutation (CNV) gene is present among the genes to be tested, the determination unit 130 determines that a drug (for example, an anti-cancer agent) corresponding to the detected copy number variation (CNV) It is possible to perform an annotation for identifying the user.

3 is a flow diagram of a method for generating a set of reference data in accordance with an embodiment. Referring to FIG. 3, the generation of the reference data set includes the steps of time-series processing in the reference data generation unit 110 described above.

In step 301, the reference data generator 110 obtains the lead-depths corresponding to the reference genes for each of a plurality of people (for example, normal persons).

In step 302, the reference data generator 110 clusters people into different groups according to the distribution of the obtained lead-depths.

In step 303, the reference data generator 110 normalizes the lead-depths obtained for each of the reference genes for each group.

In step 304, the reference data generator 110 acquires standard depths of reference genes representing each of the groups.

FIG. 4 is a diagram for explaining obtaining lead-depths corresponding to reference genes for each of a plurality of people (for example, normal persons) according to an embodiment. The description of FIG. 4 may be related to the method performed in step 301 of FIG.

Referring to FIG. 4, the reference data generator 110 may obtain the lead-depths by performing the deep sequencing using the gene data 401 obtained from the database (DB) 40.

The database (DB) 40 stores gene data 401 of a plurality of people (for example, normal persons) classified into the normal population 400. Genetic data 401 may be obtained using various sequencing means such as next generation sequencing (NGS), microarrays, etc. for biological samples taken from a plurality of people. On the other hand, the gene data 401 may be data on a whole genome, or data on a hapmap (HapMap).

The database (DB) 40 stores genetic data 401 of people who are known in the art such as NCBI, GEO, and the like, or who are recruited to analyze the test genes of the test subject To be built.

The reference data generation unit 110 performs deep sequencing on genes of individual individuals of the normal population 400 included in the gene data 401 (i.e., reference genes). For example, the reference data generator 110 may perform deep sequencing on the reference genes 411 of the " person 1 " 410 included in the normal population 400. As a result of the deep sequencing on the reference gene 411, the leads 415 are aligned in the gene 1, ..., and the gene n (n is a natural number) included in the reference genes 411, (Lead-depths) of the leads 415 aligned to each of the plurality of leads 415 are obtained. Similarly, the reference data generator 110 also performs the deep sequencing on the reference genes 421 of the " person 1 " 420 included in the normal population 400 and performs the deep sequence on the reference genes 421 (Lead-depths) of the leads 425 that have been formed. The reference data generator 110 may acquire the data of the lead-depths by performing the deep sequencing on the reference genes of the individuals of the normal population 400 included in the gene data 401.

5 is a diagram for explaining deep sequencing for exon regions according to an embodiment.

Referring to FIG. 5, deep sequencing of reference genes corresponding to genes of individuals in the normal population 400 may be performed by using the depths of leads (aligned with exons) except the intron regions 505 - depths). For example, if an individual reference gene (nucleic acid 500) comprises gene a, gene b and gene c, the result of the deep sequencing is the depth of the leads 510 aligned to exon a1 in gene a, The depth of leads aligned with exon a2, the depth of leads aligned with exon b1 in gene b, the depth of leads aligned with exon b2, and the depth of leads aligned with exon c in gene c. However, the present embodiments are not limited thereto, and the results of the deep sequencing may include data of the depths of the leads arranged in the intron portions 505.

On the other hand, the deep sequencing of the exon regions shown in FIG. 5 is applied not only to the reference genes, but also to the test genes obtained from the test subject. That is, the analysis unit 120 in FIG. 2 can analyze the depths of the leads aligned in each of the exon regions in the test genes by performing deep sequencing on the exon regions in the test genes.

FIG. 6 is a diagram illustrating clustering of people into different groups according to the distribution of lead-depths obtained from the normal population 400 according to an embodiment. The description of FIG. 6 may be related to the method performed in step 302 of FIG.

Since individuals in the normal population 400 have different genes, the depths corresponding to specific genes (or specific exons) analyzed by deep sequencing may be different for each individual. Alternatively, due to the chemical treatment (e.g., FFPE (Formalin-fixed, paraffin-embedded)) of the biological samples obtained from individuals, and the deep sequencing error, etc., a depth for each of the individual reference genes May be different from one another. Therefore, the reference data generation unit 110 groups individuals having a similar tendency in the distribution of the depths, and clusters individuals of the normal population 400 into different groups. Here, the clustering can be performed by statistically analyzing the distribution of the lead-depth for each reference gene (exon) using a known trend analysis algorithm, clustering algorithm, and the like.

Referring to FIG. 6, as a result of performing deep sequencing on the reference genes of the people belonging to the group 1, the reference genes of the people belonging to the group 1 may have a tendency that the distribution of each gene and the depth pair is similar. The same goes for the other groups. For example, reference genes of people belonging to group 1 may be obtained from biopsy samples of people belonging to group 1, and reference genes of people belonging to group M (M is natural number) are referred to as FFPE May be obtained from the samples.

FIG. 7 is a diagram for explaining the standard depths of reference genes, which represent a group according to an embodiment. The description of FIG. 7 may relate to the methods performed in steps 303 and 304 of FIG.

Referring to FIG. 7, when the clustering is completed, the reference data generator 110 normalizes the lead-depths obtained for the respective reference genes for each group to generate reference data for each of the reference genes Obtain standard depths.

For a reference gene (e.g., "exon 1"), when the depth has a variable value for each person belonging to group x, the reference data generator 110 calculates the average of various depths for "exon 1" , It is possible to standardize the depth for " exon 1 ". Similarly, the reference data generator 110 calculates the average of various depths for each of the other reference genes (e.g., "Exon 43", "Exon 3543", "Exon 5623" Exon) can be calculated. In this way, the reference data generator 110 can acquire the standard depths of the respective reference genes, which represent each of the clustered groups. Meanwhile, in the present embodiment, for convenience of explanation, it has been described that the mean value of the depths is calculated to take the representative value. However, in the present embodiments, the representative value of the depths may be calculated by using other kinds of statistical quantities.

FIG. 8 is a diagram for explaining performing the deep sequencing on the test genes obtained from the biological sample of the subject according to one embodiment. FIG.

Referring to FIG. 8, the analysis unit 120 of FIG. 2 analyzes the gene data 30 of the subject 800 by performing deep sequencing on the genes to be tested, Lt; / RTI >

The gene data 30 of the subject 800 may be obtained through the next generation sequencing (NGS) on the biopsy sample 810 or the FFPE sample 825 taken from a part of the tissue of the subject 800. [ Here, the FFPE sample 825 is a sample by the FFPE processing 820 on some tissues of the test object 800.

The analysis unit 120 in FIG. 2 analyzes the depths of the leads aligned with the tested genes in the test body 800 according to the deep sequencing methods described above with reference to FIGS. 4 and 5, 830).

9 is a flowchart of a method for determining whether a copy number variation (CNV) gene is present according to an embodiment. Referring to FIG. 9, the determination of the copy number variation (CNV) gene includes the steps of time series processing in the determination unit 130 described above.

In step 901, the determination unit 130 determines a group having the smallest statistical difference between the distribution of the depths analyzed from the genes tested and the distribution of the standard depths among the groups clustered by the reference data generation unit 110 do. That is, the determination unit 130 determines at least one group among the clustered groups (for example, the groups in FIG. 6) having a statistical tendency similar to the distribution of the analyzed probabilities from the tested genes. At this time, the determination unit 130 can determine the group having the smallest standard deviation between the distribution of the depths analyzed from the test genes and the distribution of the standard depths. However, other statistics may be used in addition to the standard deviation, in order to select a group having a tendency similar to the distribution of the analyzed depths from the test genes.

In step 902, the determination unit 130 compares the analyzed depths analyzed from the test genes with standard depths corresponding to the determined group. More specifically, the determination unit 130 compares the depth of each of the tested genes (exons) with the depth of the corresponding reference gene (corresponding exon). For example, when it is assumed that exons 1 and 43 exist in both the test genes and the reference genes, the determination unit 130 determines whether or not the exon 1 and exon 43 analyzed by the analysis unit 120 The depth is compared with the standard depth of "Exxon 1", and the depth of "Exxon 43" analyzed by the analysis unit 120 is compared with the standard depth of "Exxon 43". Here, "exon 1" and "exon 43" are arbitrary terms for indicating that they are different exons.

In step 903, the determination unit 130 determines whether a copy number variation (CNV) gene exists as a result of the comparison. At this time, the determination unit 130 determines that there is an exon region that is not statistically significant in the difference of the depths in the exon regions corresponding to each other among the exon regions of the test genes , And a copy number variation (CNV) gene.

More specifically, assuming that the threshold for determining that the difference in depth is not significant is set to be four times the standard depth, the determination unit 130 determines whether the depth of any of the exons analyzed by the analysis unit 120 satisfies the standard It can be judged that the copy number mutation (CNV) gene is present in the case of exceeding 4 times of the depth. However, the threshold value may be variously changed without being limited thereto. For example, when the standard depth of " exon 1 " is 1000, the threshold for determining significance may be 4000. Therefore, when the depth of the exon 1 of the subject analyzed by the analysis unit 120 is 5000, the determination unit 130 determines that the gene of "exon 1" is a copy number variation (CNV) gene .

FIG. 10 is a diagram for explaining whether or not a copy number variation (CNV) gene is present according to an embodiment.

Referring to FIG. 10, the depths indicated by solid lines correspond to reference genes (exons), the dashed lines correspond to reference genes (exons), and the dashed lines correspond to the test genes (exons).

The determination unit 130 compares the depths of the exons analyzed by the analysis unit 120 with the standard depths, as described above with reference to the drawings. The determination unit 130 determines whether or not the exon region ("exon a") in which the difference in the depth between the exon regions corresponding to each other among the reference genes and the exon regions of the test genes is not statistically significant, (CNV) gene can be judged to exist because the test gene of "exon a" is identified as a copy number mutation (CNV) gene.

Meanwhile, when it is determined that the copy number mutation (CNV) gene is present among the genes to be tested, the determination unit 130 outputs an annotation for identifying a drug (for example, an anticancer agent) corresponding to the copy number variation (CNV) Can be performed.

11 is a flowchart of a method for analyzing a gene according to an embodiment. Referring to FIG. 11, the gene analysis method includes steps of time-series processing in the gene analysis apparatus 10 described in the preceding figures. Therefore, even if omitted below, the contents described in the preceding drawings can also be applied to the gene analysis method of FIG.

In step 1101, the reference data generator 110 generates a set of reference data on the depths of the leads aligned on each of the reference genes by performing deep sequencing on the reference genes.

In step 1102, the analysis unit 120 analyzes the depths of the leads aligned on each of the tested genes by performing deep sequencing on the tested genes.

In step 1103, the determination unit 130 determines whether a copy number variation (CNV) gene exists among the tested genes by comparing the analyzed depths to the depths of the reference genes included in the reference data set.

12 is a block diagram illustrating hardware configurations of a computing device according to one embodiment.

12, the computing apparatus 1 includes a genetic analysis apparatus (processor) 10, a data interface 11, and a memory 12. [ On the other hand, since the computing device 1 shown in Fig. 12 is only shown in order to prevent the characteristic of the present embodiment from being blurred, only the components related to the present embodiment are shown. Therefore, Components may be further included.

The data interface 11 receives the genetic data 20 of the normal population and the genetic data 30 of the subject described above with reference to Fig. That is, the data interface 11 may be implemented as hardware of a wired / wireless network interface for the computing device 1 to communicate with other external devices. The data interface 11 transmits the received gene data 20 and 30 to a gene analysis apparatus (processor)

The data interface 11 can receive genetic data 20 of a normal population from the database DB (40 in Fig. 4). The data interface 11 can receive the gene data 30 of the subject from an external next-generation sequencing device, a microarray, or the like for sequencing the gene to be tested.

The memory 12 is a hardware for storing data to be processed and results of processing in the computing device 1 and may be a memory chip such as a random access memory (RAM), a read only memory (ROM) drive, solid state drive (SSD), and the like. That is, the memory 12 can store the gene data 20 and 30 received by the data interface 11, and can store the reference data set processed by the gene analysis apparatus (processor) 10, Deep sequencing data, and data on the identified copy number variation (CNV) gene.

A genetic analysis apparatus (processor) 10 is a module implemented with one or more processing units, which may be implemented as a combination of a microprocessor having an array of logic gates and a memory module in which a program executable in the microprocessor is stored have. The gene analysis apparatus (processor) 10 may be implemented as a module of an application program. The gene analysis apparatus (processor) 10 is a hardware apparatus for processing the gene analysis described in Figs. 1 to 11 above.

Information on the copy number variation (CNV) gene identified by the genetic analysis apparatus (processor) 10 is transmitted to another external device, for example, a display device, another computing device, etc., via the data interface 11, Or on an external network, e.g., the Internet, a public database (DB) server.

According to the embodiments described above, even if the normal blood of the subject (for example, a cancer patient) can not be ensured, the biopsy sample of the cancer tissue of the subject or the FFPE sample alone can be used to measure the copy number mutation Can be detected. Furthermore, even though the genes of the cancer tissues (test genes) obtained from the subject are chemically slightly damaged by the FFPE treatment, reference genes of similar conditions (FFPE treatment) are referred to to determine the copy number mutation (CNV) gene (CNV) gene can be detected accurately.

An apparatus according to the present embodiments may include a processor, a memory for storing and executing program data, a permanent storage such as a disk drive, a communication port for communicating with an external device, a touch panel, a key, User interface devices, and the like. Methods implemented with software modules or algorithms may be stored on a computer readable recording medium as computer readable codes or program instructions executable on the processor. Here, the computer-readable recording medium may be a magnetic storage medium such as a read-only memory (ROM), a random-access memory (RAM), a floppy disk, a hard disk, ), And a DVD (Digital Versatile Disc). The computer-readable recording medium may be distributed over networked computer systems so that computer readable code can be stored and executed in a distributed manner. The medium is readable by a computer, stored in a memory, and executable on a processor.

This embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented in a wide variety of hardware and / or software configurations that perform particular functions. For example, embodiments may include integrated circuit components such as memory, processing, logic, look-up tables, etc., that may perform various functions by control of one or more microprocessors or other control devices Can be employed. Similar to how components may be implemented with software programming or software components, the present embodiments may be implemented in a variety of ways, including C, C ++, Java (" Java), an assembler, and the like. Functional aspects may be implemented with algorithms running on one or more processors. In addition, the present embodiment can employ conventional techniques for electronic environment setting, signal processing, and / or data processing. Terms such as "mechanism", "element", "means", "configuration" may be used broadly and are not limited to mechanical and physical configurations. The term may include the meaning of a series of routines of software in conjunction with a processor or the like.

The specific implementations described in this embodiment are illustrative and do not in any way limit the scope of the invention. For brevity of description, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of such systems may be omitted. Also, the connections or connecting members of the lines between the components shown in the figures are illustrative of functional connections and / or physical or circuit connections, which may be replaced or additionally provided by a variety of functional connections, physical Connection, or circuit connections.

In this specification (particularly in the claims), the use of the terms " above " and similar indication words may refer to both singular and plural. In addition, when a range is described, it includes the individual values belonging to the above range (unless there is a description to the contrary), and the individual values constituting the above range are described in the detailed description. Finally, if there is no explicit description or contradiction to the steps constituting the method, the steps may be performed in an appropriate order. It is not necessarily limited to the description order of the above steps.

The present invention has been described with reference to the preferred embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

Claims (19)

Generating a set of reference data for the depths of the leads aligned with each of the reference genes by performing a deep sequencing on the reference genes;
Analyzing the depths of the leads aligned with each of the tested genes by performing the deep sequencing on the tested genes; And
Comparing the analyzed depths with depths of the reference genes included in the reference data set to determine whether a copy number variation (CNV) gene is present among the tested genes How to analyze. The method according to claim 1,
The analyzing step
And analyzing the depth of the leads aligned with the exon regions of the tested genes. 3. The method of claim 2,
The determining step
(CNV) gene is determined by comparing the depths between the reference genes and the test genes for the same exon region. The method according to claim 1,
The determining step
When there is an exon site in which the difference in depth between the reference genes and the exon regions corresponding to each other among the exon regions of the tested genes is not statistically significant, (CNV) gene is present. The method according to claim 1,
The generating step
Obtaining the lead-depths corresponding to the reference genes for each of the people through the deep sequencing of gene data of a plurality of people;
Clustering the people into different groups according to the distribution of the obtained lead-depths; And
And normalizing the lead-depths obtained for each of the reference genes for each group to obtain standard depths of the reference genes representing each of the groups,
The reference data set
And for each of the groups, data representative of the standard depths of each of the reference genes. 6. The method of claim 5,
The determining step
Determining a group having the smallest statistical difference between the distribution of the analyzed depths and the distribution of the standard depths among the groups; And
Comparing the analyzed depths with standard depths corresponding to the determined group to determine whether the copy number variation (CNV) gene is present. 6. The method of claim 5,
Further comprising obtaining the genetic data of the people from public genome data or public map (HapMap) data. The method according to claim 1,
The reference genes or the test genes
Biopsy tissue, formalin-fixed, paraffin-embedded (FFPE) tissue. The method according to claim 1,
Further comprising performing annotation to identify a drug corresponding to the copy number mutation (CNV) gene when it is determined that the copy number mutation (CNV) gene is present among the tested genes. A computer-readable recording medium storing a program for causing a computer to execute the method according to any one of claims 1 to 9. A reference data generator for generating a reference data set related to the depths of the leads arranged in each of the reference genes by performing a deep sequencing on the reference genes;
An analyzer for analyzing the depths of the leads aligned to each of the tested genes by performing the deep sequencing on the tested genes; And
And determining whether a copy number variation (CNV) gene exists among the test genes by comparing the analyzed depths with depths of the reference genes included in the reference data set. Analyzing device. 12. The method of claim 11,
The analyzer
And analyzing the depth of the leads aligned with the exon regions of the tested genes. 13. The method of claim 12,
The determination unit
And comparing the depths of the reference genes and the test genes for the same exon region to determine the presence of the copy number mutation (CNV) gene. 12. The method of claim 11,
The determination unit
When there is an exon site in which the difference in depth between the reference genes and the exon regions corresponding to each other among the exon regions of the tested genes is not statistically significant, RTI ID = 0.0 > (CNV) < / RTI > 12. The method of claim 11,
The reference data generator
Through the deep sequencing of gene data of a plurality of people, obtaining lead-depths corresponding to the reference genes for each of the people,
And clustering the people into different groups according to the distribution of the obtained lead-depths,
Acquiring standard depths of each of the reference genes representing each of the groups by standardizing the lead-depths obtained for each of the reference genes for each group,
The reference data set
And for each of the groups, data representative of the standard depths of each of the reference genes. 16. The method of claim 15,
The determination unit
Determining a group having the smallest statistical difference between the distribution of the analyzed depths and the distribution of the standard depths among the groups,
And comparing the analyzed depths with standard depths corresponding to the determined group to determine whether the copy number variation (CNV) gene is present. 16. The method of claim 15,
The reference data generator
And obtaining the genetic data of the people from public genome data or public map (HapMap) data. 12. The method of claim 11,
The reference genes or the test genes
Biopsy tissue, formalin-fixed paraffin-embedded (FFPE) tissue. 12. The method of claim 11,
The determination unit
And an annotation for identifying a drug corresponding to the copy number mutation (CNV) gene when it is determined that the copy number mutation (CNV) gene is present among the tested genes.

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