KR20170125278A - Method and apparatus for determining the reliability of variant detection markers - Google Patents

Abstract

The present invention relates to a method and an apparatus for accurately determining the reliability of a single nucleotide variation detection marker. The method for determining reliability of a single nucleotide variation detection marker comprises: a step of obtaining a read by performing targeted sequencing on a predicted single nucleotide variation position of a gene to be inspected from a nucleic acid sample containing the gene to be inspected; a step of mapping the read to a reference genome and calculating a quality control (QC) score from the depth of the read aligned in the reference genome; and a step of evaluating the reliability of the single nucleotide variation detection marker in accordance with the calculated QC score.

Description

[0001] The present invention relates to a method and an apparatus for determining reliability of a detection marker for a variation,

And more particularly, to a method and an apparatus for determining the reliability of a detection marker.

A genome is any genetic information that a creature has. Techniques for sequencing one individual's genome include DNA chip, next generation sequencing technology, and next generation sequencing technology. Next-generation sequencing can be used interchangeably with large-scale parallel sequencing or second-generation sequencing.

Analysis of genetic information such as nucleotide sequence and protein is 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 nucleotide sequences, proteins, and the like are key data that can provide information on current and future disease-related information to prevent disease or to select optimal treatment methods in the early stages of the disease. Techniques for precisely analyzing and diagnosing mutations such as SNV (Single Nucleotide Variant), CNV (Copy Number Variation), InDel (Insertion and Deletion) and translocation related to diseases using these genetic information of living organisms are under study.

To a method for accurately determining the reliability of a single nucleotide mutation detection label.

To an apparatus for accurately determining the reliability of a single nucleotide mutation detection label.

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.

One aspect includes performing target sequencing on a predicted single nucleotide variant (SNV) position of a test gene from a nucleic acid sample containing the test gene to obtain a read;

Mapping the lead to a reference dielectric and calculating a Quality Control (QC) score from the depth of the lead aligned to the reference dielectric; And

And evaluating the reliability of the single nucleotide mutation detection label in accordance with the calculated QC score. The reliability of the single nucleotide mutation detection label is determined by the reliability of the single nucleotide mutation detection label.

The term "single nucleotide variant (SNV)" refers to the substitution of a single nucleotide, as opposed to a copy number variant (CNV) in a gene where a relatively large region is deleted or amplified and repeatedly appear.

The term "QC" may be used interchangeably in the same sense as quality control or quality control, and in order to obtain a reliable result in an experiment or diagnosis for judging whether the single nucleotide mutation is present, the quality of the mutation detection marker, It means that the depth of the lead is sufficiently secured with respect to the predicted position.

1 is an overall flowchart of a method for determining the reliability of a single nucleotide mutation detection label. Referring to FIG. 1, a method for determining the reliability of a mutation detection label includes performing target sequencing for a single nucleotide mutation predicted site to obtain a lead 110, mapping a lead to a reference dielectric 120, Calculating a QC score from the depth of the aligned leads 130, and evaluating 140 the reliability of the variation detection beacons.

The step 110 performs targeted sequencing on a specific region including a single nucleotide variation (SNV) prediction position of the test gene to obtain a lead.

The nucleic acid sample analyzed by the step (110) may include DNA and RNA obtained from the biological material of the subject, and may be obtained by a known DNA or RNA extraction method.

Target sequencing or panel sequencing based on next generation sequencing (NGS) can be performed on a single nucleotide mutation prediction site of the test gene. More specifically, it obtains leads for each single nucleotide variation (SNV) prediction gene by performing targeted deep sequencing.

The term "target dip sequencing" is a technique for sequencing nucleic acids such as DNA fragments, RNA fragments, etc., by repeatedly aligning leads to nucleic acids such as DNA fragments, RNA fragments and the like.

The term "lead" means sequence information of one or more nucleic acid fragments. The lid may be from about 10 bp (base pair) to about 2000 bp, from about 15 bp to about 1500 bp, from about 20 bp to about 1000 bp, from about 20 bp to about 500 bp, or from about 20 to about 200 bp.

The term " depth "can be used interchangeably with the term" read-depth "

As a result of the sequencing, gene data of the FASTQ file format can be obtained. The FASTQ format is a text-based format that stores biological sequences, such as nucleic acid sequences, and their corresponding quality scores. However, it is possible to analyze sequencing data in other formats without being restricted to the FASTQ format.

The reference genomic or single nucleotide variation (SNV) predictor is located at the National Center for Biotechnology Information (NCBI), the Gene Expression Omnibus (GEO), the Food and Drug Administration (FDA), the My Cancer Genome, or the KFDA (Food and Drug Administration) (DB), as is well known in the art. That is, the reference genome may be obtained from public genomic data or from public HapMap data. The target site of the reference genome and the single nucleotide variation (SNV) predictor of the test gene may be, for example, the same exon or intron, and may be the same sequence number on the chromosome of the same number.

The sample may be obtained from biopsy tissue, formalin-fixed tissue or paraffin-embedded (FFPE) tissue of a subject. The sample may be obtained from biopsy tissue based on the amount of DNA input, or may be obtained from FFPE tissue based on DNA concentration. The amount of DNA injected for use in the hybridization step is important for a sample obtained from the biopsy tissue, and more than 400 ng, 600 ng or 800 ng of DNA can be injected for use in the hybridization step. Samples obtained from FFPE tissue are important for DNA concentration and may be 10 ng / μl, 20 ng / μl or 30 ng / μl or more. Within this range, the depth of the lead is sufficiently secured, so that the reliability of the single nucleotide mutation detection label can be improved.

In step 120 of mapping the leads to the reference dielectric, each lead is aligned and mapped to the target location of the reference dielectric. Sequence information mapped to only one genome position in the reference genome can be designated as unique sequence information. You can assign a lead to the chromosome location based on the unique sequence number assigned. The mapping can position the entire leader sequence at the most similar part of the target locus of the reference genome (Global alignment), or position some of the leader sequence to the most similar of the target sites of the reference genome , Local alignment). As a result, the depth data generated by the step 120 may include data representing the depth of the lead mapped to the nucleic acid of each of the target positions of the reference dielectric.

3 illustrates a method of target sequencing a biopsy sample 410 or an FFPE treated sample 425 of a subject 400 and determining the depth of the lead aligned to the nucleic acid (1, 2, 3, 4 or 5) 430, respectively. May be related to the method performed in step 120 of FIG.

The QC score calculation step 130 calculates a QC score (quality control score) based on the data representing the depth. QC scores can take into account depth, component bias, standard deviation, or coefficient of variation. In step (130), for a total of n target positions, a QC score can be calculated according to the following equations (1) and (2).

[Equation 1]

 &Quot; (2) "

.

.

In Equation 1, D _i means the average of the depth of the lead mapped to each nucleic acid within the depth analysis interval for the i-th target spot of the chromosome. In order to obtain D _i , the depth of the neighboring digits adjacent to the i-th target site can be considered. Depth analysis is performed from the target position of the reference dielectric to the nucleic acid at the c-th position in the 5 'direction and the nucleic acid at the c- And the average of the read lengths is calculated from the depths of the leads mapped to the respective 2c + 1 nucleic acids in the depth analysis interval. This average value can be defined as D _i . The c may be 0 or a natural number. The c may be from 0 to 10, from 0 to 7, from 0 to 5, from 0 to 3, or from 0 to 2. For example, in consideration of the fact that three codons are translated into one amino acid, it is determined as a depth analysis interval from the target site to the nucleic acid at the second position in the 5 'direction from the target site and the nucleic acid at the second position in the 3' direction . With reference to FIG. 6, for example, the depth of the lead mapped to each nucleic acid in the 5 'direction and the 3' direction from the target position, that is, the ± second position, is analyzed together. The depth of the lead mapped to the nucleic acid at positions 11168334 and 11168335 in the 5 'direction with respect to the 11168336 position of the chromosome 1 and the length of the nucleic acid at positions 11168337 and 11168338 in the 3' direction based on 11168336 digits of the chromosome 1 , And the average of the five-digit depths can be determined as the target position, D _i , at 11168336. [

I _Di Has a value of 1 when the depth (D _i ) of the lead mapped to each nucleic acid in the depth analysis section for the i th target spot of the chromosome is greater than or equal to the minimum depth (d), and 0 Is an indicator function having a value of.

S _i is the strand bias of the lead mapped to each nucleic acid within the depth analysis interval for the ith target spot of the chromosome. In Equation (1), S _i is calculated according to Equation (2). The leads may be arranged in a forward or reverse direction and may be aligned so as not to be offset in either direction. The closer the number of leads in the forward direction and the number of leads in the reverse direction, the closer to zero. For example, when S _i satisfies the condition of less than 0.9, reliability is improved in determining whether or not a single nucleotide variation exists. I _Si Is an indicator function having a value of 1 when the component bias (S _i ) of the lead mapped to each nucleic acid in the depth analysis interval is less than 0.9 and a value of 0 when the deviation is less than 0.9.

n is the total number of target positions.

d is the minimum depth required to determine whether a single nucleotide variation is present. The minimum depth d may be determined based on detection sensitivity, detection limit of variation, number of supporting reads, or a combination thereof.

The term " detection limit of mutation "means the minimum detectable gene frequency of a mutation.

The term "detection sensitivity" refers to how well a mutation can be detected in the presence of a mutation in the presence or absence of the mutation gene.

The term "support lead" means a lead that contains a variation in its target place.

The detection limit and the detection sensitivity of the allele frequency of the mutation can be set and the minimum depth for sufficiently securing the number of support leads supporting the detection limit of the mutation can be determined. In this case, the detection limit of the allele frequency of the mutation can be set differently depending on the state of the sample.

The minimum depth d of the step 130 may be calculated according to the following equation (3).

 &Quot; (3) "

r is the number of supporting leads. May be the number of supporting leads required to achieve the desired detection sensitivity.

α is the detection limit of the mutation.

The QC scores for the n total target positions are calculated from the mean (D _i ) of the depths of the leads mapped to the respective nucleic acids in the depth analysis interval and the biases (S _i ) of the lead mapped to the respective nucleic acids in the depth analysis interval Is defined as a value obtained by dividing the number of target positions satisfying simultaneously, i.e., the number of target positions where each of I _Di and I _Si satisfy a value of 1, by the number n of total target positions. The QC score may be closer to 100 as the ratio of the number of target positions that simultaneously satisfy the average of the depths (D _i ) and the slopes of the compositional deviations (S _i ) with respect to the total number of target positions. If the QC score of 50 may indicate that only a total of half the target position, satisfies the average (D _i) and component deflection (S _i) based on the depth at the same time.

In the step 140 of assessing the reliability of the mutation detection label, the reliability of the mutation detection label can be evaluated from the ratio of the reliable mutation detection label among the mutation detection labels for the total n target positions. That is, the QC score can indicate the percentage of the mutation detection markers capable of reliably detecting a single nucleotide mutation, of the mutation detection marks for the total n number of target positions. The closer the QC score to the total n target positions is closer to 100, the more reliable the detection result of a single nucleotide variation near n.

The technique for determining the presence or absence of a conventional mutation is focused on detecting a mutated gene in terms of research. When the conventional mean depth, 100x OnTarget Rate, and Q30 / FASTQ TotalBases (Gb) are used, there is a problem that the utilization for diagnosis of a specific variation is low. In clinical practice, most target sequences are sequenced. Therefore, it is more appropriate to use the depth of the target spot as a criterion for judging the reliability of detection of the mutation at the target spot. Furthermore, in clinical cases, when a mutation gene is detected not only in a case where a mutation gene is detected from a predicted position of a subject, but also when a mutation gene is not detected, if the depth of the mutation prediction site is grasped, It is possible to provide information on whether or not it is judged whether or not there is a shortage.

In another aspect, there is provided a nucleic acid analyzing apparatus comprising: a sequencing unit for performing a target sequencing on a predicted single nucleotide position of a gene of a test sample from a nucleic acid sample containing the test gene to obtain a lead;

A mapping unit for mapping the lead to a reference dielectric;

A QC score calculating unit for calculating a QC score from the depth of the lead aligned with the reference dielectric; And

And a reliability evaluation unit for evaluating the reliability of the single nucleotide mutation detection mark according to the calculated QC score.

The reliability determination unit 300 of the single nucleotide variation detection mark shown in FIG. 2 implements the above-described reliability determination method. The apparatus includes a computer readable medium or a system including the same. Other general components than the components shown in FIG. 2 may be further included.

The sequencing unit 310 performs a target sequencing on a specific region including a single nucleotide variation (SNV) prediction position of the test gene to obtain a lead. For example, a target dip sequence is performed to obtain leads for each single nucleotide variation (SNV) prediction gene.

The sample may be obtained from biopsy tissue or FFPE tissue of the subject. The sample may be obtained from biopsy tissue based on the amount of DNA input, or may be obtained from FFPE tissue based on DNA concentration.

The mapping unit 320 analyzes the depth by aligning the respective leads to the target positions of the reference dielectric. The depth data generated by the mapping unit 320 may include data indicating a depth of a lead mapped to a nucleic acid of each of the target positions of the reference dielectric.

The QC score calculating unit 330 calculates a QC score based on the data representing the depth. QC scores can take into account depth, component bias, standard deviation or coefficient of variation. The QC score for a total of n target positions in the calculation unit 330 can be calculated according to the following equations (1) and (2).

[Equation 1]

 &Quot; (2) "

.

.

As previously described, in Equation 1, D _i is the average of the depth of the lead mapped to each nucleic acid in the depth analysis interval for the i th target spot of the chromosome, and I _Di is the indicator function. In order to obtain D _i , the depth of the neighboring digits adjacent to the i-th target site can be considered. Depth analysis is performed from the target position of the reference dielectric to the nucleic acid at the c-th position in the 5 'direction and the nucleic acid at the c- And the average of the read lengths is calculated from the depths of the leads mapped to the respective 2c + 1 nucleic acids in the depth analysis interval. This average value can be defined as D _i . The c may be 0 or a natural number. The c may be from 0 to 10, from 0 to 7, from 0 to 5, from 0 to 3, or from 0 to 2.

As described above, in Equation 1, S _i denotes the component bias of the lead mapped to each nucleic acid in the depth analysis region for the i-th target site of the chromosome, and I _Si Is an indicator function. n is the total number of target positions. d is the minimum depth required to determine whether a single nucleotide variation is present. The minimum depth d may be determined based on detection sensitivity, detection limit of variation, number of supporting leads, or a combination thereof.

The minimum depth d in the calculation unit 330 may be calculated according to the following equation (3).

&Quot; (3) "

As described above, r is the number of supporting leads and? Is the detection limit of the variation.

As described above, the QC score for a total of n target sites is calculated by multiplying the average (D _i ) of the depths of the leads mapped to the respective nucleic acids in the depth analysis interval and the mean Define the number of target positions that simultaneously satisfy the bias (S _i ) criterion, that is, the number of target positions where each of I _Di and I _Si satisfy a value of 1 divided by the total number of target positions n.

The reliability evaluation unit 340 can evaluate the reliability of the mutation detection mark from the ratio of the reliable mutation detection mark among the mutation detection markers for the total of n target positions. That is, the QC score can indicate the percentage of the mutation detection markers capable of reliably detecting a single nucleotide mutation, of the mutation detection marks for the total n number of target positions. The closer the QC score to the total n target positions is closer to 100, the more reliable the detection result of a single nucleotide variation near n.

Additionally, the device 300 may include an output. The output unit may be a display device for displaying the reliability of the variation detection mark. The output unit may present reliability of the variation detection mark by a QC score, a grade, a binary variable of PASS or FAIL, an image, a graph, and the like. Any device is also possible as long as other users can display the reliability of the variation detection mark. With reference to Fig. 9, for example, a single nucleotide mutation detection tag (denoted only N in Y or N) is used to identify each chromosome target spot, a lead mapped to five nucleotides within a depth analysis interval for each target spot , The reliability of the mutation detection mark is evaluated from the component bias, the coefficient of variation and the standard deviation of the lead mapped to the five nucleic acids in the depth analysis interval, and the QC score, and the result is indicated as PASS or FAIL ) Can be provided.

The single nucleotide mutation detection label reliability determination apparatus 300 is shown to include both the sequencing unit 310, the mapping unit 320, the QC score calculation unit 330, and the reliability evaluation unit 340 according to FIG. 2 However, the present invention is not limited thereto. That is, each of the sequencing unit 310, the mapping unit 320, the QC score calculation unit 330, and the reliability evaluation unit 340 may be a part of a device independent of each other or a part of an independent device, Groups may be constituted as a part of a whole or an independent apparatus of one independent apparatus and each constitution may be a single nucleotide mutation detection mark reliability determination apparatus 300 as a whole.

Another aspect provides a computer readable recording medium on which a program for executing a reliability determination method of a single nucleotide mutation detection mark is recorded.

The method may be implemented in the form of software readable by various computer means and recorded in a computer-readable recording medium. Here, the recording medium may include program commands, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the recording medium may be those specially designed and constructed for the method according to the above, or may be available to those skilled in the art of computer software.

For example, the recording medium may be an optical recording medium such as a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, a compact disk read only memory (CD-ROM), a digital video disk (DVD) Includes a hardware device that is specially configured to store and execute program instructions such as a magneto-optical medium such as a floppy disk and a ROM, a random access memory (RAM), a flash memory, do. Examples of program instructions may include machine language code such as those generated by a compiler, as well as high-level language code that may be executed by a computer using an interpreter or the like. Such a hardware device may be configured to operate as one or more software modules to perform the operations of the above-described method, and vice versa.

Although the present specification and drawings describe exemplary device configurations, the functional operations and subject matter implementations described herein may be embodied in other types of digital electronic circuitry, or alternatively, of the structures disclosed herein and their structural equivalents May be embodied in computer software, firmware, or hardware, including, or in combination with, one or more of the foregoing. Implementations of the subject matter described herein may be embodied in one or more computer program products, that is, a computer program product encoded on a type of program storage medium for execution by, or in control of, And can be implemented as a module as described above. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter that affects the machine readable propagation type signal, or a combination of one or more of the foregoing.

A computer program (also known as a program, software, software application, script, or code) that is embedded in the apparatus according to the above method and that executes the method may be any of a compiled or interpreted language, a programming language including a priori or procedural language And may be deployed in any form including stand-alone programs or modules, components, subroutines, or other units suitable for use in a computer environment. A computer program does not necessarily correspond to a file in the file system. The program may be stored in a single file provided to the requested program, or in multiple interactive files (e.g., a file storing one or more modules, subprograms, or portions of code) (E.g., one or more scripts stored in a markup language document). A computer program may be deployed to run on multiple computers or on one computer, located on a single site or distributed across multiple sites and interconnected by a communications network.

As described above, in determining whether a single nucleotide mutation is present from the test gene, it is useful for imparting reliability to the mutation detection result, and in particular, for detecting tumor-specific gene mutation.

Brief Description of the Drawings Figure 1 is a diagram showing an overall flow of a method for determining the reliability of a single nucleotide mutation detection label.
2 is a diagram showing the configurations of an apparatus for determining the reliability of a single nucleotide mutation detection label.
Fig. 3 is a diagram showing the target sequencing and mapping performed on the test genes obtained from the test subject. Fig.
FIG. 4 is a diagram showing the detection limit, depth, and detection sensitivity of a variation referenced to obtain the minimum depth.
5 is a diagram showing a target spot selected from a single nucleotide variation list of chromosomes.
FIG. 6 is a diagram showing the results of analyzing the mean (D _i ) and the component bias (S _i ) of the depths of the leads mapped to respective nucleic acids in the depth analysis section for one target spot.
7 is a graph showing the QC score and average depth calculated from the genes of each of the 1283 biopsy samples and 1012 FFPE samples.
FIG. 8 is a graph showing an average depth and a QC score, and a depth at a chromosome target spot.
Figure 9 is a diagram showing a single nucleotide mutation detection label and its reliability.
FIG. 10 is a diagram showing depths by chromosome target digits using different DNA extraction kits. FIG.

Hereinafter, the present invention will be described in more detail with reference to examples. However, these examples are for illustrative purposes only, and the scope of the present invention is not limited to these examples.

<Sensitivity to detection and detection limit Depth  Decision>

To determine how much lead depth is required depending on the detection limit, we refer to the detection limit and depth sensitivity sensitivity of the variant caller provided by MuTect. The supporting leads required to achieve the desired detection sensitivity can be determined by the performance of the detector to detect the variation. In the case of MuTect, detection of mutation is possible with detection sensitivity of 99% with 10 supporting leads. Referring to FIG. 4, when the detection limit of the mutation is more than 0.04, there must be at least 10 supporting leads to have a detection sensitivity of 99%, and even if the detection limit of this variation is 0.04 or less, And can have a detection sensitivity of 99%.

For example, the detection limit of the mutation was set at 5% in consideration of the fact that the biopsy sample had a detection limit of 2% and the FFPE sample had a relatively poor sample condition. According to Equation 3, the minimum depth (d) required to determine whether a single nucleotide variation exists at r = 10,? = 0.02 when trying to detect a 2% variation and using MuTect as a detector is 500 . When 5% variation is detected and MuTect is used as a detector, the minimum depth (d) required to determine whether a single nucleotide variation exists at r = 10, a = 0.05 is 200.

&Lt; Single nucleotide mutation ( SNV ) Predicted spot selection>

A single nucleotide mutation (SNV) listed in the FDA approved drug target mutation, My Cancer Genome, or KFDA was investigated for target sequencing, and 137 chromosomal loci except for overlapping mutations (See FIG. 5).

<Target Sequencing>

GDNA was extracted from 1283 human biopsy samples and 1012 FFPE samples using a DNA prep kit and the DNA concentration and purity were measured using Nanodrop and Qubit 2.0 fluorescence spectrophotometer. The library was then constructed according to the guidelines and sequenced using the TruSeq Rapid PE Cluster kit and the 100bp paired-end mode of the TruSeq Rapid SBS kit on an Illumina HiSeq 2500 sequencing platform.

< QC score  Output>

The data of the sequenced leads was localized to the hg19 human reference sequence. After filtering leads out of the region containing low quality lead, single nucleotide mutation (SNV) predictors, mutation detection markers were obtained by confirming the presence of the mutation gene in MuTect 1.1.4.

At this time, SAMTOOLS mpileup was used to calculate the depth of the lead mapped to each nucleic acid in the depth analysis section. The final BAM file just before entering the mutation detector was used. And the QC score was calculated by substituting in Equation 1 above.

<Reliability Evaluation 1>

The QC score was calculated to evaluate the percentage of the total 137 target sites that could reliably detect a single nucleotide variation.

The average depth of 1283 biopsy samples and 1012 FFPE samples and the QC scores at 137 chromosome target sites were checked (see Table 1). The average depth of the FFPE sample was about 728 and the biopsy sample was about 954, which is much higher than the sensitivity of the detection sensitivity considering the detection limit. On the other hand, when the QC score was examined, the FFPE sample was about 92.7 and the biopsy sample was about 97.3. That is, it was confirmed that the average detection index of 7.3% in the FFPE sample and 2.7% in the biopsy sample did not meet the required minimum depth standard.

Sample Type N Average depth QC score FFPE 1012 727.8 ± 279.2 92.7 ± 22.3 Biopsy (FF) 1283 954.3 + - 181.5 97.3 ± 9.1

Referring to FIG. 7, it can be seen that some of the entire samples do not meet the detection limit, even though they have a sufficiently high average depth. In particular, since the quality of the FFPE sample is relatively low compared to the biopsy sample, the QC score may be very low even when the average depth is high. That is, the QC score can account for the quality of the deviation detection mark that the average depth missed.

<Reliability Evaluation 2>

Two arbitrary FFPE samples were selected and the depth average and QC score at 137 chromosomal sites were calculated. For the A sample, the average depth was 324.1 and the QC score was 100. For B samples, the average depth was 634.4 and the QC score was 45.26. FIG. 8 also shows the depths at each of the 137 chromosome target positions. The average depth of the A sample was lower than the average depth of the B sample, but the distribution of the depth was uniform and it was found that all the target positions met the minimum depth criterion.

FFPE sample Average depth QC score A sample 324.1 100 B sample 634.4 45.26

< QC score  Checking Factors to Control>

QC scores were used to identify the key factors that could affect the QC score before performing the target sequencing. For convenience, the QC score was binary and classified as PASS or FAIL based on 80.

For 1012 FFPE samples, the QC score of 696 FFPE samples (97.6%) among the 713 FFPE samples satisfying the DNA concentration of Qubit of 26.5 ng / μl or more after PrePCR satisfied 80 or more. Also, for the 713 FFPE samples, the QC score of 668 FFPE samples (98%) among the 702 FFPE samples satisfying the molarity of 3.92 nM after postPCR satisfied 80 or more.

For 1012 FFPE samples, the QC score of 212 FFPE samples (70.9%) out of 299 FFPE samples with a DNA concentration of Qubit less than 26.5 ng / μl after PrePCR satisfied 80 or better. For the 299 FFPE samples, the QC score of 184 FFPE samples among the 213 FFPE samples satisfying an average library size of greater than 274 satisfied 80 or more. However, for the 299 FFPE samples, only the QC scores of the 26 FFPE samples of 80 out of 84 FFPE samples with an average library size of less than 274 met 80. [ That is, it can be seen that the FFPE sample can obtain a high QC score by primarily controlling the DNA concentration generated by the library production, the DNA molar concentration after postPCR, and the average library size.

For 1246 biopsy samples, among 1160 biopsy samples with a DNA amount exceeding 648.06 ng in the hybridization step, the QC score of 1147 biopsy samples (98.9%) satisfied 80 or more. It can be seen that the biopsy sample can obtain a high QC score by controlling the amount of DNA injected in the hybridization step.

< DNA  extraction Of kit  Quality evaluation>

DNA was extracted from the same FFPE sample using two kinds of DNA extraction kits and the quality items (DNA purity, GC concentration and average library size) and QC score were calculated by the above procedure.

Kit Type DNA purity
(260/280 ratio) GC concentration
(%) Average library size (bp) Average depth QC score A kit 1.895 + 0.067 49.2 ± 1.8 300.3 ± 22.5 728 ± 279 93.5 ± 21.6 B kit 1.875 + 0.076 51.5 ± 3.5 313.3 ± 23.4 775 ± 326 77.7 ± 23.7

When DNA extraction was performed using the A kit and the B kit, there was a significant difference in the QC scores even when there was no significant difference between the average depth and the quality of the Picard tool.

Referring to FIG. 10, the depth of each chromosome target spot was compared with a sample obtained by extracting DNA using an A kit. The distribution of the depth was uniform and the minimum depth standard was further secured Able to know.

110: step of obtaining lead
120: mapping step
130: QC score calculation step
140: reliability evaluation step
300: reliability determining device
310: Sequencing unit
320:
330: QC score calculating unit
340: Reliability Evaluation Unit
400:
410: biopsy sample
425: FFPE treated sample
430: Depth of lead aligned with reference dielectric
1,2,3,4,5: Nucleic acid

Claims (19)

Performing targeted sequencing on a predicted single nucleotide variant (SNV) position of the test gene from a nucleic acid sample containing the test gene to obtain a read;
Mapping the lead to a reference dielectric and calculating a Quality Control (QC) score from the depth of the lead aligned to the reference dielectric; And
And evaluating the reliability of the single nucleotide mutation detection label according to the calculated QC score.
A method for determining the reliability of a single nucleotide mutation detection label. 2. The method according to claim 1, wherein the QC score is determined as a depth analysis interval from a nucleic acid at a c-th position in a 5 'direction to a nucleic acid at a c-th position in a 3' direction from a target site of a reference dielectric, Is calculated from the depth of the lead mapped to each one of the nucleic acids (c is 0 or a natural number). 3. The method of claim 2, wherein c is two. The method of claim 1, wherein the QC score is calculated according to the following equation:
[Equation 1]

Where D _i is the average of the depth of the lead mapped to each nucleic acid in the depth analysis interval for the ith target spot,
I _Di D _i Has a value of 1 when d is greater than d, and D _i Is an indicator function having a value of 0 when d is less than d,
S _i is the component bias of the lead mapped to each nucleic acid in the depth analysis interval for the ith target spot,
I _Si S _i Has a value of 1 when it is less than 0.9, S _i Is an indicator function having a value of 0 when it is 0.9 or more,
n is the total number of target positions,
d is the minimum depth required to determine whether a single nucleotide variation is present,
In the above equation (1), S _i is calculated according to the following equation (2)
&Quot; (2) &quot;

. 5. The method of claim 4, wherein the minimum depth (d) is calculated from a detection sentivity, a detection limit of variation, a number of supporting reads, or a combination thereof. The method of claim 5, wherein the minimum depth (d) is calculated according to the following equation:
&Quot; (3) &quot;

Where r is the number of supporting leads and? Is the detection limit of the mutation. The method of claim 1, wherein the sample is obtained from biopsy tissue or formalin-fixed, paraffin-embedded (FFPE) tissue. 8. The method of claim 7, wherein the sample is obtained from biopsy tissue based on the amount of DNA input, or obtained from FFPE tissue based on DNA concentration. 2. The method of claim 1, wherein evaluating the reliability is based on the ratio of the reliable detection markings of the mutation detection markers to the total n target positions. The computer-readable recording medium according to any one of claims 1 to 9, wherein a program for executing a reliability determination method of a single nucleotide mutation detection mark is recorded. A sequencing unit for performing a target sequencing to a predicted single nucleotide position of a test gene from a nucleic acid sample containing the test gene to obtain a lead;
A mapping unit for mapping the lead to a reference dielectric;
A QC score calculating unit for calculating a QC score from the depth of the lead aligned with the reference dielectric; And
And a reliability evaluation unit for evaluating the reliability of the single nucleotide mutation detection mark according to the calculated QC score.
An apparatus for determining the reliability of a single nucleotide mutation detection label. 12. The method of claim 11, wherein the QC score is determined as a depth analysis interval from a nucleic acid at a c-th position in a 5 'direction to a nucleic acid at a c-th position in a 3' direction from a target site of a reference dielectric, (C is zero or a natural number) that is calculated from the depth of the lead mapped to each one of the nucleic acids. 14. The apparatus of claim 12, wherein c is two. 12. The apparatus of claim 11, wherein the QC score is calculated according to:
[Equation 1]

Where D _i is the average of the depth of the lead mapped to each nucleic acid in the depth analysis interval for the ith target spot,
I _Di D _i Has a value of 1 when d is greater than d, and D _i Is an indicator function having a value of 0 when d is less than d,
S _i is the component bias of the lead mapped to each nucleic acid in the depth analysis interval for the ith target spot,
I _Si S _i Has a value of 1 when it is less than 0.9, S _i Is an indicator function having a value of 0 when it is 0.9 or more,
n is the total number of target positions,
d is the minimum depth required to determine whether a single nucleotide variation is present,
In the above equation (1), S _i is calculated according to the following equation (2)
&Quot; (2) &quot;

. 15. The apparatus of claim 14, wherein the minimum depth (d) is calculated from a detection sentivity, a detection limit of variation, a number of supporting reads, or a combination thereof. 16. The apparatus of claim 15, wherein the minimum depth (d) is calculated according to: &lt; EMI ID =
&Quot; (3) &quot;

Where r is the number of supporting leads and? Is the detection limit of the mutation. 12. The apparatus of claim 11, wherein the sample is obtained from biopsy tissue or FFPE tissue. 18. The apparatus of claim 17, wherein the sample is obtained from biopsy tissue based on the amount of DNA input, or is obtained from FFPE tissue based on DNA concentration. 12. The apparatus of claim 11, wherein the evaluating unit evaluates the reliability from a ratio of reliable mutation detection marks among mutation detection marks for a total of n target positions.

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