WO2016076524A1

WO2016076524A1 - Standardized quantitative analysis method for nucleic acid, applying sing-pcrseq method

Info

Publication number: WO2016076524A1
Application number: PCT/KR2015/009823
Authority: WO
Inventors: 정상균; 오수아
Original assignee: 한국한의학연구원
Priority date: 2014-11-14
Filing date: 2015-09-18
Publication date: 2016-05-19
Also published as: KR101738481B1; KR20160057792A

Abstract

The present invention relates to a quantitative analysis method for nucleic acids in a sample, the method applying a spiking-in neighbor genome-coupled competitive PCR amplicon sequencing (SiNG-PCRseq) method in which a neighbor genome is mixed with a sample to be quantified, and then co-amplification is carried out by using similarities between neighboring sequences. Specifically, the SiNG-PCRseq method of the present invention can provide a standardized and relative DNA quantitative value under various experimental conditions including a variable element such as a cell line and a polymerase, and when compared with a RNA-seq method to be used as a conventional method for quantitative analysis of nucleic acids, the provided standardized DNA quantitative value accurately and conveniently provides a relative quantitative value with respect to nucleic acids in a sample irrespective of the experimental conditions or the type of sample, and thus the SiNG-PCRseq method of the present invention is useful for a standardized quantitative analysis method for nucleic acids in a sample.

Description

Standardized Quantitative Analysis of Nucleic Acids Using the SING-PCRSEQ Method

The present invention relatives after mixing the dielectric for the determination target sample utilizing the similarity between closely related species sequence by co-amplification (co-amplification) sequence analysis of the competition in which PCR amplicons (piking- s i n g n eighbor enome-coupled competitive PCR amplicon seq uencing (SiNG-PCRseq) method, the method of quantifying the nucleic acid in the sample.

Accurate quantitation of the transcripts of cells is necessary to provide a fundamental perspective for studying the physiological characteristics and functions of cells. The analytical basis for the precise analysis of transcriptomes is the current trend to shift microarrays from technology to next generation sequencing (NGS) (Ozsplak, F. & Milos, PM, Nat). Rev. Genet., 12: 87-98, 2011; Metzker, ML, Nat. Rev. Genet., 11: 31-46, 2010; Blencowe, BJ et al ., Genes. Dev., 23: 1379-1386. , 2009). This change is due to technological advances that allow NGS technology to quantify large portions of transcripts at the single nucleotide level.

The recently developed RNA-seq method, which has been developed as a nucleic acid quantification method, can generate hundreds of millions of sequence reads through a single run, thus allowing for abundance ratios and sequence variants between sequences at the whole transcript level. Has the advantage of providing information (Mortazavi, A. et al. , Nat. Methods, 5: 621-628, 2008; Wang, Z. et al. , Nat. Rev. Genet., 10: 57-63 , 2009). RNA-seq methods allow quantitative comparisons between different samples for the same sequence with high efficiency and reproducibility. However, the relative quantitative value between different transcripts in the same cell is difficult to deal with when the biase appears, so it is impossible to quantitatively analyze nucleic acids by RNA-seq method alone. Have

The bias was induced during analytical procedures such as random 6-mer (hexamer) priming, fragmentation and colony formation of flow cells for both reverse transcription and double strand cDNA formation (Hansen, KD et al. , Nucleic acids research, 38: e131, 2010; Oshlack, A. & Wakefield, MJ, Biology direct., 4: 14, 2009; A. Roberts et al. , Genomic biology, 12: R22, 2011), resulting in distortion of quantitative topography of transcript members. . In addition to analysis at the entire transcript level, RNA-seq methods have been developed that target specific groups of transcripts, including tiling arrays (Mercer, R. et al. , Nature biotechnology, 30: 99-104, 2012). Or constructing a library of target sequences captured using soluble probes (Levin, JZ et al. , Genomic biology, 10: R115, 2009) or spiked-in by multiplexed competitive PCR. There are methods with steps.

In terms of quantitative representation of inter-sequence, capture approaches still have the limitation of involving procedural biases similar to RNA-seq methods. In contrast, the competitive PCR approach provides a platform for correcting procedural biases by calibrating the quantitative value of the subject sequence, using the corresponding competitor template knowing the quantitative value. Since the competitor template also undergoes the same level of biochemical reactions as the corresponding target sequence, it can be assumed that it can have the same degree of bias as the corresponding target sequence (Blomquist, TM et al. , PLoS ONE, 8: e79120, 2013). This method, however, is essential to maintain a rigorous quantitative relationship between competitors when creating a mixture of competitor templates, but there are limited means to ensure the rigor when this process is carried out between different experimenters.

Thus, despite performing statistical or experimental adjustments with standard molecules bound for accurate quantitative analysis (Jiang, L. et al. , Genomic research, 21: 1543-1551, 2011; Schwartz, S et al. , PLoS ONE, 6: e16685, 2011; Zook, JM et al. , PLoS ONE, 7: e41356, 2012), lacking certainty that these deflections can be completely compensated for There is a need for development of reliable nucleic acid quantitative analysis methods.

In this regard, a method of determining the relative quantitative value of other sequences using a quantitative competitor PCR strategy has been reported (Jeong, S. et al. , DNA Res., 19: 209-217, 2012). In this method, competitor templates formed competitor arrays through plasmid insertion to enable accurate determination of relative quantitative ratios between each other. In other words, by recombining the copy number of each competitor template in one plasmid can provide an accurate quantitative relationship of the competitor template. When applied in combination with melting analysis for quantitating alleles, the method has the advantage of being very accurate and sharp in quantifying small numbers of sequences (Jeong, S. et al. , Genome Res., 17: 1093-1100, 2007).

Accordingly, the present inventors have expanded the concept of nucleic acid quantitative analysis methods developed before, and have tried to develop accurate and simple ultra-fast nucleic acid quantitative analysis methods. As a result, competitors' genomes (e.g. use, as long competitor PCR amplicon sequencing associated with the amplifier closest relative dielectric by quantifying the competitor sequences within the amplicon to enable the next generation of high-speed sequencing (piking- s i n g n eighbor enome-coupled competitive PCR amplicon seq uencing, SiNG-PCRseq) method was developed. In the present invention, SiNG-PCRseq quantification of human cDNA using a sample of cDNA obtained from a human-derived fibroblast or induced pluripotent stem cells (iPSC) and a human and related species orangutan gDNA When the analysis was performed, it was confirmed that it can provide a standardized relative DNA quantitative value in a variety of variable experimental conditions, such as cell lines, polymerase, the standardized DNA quantitative value provided is used as a conventional nucleic acid quantitative assay As compared with the RNA-seq method, it was confirmed that the accurate quantitative information is provided, the SiNG-PCRseq method of the present invention can be usefully used in the standardized analysis method for quantifying nucleic acid in a sample.

[Preceding technical literature]

[Non-Patent Documents]

Ozsolak, F. & Milos, P.M., RNA sequencing: advances, challenges and opportunities, Nat. Rev. Genet., 12: 87-98 (2011).

Metzker, M.L., Sequencing technologies-the next generation, Nat. Rev. Genet., 11: 31-46 (2010).

3. Blencowe, BJ et al. , Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes, Genes Dev., 23: 1379-1386 (2009).

4. Mortazavi, A. et al. , Mapping and quantifying mammalian transcriptomes by RNA-seq, Nat. Methods, 5: 621-628 (2008).

5. Wang, Z. et al. , RNA-seq: a revolutionary tool for transcriptomics, Nat. Rev. Genet., 10: 57-63 (2009).

6. 't Hoen, PA et al. , Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories, Nat. Biotechnol., 31: 1015-1022 (2013).

7. Hansen, KD et al. , Biases in Illumina transcriptome sequencing caused by random hexamer priming, Nucleic Acids Res., 38: e131 (2010).

8. Oshlack, A. & Wakefield, M.J., Transcript length bias in RNA-seq data confounds systems biology, Biol. Direct., 4: 14 (2009).

9. Roberts, A. et al. , Improving RNA-seq expression estimates by correcting for fragment bias, Genome Bio., L 12: R22 (2011).

10. Jiang, L. et al. , Synthetic spike-in standards for RNA-seq experiments, Genome Res., 21: 1543-1551 (2011).

11. Schwartz, S. et al. , Detection and removal of biases in the analysis of next-generation sequencing reads, PLoS ONE, 6: e16685 (2011).

12. Zook, JM et al. , Synthetic spike-in standards improve run-specific systematic error analysis for DNA and RNA sequencing, PLoS ONE, 7: e41356 (2012).

13. Mercer, TR et al. , Targeted RNA sequencing reveals the deep complexity of the human transcriptome, Nat. Biotechnol., 30: 99-104 (2012).

14. Levin, JZ et al. , Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts, Genome Bio., L10: R115 (2009).

15. Blomquist, TM et al. , Targeted RNA-sequencing with competitive multiplex-PCR amlicon libraries, PLoS ONE, 8: e79120 (2013).

16. Jeong, S. et al. , Accurate measurement of the relative abundance of different DNA species in complex DNA mixtures, DNA Res., 19: 209-217 (2012).

17. Jeong, S. et al. , Accurate quantitation of allele-specific expression patterns by analysis of DNA melting, Genome Res., 17: 1093-1100 (2007).

18. Untergasser, A. et al. , Primer3-new capabilities and interfaces, Nucleic Acids Res., 40: e115 (2012).

An object of the present invention is closely related species then mixing the dielectric for the determination target sample utilizing the similarity between closely related species sequence by co-amplification (co-amplification) sequence analysis of the competition in which PCR amplicons (piking- s i n g n eighbor enome- Through the coupled competitive PCR amplicon seq uencing (SiNG-PCRseq) method, a high-throughput nucleic acid quantification method that can accurately and easily obtain the relative quantitative values of different nucleic acids in a sample regardless of the experimental conditions or the type of sample. To provide.

In order to achieve the above object, the present invention

i) mixing DNA or cDNA isolated from a subject-derived cell with genomic DNA (gDNA) isolated from a cell of the species of late myelin;

ii) amplifying the DNA sample mixed in step i) by multiplex PCR to obtain an amplicon;

iii) sequencing the sequence library comprising the amplicons obtained in step ii) and comparing it with a reference sequence to quantify the read count;

iv) calculating the fraction of sequence (γA) of the target sequence (SEQ ID A) to the amplicon obtained in step ii) using the read value quantified in step iii);

v) calculating a deflection correction value γ A _c that corrects procedural deflection using the ratio value γ A calculated in step iv);

vi) Calculating the relative abundance of A to B (RA _{A / B} ) of the target sequence (SEQ ID NO: A) to the near-missing sequence (SEQ ID NO: B) from the bias correction value γ A _c calculated in step v). Making; And

vii) providing a standardized quantity of nucleic acid in a sample by dividing the relative abundance ratio (RA _{A / B} ) calculated in step vi) by a representative value thereof and calculating standardized quantities (StQs). do.

In addition, the present invention

i) calculating standardized quantitative values between different genes of a subject using the standardized quantitative method of the present invention; And

ii) calculating a standardized ratio between different genes in one species of interest using the standardized quantitative value of step i). to provide.

In addition, the present invention

i) Gene transcripts which are characterized by a disease state having a transitional characteristic to the disease or disease, for the target cell isolated from the diseased or suspected disease, using the standardized quantitative method of the present invention Or calculating a standardized quantity of target sequences in the genome; And

ii) diagnosing the gene related disease comprising comparing the standardized quantitative value of step i) with a standardized quantitative value of the same nucleotide sequence generated from a transcript or genome of a cell isolated from a homogenous normal individual. It provides a method of providing information for.

Sequencing of competitive PCR amplicons by mixing the related species of the present invention to the sample to be quantified and co-amplifying the similarity between the related species of the related species ( s piking- i n neighbor g enome-coupled competitive PCR amplicon seq uencing (SiNG-PCRseq) method can provide standardized relative DNA quantification even under various experimental conditions including variable elements such as cell lines, polymerases, and the standardized DNA quantification provided above provides Compared to the RNA-seq method used as an analytical method, the relative quantitative value of the different nucleic acids in the sample can be provided more accurately regardless of the experimental conditions or the type of sample, the SiNG-PCRseq method of the present invention It can be usefully used in standardized assay methods for quantifying nucleic acids.

1 is a schematic diagram illustrating the steps of the quantitative method of the present invention:

Figure 1a shows a schematic diagram showing the cDNA quantification method step of the present invention, to show that the relative quantitative value of the human (HS) and orangutan (PA) sequences are expressed step by step, four experimental genes (Gene A to Expressed as an arbitrary read number for Gene D);

1B is a diagram showing an experimental set configured to divide a sample group of the cDNA quantification method of the present invention.

Figure 2 is a diagram showing the results of electrophoresis to confirm the integrity of the intermediate (intermediary) amplicon DNA in the preparation of cDNA amplicon library for sequence analysis.

Figure 3 shows the size of the read for each sample due to the construction of the cDNA amplicon library. The total read size and informative reads for use in quantitative analysis for each sample are shown in bars, the usage rate of the sequenced reads in the present invention is shown in diamonds, and inter In the ideal case excluding the absence of -species variation (ISV), the utilization of sequenced reads is shown as circles.

4 is a diagram showing a pairwise comparison of Pearson correlation for each sample due to the construction of the cDNA amplicon library.

FIG. 5 is a distribution diagram showing the dropping of the fraction of human sequence (γHS) to amplified PCR amplicons in a mixed sample of human and orangutan genes.

6 is a diagram showing the correlation between the resulting γHS according to the difference in the polymerase by dropping the γHS for each amplicon in the sample as a scatter plot.

FIG. 7 is a diagram showing a box plot for confirming a relationship between a variation type in an orangutan sequence variation and a procedural bias in calculating γHS in quantitative analysis using SiNG-PCRseq.

FIG. 8 is a diagram showing a distribution diagram in which γHS _C calculated by correcting a deflection of γHS of an amplicon in a sample is dropped as a scatter diagram. FIG.

FIG. 9 is a scatter diagram illustrating root mean square deviation (RMSD) obtained for γHS _C calculated by correcting a deflection of γHS of an amplicon in a sample. FIG.

FIG. 10 is a plot showing standardized quantities (StQs) for each amplicon amplified in five gDNA samples. The area marked in black on the lower axis represents the amplicon of the autosomal gene, and the area marked in gray at the right end of it represents the amplicon of the X-chromosome associated sex chromosome gene.

FIG. 11 shows the steps for quantifying cDNA from SiC-PCRseq method from cDNA samples derived from human fibroblast (Fib; Fib; F1 to F3) and induced pluripotent stem cell lines (iPSC; I1 to I3):

FIG. 11A shows the relative abundance of HS to PA (RA _{H / P} ) of a human sequence with respect to a competing orangutan sequence in a cDNA sample derived from iPSC and a cDNA sample derived from Fib. Where * denotes the portion where the coefficient of variation (CV) changes significantly;

FIG. 11B shows the distribution of standardized quantitative values (StQs) for cDNA samples derived from iPSC and cDNA samples derived from Fib; FIG.

FIG. 11C shows the distribution of coefficient of variation (CV) for each amplicon by averaging StQs values calculated from three samples for each cell line of iPSC and Fib.

12 is a diagram showing a scatter plot showing the difference in StQs values due to the difference in hTaq and fTaq polymerase in quantifying cDNA samples derived from various cell lines using the SiNG-PCRseq method of the present invention.

Figure 13 is a view confirming the difference in quantitative analysis between the SiNG-PCRseq method of the present invention and the existing quantitative method;

FIG. 13A is a scatter plot showing the distribution of StQs values quantified by RNA-seq method and SiNG-PCRseq method for the same sample of iPSC and Fib cDNA; FIG.

13B shows the fold difference (I / F) of StQs values quantified by RNA-seq method and SiNG-PCRseq method in samples of iPSC and Fib cDNA. In comparison of multiples of I / F, genes showing a difference of two or more times are indicated by empty squares, and kernel density analysis using the same is shown by the solid line and the right axis.

The present invention

vi) Calculating the relative abundance of A to B (RA _{A / B} ) of the target sequence (SEQ ID NO: A) to the near-missing sequence (SEQ ID NO: B) from the bias correction value γ A _c calculated in step v). Doing; And

In the present invention, the term "reference sequence" may be a known sequence capable of determining the identity of a nucleotide sequence by the species and / or gene name derived. For example, the reference sequence may be, but is not limited to, a sequence retrieved from an NCBI reference sequence database.

In the present invention, the term "lead value" may refer to the number of sequencing performed for each reference sequence.

In the present invention, the term "representative value" may be a relative abundance ratio of the relative abundance gene of a specific base sequence or a relative abundance ratio derived for all or some nucleotide sequences. The representative value may mean, but is not limited to, an arithmetic mean, geometric mean, harmonic mean, variance, and standard deviation of two or more relative abundances.

The subject of step i) may be any microorganism, yeast, mammalian, etc. that extracts genes from its cells and wishes to quantitate any of them, the related species of which have been reported genetically related to the subject for the subject. Also may be used. For example, the subject is a human, and a related species thereof may be, but is not limited to, an orangutan, a chimpanzee, a monkey, or a gorilla.

The cells of step i) can be any cell that can extract DNA therefrom or synthesize cDNA using its RNA. Specifically, when the cell of interest is a human-derived cell, it may be human lymphoblast, human fibroblast, or human induced pluripotent stem cells (iPSC), but is not limited thereto.

The multiple PCR of step ii) may be multiple competitive PCR, but is not limited thereto.

The ratio value γA of step iv) may be calculated using Equation 7 below, but is not limited thereto;

[Equation 7]

The deflection correction value γ A _C of step v) may be calculated using Equation 4 below, but is not limited thereto;

[Equation 4]

Wherein γA and γB represent the fraction of the target sequence A and the related species B in the amplified amplicons in the mixed sample;

pA and pB represent the mixing ratios mixed in the reference sample in which the target sequence A and the related species sequence B were mixed in different moles;

γA _Ref and γA _Ref represent the ratio of subject sequence A and myoma sequence B to amplified amplicons in a reference sample in which subject sequence A and myoma sequence B are mixed in different moles;

n may be an integer of 1 or more, wherein n represents the number of mixed samples used for the deviation correction.

The relative abundance ratio RA _{A / B} of step vi) may be calculated using Equation 8 below, but is not limited thereto;

[Equation 8]

The standardized quantitative values StQs of step vii) may be calculated using Equation 9, but are not limited thereto;

[Equation 9]

Representative RA _{A / B} value in the sample in [Equation 9] refers to the representative value of step vii) in the standardized quantitative method of the present invention, the relative abundance or all base sequence or part of a specific one base sequence It can be the average value of the relative abundances derived for the sequences. The representative value may mean, but is not limited to, an arithmetic mean, geometric mean, harmonic mean, variance, and standard deviation of two or more relative abundances.

In a specific embodiment of the present invention, the inventors have found that closely related species of the mixture after the quantification target sample dielectric utilize similarities between closely related species sequence by co-amplification (coamplification) which compete sequencing (piking- s i n g n eighbor enome In order to obtain the genomic DNA of the present invention for use as a target cell of the -coupled competitive PCR amplicon seq uencing (SiNG-PCRseq) method, the orangutan-derived cell line and the human-derived cell line were each cultured and then cultured. GDNA or cDNA was obtained from the cells.

In addition, the present inventors selected 263 gene sequences as reference sequences for human genes, and prepared 20 pairs of primer pairs to prepare primer sequences for multiplex PCR. The total was divided into 24 groups. In addition, amplicons were amplified by multiple competitive PCR, and intermediate amplicon DNA was prepared to construct a sequence library. As a result, mediator amplicons according to mixing ratios and polymerase types in various samples were obtained. It was confirmed that DNA completeness and consistency between samples were maintained (see FIG. 2).

In addition, the present inventors quantified the amplicon library by quantifying the reference sequence and the amplified amplicon library to obtain read values for each amplicon. Thus, the inventors used two different polymerases that were used as polymerases in the amplicon polymerization. It was confirmed that there was a high correlation between each sample in the used hTaq set and fTaq set, but the correlation between the two sets is inferior (see FIGS. 3 and 4).

In addition, the present inventors obtained the ratio of human sequence (γHS) to amplified PCR amplicons in order to apply the SiNG-PCRseq method of the present invention to a gDNA sample, respectively, in a sample It was confirmed that procedural biases were indicated by a non-flat plot pattern, in which the read value of the amplicon of the amplicon was not shown (see FIG. 5), which is a process of repeating a plurality of PCR reactions and purifications. It is confirmed that a quantitative deviation occurs in and is essentially related to the mutant structure of the gene sequence (see FIGS. 6 and 7).

In addition, in the quantitative analysis of the SiNG-PCRseq of the present invention, the present inventors modified the previously reported method to correct the deflection of the amplicon γHS appearing in the variation of the sequence to the γHS of the amplicon in the sample As a result of calculating the deflection correction value γHS _C which corrected the deflection, it was confirmed that the deflection shown in γHS was efficiently corrected to show an evenly arranged quantitative pattern (see FIGS. 8 and 9).

In addition, the present inventors obtain the relative abundance ratio (RA _{H / P} ) of the human sequence to the orangutan sequence, which is the competition sequence in the sample, in order to calculate the final quantitative value as the final quantitative value, and each of them is RA _{H of the} autosomal sequences. _The standardized quantities (StQs) were calculated as relative to the average value of _{/ P} , and the relative quantitative ratios of each of the sequences were accurately reflected, and the SiNG-PCRseq of the present invention was repeated through different experimental conditions. It was confirmed that the accuracy (accuracy) is maintained even when (see FIG. 10).

In addition, the inventors confirmed that the SiNG-PCRseq of the present invention is actually reproducible under different experimental conditions even in cDNA samples derived from various cell lines. For a cDNA sample prepared from Fib and iPSC two different polymerase a (hTaq, fTaq) used in SiNG-PCRseq method was applied to, through the Sequencing Analysis calculated RA _{H /} a _P value RA of all the base sequence _{H / As} a result of normalization with an average of _P values, it was successful to obtain constant normalized values regardless of the relative amount of orangutan gDNA mixed in the sample (see FIG. 11). In addition, it was confirmed that the correlation (R ² ) of the standardized quantitative values obtained using different polymerases showed high reproducibility of 0.92 or more (see FIG. 12 and Table 6).

In addition, the present inventors quantitative analysis between genes in a sample based on RNA-seq in order to check whether the quantitative result of the gene in the sample by the SiNG-PCRseq method of the present invention is different from the conventional method for quantifying genes. between the performance compared result, RNA-seq method and the correlation of SiNG-PCRseq the StQs value quantified by the method relationship (R ²⁾ is very low with out a sample two assay for each other quantitative comparison of the different sequences within the The difference was confirmed (see FIG. 13A). However, it was confirmed that there is no significant difference between the two quantification methods to reveal the quantitative difference between the different samples for the specific nucleotide sequence (see Fig. 13b).

Thus, the SiNG-PCRseq method of the present invention can provide standardized relative DNA quantitative values in a variety of variable experimental conditions such as cell lines, polymerases, and the standardized DNA quantitative values provided above can be used as conventional nucleic acid quantitative assays. Compared with the RNA-seq method, the relative quantitative value of the nucleic acid in the sample is accurately and easily provided regardless of the experimental conditions or the type of the sample. It can be useful for the method.

In addition, the present invention

The method can be useful for determining the cytological and histological identity by applying to the analysis of unknown biosamples.

In addition, the present invention

As used herein, the term "normal individual" may refer to a healthy individual who has or is unlikely to develop a disease.

The standardized quantitative value used in the method for providing information for diagnosing the gene-related disease may be a distribution of the calculated standardized quantitative value, but is not limited thereto.

As a subject of step i), cells such as microorganisms, yeasts, mammals, etc. may be used as cells for extracting and quantitating genes, which are reported to be genetically related to the cells of the subject. Anything can be used. Specifically, the subject is a human, and a related species thereof may be an orangutan, a chimpanzee, a monkey, or a gorilla, but is not limited thereto.

The cell of step i) can be any cell that can be used to extract DNA from the cell of interest. For example, the cells may be fibroblasts or induced pluripotent stem cells, but are not limited thereto.

The "gene transcript characterizing the disease" of step i) may mean a gene transcriptome known to be associated with the disease, such as its expression increases or decreases when a particular disease occurs.

"Disease state with transitional characteristics to disease" may refer to a gene transcript known to be associated with prognostic or early symptoms appearing before the onset of the disease.

For example, when the standardized quantitative value of step i) in step ii) is compared with the standardized quantitative value of the same genes calculated from cells isolated from homogenous normal individuals, the disease develops or develops when different values are shown. Determining that there is a possibility to further include after step ii).

Specifically, a disease associated with the gene when the distribution of the standardized quantitative value derived from the target cell has moved to a lower or higher region compared to the standardized quantitative distribution of the same gene produced from cells isolated from homogenous normal individuals. It can be judged that this disease has or is likely to occur.

That is, the method of providing information for diagnosing a disease related to a gene using a standardized quantitative value of the gene according to the present invention is not limited to a specific gene, and may be applied regardless of the type of gene.

The SiNG-PCRseq method of the present invention can provide standardized relative DNA quantitative values under various experimental conditions including variable elements such as cell lines and polymerases, and the standardized DNA quantitative values provided above can be used as conventional nucleic acid quantitative assays. Compared with the RNA-seq method used, the relative quantitative value of the nucleic acid in the sample can be provided accurately and simply regardless of the experimental conditions or the type of the sample. Standardized quantitative values can provide useful information on diagnosis of gene-related diseases.

In addition, the present invention

i) by providing a method for providing information for diagnosing the disease associated with the gene may provide a method for treating the disease, comprising administering a therapeutic agent for the disease to the individual diagnosed with the disease.

The agent for treatment of the disease may optionally use a known agent.

The subject may mean any animal, including humans, who have or are likely to develop a diagnosed disease. The animal may be a mammal such as, but not limited to, a human, a cow, a horse, a sheep, a pig, a goat, a camel, a antelope, a dog, a cat, and the like, which require treatment of similar symptoms.

The therapeutic agent may be administered in a pharmaceutically effective amount.

Hereinafter, the present invention will be described in more detail with reference to Examples. These examples are intended to illustrate the present invention more specifically, but the scope of the present invention is not limited by these examples.

<실시예 1> 오랑우탄 및 인간 유래 세포주의 배양 및 유전체 DNA 수득Example 1 Culture of Orangutans and Human-derived Cell Lines and Obtaining Genomic DNA

In order to obtain the genomic DNA of the present invention, orangutan-derived cell lines and human-derived cell lines were cultured, respectively, and gDNA or cDNA was obtained from the cultured cells.

Specifically, a male orangutan-derived lymphoblastoid cell line (Public Health England, UK) was prepared and cultured in RPMI1540 medium containing 15% fetal bovine serum (FBS). As human fibroblasts, fibroblasts (System Biosciences) derived from human male foreskin were prepared and cultured in DMEM medium containing 15% FBS. Human derived induced pluripotent stem cells (iPSCs) are derived from a human unknown lymphoblastic cell line (I-1210 / 002 / 002-02) provided under the approval of the Institutional Audit Committee of the Korean Institute of Oriental Medicine. Were prepared and incubated in mTesR1 conditional medium (Stemma Technologies Inc.) containing 0.5 μM sodium butylate (Sigma, USA) and 25 μM SB431542 (Sigma, USA).

Cultured orangutan-derived lymphoblastic cell lines were isolated from genomic DNA (gDNA) of orangutans using G-DEX ™ IIC (iNtRON Biotechnology, Korea) according to the manufacturer's protocol. Human-derived fibroblast line (Fib) and human-derived pluripotent stem cell (iPSC) were extracted with whole cell RNA using Trizol® Reagent (TRIzol ^® Reagent; Life Technologies, USA) following the manufacturer's protocol Human cDNA samples were prepared using cDNA synthesis kit (cDNA synthesis kit; BioRad, USA) according to the protocol provided by the manufacturer using 1 μg of the extracted RNA as a template. The prepared gDNA or cDNA was mixed with a composition as shown in the following [Table 1] to [Table 3] to prepare a DNA sample. Tables 1 to 3 show the mixing ratios of human genomic DNA (gDNA) samples, human fibroblast line-derived cDNA samples, and human induced pluripotent stem cell-derived cDNA samples, respectively.

Table 1

Sample name	G10	G9	G7	G5	G3	G1	G0
Human (HS) (20 ng)	10	9	7	5	3	One	0
Orangutan (PA) (20 ng)	0	One	3	5	7	9	10

TABLE 2

Sample name	F1	F2	F3
Human (HS) (2.5 ng)	4	2	One
Orangutan (PA) (1 ng)	One	One	One

TABLE 3

Sample name	I1	I2	I3
Human (HS) (2.5 ng)	4	2	One
Orangutan (PA) (1 ng)	One	One	One

<실시예 2> 다중 중합효소 연쇄 반응(multiplex PCR)을 위한 프라이머 염기서열의 제작 및 다중 경쟁적 중합효소반응(multiple competitive PCR) 수행을 통한 앰플리콘의 증폭<Example 2> Amplicon amplification by preparing primer sequences for multiplex PCR and performing multiple competitive PCR

<2-1> 정량 대상 유전자의 선별<2-1> Selection of genes to be quantified

Among the cDNAs prepared from human-derived cells, in order to select genes of the present invention, stretches were searched for orangutan genome sequences and homologous sequences.

Specifically, gene mRNA sequences were retrieved from the NCBI reference sequence database. Then, the searched mRNA sequence is arranged and compared with the genome sequence (NCBI Pongo_pygmaeus_abelii-2.0.2 assembly) of Sumatra orangutan (Pongo abelli) through BLAST. By searching for homologous sequences including species variation (ISV), 263 gene sequences were selected as reference sequences for human genes.

<2-2> 프라이머 염기서열 군의 제작<2-2> Preparation of Primer Sequence Group

In order to amplify the selected gene sequence by multiplex PCR, primer sequences were selected and divided into groups.

Specifically, for the 263 reference sequence genes selected in Example <2-1>, they bind to both the human cDNA sequence and the orangutan gDNA sequence in the sample at the same time, and average the size of the standard 67 bp through multiple PCR (standard deviation, A primer to identify the origin of a species by amplifying a fragment of SD = 10.4) and amplifying a site capable of recognizing a small number of heterologous (ISV) nucleotides in the amplified amplicon product Pairs were designed using the previously reported Primer3 program (Untergasser, A. et al. , Nucleic Acids Res., 40: e115, 2012). The selected primer sequences were divided into a total of twenty-four groups of primer pairs in a manner that avoids unintended production of amplicons, which can be produced when using multiple primers for a single transcript.

<2-3> 다중 PCR 수행을 통한 기준 서열(reference sequence)의 동시 증폭<2-3> Simultaneous Amplification of Reference Sequence by Performing Multiple PCR

In order to simultaneously amplify orangutan derived gDNA mixed with human-derived gDNA or cDNA present in the same sample, multiple PCR was performed to amplify the amplicons.

Specifically, G10 to G0 human gDNA samples, F1 to F3 human Fib cDNA samples, and I1 to I3 human iPSC cDNA samples prepared in Example 1 and mixed with gDNA derived from orangutans were prepared. Then, each prepared DNA sample was used as a template, and either a Solg ™ h-Taq DNA polymerase (hTaq; Solgent, Korea) or FastStart Taq polymerase (fTaq; Roche, Switzerland) was used as a polymerase. Using each, each of the 24 primer groups divided in Example <2-2> was used to perform multiple competitive PCRs for a total of 24 rounds per each DNA sample. The reaction conditions of the multiple PCR are as described in the following [Table 4].

Table 4

Temperature (℃)	time	Remarks
95	15 minutes	Enzyme activation
95	20 seconds	40 cycle iterations
55	25 seconds
65	2 minutes

<2-4> 서열분석을 위한 중간체(intermediary) 앰플리콘의 제조<2-4> Preparation of Intermediary Amplicons for Sequencing

To analyze the sequence of the amplified amplicon product, 5'-end phosphorylation of the amplicon, adapter ligation, flow cell attachment, sequencing primer binding And two additional PCR amplifications for attaching a sequence module to the amplicon to enable barcoding.

Specifically, each of the amplicons obtained by performing multiple PCR in Example <2-3> was purified using an Expin ™ PCR kit (GeneAll, South Korea). 1 µg of the purified amplicon group was obtained and phosphorylated using the T4 polynucleotide kinase (T4 Polynucleotide Kinase; Promega, USA) according to the manufacturer's protocol to phosphorylate the 5'-end of the amplicon. The phosphorylated 6 ng amplicon was then attached with a 10 μg Y-shaped adapter molecule (15 μM) using a T4 ligase (Promega) to illuminate sequencing. platform).

After attachment, PCR was performed to attach a sequence module to each purified amplicon. The purified amplicon 4 pg, MP1 and MP2 primers, and hTaq or fTaq polymerase was mixed and the first PCR reaction, the reaction conditions were as described in Table 5 below. The 0.05-fold volume of the amplicon product of the first PCR reaction was then mixed with one of the IdxPs and the primers of MP1, and subjected to a second PCR according to the reaction conditions of Table 5 below to attach the sequence module. The sequence library was constructed by preparing intermediate amplicon DNA.

Table 5

Primary PCR			2nd PCR
Temperature (℃)	time	Remarks	Temperature (℃)	time	Remarks
95	10 minutes	Enzyme activation	95	10 minutes	Enzyme activation
95	10 sec	20 cycle repetition	95	10 sec	10 cycle repetition
65	30 seconds		65	30 seconds
72	2 minutes		72	2 minutes

<2-5> 중간체 앰플리콘 DNA의 완성도(integrity) 확인<2-5> Confirmation of Integrity of Intermediate Amplicon DNA

In the sequence library constructed through the preparation of the intermediate amplicon DNA, in order to confirm the integrity of the mediator amplicon DNA and the consistency between the samples according to the mixing ratios and polymerase types in various samples, The electrophoresis was performed to identify the amplicons in the sample at each stage of multiple competitive PCR.

Specifically, sequence libraries amplified and constructed using hTaq or fTaq were obtained from various samples prepared in Examples <2-3> and <2-4>. Then, the amplicon product amplified by ˜21-plexing PCR in Example <2-3>, a mixed amplicon pool, and the intermediate amplicon in Example <2-4>. Primary PCR and secondary PCR products for preparing DNA were mapped by electrophoresis on a 12% polyacrylamide gel.

As a result, it was confirmed that sequence libraries constructed from various samples were prepared using polymerase pairs of hTaq or fTaq as shown in FIG. 2, and they were confirmed to be consistent between samples (FIG. 2).

<실시예 3> 앰플리콘 DNA 라이브러리의 서열분석Example 3 Sequencing of Amplicon DNA Libraries

<3-1> 시료-특이적인 기준 서열(reference sequence)의 서열분석<3-1> Sequencing of Sample-Specific Reference Sequences

In baseline sequence analysis of human and orangutans retrieved from the NCBI database, sequences of amplicon sequence libraries constructed from pure human gDNA and orangutan gDNA were analyzed because sequence variations were not readily available in the present invention.

Specifically, the amplicon sequence library was constructed by performing the method of <Example 2> from the G10 or G0 sample prepared in <Example 1> from the template. The constructed sequence library is then divided into groups in equal amounts, followed by multiple parallel sequencing using the Illumina HiSeq 2500 platform, which is capable of reading 100 bp sequences at single-ends. Each sequence was analyzed. Then, BLAST was performed to compare the amplicon reads of the analyzed sequence with the NCBI reference sequence database. After execution, reads corresponding to a reference sequence of at least 75 bp in arranged length exhibiting at least 90% query coverage were obtained. Then, the reads of each amplicon target were arranged in multiples to determine the matched sequence structure. At this time, sequences occupying at least 25% of each nucleotide portion of the array were present.

We identified a sequence library with heteromorphic, heterozygous positions removed, and a high degree of sequencing depth of at least 100, indicating that human / orangutan sequence pairs with different sequences between human and orangutans It was confirmed that it was constructed as a specific reference sequence. The constructed reference sequence was identified as an amplicon corresponding to 248 genes containing 425 ISVs, and the remaining 70 sequences were identified as 4 genes that were not amplified and 66 genes that did not include ISVs.

<3-2> 다중 경쟁적 PCR로 증폭된 앰플리콘의 리드 확인<3-2> Read Confirmation of Amplicons Amplified by Multiple Competitive PCR

To calculate their fraction from samples mixed with human cDNA and orangutan gDNA, reads of amplicon products amplified by multiple competitive PCR were identified.

Specifically, the amplicon mixed sample constructed and identified in the sequence library in <Example 2> was compared with the sample-specific reference sequence constructed in Example <3-1> by BLAST analysis. After comparison, for each amplicon target, a sequence derived from the human mRNA and the orangutan genome, for each amplicon target, was targeted, with a sequence that exhibited 100% sequence identity at 75 bp or greater and at least 90% query coverage. The number was counted.

As a result, as shown in Figure 3 amplicon sequence amplified by hTaq polymerase from the samples of I1 to I3, F1 to F3, G10, G9, G7, G5, G3, G1 and G0, respectively, when aligned with the reference sequence , 100% homology in the 1.4 to 2.9 million read range, and the amplicon sequence samples amplified with fTaq polymerase showed 100% homology in the range of 1.2 to 1.9 million reads when arranged with the reference sequence. It was confirmed that the read range corresponds to about 61% of the total sequence (FIG. 3).

<3-3> 동일 시료에 대한 중합효소(polymerase) 종류에 따른 상관관계의 확인<3-3> Confirmation of Correlation by Polymerase Type for the Same Sample

When performing quantitative analysis with the method of the present invention using the same sample, Pearson between the read values of amplicons calculated from different samples to see if there could be a difference in the results in the technical configuration including the polymerase. Pearson correlation was calculated. The calculated Pearson correlation coefficients were shown from various samples comparing hTaq sets using Solg ™ hTaq as polymerase and fTaq sets using FastStart Taq as polymerase in amplicon polymerization.

As a result, as shown in FIG. 4, when the correlation between the hTaq set and the fTaq set was compared with the same sample, the correlation was low. However, when the same polymerase set was used, the read height of each amplicon in the other sample was With a very high correlation, the two polymerases have different amplification properties, but it was confirmed that the amplification performance of each polymerase between samples was continuous (FIG. 4).

<실시예 4> gDNA 시료를 이용하여 근연종 유전체를 정량 대상 시료에 혼합한 뒤 근연종 서열 간의 유사성을 활용하여 공동 증폭(co-amplification)시킨 경쟁 서열분석(spiking-in neighbor genome-coupled competitive PCR amplicon sequencing, SiNG-PCRseq) 방법의 적용<Example 4> Spike-in neighbor genome-coupled competitive PCR using a gDNA sample mixed with the species related to the quantitative sample and co-amplified using similarity between the species Application of amplicon sequencing and SiNG-PCRseq method

<4-1> PCR 앰플리콘에 대한 인간 서열의 비율(fraction) 계산<4-1> Fraction calculation of human sequence to PCR amplicon

To determine the ratio of human-derived sequences contained in the amplified amplicons from gDNA mixed samples of human and orangutans, the fraction of human sequence (γHS) to the amplified PCR amplicons was determined.

Specifically, multi-competitive PCR using hTaq or fTaq by the method of <Example 2> and <Example 3> from the G10, G9, G7, G5, G3, G1 and G0 samples prepared in <Example 1> Was performed to amplify the amplicons and quantify the reads of the sequence library. From the quantified read values, an amplicon showing an average value of 200 leads or less per sample for the same amplicon and an amplicon showing impurity of 5% or more in the G0 and G10 samples was removed. Then, the quantitative value of the read for each amplicon in each sample was used in the following [Equation 1] to obtain the ratio (γHS) to the human-derived sequence included in each amplicon. Then, for each hTaq set and fTaq set, amplicons showing higher mean γHS based on the hTaq set were dropped in order starting from the left. In addition, from the hTaq set and the fTaq set, γHS of amplicons containing the same sequence were averaged and scattered on a scatter diagram.

[Equation 1]

As a result, as shown in FIGS. 5 and 6, in one sample, a non-flat plot pattern is shown in which the read values of each amplicon do not show a straight line, which is different from each other. It was confirmed that the same pattern in the G10, G9, G7, G5, G3, G1 and G0 samples containing the human sequence (Fig. 5). Through this, a large portion of the amplicon amplified in the sample was directly quantified for any of the mutations in the human or orangutan sequence, confirming that it exhibits a procedural bias (FIG. 5).

In addition, when the average of γHS for each amplicon containing the same sequence in all samples of G10, G9, G7, G5, G3, G1, and G0 was scattered, they showed a linear distribution with a slope of 1.03. It was confirmed that there is a very close relationship between the hTaq set and the fTaq set, and thus, the extent and preference of the procedural biases identified above are not random, but are intrinsically related to the mutation structure of the gene sequence. It was confirmed that (Fig. 6).

<4-2> 변이 유형에 따른 γHS의 평균 분포 확인<4-2> Check the average distribution of γHS according to the variation type

In quantitative analysis using SiNG-PCRseq, in order to determine the cause of procedural bias in γHS, the relationship between the variation type and the bias in the variation of the orangutan sequence was examined.

Specifically, from the G10, G9, G7, G5, G3, G1 and G0 samples prepared in <Example 1> by the method of Example <4-1> to obtain the γHS contained in the amplicon in each sample This was averaged. Then, the sequence variation of the orangutan in the amplicon was confirmed, and when the mutations were found in guanine (G) and cytosine (C) nucleotides showing strong hydrogen bonds (Stronger; S), adenine (weak hydrogen bonds) A) and thymine (T) nucleotides were divided (Weaker; W) and the degree of hydrogen bonding was neutral (Neutral; N). The divided S, W and N groups were analyzed by Student's t-test using the average of γHS, and a box plot was drawn to confirm the degree of distribution. As a result, as shown in FIG. 7, the box plot showed a p value of 2.54 × 10 ⁻⁹ , and in the case where the variant sequence is represented by guanine (G) and cytosine (C) residues (S), It was confirmed that a relatively high γHS average was shown in comparison with the case where the mutation was caused by the adenine (A) and thymine (T) residues (N) (FIG. 7). This difference was confirmed in the quantitative analysis by SiNG-PCRseq of the present invention, it can be seen through the process of repeating a plurality of PCR reactions and purification.

<실시예 5> 시료 내 앰플리콘의 γHS에 대한 편향 보정(Bias correction)Example 5 Bias Correction of γHS of Amplicons in Samples

In the quantitative analysis using the SiNG-PCRseq of the present invention, in order to correct the deflection of the amplicons for γHS in sequence variation, the degree of deflection in one sequence is proportional to the ratio of the total amplicons. Calculated by application. In the above rule, a procedural bias between sequence variations results in a difference in γHS in the amplicon, which is efficient using γHS obtained from a reference amplicon calculated by analyzing two competing sequences having the same molar composition. (Jeong, S. et al. , Genome Res., 17: 1093-1100, 2007), it was calculated using a formula modified to suit the present invention.

Specifically, as previously known, two different competing sequences, A and B, are mixed in the same mole number, from a single reference mixture having the same contribution, which is the ratio of sequence A in the sample. The following formula (2) was used, which is a method for obtaining γA _c , which is a ratio of correcting the deflection of γA.

[Equation 2]

Wherein γA and γB represent the ratio of sequences A and B in the amplified amplicons in the mixed sample; γA _H and γB _H represent the ratio of sequences A and B in the amplified amplicons in the reference sample in which the A and B sequences were mixed in equal moles.

In the SiNG-PCRseq of the present invention, the genes of human (HS) and orangutan (PA), which are competing sequences in the reference sample, are not mixed in the same molar number, but the mixing ratio of each sequence in the mixed sample is known, and thus reflected. By modifying Equation 2 above, Equation 3 was obtained.

[Equation 3]

Wherein γA and γB represent the fraction of sequences A and B in the amplified amplicon in the mixed sample; pA and pB represent the mixing ratios mixed in the reference sample in which sequences A and B were mixed in different ratios; γA _Ref and γB _Ref represent the ratio of sequences A and B to the amplified amplicons in the reference sample in which sequences A and B were mixed at different ratios.

In addition, in the SiNG-PCRseq of the present invention, since multiple amplicons are included in the reference sample and the mixed sample through multiple PCR amplification, the equation [3] is modified to equally reflect the correction power of each reference sample. It is shown as Equation 4 below.

[Equation 4]

Then, from the G10, G9, G7, G5, G3, G1 and G0 samples prepared in <Example 1> by using the method of Example <4-1> to obtain the γHS contained in the amplicon in each sample ΓHS _C was calculated by correcting the deflection of γHS with respect to the amplicon in the sample by applying it to Equation 4 above. At this time, in order to correct the deflection of each sample, the calibration was performed using other samples as reference samples, and the pA and pB values of the other samples used the average value of the HS values of the corresponding samples and the average value of PA. This is because the bias of each amplicon is random with respect to human and orangutan sequences, and the types of mutation sequences in the two species are random, and since the number of these amplicons is large enough, the average value of the amplicons is eventually mixed and approximated by the first two gDNAs. Because I will. The calculated γHS _C was instilled in sequence, starting from the left, with amplicons showing a higher mean γHS _C. In addition, the root mean square deviation (RMSD) of the calculated γHS _C was obtained and shown in a scatter diagram.

As a result, as shown in Figure 8, in all the samples confirmed the γHS _C corrected for the deflection shown in γHS, it was confirmed that this shows a uniformly arranged quantitative pattern by efficiently correcting the deflection shown in γHS (Fig. 8). ). In addition, it was confirmed that the coefficient of variation of γHS _C for each sample was in the range of 0.1 to 0.2. When comparing γHS _C values between the hTaq set and the fTaq set, the degree of deviation from the expected value was hTaq. Although it is insignificant in the set, it is confirmed that the fTaq set is greatly influenced by the depth of the lead (FIG. 9).

<실시예 6> 시료 내 경쟁서열에 대한 인간 서열의 상대적인 존재비(relative abundance of HS to PA; RAH/P)의 산출Example 6 Calculation of relative abundance of HS to PA (RAH / P) of the human sequence relative to the competition sequence in the sample

The quantitative value is used to reflect the difference in the number of copies that can appear between the autosomal sequence and the sex chromosomal sequence, which is an X-associated sequence according to the male genome, from the γHS _C s corrected bias according to amplicons. The relative abundance of HS to PA (RA _{H / P} ) of the human sequence with respect to the orangutan sequence, which is a competition sequence in the sample, was obtained.

Specifically, γHS _C was calculated by the method of Example <4-2> from the G9, G7, G5, G3 and G1 samples prepared in <Example 1>. Then, the calculated γHS _C was applied to Equation 5 below to convert the relative abundance of human sequences (RA _{H / P} ) in each sample.

[Equation 5]

<실시예 7> 시료 내 인간 서열의 표준화된 정량값의 산출Example 7 Calculation of Standardized Quantitative Values of Human Sequences in Samples

In order to calculate the final quantitative value from the relative abundance ratio (RA _{H / P} ) of the human sequence to the orangutan sequence, the competition sequence in the sample, standardized quantities (StQs) were obtained.

Specifically, from the G9, G7, G5, G3 and G1 samples prepared in Example 1, the relative abundance ratio (RA _{H / P} ) of the human sequences in each sample was calculated by the method of <Example 6>. . Then, RA _{H / P} quantitative values were applied to Equation 6 below to obtain standardized quantitative values (StQs) as relative quantitative values for the average RA _{H / P} of all autosomal sequences in each sample. .

[Equation 6]

<실시예 8> 다양한 인간 세포주 유래 cDNA 시료에 대한 SiNG-PCRseq 방법의 적용Example 8 Application of SiNG-PCRseq Method to Various Human Cell Line-derived cDNA Samples

<8-1> 인간 섬유아세포주(Fib) 및 유도만능줄기세포주(iPSC) 유래의 cDNA 시료로부터 SiNG-PCRseq 방법을 적용한 cDNA 정량<8-1> Determination of cDNA by SiNG-PCRseq Method from cDNA Samples Derived from Human Fibroblast Line and Fibroblast Cell Line

In order to confirm that the SiNG-PCRseq method of the present invention can be equally effectively applied to cDNA samples derived from various cell lines, cDNA samples synthesized from Fib and iPSC were applied to the SiNG-PCRseq method.

Specifically, human Fib cDNA samples F1 to F3 and human iPSC cDNA samples I1 to I3 prepared in Example 1 and mixed with gDNA derived from orangutans were prepared. Then, the cDNA samples were subjected to the SiNG-PCRseq method using the method of <Example 3> to <Example 7> using hTaq or fTaq polymerase, respectively, for the amplicons amplified in each sample. γHS, γHS _C , RA _{H / P} and StQs values were determined. In calculating the γHS _C value for the correction of bias, quantitative analysis was performed excluding amplicons having a read value of 200 or less in each sample, and amplicons whose sequence was different from the gDNA sequence. After the calculations, for iPSC samples and Fib samples, amplicons showing higher RA _{H / P} and StQs values based on the hTaq set were dropped in order starting from the left.

As a result, as shown in Figure 11 and Table 6 below, when the RA _{H / P} value of each of the hTaq set iPSC and Fib cell cDNA samples were calculated, the composition of the DNA mixed in each sample significantly It was confirmed to reflect (FIG. 11A and Table 5). From this, when the StQ values for the amplicons in each sample were calculated, the StQ values of the iPSC or Fib samples obtained from three samples over a wide quantitative range representing RA _{H / P} of 0.05 or more had the same distribution of scatter. It was confirmed that these can be used as standard quantitative values, respectively (FIG. 11B). In addition, when the average of StQs values calculated from three samples for each cell line of F1 to F3 or I1 to I3 was obtained, the iPSC sample was obtained when the coefficient of variation (CV) for each amplicon was shown. Indicates a CV average of 0.13, and the Fib sample confirmed a CV average of 0.16, confirming a low maintenance pattern of less than 0.5 (FIG. 11C). Through these results, it was confirmed that the SiNGPCR-seq method of the present invention can be used for various samples by correcting the ratio of amplified gDNA in the cDNA sample.

Table 6

Sample name		RA _{H / P} Average (Std)
Fibroblast line	F1	3.0
	F2	1.1
	F3	0.5
Induced pluripotent stem cell line	I1	5.3
	I2	2.5
	I3	1.0

<8-2> 다양한 인간 세포주 유래 cDNA 시료 정량에 있어서 중합효소에 따른 영향 확인<8-2> Confirmation of the Effect of Polymerase on Quantification of cDNA Samples from Various Human Cell Lines

In quantifying cDNA samples from various cell lines using the SiNG-PCRseq method of the present invention, it was confirmed whether the continuous representation of the quantitative value could be maintained due to the difference in the hTaq and fTaq polymerases.

Specifically, γHS, γHS _C , RA _{H / P} and StQs values for amplicons amplified in each sample for Fib and iPSC cell cDNA were obtained using the method of Example <8-1>. In the fTaq set, the read value of the amplified amplicon sequence was not high and was often excluded in the calculation of γHS. Therefore, the hTaq targets 365 amplicons (86%), whereas in the fTaq set, 240 amplicons are used. Quantitative analysis was performed on only (56%). Then, the relationship between the hTaq set and the fTaq set using the StQs value calculated from I1 to I3 or the StQs value calculated from F1 to F3 is shown as a scatter plot.

As a result, as shown in FIG. 12, in the 240 amplicons targeted to both the hTaq set and the fTaq set, the StQs value of the iPSC sample showed a correlation (R ² ) of 0.93 with a regression slope of 1.02. , StQs value of the Fib sample was confirmed to show a regression slope of 0.93 and R ² value of 0.92, which confirmed that the SiNG-PCRseq method of the present invention can be used reproducibly under other reaction conditions (Fig. 12). .

<실시예 9> 본 발명의 SiNG-PCRseq 방법 및 기존의 정량 방법 간의 정량 분석 차이 확인Example 9 Confirmation of Quantitative Differences Between SiNG-PCRseq Methods and Existing Quantitative Methods of the Present Invention

In order to check whether the quantification result of a gene in a sample using the SiNG-PCRseq method of the present invention is different from a conventional quantification method of a gene, a quantification of a gene in a sample through an inter-sequence quantitative analysis based on RNA-sequence Analyzes were performed and compared.

Specifically, γHS, γHS _C , RA _{H / P} and StQs values for amplicons amplified in each sample for Fib and iPSC cell cDNA were obtained using the method of Example <8-1>. In addition, for the same sample as described above, DNA quantitative analysis was performed in a sample through relative quantitative analysis (RNA-seq) between RNA-sequences according to a previously reported method (Blomquist, TM et al. PLoS ONE 8, e79120). , 2013). Read counts for reference mRNA, a template of cDNA for amplicon amplification, were normalized to length to obtain RPKM (Read Per Kilobase per Millon mapped reads) values. Then, a kernel density plot was shown, indicating the significance by comparing the quantitative analysis between samples by SiNG-PCRseq method and RNA-seq method.

In order to effectively compare the results of the two methods, genes exhibiting RA _{H / P} of 0.05 or more in all samples in the SiNG-PCRseq method and 40 or more read values in the RNA-seq method were used. RA _{H / P} and RPKM values were standardized by averaging the values obtained from the selected genes.

As a result, as shown in FIG. 13, the gene-to-gene correlation (R ² ) of StQs values quantified by the RNA-seq method and the SiNG-PCRseq method showed 0.46 and 0.43 in iPSC and Fib, respectively. This indicates that there is a significant difference in obtaining a relative quantitative value between different sequences between the two methods, which is due to the lack of bias correction of the different sequences of RNA-seq entirely (FIG. 13A). The difference in the standard quantitative value (StQs) between the two cell lines of iPSC and Fib has an R ² value of 0.88, confirming that there is no significant difference between the two methods in comparative quantification of the same sequence between different samples (FIG. 13B). .

Claims

i) mixing DNA or cDNA isolated from a subject-derived cell with genomic DNA (gDNA) isolated from a cell of the species of late myelin;

ii) amplifying the DNA sample mixed in step i) by multiplex PCR to obtain an amplicon;

iii) sequencing the sequence library comprising the amplicons obtained in step ii) and comparing it with a reference sequence to quantify the read count;

iv) calculating the fraction of sequence (γA) of the target sequence (SEQ ID A) to the amplicon obtained in step ii) using the read value quantified in step iii);

v) calculating a deflection correction value γ A c that corrects procedural deflection using the ratio value γ A calculated in step iv);

vi) Calculating the relative abundance of A to B (RA A / B ) of the target sequence (SEQ ID NO: A) to the near-missing sequence (SEQ ID NO: B) from the bias correction value γ A c calculated in step v). Doing; And

vii) calculating the standardized quantities (StQs) by dividing the relative abundance ratio (RA A / B ) calculated in step vi) by the representative value thereof.
The method of claim 1, wherein the subject of step i) is a human, and a related species thereof is an orangutan, a chimpanzee, a monkey, or a gorilla.
The method of claim 1, wherein the cells of step i) are lymphocytes, fibroblasts, or induced pluripotent stem cells (iPSCs). .
The method of claim 1, wherein the multiple PCR of step ii) is multiple competitive PCR.
The method of claim 1, wherein the ratio value γA of step iv) is calculated using Equation 7 below;

[Equation 7]

.
The method of claim 1, wherein the deflection correction value γAc of step v) is calculated using Equation 4 below;

[Equation 4]

Wherein γA and γB represent the fraction of the target sequence A and the related species B in the amplified amplicons in the mixed sample;

pA and pB represent the mixing ratios mixed in the reference sample in which the target sequence A and the related species sequence B were mixed in different moles;

γA Ref and γB Ref represent the ratio of the subject sequence A and the related species B to the amplified amplicons in the reference sample in which the target sequence A and the related species B are mixed in different moles;

n is an integer of 1 or more.
According to claim 1, The relative abundance ratio (RA A / B ) of the step vi) is characterized in that calculated using the following Equation 8, standardized quantification method of nucleic acid in a sample;

[Equation 8]

.
The method of claim 1, wherein the standardized quantities (StQs) of step vii) are calculated using the following Equation (9);

[Equation 9]

Wherein RA represents in the sample A / B value of RA A / B average, RA of the specific base sequence A / B is one or any of the RA A / B average value of some sequences.
i) calculating a standardized quantitative value between different genes of a subject using the standardized quantitative method of claim 1; And

ii) calculating a standardized ratio between different genes in one species of interest through the standardized quantitative value of step i).
i) Gene transcripts which are characterized by a disease state having a transitional characteristic to the disease or disease, for the target cell isolated from the diseased or suspected disease, using the standardized quantitative method of the present invention Or calculating a standardized quantity of target sequences in the genome; And

ii) diagnosing the gene related disease comprising comparing the standardized quantitative value of step i) with a standardized quantitative value of the same nucleotide sequence generated from a transcript or genome of a cell isolated from a homogenous normal individual. How to provide information for.