WO2016068627A1 - Method for analyzing absolute copy number variation based on single sample - Google Patents

Abstract

Provided is a method for analyzing absolute copy number variation based on a single sample, the method comprising: receiving experiment sample sequence data generated in a dielectric sequencer; calculating a target region ratio (TRR) on the basis of a read count in which the received experiment sample sequence data is read mapped to chromosomal position-specific standard reference sequence data; estimating the purity of an experiment sample and the average copy number of an experiment sample in at least one target area; and predicting the absolute copy number variation of the experiment sample on the basis of the calculated and estimated parameters.

Description

 【Specification】

 [Name of invention]

 How to analyze absolute copy number variation based on a single sample

 The present invention relates to a method for analyzing an absolute copy number variation based on a single sample, and relates to a method for analyzing an absolute copy number in at least one target region for an experimental sample using only an experimental sample without a control sample. [Technique to become background of invention]

 Copy number variation (CNV) is a structural

It is a form of structural variation (SV). In general, CNV refers to amplification or deletion of DNA fragments of lkb or more. CNV is present at a very high frequency of more than 10 percent in the human population, and the average size of CNV in an individual's genome is 3.5 ± 0.5 Mbp (0.1 percent). Many studies have demonstrated that CNV is associated with complex diseases such as autism, schizophrenia, Alzheimer's disease, and cancer.

 Recently, with the development of Next Generation Sequencing (NGS) technology, CNV has been detected at high resolution (<10kbp) using reads generated by sequencing devices (Alkan C). et al., Nature Genetics

41: 1061-1067; J.L. Hayes et al, Genomics, vol. 102, Issue 3, pp. 174-181, 2013; Chiang DY et al., Nature Methods 6: 677-681).

 However, when a cancer sample is analyzed using a large scale parallel sequencing device, normal cells are mixed in the cancer sample, and thus, prior knowledge of the cancer sample is required to analyze accurate CNV of the cancer sample. In addition, since most of the clinical samples do not have a normal sample as a control group, the CNV may be obtained only by the results of the cancer sample.

It is not easy to analyze correctly. Therefore, there is a need for the development of a new method for analyzing CNV with only cancer samples without normal samples.

(Non-Patent Document 1) Alkan C et al., Nature Genetics 41: 1061-1067; JL Hayes et al., Genomics, vol. 102, Issue 3, pp. 174-181, 2013; Chiang DY et al., Nature Methods 6: 677-681 [Content of invention]

 Problem to be solved

 One example of the present invention is to provide a method for analyzing an absolute copy number in at least one target region for a test sample using only a test sample without a control sample.

 Another example of the invention provides at least one target for a test sample

A computer readable method for analyzing absolute copy numbers in a region is provided.

 Another example of the invention provides a computer program or computer executable instruction stored on a computer readable storage medium for carrying out a method of analyzing absolute copy numbers in at least one target area for an experimental sample. It provides a computer-readable storage medium (or recording medium) recorded.

 Another example of the invention provides at least one target for a test sample

A computer readable storage medium (or recording medium) containing a computer program stored on a computer readable storage medium or a computer executable instruction for executing a computer readable method for analyzing an absolute copy number in an area. To provide.

 However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

[Measures of problem]

 As a technical means for achieving the above-described technical problem, an embodiment of the present invention, the read count by reading the mapping of the test sample sequence data to the standard reference sequence data for each chromosomal position (Chromosomal Position) (Read Count) ), Calculating a target region ratio (TRR) based on the read count, estimating the purity of the test sample and the average number of replicates of the test sample in at least one target region, calculating and estimated parameters Predicting an absolute number of replicates of at least one target region in the experimental sample based on.

 Specifically, one example of the present invention is a sample sequence information on the chromosome

Read Mapping to Standard Reference Sequence Information by Chromosomal Position Mapping to obtain a read count;

 Calculating a sample target region ratio (TRR) based on the read count;

 Estimating the purity of the sample and the average number of copies of the sample; And

 Determining an absolute copy number of the sample based on the calculated TRR and estimated purity and mean copy number

 It relates to a method for determining the absolute copy number variation of a sample based on a single sample.

 Estimating the purity of the test sample and the average copy number of the test sample, (i) from the read mapping results, the allele (A Allele) having the same sample sequence information and reference sequence information, and the sample sequence information and reference Calculating a B allele frequency (BAF) of the different alleles based on the frequency of alleles having different sequence information;

 (ii) segmenting the sample sequence information based on the frequency rate of the different alleles;

 (iii) applying the divided at least one segment to a copy number model of the frequency ratio with respect to sample purity to extract a purity candidate and a copy number candidate of the sample; And

 (iv) filtering the purity candidate and the copy number candidate of the sample by using the filtering parameter to determine the purity and the average copy number of the sample.

 Read mapping of test sample sequence data to standard reference sequence data to obtain a read count, calculating a target region ratio (TRR) based on the read count, and purity and average of the test sample in at least one target region. By estimating the number of copies, it is possible to provide an analysis method of absolute copy number variation based on a single sample that can determine the absolute number of copies of the test sample in at least one target region without the control sample sequence data and lead mapping as the control of the test sample. have.

The present invention and another example provide a computer program stored in a computer readable storage medium for carrying out the steps of the computer readable method. A further example of the present invention provides a computer readable storage medium (or recording medium) containing computer executable instructions for executing the steps of the computer readable method. 【Effects of the Invention】

 According to any one of the above-described means for solving the problems of the present invention, the number of copies of the test sample can be determined without the process of lead mapping of the control sample, the control sample sequence data, and the control sample of the control sample. In addition, even if the cancer sample is mixed with normal cells, or if the control sample or normal cells do not exist, the absolute number of copies in at least one target region can be known, thus providing accurate replication in the desired target region. The number can be determined, and even if no control sample is present, it can be useful for discovering somatic cell clone variation. [Brief Description of Drawings]

 1 is a block diagram illustrating an analysis system for copy number variation based on a single sample according to an embodiment of the present invention.

 FIG. 2 is a block diagram for explaining an apparatus for analyzing copy number variation illustrated in FIG. 1.

 FIG. 3 is a block diagram illustrating a method for analyzing copy number variation performed in the apparatus for analyzing copy number variation shown in FIG. 1.

 FIG. 4 is a diagram for describing lead mapping performed in the apparatus for analyzing copy number variation illustrated in FIG. 1.

 FIG. 5 is a diagram comparing a simulation result graph when using the copy number predictor shown in FIG. 1 and using the existing copy number predictor.

 6 is a flowchart illustrating a method of analyzing absolute copy number variation based on a single sample according to an embodiment of the present invention.

 7 illustrates a computer readable storage medium for executing the method for analyzing sample sequence information according to an embodiment of the present invention.

8 is a sample sequence executed in the analysis device for copy number variation shown in FIG. It is a block diagram for demonstrating the information analysis method.

 FIG. 9 is a diagram for describing a method of calculating a frequency rate performed by the apparatus for analyzing copy number variation shown in FIG. 1.

 FIG. 10 is a diagram for describing a segmentation method performed in the apparatus for analyzing copy number variation shown in FIG. 1.

 FIG. 1 is a diagram for describing a node definition method for candidate extraction performed in the apparatus for analyzing copy number variation shown in FIG. 1.

 FIG. 12 is a diagram for describing a filtering method performed in the apparatus for analyzing copy number variation illustrated in FIG. 1.

 FIG. 13 is a view for explaining an estimation method performed in the analysis apparatus for copy number variation shown in FIG. 1.

 FIG. 14 is a diagram comparing graphs of sample purity simulation values with respect to sample estimate values estimated by the apparatus for analyzing copy number variation shown in FIG. 1.

 15 is a flowchart illustrating a method of estimating sample purity and number of copies according to an embodiment of the present invention.

[Specific contents to carry out invention]

 DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. In the drawings, parts irrelevant to the description are omitted for simplicity of explanation, and like reference numerals designate like parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. . In addition, when a part is said to "include" a certain component, which means that it may further include other components, except to exclude other components, unless specifically stated otherwise, one or more other features It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, parts, or combinations thereof. do.

 In this specification, unless otherwise defined, a target region and a target base sequence mean a selected region (target region) and a base sequence (target base sequence) of the region, respectively, to be analyzed in the genome or chromosome. The target region and target base sequence may be present in one or more for one sample. For example, the target region may be an arbitrary section to be analyzed in full-length genome analysis, and may be a region for designing and selecting probes for sequencing at library prep in targeted sequencing. .

 In the present specification, "about" described before the numerical value may be used to include a variation (incremental) of 10%, 5%, or 3% of the numerical value unless otherwise defined.

 Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

 1 is a block diagram illustrating an analysis system for copy number variation based on a single sample according to an embodiment of the present invention. Referring to FIG. 1, the analysis system 1 for copy number variation may include a genome decoder 100 and an analysis apparatus 300 for copy number variation. However, since the analysis system (1) of the copy number variation of FIG. 1 is only an embodiment of the present invention, the present invention is not limitedly interpreted through FIG. 1.

In this case, each component of FIG. 1 is generally connected through a network 200. For example, as shown in FIG. 1, the genome decoder 100 and the copy number analysis device 300 may be connected through the network 200. At this time, the genome reader 100 and the analysis device 300 for copy number variation may be directly connected. In addition, since only the experimental sample sequence data generated by the genome decoder 100 needs to be received by the analysis device (0) of copy number variation, it may include both direct and indirect connections. Here, the network 200 refers to a connection structure capable of exchanging information between respective nodes such as terminals and servers. An example of such a network 200 is WCDMA, Internet, or LAN (Local). Area Network (Wireless LAN), Wireless Local Area Network (WLAN), Wide Area Network (WAN), Personal Area Network (PAN), El network using ATM, 3G, 4G, LTE, Wi-Fi, etc. It doesn't work. In addition, the genome decoder 100 and the analysis apparatus 300 for copy number variation disclosed in FIG. 1 are not limited to those shown in FIG. 1. The genome decoder 100 may amplify DNA sequences, photograph fluorescent labels, and the like by photographing means, and perform image processing to parallelize DNA genetic information. In this case, the genome decoder 100 may be applied to a field for determining gene mutation, DNA copy number, and chromosome rearrangement. For this purpose, the genome decoder 100 may read a single DNA several times. The number of reads may be defined as a read count, and the read count may also be defined as a depth.

 The copy number variation analysis device 300 read-maps the experimental sample sequence data to standard reference sequence data to calculate a read count, and calculates a target region ratio (TRR) based on the read count. Can be calculated. In this case, the analysis apparatus 300 for copy number variation does not require the control sample sequence data of a control sample of a control sample in the process of calculating the TRR, which will be described in detail through the following equation. In addition, the apparatus 300 for analyzing the copy number variation estimates TRR, which is a predetermined number of purity, average number of copies, and number of copies of the experimental sample, and calculates and estimates the parameters, namely, TRR, purity, average number of copies, and average number of copies. The absolute number of replicates of the experimental sample can be predicted based on parameters including the number TRR. In this case, a parameter including TRR, in which purity, average number of copies, and average number of copies are preset numbers, may borrow a prediction value from the outside. In this case, the analysis apparatus 300 for copy number variation may be implemented as a computer that can access a server or a terminal at a remote location through the network 200. Here, the computer may include, for example, a notebook, a desktop, a laptop, and the like.

 FIG. 2 is a block diagram illustrating an apparatus for analyzing copy number variation shown in FIG. 1, and FIG. 3 illustrates a method for analyzing copy number variation performed in the apparatus for analyzing copy number variation shown in FIG. 1. FIG. 4 is a block diagram for explaining lead mapping performed in the apparatus for analyzing copy number variation shown in FIG. 1, and FIG. 5 illustrates a case where the copy number predictor shown in FIG. It is a figure which compared the simulation result graph in the case of using a number prediction apparatus.

2 and 3, the copy number predicting apparatus 300 according to an embodiment of the present invention includes a receiver 310, a calculator 330, an estimator 350, and a predictor 370. can do. The receiver 310 may receive the experiment sample sequence data generated by the genome decoder 100 (S3 100). In this case, the test sample sequence data may be data having a plurality of read counts by reading the test sample in the genome reader 100 a plurality of times. In this case, the test sample may be a cancer sample.

 The calculation unit 330 converts the received experimental sample sequence data onto chromosomes.

A target region ratio (TRR) may be calculated based on the read count of read mapping of standard reference sequence data for each position (S3200, S3300) (S3400).

 The reason why the control sample sequence data, which is a control of the experimental sample, is not required in the analysis apparatus 300 for copy number variation according to an embodiment of the present invention will be described. Referring to FIG. 4, a read count is calculated by (a) assuming that the test sample sequence data and the control sample sequence data are present, and (b) lead mapping the control sample sequence data to standard reference sequence data which is a human standard sequence. The TRR of the target area may be obtained based on the obtained read count.

 In a conventional analysis method using both experimental sample data and control sequence data, the TRR is the ratio of the read count of at least one target region located in the experimental sample data and the control sequence data, which is the ratio of the read count in the target region ί. The target region ratio (TRR) may be expressed by Equation 1 below.

 [Equation 1]

 TRR =

n _j T In Equation 1, TRR is the ratio of the read count of the test sample and the control sample in the specific target area, ^ is the mapped read count of the test sample and the control sample in the target area ί, Ν is the mapped control Is the total read count of the sample, and Τ is the total read count of the mapped test samples, which is the sum of ti in at least one target area. For example, the test sample may be a cancer sample and the control sample may be a normal sample.

In the present invention, TRR is calculated for at least one target region, and TRR may be derived as in Equation 2 and Equation 3 below. Thus, as shown in Equation 2 or Equation 3, the TRR according to the present invention is at least lead mapped. It may be calculated based on the read count in one target area and the ratio of the total sum of the read counts.

Specifically, in the TRR of Equation 1 defined in the conventional analytical method using both the experimental sample data and the control sequence data, d is the unit read count, that is, the sequencing depth in the target region with the number of copies 1, the target region When the number of copies of i is j, the expected read count Ri is R ^j = jd. Where d can be divided into dt (test sample) and dn (control sample) according to the sample. Is a read count mapped to the i th target region of the control which is the basis of the control sample sequence data, and can be expressed as 2 of the control, where Ν is the total read count of the control, where Ν is the sum of η from the target region 1 to L 2 may be expressed as 1. T is the total read count of the test sample, and L is the total number of target areas.

[Equation 2]

TRR = ^-L

 τ

[Equation 3]

2d _n T

 L

 T

 In Equations 2 and 3,

丁 1! ^ Means TRR when the i th target region has a copy number j, and the definition of ti, n _b N, T is the same as Equation 1,

d _n is the unit read count of the control sample,

 T is the total lead count of the mapped test samples, which is the sum of ti in at least one target region i.

It can be seen from Equation 3 that control sample sequence data of the control sample is not required. That is, the final 11 is required only t _b T, L value, because ti, T, L are all obtained from the test sample as described above. Thus The method for analyzing absolute copy number variation based on a single sample according to an embodiment of the present invention calculates the TRR using only the experimental sample and replicates the present invention, even if there is no control sample sequence data of a control sample which is a control of the experimental sample. The number variation can be predicted.

T, which is the sum of ^ in at least one target region, may be summarized as in Equation 4 below. In general, since the test sample is mixed with control cells such as normal cells, assuming that the purity of the test sample (eg, cancer cells) is α, the total read count Τ can be calculated as follows. Where τ is the total lead count of the target region of the test sample and Lj is the total number of target regions with the number of copies j. Since the average copy number J of a pure experimental sample, for example, a dark sample, can be expressed by Equation 5 below, Τ can be expressed by Equation 6 below. Like Τ, ti can be expressed as equation (7). At least one target area, ie _. When the i th target region has the number of copies of the test sample, j is the number of copies, and when the purity of the test sample is _α , ti, the lead count of the test sample sequence data in the at least one target area, is represented by the following equation. It can be defined as 7.

[Equation 4]

[Equation 6]

Γ = 2 (1 — a) Ld _t + aJL d _t

[Equation 7]

t _t- = 2 (1 — a) d _t + jid _{t Using} Equation 6 and Equation 7, the TRR of Equation 2 is represented by j replication number and the TRR at the first target area is expressed by Equation 8 below. It can be arranged together. [Equation 8]

TRR

 쒜

 2 (1-a) L-ajl

 2 丄 (j ᅳ

 2 + g ~ 2) a 2 + 0-2) Here, if the right left term of Equation 8 is defined as A as shown in Equation 9 below, and A is substituted into Equation 8, it can be expressed as have.

[Equation 9]

₌ 2

^ 一- ₊ _

Equation 10 TRR = A + ^Vl

^{1 "'} 2 + (J-2) a

 In Equations 9 and 10,

 α is the purity of the test sample,

 J is the average copy number of the test sample, and

 j is the number of copies of the i th target region of the test sample.

The information on the numerical values of J and _α can be expressed by Equation 10 by arranging them as A as in Equation 8. Here, J and α can receive input from the outside. That is, an estimate may be received from an external pathology specialist, estimated using a micro array, or received through another method (S350O).

The prediction unit 370 may predict the absolute copy number of the experimental sample based on the calculated and estimated parameters. Here, the calculated parameter may be TRR, and the estimated parameters may be J and α. That is, if the prediction unit 370 defines S and ^J as an absolute copy number score as in Equation 1 1 based on the calculated and estimated parameters, the equation 10 may be converted as shown in Equation 12, and the absolute copy number ji in the i th target region may be calculated.

[Equation 1 1]

 2-j

[Equation 12]

 , = 2-s

 In Equations 1 1 and 12,

S and ^J are absolute copy scores where the i th target region of the test sample has a copy number j,

A is as defined in Equation 9, can be calculated through the estimated parameters J and α,

TRR, ^J is a TRR having a copy number j in the i-th target region of the test sample, and TRR ¹ is a TRR of the test sample having a read count of 1 in at least one target area.

In the analysis method of absolute copy number variation based on a single sample according to an embodiment of the present invention, it is ultimately to know ji, that is, the absolute copy number j in the i th target region, and the calculated TRR and the estimated and estimated The absolute copy number score Si ¹ can be calculated by J and α. TRR is calculated using a test sample, for example, a read count measured from a cancer sample, and a value obtained by dividing A by subtracting A (TRR ^j -A) by (TRR'-A) is obtained. The number could be determined.

TRR ¹ means the TRR of a test sample having a read count of ¹ in at least one target region, and A can be calculated through the estimated parameters J (average number of copies of the test sample) and _α (purity of the test sample). have. A TRR, which is a preset number of copies in at least one target region, may be estimated. Here, the preset copy number may be ¹ and may be represented by TRR ¹ , where TRR ¹ may mean a TRR having 1 copy number in at least one target region. Here, the absolute copy number of the test sample may be the absolute copy number of the test sample in at least one target region.

 As described above, the method for analyzing absolute copy number variation based on a single sample according to an embodiment of the present invention may be performed even if the control sample sequence data other than the control sample does not exist, that is, only the test sample is used. It is possible to predict an absolute copy number in at least one target region. At this time, if the absolute number of copies in the at least one target region can be identified through the method for analyzing the absolute number of copies variation based on a single sample according to an embodiment of the present invention, accurate treatment and clinical experiment of the patient may be possible.

 Referring to Figure 5, (a) shows the results of the simulation of the TRR according to the chromosomal position, (b) shows the results of simulating S and j according to the chromosomal position according to an embodiment of the present invention do. (a) shows a simulation result of 50% of the cancer sample purity, and since only the TRR is shown, the absolute number of copies cannot be known, but (b) shows that each segment is listed according to the absolute number of copies. It can be seen that the absolute number of copies in the target area can be identified. The method of analyzing the absolute copy number variation based on the single sample of FIGS. 2 to 5 may be easily inferred from the same or described contents, and thus descriptions thereof will be omitted.

 6 is a flowchart illustrating a method of analyzing absolute copy number variation based on a single sample according to an embodiment of the present invention. Referring to FIG. 6, the apparatus for analyzing copy number variation receives experimental sample sequence data generated by a genome sequencer (S6100).

 Then, the apparatus for analyzing the copy number variation is based on a read count in which the received test sample sequence data is read mapped to standard reference sequence data for each chromosomal position. Region Ratio) is exported (S6200).

 In addition, the analysis apparatus for copy number variation estimates the purity of the test sample and the average copy number of the test sample in the at least one target region (S6300).

Finally, the analysis apparatus for copy number variation predicts the absolute copy number of the experimental sample based on the calculated and estimated parameters (S6400). Such matters that are not described for the analysis of the absolute copy number variation based on the single sample of FIG. 6 are the same as those described above for the method for analyzing the absolute copy number variation based on the single sample through FIGS. 1 to 5. Since it can be easily inferred from the description, it will be omitted.

 Another example of the invention relates to a method for analyzing absolute copy number variation based on a single sample. In a specific embodiment, the method for analyzing absolute copy number variation based on a single sample according to the present invention may comprise the following steps:

 (1) reading mapping the sample sequence information to reference sequence information for each chromosome position to obtain a read count;

 (2) calculating a target region ratio (TRR) in at least one target region in a sample based on the read count;

 (3) estimating the purity of the sample in the at least one target region and the average number of copies of the sample; And

 (4) determining an absolute copy number of at least one target region in the sample based on the calculated TRR and the estimated purity and average copy number.

 Hereinafter, an analysis method of sample sequence information based on a single sample for each step will be described in detail.

(1) obtaining a lead count of an experimental sample

 In the method for analyzing absolute copy number variation based on a single sample of a sample sequence according to the present invention, (1) the sample sequence information is read by read mapping to reference sequence information for each chromosome position. The step of obtaining the count can be performed.

The sample sequence information and the reference sequence information can be obtained by a conventional sequence information analysis method. For example, as a method of analyzing through a sequencer, large-scale parallel sequencing such as next-generation sequencing can be performed on a test sample. Alternatively, the obtained sequence information may be prepared in a form stored in a data storage medium or obtained through a network data transmission / reception apparatus. In one embodiment of the invention, it may be received using the genome sequencer 100 shown in the sequence information analysis system 1 of FIG. 1, provided that the sample sequence information analysis system 1 of FIG. Since only one embodiment of the present invention, the present invention is not limited to FIG. 1.

 The sample sequence information means sequence information of a sample to be analyzed, and the reference sequence information is a reference genome sequence, which is a genome sequence representing one species. A reference genome of a human may now be constructed based on published (eg, UCSC, NCBI, etc.) reference genomic sequences such as build 37 (GRCh37), hgl 8, hgl9, hg38.

The sample sequence information or the reference sequence information may be obtained by, for example, a large-scale parallel sequencing method in the next-generation sequencing method, and the sequence information, the read depth, or the read count number may be obtained by using the next-generation sequencing method. In this case, the polynucleotide fragment is a _rea d used for next generation sequencing, the number of polynucleotide fragments is read count or read depth, and the average polynucleotide fragment number is It may be an average number of leads. The term, "massively parallel sequencing

"sequencing" means that a single genome is innumerable polynucleotides

The sequence of each fragment is randomly decomposed into fragments, the sequences of each fragment are read simultaneously, and the sequence data thus obtained are combined using bioinformatics to collectively decipher a large amount of genomic information.

 The next-generation sequencing method is, for example, 454 platform (Margulies, et al., Nature (2005) 437: 376-380), Illumina Genome Analyzer (or Solexa ™ platform), Illumina HiSeq2000, HisSeq2500, MiSeq, NextSeq500, Life Tech Ion PGM, Ion Proton, Ion S5, Ion S5XL, or SOLiD (Applied Biosystems) or Helicos True Single Molecule DNA Sequencing Technology (Harris, et al., Science (2008) 320: 106 109), single molecule from Pacific Biosciences, and And / or by real-time (SMRT ™) technology or the like. In addition, large-scale parallel sequencing that is possible on nanopore sequencing (Soni and Meller, Clin Chem (2007) 53: 1996-2001) has been carried out by the

Sequencing is possible with high order multiplexing in a parallel fashion (Dear, Brief Funct Genomic Proteomic (2003) 1: 397-416). Each of these platforms sequences single molecules that are either clonally expanded or not amplified of nucleic acid fragments. Sequence information of polynucleotide fragments can be obtained using commercially available sequencing instruments. In addition, the sequencing may be performed by various other known sequencing methods and / or modifications thereof.

 The sample sequence information may be whole genome sequence information or sequence information of a selected target region. In this specification, unless otherwise defined, a target region and a target base sequence mean a selected region (target region) and a base sequence (target base sequence) of the region, respectively, to be analyzed in the genome or chromosome. The target region and target base sequence may be present in one or more for one sample. For example, the target region is an arbitrary region to be analyzed in whole genome sequencing, and in target sequencing, a region for designing and selecting probes for sequencing at library prep. Can mean come. The sample sequence information or reference sequence information may be obtained by, for example, a large-scale parallel sequencing method in the next generation sequencing method, and sequence information, read depth, or read count number may be obtained using the next generation sequencing method. Sample sequence information through the next generation sequencing method. The entire genome sequence information or a specific selection region, that is, a target region may be selected to perform next generation sequencing, and the sequence information of the target region may be used as sample sequence information. The targeted sequencing method using the NGS method is, for example

Using a technique called Sequence Capture, you can selectively analyze parts of specific genomes.

Specifically, in the obtaining of the read count, the sample sequence information, for example, the sample sequence information obtained from the genome sequencer 100 may be mapped to the reference sequence information for each position on the chromosome (S3200). For example, it may be performed by the calculation unit 330 of the analysis device 300 of the sample sequence information of FIG. In this case, the sample sequence information may be data having a plurality of read counts by reading a plurality of test samples from the genome sequencer 100. In this case, the test sample may be a cancer sample. For example, assuming that the genome sequencer 100 has 250 read counts, the number of read counts for each target region of the sample sequence information may be calculated while reading the 250 test sample sequence information. In this case, the read count may be calculated in at least one target region located in the sample sequence information. (2) estimating the purity of the sample and the average number of copies

 In the method for analyzing the absolute copy number variation based on a single sample of the sample sequence according to the present invention, the purity of the test sample in at least one target region and the average copy number of the test sample can be estimated.

 Estimating the purity of the test sample and the average number of copies of the sample,

 (i) From the read-mapped result, based on the allele (A Allele) having the same sample sequence information and the reference sequence information and the frequency of the allele (B Allele) having different sample sequence information and the reference sequence information, the different Calculating a frequency of allele (B allele frequency, BAF);

 (ii) segmenting the sample sequence information based on the frequency rate of the different alleles;

(iii) applying the divided at least one segment to a copy number model of the frequency ratio with respect to sample purity to extract a purity candidate and a copy number candidate of the sample; And ^'

 (iv) filtering the purity candidate and the copy number candidate of the sample by using the filtering parameter to determine the purity and the average copy number of the sample.

It can be done by including the method.

 According to the present invention, a method for estimating the purity of an experimental sample and an average copy number of a test sample in a single sample may be performed by measuring at least one parameter including the purity of the test sample and the average copy number, and using the parameters to detect somatic mutations. In addition to improving accuracy, even if no control sample exists, it can be useful for discovering somatic cell copy variation.

 In the following, it will be described in detail by dividing step (2) into detailed steps.

 ii) calculating frequency rates of different alleles

In the present invention, the step (i) is based on an allele having the same sample sequence information and reference sequence information and a different allele frequency (BAF: B Allele Frequency) based on the frequency of alleles having different sample sequence information and reference sequence information. ) Can be calculated. Specifically, referring to FIG. 9, when a replication deletion, duplication, translocation, inversion, etc. occurs in normal cells, the sample may be a cancer cell sample modified with cancer cells. If the number of copies of the same allele between the sample sequence information and the reference sequence information is n, the number of copies of alleles different from the sample sequence information and the reference sequence information is m, and the purity of the sample is α, different from the same allele (Α). The frequency of the allele β can be defined as in Equation 13 and Equation 14, respectively.

[Equation 13]

F _a = na + 1 (1―) ― 1 + (ri― 1) «

[Equation 14]

 Ff, = m + 1 (1. ᅳ «) '= 1 + (雄 ― 1) α

 In Equations 13 and 14, η is the number of copies of the same allele, m is the number of copies of the different alleles and m and n are each 0 or natural numbers,

 α is the purity of the sample,

 Fa is the frequency of the same allele (A),

 Fb is the frequency of different alleles (B).

 For example, the purity of a sample can be expressed in terms of the purity (tumor purity or tumor cellularity) of the total number of cells in the sample. Can be. In addition, when performing a biopsy of the cancer sample means a ratio of only cancer-derived cells excluding normal cells (stroma cells, white blood cells, etc.) contained in the sample.

 In this case, when the frequency ratio of the different alleles in the sequence information of the test sample is compared with the reference sequence information, BAF, BAF may be defined as shown in Equation 15 below. That is, the frequency of different alleles (BAF) in a sample is the frequency of the total alleles, and the ratio of the frequency of different alleles (BAF) to the sum of the frequency of the same allele and the frequency of the different alleles between the sample and the reference sample. = (Fb) / (Fa + Fb)). Substituting the formulas of Equations 13 and 14 into Fa and Fb described in Equation 15, respectively, it can be expressed as shown in Equation 15's right formula.

[Equation 15] F _h 1 + Cm― l)

BAF = = ^{· ■} , ^„,,

F _a + F _b 2 + (H + m-2) a

 In Equation 3, the definitions of ^, 1 ^, 01, 1¾ and Fb are the same as Equations 13 and 14.

 Thus, the frequency rate of different alleles can be calculated based on the number of copies of the same allele, the number of copies of the different alleles, and the purity of the experimental sample.

(ii) segmentation of sample sequence information;

 In the method of analyzing information of a sample sequence according to the present invention, step (ii) may segment the sample sequence information based on BAFs of different alleles of the sample sequence information.

 In an example of the present invention, as shown in FIG. 10 (c), the division of sequence information has a region in which the average of the frequencies of different alleles differs from each other.

Finding and dividing a segment, for example, grabbing a random area and t-testing the mean. The division of the sequence information may be performed by various methods, and the division method includes, for example, a circular binary segmentation (CBS) method, but is not limited thereto.

 In the present specification, the segment refers to a group of sequence information, in which the average of different allele BAFs in the sequence information of the sample is the same, and refers to the black bar portion shown in FIG. 10 (c). Referring to FIG. 10, (a) is a BAF graph of a control sample, and (b) is a BAF graph of an experimental sample. Here, in order to divide the sample sequence information, the BAF graph may be segmented using CBS (CircuIar Binary Segmentation) or another segmentation method.

(iii) copy number and sample purity candidate extraction of different alleles

 Step (iii) in the information analysis method of the sample sequence according to the present invention, by applying at least one segment to the copy number model of the frequency rate for the sample purity, it is possible to extract the copy number and sample purity candidate of the sample. have.

Here, the copy number model of the frequency rate with respect to the sample purity may be a nm plot model. By applying the segment defined in the partitioning step to the nm plot model, each node (^) _£ 161,) (162 ... 0 (6) can be defined, where nodes are (n, m, a, F _a , Since the value of F _b ) is included, when candidate nodes are selected, copy numbers and sample purity candidates of different alleles can be extracted. Here ，！ ！！ ，！ ^ ， ^ The value of the above formula

As defined in 13 and 14;

 Equation 15 may be converted as shown in Equation 16. Substituting each segment into the n-m plot model, an α candidate may be derived, and this is defined as a node candidate or a sample purity candidate. Further, candidate values of the copy number (m, n) can also be obtained from the sample purity candidate values.

[Equation 16]

1-2BAF

 1-m 4- (n 4- in-2) BAF

 For example, assuming that η is 0, m is 1, and the BAF of a segment is 0.7, and each parameter is substituted into Equation 4, 0.5 is obtained, and α, ηι, and α are represented by Equations 13 and 14 If the above parameter is substituted, Fa is 0.3 and Fb is 1.0, so the node in this case is (0,1,0.57,0.3, 1.0).

(iv estimating purity and mean copy number of samples

 In the method of analyzing information of a sample sequence according to the present invention, the step (iv) may include testing the sample purity and the number of copies filtered through at least one filter among candidates for the sample purity and the number of copies extracted in the step (Hi). It can be estimated by setting the sample purity and the number of copies of the sample, respectively.

 Here, the at least one filter may include at least one filter selected from the group consisting of a ratio filter, a copy number filter, and a unit filter. Preferably, the filter may be filtered using all of the ratio filter, the copy number filter, and the unit filter. Can be.

 In this case, the ratio filter may be a filter for filtering whether or not the TRR ratio based on the read count in at least one target region with respect to a target region ratio (TRR) having a predetermined number of read counts is equal to the following equation. Can be defined as 17.

[Equation 17]

 In the estimating step, all of the candidates for sample purity obtained in the extracting step using the ratio filter leave only candidates having the same ratio (r) in Equation 75.

Can be filtered out. TRR is a measured value obtained from lead mapping of sequence information of an experimental sample.

 The copy number filter may filter whether the average copy number of the test sample is the same and may be defined as in Equation 18 below.

[Equation 18]

 ᅳ 2 (1 ᅳ TRR) + (/ 一 2 + 2TRR) a

 ᅳ TRR The estimating step includes all candidates having the same average number of copies (J) in Equation 18 among the sample candidates obtained in the extraction step using the copy number filter.

Can be filtered out.

 The unit filter may be a filter for filtering whether the read count of the unit region is the same among at least one target region, and may be defined as in Equation 19 below.

[Equation 19]

, _ ^F a + ^F b

(2 + ^■ w + — Z) tx]

 Here, d may mean a unit read count, and may be a read count of a unit area in which at least one target area has a copy number of 1. That is, the estimating step may filter out all of the sample candidate certificates obtained in the extraction step by using the unit filter, leaving all of the candidates having the same number (d) of Equation 19 left behind.

Specifically, referring to FIG. 12, in the extraction step, the candidate extracted in the extraction step is defined as nodes 1 to 6 (nodel, ..., node6), and at least one filter is simultaneously or sequentially used. The obtained sample candidate may be removed, i.e. the node may be removed. Here, in the three filtering processes illustrated in FIG. 12, since at least one filter may be used repeatedly, three filters may be used. It is not meant to be written sequentially.

 Referring to FIG. 13, the remaining nodes, that is, candidates, may be identified through filtering. That is, when nodes 3 and 5 are finally selected, it can be seen that the segments correspond to the case where the purity of the test sample corresponds to 0.7 purity.

 Therefore, when performing the information analysis method of the sample sequence according to an embodiment of the present invention, the sample purity, Fa, Fb, the number of copies of the same allele n, the number of copies of different alleles based on the information of the last remaining node m can be found.

 Further, on the basis of the sample purity of the remaining node, Fa, Fb, the same allele copy number n, and the multiple allele m of the different alleles, using Equation 5, Equation 6 and Equation 7, average copy of the sample. A parameter including the number J and the unit read count d can be calculated.

 As a specific example, FIG. 15 is a flowchart illustrating a method of analyzing sample sequence information according to an embodiment of the present invention. Referring to FIG. 15, the apparatus for analyzing sample sequence information receives sample sequence information generated by a genome sequencer and read-maps reference sequence information for each chromosome position (SI). 100).

 Then, the analyzer for analyzing the sample sequence information includes different alleles based on the frequency of the allele (A Allele) having the same sample sequence information and the reference sequence information and the allele (B Allele) having different sample sequence information and the reference sequence information. Gene

Calculate the frequency rate (S1200).

 In addition, the analyzer for analyzing sample sequence information divides the sample sequence information based on BAF (S 1300).

 The apparatus for analyzing sample sequence information applies the divided at least one segment to a copy number model having a frequency ratio to sample purity to extract copy numbers and sample purity candidates of different alleles (S 1400).

 Finally, the apparatus for analyzing sample sequence information estimates the purity and average copy number of the experimental sample using at least one filter (S1500).

(3) calculating the TRR of the target region

Absolute copy number variation based on a single sample of the sample sequence according to the invention In the analysis method, the number of read counts per target region of the sample sequence information can be calculated while reading the sequence information of the experimental sample. In this case, the read count may be calculated in at least one target region located in the sample sequence information.

 Referring to FIGS. 2 and 3, the calculation unit 330 may read the received test sample sequence data to read counts that are read mapped to standard reference sequence data for each chromosomal position. Based on (S3200, S3300), a target region ratio (TRR) may be calculated (S3400).

 The reason why the control sample sequence data, which is a control of the experimental sample, is not required in the analysis apparatus 300 for copy number variation according to an embodiment of the present invention will be described. Referring to FIG. 4, (a) assuming that the test sample sequence data and the control sample sequence data are present, (b) read mapping of control sample sequence data to standard reference sequence data, which is a human standard sequence, to calculate read counts. The TRR of the target area may be obtained based on the obtained read count.

 In a conventional analysis method using both experimental sample data and control sequence data, TRR is the ratio of the read count of at least one target region located in the experimental sample data and the control sequence data, which is the ratio of the read count in target region i. The target region ratio (TRR) may be expressed by Equation 1 below.

 Formula

TRR =

 Τ

In Equation 1, TRR is the ratio of the read count of the test sample and the control sample in the specific target area i, is the mapped read count of the test sample and the control sample in the target area i, Ν is the total of the mapped control sample Is the read count and Τ is the total read count of the mapped samples, the sum of ^ in the at least one target region. For example, the test sample may be a cancer sample and the control sample may be a normal sample. In the present invention, TRR is calculated for at least one target region, and TRR may be derived as in Equation 2 and Equation 3 below. Therefore, as shown in Equation 2 or Equation 3, the TRR according to the present invention is based on the ratio of the total sum of the read count and the read count in the at least one lead-mapped target region. Can be calculated based on this.

Specifically, in the TRR of Equation 1 defined in the conventional analytical method using both the experimental sample data and the control sequence data, d is the sequencing depth in the target region with unit read number (lead depth), that is, copy number 1. Then, when the number of copies of the target area i is j, the expected lead count R ^J becomes R ^J = jd. Where d can be divided into dt (test sample) and dn (control sample) according to the sample. Is a read count mapped to the i th target region of the control which is the basis of the control sample sequence data, and can be expressed as 2 of the control, where Ν is the total read count of the control, where Ν is the sum of η from the target region 1 to L It may also be expressed as 2d _n L. T is the total read count of the target region of the test sample, and Lj is the total number of target regions with the copy number j.

[Equation 2] TRR = ^ -L

[Equation 3]

 N

n _t T

2d T

T

 In Equations 2 and 3,

TRRr ¹ means TRR when the i th target region has a copy number j, and the definitions of ti, n _b N, T are the same as in Equation 1,

d _n is the unit read count of the control sample,

 L is the total number of target areas.

It can be seen from Equation 3 that control sample sequence data of the control sample is not required. That is, TRRf ^ finally requires only the values of t T, L, because ti, T, L are values obtained from the test sample as described above. Thus The method for analyzing absolute copy number variation based on a single sample according to an embodiment of the present invention calculates the TRR using only the experimental sample and replicates the present invention, even if there is no control sample sequence data of the control sample which is a control of the experimental sample. The number variation can be predicted. T, which is the sum of ti in at least one target region, may be summarized as in Equation 4 below. In general, since the test sample is mixed with control cells such as normal cells, assuming that the purity of the test sample (eg, cancer cells) is α, the total read count Τ can be calculated as follows. Is the total read count of the target region of the test sample, and Lj is the total number of the target region having the number of copies j. Since the average copy number J of a pure experimental sample, for example, a dark sample, can be expressed by Equation 5 below, Τ can be expressed by Equation 6 below. Like Τ, ti can be expressed as equation (7). If the at least one target region, i.e. the target region, has a number of copies of the test sample with j, the number of copies is represented by ji, and if the purity of the test sample is α, reading of the experimental sample sequence data in the at least one target region The count ti may be defined as shown in Equation 7 below.

[Equation 4]

T = (1-a ^ 2d _t + ld _t + ^ 2d _t +-

L LI L2

[Equation 5]

Equation 61

T = 2 (1― a) Ld _t + ajl ά

[Equation 7]

t _f = 2 (1― a) d _t + jid Using Equations 6 and 7 described above, the TRR of Equation 2 can be summarized as in Equation 8 below in the i-th target region having the number of j copies.

[Equation 8]

TRR {

여기서， 수학식 8의 우측 좌항을 하기 수학식 9와 같이 A로 정의하고 , A를 수학식 8에 대입하면, 하기 수학식 10으로 표시할 수 있다.

Here, if the right left term of Equation 8 is defined as A as shown in Equation 9 below, and A is substituted into Equation 8, Equation 10 may be expressed.

[Equation 9]

 2

^Λ 2 + 0-2) α

[Equation 10]

 In Equations 9 and 10,

 α is the purity of the test sample,

 J is the average copy number of the test sample, and

 j is the number of copies of the i th target region of the test sample.

The information on the numerical values of J and _α can be expressed by Equation 10 by arranging them as A as in Equation 8. Here, J and α can receive input from the outside. That is, an estimate may be received from an external pathologist, or may be estimated using a micro army, or may be received through another method (S3500).

Specifically, an example of the method of obtaining the average copy number J and the sample purity α is an experiment. Estimating the purity of the sample and the average number of copies of the sample, (i) from the read mapping result, the allele (A Allele) having the same sample sequence information and reference sequence information, and the sample sequence information and reference sequence information Calculating a frequency of B allele frequency (BAF) based on the frequency of different alleles (B Allele); (ii) segmenting the sample sequence information based on the frequency rate of the different alleles; (iii) the divided at least one

Applying a segment to the copy number model of the frequency rate for sample purity to extract a purity candidate and a copy number candidate of the sample; And (iv) filtering the purity candidate and the copy number candidate of the sample by using the filtering parameters to determine the purity and the average copy number of the sample. An example of the method of obtaining the average copy number J and the sample purity α is as described in detail in the step (2) estimating the purity and the average copy number of the sample. The method for estimating the purity of the test sample and the average copy number of the test sample in the single sample may include measuring at least one parameter including the purity of the test sample and the average copy number, and using the parameters to improve the accuracy of the discovery of the somatic mutation. In addition, even in the absence of a control sample, it may be useful for discovering somatic cell copy number variation.

(4) In the method for analyzing the absolute copy number variation based on a single sample of the sample sequence according to the present invention, which determines the absolute copy number of the sample based on the calculated and estimated parameters, the experiment based on the calculated and estimated parameters The absolute copy number of the sample can be determined. Here, the calculated parameter may be TRR, and the estimated parameters may be J and a. That is, on the basis of the calculated and estimated parameters, if 8 is defined as the absolute copy number score as shown in Equation 1 1, Equation 10 may be converted as shown in Equation 12 below. The absolute copy number j, can be calculated. The absolute copy score is a score that can be calculated using a simple formula, and has a negative relationship with the absolute copy number. The clinical significance of the absolute copy score is that the absolute copy number can be expressed as an integer such as 0, 1, 2, 3 than the previous threshold method, which allows more accurate definition of the copy variation. There is an advantage.

 When the average number of copies J and purity are externally input, the TRR 'having the number of copies 1 can be theoretically calculated and the TRR value of the target region can be obtained from the measured value, so the following s value in the target region can be calculated. That is, by dividing the TRR value, only j desired to be calculated remains and the remaining variables are canceled out.

[Equation 1 1]

 = 2 ᅳ [Equation 12]

 h =

 In Equations 11 and 12,

S and ^J are absolute copy scores in which the i th target region of the test sample has a copy number j, A is as defined in Equation 9, and can be calculated through the estimated parameters J and α,

TRR, 'is a TRR having a copy number j in the i-th target region of the test sample, and TRR ¹ is a TRR of the test sample having a read count of 1 in at least one target region.

In the method for analyzing the absolute copy number variation based on a single sample according to an embodiment of the present invention, it is ultimately to know j, that is, the absolute copy number j in the i th target region. the absolute number of replication by J and the score _Sf α i may be calculated. TRR is calculated from the test sample, for example, a cancer sample, and the value obtained by dividing A by subtracting A (TRR ^J -A) by (TRR'-A) is obtained. The number can be determined.

TRR ¹ is the number of test samples with a lead count of ¹ in at least one target area.

TRR, A can be calculated through the estimated parameters J (average number of replicates of the test sample) and α (purity of the test sample). Number of copies in at least one target region TRR, which is a preset number, may be estimated. Here, the group may be a copy number set to 1, can be expressed as ¹ TRR, ^1, TRR can mean a number TRR of replication in at least one of a target area.

 Here, the absolute copy number of the test sample may be the absolute copy number of the test sample in at least one target region. For example, in the case of ERBB2 gene, which is widely known in breast cancer, the number of copies often exceeds 5, and the proposed method can calculate the absolute number of copies over the normal number. On the other hand, the methods and information described herein provide a computer program stored in a computer readable storage medium for carrying out the steps of the method capable of executing the steps described above. The computer program stored in the computer readable storage medium may be combined with hardware. A computer program stored in the computer readable storage medium is a program for executing the steps in a computer, wherein all the above steps are executed by one program or by two or more programs executing one or more steps. Can be. Programs or software stored on the computer readable storage medium may be any, including, for example, on a communication channel such as a telephone line, the Internet, a wireless connection, or the like, or on a portable medium such as a computer readable disk, a flash drive, or the like. It can be delivered to a computer device through known delivery methods.

 Another example also provides a computer readable storage medium (or recording medium) containing computer executable instructions for executing the steps of the method.

 The computer readable medium may include both computer storage media and communication media.

Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. For example, . Computer storage media may include RAM, ROM, EEPROM, flash memory (eg, USB memory, SD memory, SSD, CF memory, xD memory, etc.), magnetic disks, laser disks, or other Can be used to store memory, CD-ROM, digital versatile disk or other optical disk, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device or desired information and accessible by computer One or more of all possible media may be selected, but is not limited thereto.

 Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transmission mechanism, and includes any information delivery media. For example, the communication medium may be selected from one or more of a wired medium such as a wired network or a direct-wired connection, and a wireless medium such as an acoustic medium, an RF, an infrared ray, and other wireless mediums. .

 Combinations of one or more of the above may also be included within the scope of computer readable media.

 Combinations of one or more of the above may also be included within the scope of computer readable media. An example of a computer readable medium according to one embodiment of the present invention is shown in FIG. 7, for example as one component of computer system 500, the computer system comprising one or more processors 510, one or more computer readable storage. May include a medium 530 and a memory 520

 The above description of the present invention is for illustration, and the present invention belongs to

Those skilled in the art will understand that the present invention can be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

 The scope of the present invention is shown by the following claims rather than the above description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention. do.

Claims

[Patent Claims]  [Claim 1]  Reading mapping the sample sequence information to standard reference sequencing information for each chromosome position to obtain a read count;  Calculating a sample target region ratio (TRR) based on the read count; .  Estimating the purity of the sample and the average number of copies of the sample; And  Determining an absolute copy number of the sample based on the calculated TRR and estimated purity and mean copy number  Including, the method for determining the absolute copy number variation of the sample based on a single sample. [Claim 2] ^"  The method of claim 1, wherein the sample sequence information is whole genome sequence information or sequence information of a selected target region.  [Claim 3]  The method of claim 1, wherein the sample sequence information is obtained by performing a next generation sequencing method.  [Claim 4]  The method of claim 1, wherein the TRR is calculated by Equation 2 based on a ratio of a sum of a read count of at least one target region located in the sample and a read count of the target region. [Equation 2

 In the formula,  Is the lead count of the test sample in the target region i, T is the total lead count of the test sample, which is the sum of _t i in at least one target area, L is the total number of target areas of the mapped sample. [Claim 5]  The method of claim 1, wherein the absolute copy number of the test sample is an absolute copy number of the test sample in the at least one target region.  [Claim 6]  2. The method of claim 1, further comprising estimating a TRR, wherein the number of copies in said at least one target region is a predetermined number.  [Claim 7]  The method of claim 1, wherein estimating the purity of the sample and the average number of copies of the sample  From the read mapping result, the allele of the different allele is based on the frequency of the allele (A Allele) having the same sample sequence information and the reference sequence information and the allele (B Allele) having the different sample sequence information and the reference sequence information. Calculating a B allele frequency (BAF); ^Segmenting the sample sequence information on the basis of the frequency of less than alleles;  Extracting the purity candidate and the copy number candidate of the sample by applying the divided at least one segment to the copy number model of the frequency ratio with respect to sample purity; And  Analyzing the absolute copy number variation based on a single sample, using a filtering parameter to filter the purity candidate and the copy number candidate of the sample to determine the purity and the average copy number of the sample. Way.  [Claim 8]  The method according to claim 7, wherein a candidate node is selected by applying the divided one segment to a copy number model of the frequency ratio with respect to sample purity, and selecting the candidate node. Method for analyzing absolute copy number variation based on a single sample, which obtains a copy number candidate value from a node.  [Claim 9] The method of claim 7, wherein the copy number model of the frequency rate for the sample purity nm plot model is an absolute copy number variation ^analysis, based on a single sample. [Claim 10]  8. The absolute replication based on a single sample according to claim 7, wherein the frequency ratio of the different alleles is calculated based on the number of copies of the same allele, the number of copies of the different alleles, and the purity of the test sample. Method of analysis of number variation.  [Claim 1 11]  8. The method of claim 7, wherein the filtering parameter is at least one filter selected from the group consisting of a ratio filter, a copy number filter, and a unit filter.  [Claim 12]  The method of claim 1, wherein the filtering is performed by a ratio filter that filters whether or not the TRR ratio based on a read count in at least one target region to a target region ratio (TRR) having a predetermined number of read counts is matched. The method of analysis of absolute copy number variation based on a single sample, which is performed.  【Claim 13】  The method of claim 1, wherein the filtering comprises: a copy number filter for filtering whether the average copy number of the test sample is the same or a unit filter for filtering whether the read count of the unit area among the at least one target area is the same. The method of analyzing absolute copy number variation based on a single sample, which is performed.  [Claim 14]  The method of claim 7, wherein the purity and the average copy number of the sample are estimated by setting the sample purity extracted through the at least one filter of the sample purity candidates to the purity of the test sample. Method of analysis of absolute copy number variation.  [Claim 15] The method of claim 7, wherein the average copy number is calculated from Equation 18 based on the purity of the test sample and the TRR calculated from the sample sequence information. : Equation 18

 [Claim 16]  A computer readable method for analyzing sample sequence information based on a single sample, comprising performing a method of analyzing absolute copy number variation based on a single sample according to any one of claims 1 to 15.  [Claim 17] Absolute based on a single sample according to any one of claims 1 to 15 coupled to hardware _. A computer program stored in a computer readable storage medium for carrying out the method for analyzing copy number variation.  [Claim 181  A computer program coupled to hardware and stored on a computer readable storage medium for carrying out the steps of the computer readable method for analysis of absolute copy number variation based on a single sample according to claim 16.