Method for detecting target analytes in a sample

Abstract

The present invention relates to a method for detecting a target analyte in a sample or a method for determining a quantification cycle (Cq) value for a target analyte in a sample. The present invention utilizes the intersection point of a linear function connecting the first pole of an n-th derivative function or an n+1-th derivative function and the second pole of an n+1-th derivative function for the detection of a target analyte and a function representing the amplification curve of the target analyte, thereby providing superior quantitative accuracy and precision compared to conventional methods despite inter-experimental variation in real-time PCR reactions.

Description

Technology Field

[0001] Cross-reference regarding related applications

[0002] This patent application claims priority to Korean Patent Application No. 2023-0191631 filed with the Korean Intellectual Property Office on December 26, 2023, and the disclosures of said patent applications are incorporated herein by reference.

[0003] Technology field

[0004] The present invention relates to a method for detecting a target analyte in a sample or a method for determining a quantification cycle (Cq) value for a target analyte in a sample. Background Technology

[0005] Nucleic acid amplification and detection have established themselves as one of the core technologies in modern molecular biology research. The most widely used polymerase chain reaction (PCR) for nucleic acid amplification involves repeated cycles of denaturation of double-stranded DNA, annealing of oligonucleotide primers onto a DNA template, and primer extension by DNA polymerase (Mullis et al., U.S. Patents No. 4,683,195, 4,683,202 and 4,800,159; Saiki et al., (1985) Science 230, 1350-1354).

[0006] Real-time PCR is a PCR-based technique for detecting target nucleic acids in a sample in real time. To detect specific target nucleic acids, real-time PCR utilizes a signal generation means capable of generating a detectable fluorescent signal in proportion to the amount of the target nucleic acid. The generation of the fluorescent signal can be achieved by using an intercalator that generates a signal when intercalated between double-stranded DNA, or an oligonucleotide having a fluorescent reporter and a quencher molecule. A fluorescent signal with an intensity proportional to the amount of the target molecule is detected in each amplification cycle and plotted against the amplification cycle to obtain an amplification curve or amplification profile curve.

[0007] PCR amplification curves typically have multiple phases: a baseline phase where fluorescence remains nearly constant, an exponential phase where fluorescence nearly doubles with each cycle, and a plateau phase where amplification gradually decreases.

[0008] The baseline phase refers to the region during the initial stages of PCR where the fluorescence signal shows little change. In the baseline region, the level of PCR amplicon is insufficient for detection; therefore, the signal detected in this region may be attributed to background signals, including fluorescence signals from reaction reagents and measurement devices. The exponential phase indicates an increase in the fluorescence signal proportional to the increase in the amplification product. The plateau phase refers to the region where the fluorescence signal shows little increase due to the saturation of PCR amplicon and fluorescence signal levels.

[0009] In particular, Real-time PCR is widely used in many fields due to its sensitivity and specificity. For instance, it can be utilized for the qualitative or quantitative analysis of target nucleic acids through amplification. This is based on various analytical methods for detecting target analytes within samples using datasets obtained by Real-time PCR developed to date.

[0010] One method uses a predetermined threshold to determine whether the signal value in the dataset reaches or exceeds the threshold. This requires collecting as much of the actual response dataset as possible to determine the threshold.

[0011] As another method, U.S. Patent No. 6,303,305 discloses a method for determining the maximum point of the nth derivative of an amplification curve and calculating the initial concentration of nucleic acid in a nucleic acid sample from said maximum point, and U.S. Patent No. 6,503,720 discloses a method for quantifying the concentration of nucleic acid in a sample, for calculating a first, second, or nth derivative of a function with respect to an amplification signal, determining a cycle corresponding to said maximum or minimum point of said derivative function, and calculating the initial concentration in the sample from said maximum or minimum point.

[0012] However, the above methods had the problem that they could be relatively heavily affected by experimental variations in the experimental PCR reaction.

[0013] Accordingly, the inventors recognized the need to develop a technology capable of overcoming quantitative inaccuracies arising from experimental variations in real-time PCR reactions.

[0014] Throughout this specification, numerous cited literature and patent literature are referenced and their citations are indicated. The disclosures of the cited literature and patents are incorporated by reference into this specification in their entirety to more clearly explain the state of the art to which the present invention pertains and the content of the present invention. The problem to be solved

[0015] The inventors have endeavored to develop a technology that can reduce the influence of experimental variation in real-time PCR reactions and improve the accuracy of quantitative PCR. As a result, unlike conventional methods that detect a target analyte in a sample using the maximum or minimum point of a first-order, second-order, or n-th-order derivative function representing the amplification curve of the target analyte, the inventors have completed the present invention by using the intersection point of a first-order function connecting the first pole of the n-th-order derivative function or the n+1-th-order derivative function representing the amplification curve of the target analyte and the second pole of the n+1-th-order derivative function to detect the target analyte, and confirming that the quantitative accuracy and precision are superior compared to conventional methods despite the experimental variation in real-time PCR reactions.

[0016] Therefore, the objective of the present invention is to provide a method for detecting a target analyte in a sample.

[0017] Another objective of the present invention is to provide a method for determining the Quantification Cycle (Cq) value for a target analyte in a sample.

[0018] Another objective of the present invention is to provide a computer-readable recording medium comprising instructions for implementing a processor for executing the method of the present invention described above.

[0019] Other objects and advantages of the present invention will become more apparent from the following embodiments, claims, and drawings. means of solving the problem

[0020] I. Method for detecting a target analyte in a sample (First embodiment)

[0021] According to one aspect of the present invention, the present invention provides a method for detecting a target analyte in a sample, comprising the following steps:

[0022] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0023] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0024] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0025] (d) A step of detecting a target analyte in a sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c).

[0026] The inventors have strived to develop a technology that can reduce the influence of experimental variation in real-time PCR reactions and improve the accuracy of quantitative PCR. As a result, unlike conventional methods that detect a target analyte in a sample using the maximum or minimum point of a first-order, second-order, or n-th-order derivative function representing the amplification curve of the target analyte, the inventors used the intersection point of a first-order function connecting the first pole of the n-th-order derivative function or the n+1-th-order derivative function representing the amplification curve of the target analyte and the second pole of the n+1-th-order derivative function to detect the target analyte, and confirmed that the quantitative accuracy and precision are superior compared to conventional methods despite the experimental variation in real-time PCR reactions.

[0027] According to one embodiment, the method is a computer-implemented method.

[0028] FIG. 1 is a flowchart of the processes for carrying out the method of the present invention according to one embodiment of the present invention. The method of the present invention will be described with reference to FIG. 1 as follows:

[0029] Step (a): Obtain an S-shaped function representing the growth curve ( 110 )

[0030] First, the method of the present invention comprises the step of (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte.

[0031] As used herein, the term “sample” refers to any material containing or presumed to contain the nucleic acid of interest, or any material that is the nucleic acid itself containing or presumed to contain the target nucleic acid sequence of interest. Specifically, the sample includes biological samples (e.g., cells, tissues, and body fluids from biological sources) and non-biological samples (e.g., food, water, and soil). Biological samples include, but are not limited to, viruses, bacteria, tissues, cells, blood, serum, plasma, lymph, sputum, swabs, aspirates, bronchial lavage fluid, milk, urine, feces, ocular fluid, saliva, semen, brain extracts, cerebrospinal fluid (SCF), appendix, spleen, and tonsil tissue extracts, ascites, and amniotic fluid. Additionally, the sample may include naturally occurring nucleic acid molecules and synthetic nucleic acid molecules isolated from biological sources.

[0032] As used herein, the term “target analyte” may include various substances (e.g., biological and non-biological substances, e.g., chemicals). Specifically, the target analyte may include biological substances, e.g., nucleic acid molecules (e.g., DNA and RNA), proteins, peptides, carbohydrates, lipids, amino acids, biological compounds, hormones, antibodies, antigens, metabolites, and cells. More specifically, the target analyte may include nucleic acid molecules. The target analyte is present in the sample.

[0033] As used herein, the terms “target nucleic acid,” “target nucleic acid sequence,” or “target sequence” refer to a nucleic acid sequence of interest for analysis or detection. The target nucleic acid sequence includes double-stranded sequences as well as single-stranded sequences. The target nucleic acid sequence includes sequences initially present in the sample as well as sequences newly generated in the reaction.

[0034] Target nucleic acid sequences include any DNA (gDNA and cDNA), RNA molecules, and hybrids thereof (chimeric nucleic acids). The sequence may be in a double-stranded or single-stranded form. If the nucleic acid used as the starting material is double-stranded, it is preferable to convert the double-stranded form into a single-stranded or partially single-stranded form. Known methods for separating strands include, but are not limited to, heating, alkali, formamide, urea, and glycoxal treatment, enzymatic methods (e.g., helicase action), and binding proteins. For example, strand separation can be achieved by heating at a temperature of 80–105°C. A general method for achieving such treatment is disclosed in the literature [Joseph Sambrook, et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (2001)].

[0035] The target nucleic acid sequence includes any naturally occurring prokaryotic nucleic acid, eukaryotic nucleic acid (e.g., protozoa and parasites, fungi, yeast, higher plants, lower animals and higher animals, e.g., mammals and humans), viral nucleic acid (e.g., herpes virus, HIV, influenza virus, Epstein-Barr virus, hepatitis virus, polio virus, etc.), or viroid nucleic acid. The nucleic acid molecule may also be any nucleic acid molecule produced or capable of being produced by recombination, or chemically synthesized or capable of being synthesized. Thus, the target nucleic acid sequence may or may not be found in nature.

[0036] The target nucleic acid sequence should not be interpreted as being limited to a sequence known at a given time or available from a given time, but rather should be interpreted as including a sequence that may be available or known at any present or future time. That is, the target nucleic acid sequence may or may not be known at the time of carrying out the method of the present invention. In the case of an unknown target nucleic acid sequence, its sequence may be determined by one of the conventional sequencing methods prior to carrying out the present invention.

[0037] If the target analyte is a target nucleic acid molecule, the sample may undergo a nucleic acid extraction process. The nucleic acid extraction process may vary depending on the type of sample. If the extracted nucleic acid is RNA, a reverse transcription process is additionally performed to synthesize cDNA from the extracted RNA (cf. Sambrook, J. et al., Molecular Cloning . A Laboratory Manual , 3rd ed. Cold Spring Harbor Press (2001)).

[0038] According to one embodiment, the target nucleic acid sequence includes a nucleotide variation.

[0039] As used herein, the term “nucleotide variation” means the substitution, deletion, or insertion of any single or multiple nucleotides in a DNA sequence at a specific position within a sequence-like sequence of DNA segments. These sequence of DNA segments comprise a single gene or any other region of a chromosome. Such nucleotide variations may be mutations or polymorphic allele variations. For example, nucleotide variations detected in the present invention include single nucleotide polymorphisms (SNPs), mutations, deletions, insertions, substitutions, and translocations. Exemplary nucleotide variations include various variations within the human genome (e.g., variations in methylenetetrahydrofolate reductase (MTHFR)), variations associated with drug resistance in pathogens, and tumorigenic variations. As used herein, the term "nucleotide variation" includes any variation at a specific position of a nucleic acid sequence. That is, the term "nucleotide variation" includes the wild type and any mutant form thereof at a specific position of a nucleic acid sequence.

[0040] Various methods for amplifying target nucleic acid molecules are known, which are not limited to PCR (polymerase chain reaction), LCR (ligase chain reaction, see Wiedmann M, et al., "Ligase chain reaction (LCR)-overview and applications." PCR Methods and Applications, 3(4):S51-64(1994)), GLCR (gap-filling LCR, see WO 90 / 01069, EP 0439182 and WO 93 / 00447), Q-beta (Q-beta replicase amplification, see Cahill P, et al., Clin Chem., 37(9):1482-5(1991), U.S. Patent 5,556,751), and SDA (strand displacement amplification, see GT Walker et al., Nucleic Acids Res. 20(7):16911696(1992), EP 0497272), Includes NASBA (nucleic acid sequence-based amplification, see Compton, J. Nature 350(6313):912(1991)), TMA (Transcription-Mediated Amplification, see Hofmann WP et al., J Clin Virol. 32(4):289-93(2005); see U.S. Patent 5,888,779) or RCA (Rolling Circle Amplification, see Hutchison CA et al., Proc. Natl Acad. Sci. USA. 102:1733217336(2005)).

[0041] The S-shaped function used in the method of the present invention may be a function known in the art for approximating a growth curve, in particular an amplification curve, and more particularly a real-time PCR curve for a target analyte.

[0042] The term “growth curve” as used herein comprises (1) a curve consisting of a set of raw nucleic acid amplification data, (2) a modification of (1) (specifically, (i) baselining and (ii) smoothing), and (3) a derivation of (1) (specifically, (i) a modified amplification curve after nth derivative and (ii) a modified amplification curve after nth difference (e.g., slope regression)).

[0043] In this specification, the term "baselining" refers to a process of processing a data set by subtracting a value corresponding to a baseline from a signal value in each cycle of the data set, thereby obtaining a data set with the baseline subtracted.

[0044] A baseline subtracted data set can be obtained by various methods known in the art (U.S. Patent No. 8,560,247 and WO 2016 / 052991). Additionally, a data set including a pre-standardization data set and a post-standardization data set can be baselined using a quadratic function having an axis of symmetry. Baselining a data set using a quadratic function means correcting the data set by subtracting the quadratic function from the data set.

[0045] In this specification, the term "smoothing" refers to processing or refining a data set according to a predetermined algorithm to remove or minimize noise in the data set. Through the smoothing of the data set, the visual representation of the data set is enhanced.

[0046] In data analysis, data set smoothing creates a rough dataset that follows the main patterns of the data set. Data set smoothing removes noise, other fine structures, or abrupt phenomena. Through the smoothing process, the signals at data points are processed, thereby reducing the characteristics of individual data points and decreasing the signal differences between adjacent data points. This makes it easier to recognize information regarding macroscopic changes in the data set's signals.

[0047] According to one embodiment of the present invention, the S-shaped function is selected from the group consisting of a sigmoid function, a logistic function, a Gompertz function, a Chapman function, and a Richard function.

[0048] According to a more specific embodiment, the sigmoid function is expressed by the following Equation 1:

[0049] Mathematical formula 1

[0050]

[0051] In the above mathematical formula, α1, α2, α3, and α4 are parameters determined by fitting a sigmoid function to a selected part of the data set, and x is the cycle number.

[0052] In the above mathematical formula, α1 may correspond to the background value on the curve represented by the function; α2 may correspond to the maximum value on the curve represented by the function; α3 may correspond to the cycle number having the intermediate value on the curve represented by the function; and α4 may correspond to the slope of the curve represented by the function.

[0053] The sigmoid function is a special case of the logistic function and has a characteristic S-shaped curve or sigmoid curve.

[0054] According to a more specific embodiment, the logistic function is expressed by the following Equation 2:

[0055] Mathematical formula 2

[0056]

[0057] In the above mathematical formula, α1, α2, α3, and α4 are parameters determined by fitting a logistic function to a selected part of the data set, and x is the cycle number.

[0058] In the above mathematical formula, α1 may correspond to the background value on the curve represented by the function; α2 may correspond to the maximum value on the curve represented by the function; α3 may correspond to the cycle number having the intermediate value on the curve represented by the function; and α4 may correspond to the slope of the curve represented by the function.

[0059] The logistic function is a function that has a characteristic S-shaped curve or sigmoid curve.

[0060] According to a more specific embodiment, the Gompertz function is expressed by the following mathematical formula 3:

[0061] Mathematical formula 3

[0062]

[0063] In the above mathematical formula, α1, α2, α3, and α4 are parameters determined by fitting a Gompertz function to a selected part of the data set, and x is the cycle number.

[0064] In the above mathematical formula, α1 may correspond to the background value on the curve represented by the function; α2 may correspond to the maximum value on the curve represented by the function; α3 may correspond to the cycle number having the intermediate value on the curve represented by the function; and α4 may correspond to the slope of the curve represented by the function.

[0065] The Gompertz function is a type of sigmoid function that has a curve that rises steeply and then gradually approaches the horizontal line.

[0066] According to a more specific embodiment, the Chapman function is expressed by the following Equation 4:

[0067] Mathematical formula 4

[0068]

[0069] In the above mathematical formula, α1, α2, α3, and α4 are parameters determined by fitting a Chapman function to a selected part of the data set, and x is the cycle number.

[0070] In the above mathematical formula, α1 may correspond to the background value on the curve represented by the function; α2 may correspond to the maximum value on the curve represented by the function; α3 may correspond to the cycle number having the intermediate value on the curve represented by the function; and α4 may correspond to the slope of the curve represented by the function.

[0071] The Chapman function is a type of sigmoid function.

[0072] According to a more specific embodiment, the Richard function is expressed by the following mathematical formula 5:

[0073] Mathematical formula 5

[0074]

[0075] In the above mathematical formula, x is the cycle number; F(x) is the reaction fluorescence in the cycle; and F max ε is the maximum response fluorescence; b is the slope of the curve; c is the fractional period at which the response fluorescence reaches; d is the Richard coefficient; and Fb is the background fluorescence.

[0076] For further explanation of S-shaped functions, refer to the literature [Michele Guescini et al., BMC Bioinformatics 2008, 9:326].

[0077] According to one embodiment of the present invention, the step of obtaining an S-shaped function of step (a) comprises the following steps:

[0078] (a-1) A step of obtaining a first data set representing a growth curve by an amplification reaction for a target analyte; said first data set includes a plurality of data points each having a cycle number and a signal value at said cycle number;

[0079] (a-2) a step of obtaining a second data set by calculating a slope value at each cycle number for the first data set; the second data set includes a plurality of data points each having a cycle number and a slope value at said cycle number; and

[0080] (a-3) A step of calculating an S-shaped function that approximates a selected portion of the second data set.

[0081] (a-1) Obtain a first data set for the target analyte

[0082] According to the present embodiment, (a-1) a first data set representing a growth curve by an amplification reaction for a target analyte is obtained. The first data set includes a plurality of data points, each having a cycle number and a signal value at said cycle number.

[0083] The above first data set can be used interchangeably with the "amplified data set".

[0084] According to one embodiment, the first data set is obtained by a signal generation process.

[0085] As used herein, the term “signal generation process” means any process capable of generating a signal depending on the characteristics of a target analyte in a sample, namely, the activity, amount, or presence (or absence) of the target analyte, in particular the presence (or absence) of the analyte in the sample. As used herein, a signal generation process includes biological reactions and chemical reactions. Such biological reactions include genetic analysis, immunological analysis, and bacterial growth analysis, such as PCR, real-time PCR, and microarray analysis. According to one embodiment, a signal generation process includes analyzing the generation, alteration, or destruction of a chemical substance.

[0086] The signal generation process is accompanied by a change in the signal. This signal change can serve as an indicator that qualitatively or quantitatively represents the presence or absence of a target nucleic acid sequence.

[0087] Details of the “signal generation process” are disclosed in WO 2015 / 147412 filed by the applicant, the teachings of which are incorporated herein by reference in their entirety.

[0088] According to one embodiment, the signal generation process is an amplification process, in particular a signal amplification process.

[0089] According to one embodiment, the signal generation process is a process that may or may not involve the amplification of the target analyte.

[0090] Specifically, the signal generation process is a process accompanied by the amplification of a target nucleic acid molecule. More specifically, the signal generation process is a process accompanied by the amplification of a target nucleic acid molecule and capable of increasing or decreasing the signal (specifically, capable of increasing the signal) upon the amplification of the target nucleic acid molecule.

[0091] As used herein, the term "signal generation" includes the appearance or disappearance of a signal and an increase or decrease in a signal.

[0092] Specifically, the term "signal generation" means an increase in the signal.

[0093] The signal generation process may be carried out according to various methods known to those skilled in the art. For examples of such methods, refer to the description of the signal generation means below.

[0094] According to one embodiment, the signal generation process can be carried out as a process involving signal amplification along with target amplification.

[0095] According to one embodiment, as a signal generation process, the amplification reaction is carried out in such a way that the signal is amplified simultaneously with the amplification of the target nucleic acid molecule (e.g., real-time PCR). Alternatively, the signal amplification reaction is carried out in such a way that the signal is amplified without the amplification of the target nucleic acid molecule (e.g., CPT method (Duck P, et al., Biotechniques, 9:142-148 (1990)), Invader assay (U.S. Patents No. 6,358,691 and 6,194,149)).

[0096] As used herein, the term "signal" refers to a measurable output. As used herein, the term "signal value" is an expression that quantitatively represents the signal.

[0097] Signal intensity or signal changes can serve as indicators to qualitatively or quantitatively indicate the presence or absence of an analyte (target nucleic acid sequence).

[0098] Examples of useful indicators include fluorescence intensity, luminescence intensity, chemiluminescence intensity, bioluminescence intensity, phosphorescence intensity, charge transfer, voltage, current, power, energy, temperature, viscosity, light scatter, radioactivity intensity, reflectance, transmittance, and absorbance. Signal changes may include signal reduction as well as signal increase. According to one embodiment, the signal generation process is a process of amplifying the signal value.

[0099] The signal includes various signal characteristics from signal detection, such as signal intensity [e.g., RFU (relative fluorescence unit) value or, in the case of performing an amplification response, RFU value at a specific cycle, selected cycle, or endpoint], signal change shape (or pattern) or Ct value or a value obtained by mathematically processing the above characteristics.

[0100] According to one embodiment, the term "signal" includes not only the signal itself obtained at the detection temperature but also a modified signal provided by mathematically processing said signal.

[0101] According to one embodiment, when an amplification curve is obtained by real-time PCR, various signal values ​​(or characteristics) from the amplification curve can be selected and used to determine the presence of a target (intensity, Ct value, Cq value, or amplification curve data).

[0102] The signal (especially the signal strength) may vary depending not only on its detection temperature but also on the signal generating means used.

[0103] As used herein, the term "signal generating means" refers to any substance used to generate a signal indicating the characteristics of a target analyte to be analyzed, more specifically, its presence or absence.

[0104] As used herein, the term “signal generating means” means any substance used to generate a signal indicating the presence of a target nucleic acid sequence, such as, for example, oligonucleotides, labels, and enzymes. Alternatively, as used herein, the term “signal generating means” may be used to mean any method of using a substance for signal generation.

[0105] Various signal generating means are known to those skilled in the art. A signal generating means comprises a label itself and an oligonucleotide having the label. The label may include a fluorescent label, a luminescent label, a chemiluminescent label, an electrochemical label, and a metal label. The label itself, such as an intercalating dye, may serve as a signal generating means. Alternatively, a single label or an interactive double label comprising a donor molecule and an acceptor molecule may be used as a signal generating means in the form of being bound to at least one oligonucleotide. The signal generating means may include additional components for generating a signal, such as nucleic acid cleavage enzymes (e.g., 5'-nucleases and 3'-nucleases).

[0106] Signal generating means can be classified as follows according to their signal generation methods:

[0107] a. A signal generating means that generates a signal by forming a dimer between a target nucleic acid sequence and a detection oligonucleotide that specifically hybridizes to the target nucleic acid sequence.

[0108] As used herein, the term "detection oligonucleotide" is an oligonucleotide involved in the generation of a signal to be detected. According to one embodiment, the detection oligonucleotide comprises an oligonucleotide involved in the actual generation of the signal. For example, signal generation depends on the hybridization or non-hybridization of the detection oligonucleotide with another oligonucleotide (e.g., an oligonucleotide comprising a target nucleic acid sequence or a nucleotide sequence complementary to the detection oligonucleotide). According to one embodiment, the detection oligonucleotide comprises at least one label.

[0109] Signal generation by dimerization between a target nucleic acid sequence and a detection oligonucleotide is described in the Scorpion method (Whitcombe et al., Nature Biotechnology 17:804-807 (1999)), the Sunrise or Amplifluoride method (Nazarenko et al., Nucleic Acids Research, 25(12):2516-2521 (1997), and U.S. Patent No. 6,117,635), the Lux method (U.S. Patent No. 7,537,886), the Plexor method (Sherrill CB, et al., Journal of the American Chemical Society, 126:4550-4556 (2004)), the Molecular Beacon method (Tyagi et al., Nature Biotechnology v.14 MARCH 1996), the Hybeacon method (French DJ et al., Mol. Cell Probes, 15(6):363-374 (2001)), and adjacent hybridization probes. This can be achieved by various methods including the method (Bernard PS et al., Anal. Biochem., 273:221 (1999)) and the LNA method (U.S. Patent No. 6,977,295).

[0110] b. A signal generating means that generates a signal by the formation of a dimer in a manner dependent on the cleavage of a mediation oligonucleotide specifically hybridized to a target nucleic acid sequence.

[0111] As used herein, the term "mediated oligonucleotide" refers to an oligonucleotide that mediates the formation of a dimer that does not contain a target nucleic acid sequence.

[0112] According to one embodiment, the cleavage of the mediating oligonucleotide itself does not generate a signal, and the fragment formed by the cleavage is involved in a continuous reaction for signal generation.

[0113] Signal generation by a dimer formed in a manner dependent on the cleavage of the above-mentioned mediating oligonucleotide can be achieved by various known methods including the PTOCE (PTO cleavage and extension) method (WO 2012 / 096523), the PCE-SH (PTO Cleavage and Extension-Dependent Signaling Oligonucleotide Hybridization) method (WO 2013 / 115442), and the PCE-NH (PTO Cleavage and Extension-Dependent Non-Hybridization) method (WO 2014 / 104818).

[0114] c. A signal generating means that generates a signal by cleaving the detection oligonucleotide after the detection oligonucleotide is hybridized to a target nucleic acid sequence.

[0115] After the detection oligonucleotide is hybridized to a target nucleic acid sequence, signal generation by cleavage of the detection oligonucleotide can be achieved by various methods including the TaqMan probe method (U.S. Patent No. 5,210,015 and U.S. Patent No. 5,538,848).

[0116] d. A signal generating means that generates a signal by cleavage of a detection oligonucleotide in a manner dependent on the cleavage of a mediating oligonucleotide specifically hybridized to a target nucleic acid sequence.

[0117] According to one embodiment, when a mediating oligonucleotide hybridized to a target nucleic acid sequence is cleaved to release a fragment, the fragment specifically hybridizes to a detection oligonucleotide to induce cleaving of the detection oligonucleotide.

[0118] Signal generation by cleavage of the oligonucleotide in a detection method dependent on the cleavage of the above-mentioned mediating oligonucleotide can be achieved by various methods including the Invader analysis (U.S. Patent No. 5,691,142), the PCEC (PTO Cleavage and Extension-Dependent Cleavage) method (WO 2012 / 134195), and the method described in U.S. Patent No. 7,309,573.

[0119] As used in this specification, the term "signal amplification response" refers to a response that increases or decreases a signal generated by a signal generating means.

[0120] According to one embodiment, a signal amplification response means an increase (or amplification) of a signal that occurs dependently on the presence of a target analyte using a signal generating means. The signal amplification response may or may not be accompanied by amplification of the target analyte (e.g., a target nucleic acid molecule). More specifically, the signal amplification response means a signal amplification accompanied by amplification of the target analyte.

[0121] The first data set obtained by the signal amplification reaction includes cycle numbers.

[0122] As used herein, the terms “cycle number” or “cycle” refer to a unit of change in conditions in a plurality of measurements involving a change in conditions. For example, changes in conditions include changes in temperature, reaction time, number of reactions, concentration, pH, and / or number of replications of the target nucleic acid molecular sequence. Accordingly, a cycle may include time or process cycles, unit operation cycles, and recurrence cycles.

[0123] As an example, when investigating enzyme kinetics, the reaction rate of an enzyme is measured several times while regularly increasing the substrate concentration. In this reaction, the increase in substrate concentration may correspond to a change in conditions, and the unit of increase in substrate concentration may correspond to a cycle.

[0124] As another example, isothermal amplification allows for the measurement of samples multiple times during a reaction time under isothermal conditions, where the reaction time may correspond to a change in conditions and the unit of the reaction time may correspond to a cycle. For example, if a 5-minute interval such as 5, 10, or 15 minutes is set as a single reaction time, the cycle can be expressed as a 5-minute cycle, a 10-minute cycle, a 15-minute cycle, and so on. Alternatively, if the 5-minute interval is considered as a single unit, the 5-minute cycle can be represented as the 1st cycle, the 10-minute cycle as the 2nd cycle, the 15-minute cycle as the 3rd cycle, and so on.

[0125] As another example, in the case of melting analysis or hybridization analysis, changes in the signal can be measured while varying the temperature within a certain temperature range, where the temperature corresponds to a change in conditions and the temperature unit (e.g., the measured temperature) corresponds to a cycle. For example, if a 0.5°C interval is set as one reaction time, such as 40°C, 40.5°C, 50°C, 50.5°C, etc., the cycle can be expressed as a 40°C cycle, a 40.5°C cycle, a 50°C cycle, a 50.5°C cycle, and so on. Alternatively, if the 0.5°C interval is considered as a single unit, the 40°C cycle can be represented as the 1st cycle, the 40.5°C cycle as the 2nd cycle, the 50°C cycle as the 3rd cycle, and so on.

[0126] Specifically, when a series of reactions are repeated or reactions are repeated at regular time intervals, the term "cycle" refers to one unit of the above repetition.

[0127] For example, in polymerase chain reaction (PCR), a cycle refers to a reaction unit that includes the denaturation of a target molecule, annealing (hybridization) between the target molecule and the primer, and primer extension. An increase in the repetition of the above reaction may correspond to a change in conditions, and the unit of the above repetition may correspond to a cycle.

[0128] A first data set obtained by an amplification reaction includes a plurality of data points, and each data point has a cycle number and a signal value at the cycle number.

[0129] As used herein, the term "signal value" refers to a numerical value of a signal level (e.g., signal strength) actually measured in each cycle of the signal generation process, or a variation thereof. Such variation may include a mathematically processed value of the measured signal value. Examples of a mathematically processed value of the measured signal value (i.e., the signal value of the raw data set) may include a value obtained by addition, subtraction, multiplication, or division, or a logarithmic value.

[0130] As used herein, the term "data point" refers to a coordinate value comprising a cycle and a signal value at said cycle. As used herein, the term "data" refers to all information constituting a data set. For example, the cycle and signal value of an amplification reaction are each data.

[0131] Data points obtained from signal generation responses, particularly signal amplification responses, can be plotted as coordinate values ​​within an orthogonal coordinate system. In an orthogonal coordinate system, the X-axis represents the cycle of the amplification response, and the Y-axis represents the signal values ​​measured in each cycle or their variations.

[0132] As used herein, the term “data set” means a set of data points. For example, a data set may include a raw data set, which is a set of data points obtained directly from an amplification reaction using a signal generating means. Alternatively, a data set may be a modified data set obtained by a modification of a data set that includes a set of data points obtained directly from a signal generating process. The data set may include all or part of a set of data points obtained from a signal generating process. The data set may include a plurality of data points. A data set may include at least two data points. Specifically, the number of data points in a data set may be at least 2, 3, 4, 5, 10, or 20. Additionally, the number of data points in a data set may be 1,000 or fewer data points. Specifically, the number of data points in a data set may be 1,000, 500, 300, 200, 100, 90, 80, 70, or 60 or fewer. The number of data points in a data set may be 3 to 1,000. Specifically, it may be 3 to 1,000, 10 to 500, 1 to 100, 20 to 100, 20 to 80, 20 to 70, or 20 to 60. According to one embodiment, the number of data points in a data set may be 20 to 60.

[0133] In one embodiment, the first data set may be obtained by processing a plurality of data sets. When two target analytes are detected in a single reaction, a data set for each of the two target analytes may be provided by processing the data set obtained by the single reaction. For example, a data set for each of the two target analytes may be provided by obtaining two data sets by measuring signals at two different detection temperatures and processing the two data sets.

[0134] The first data set can be plotted to obtain an amplification curve.

[0135] According to one embodiment, the amplification curve is an amplification curve obtained by an amplification reaction of a target analyte (specifically, a target nucleic acid molecule).

[0136] According to a more specific embodiment of the present invention, the first data set of step (a-1) may be a raw data set, a mathematically modified data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically modified data set.

[0137] According to a more specific embodiment, the first data set of step (a-1) is a raw data set.

[0138] The term "raw data set" as used in this specification refers to a data set containing signals obtained directly from a signal generation response. For example, the raw data set includes a set of signals obtained from a signal receiving device, undergoing basic signal processing on a collection device, and then passing to a signal analysis stage.

[0139] According to a more specific embodiment, the first data set of step (a-1) is a mathematically transformed data set of the raw data set.

[0140] As used herein, the term “mathematically processed data set” refers to a data set transformed from a raw data set through mathematical processing. The mathematically processed data set is a baseline-subtracted data set obtained by removing background signal values ​​from the raw data set. A baseline-subtracted data set may be obtained by various methods known in the art (e.g., U.S. Patent No. 8,560,247).

[0141] According to a more specific embodiment, the first data set of step (a-1) is a standardized data set of the raw data set or a standardized data set of the mathematically transformed data set.

[0142] As used herein, the term "normalization" refers to a process of reducing or eliminating signal deviations between data sets of multiple responses. As used herein, the terms "correction" or "adjustment" refer to correcting or modifying data (particularly signal values) of a data set for analytical purposes. Normalization is an example of correction.

[0143] According to one embodiment, the standardized data set may be provided by a method comprising the following steps:

[0144] (i) a step of providing a normalization coefficient for calibrating the raw data set or the mathematically modified data set of the raw data set; the data set is provided from a signal generated by a signal generation response using a signal generation means; the data set includes a plurality of data points each having a cycle number and a signal value at said cycle number; the normalization coefficient is provided using a reference value, a reference cycle, and the data set; the reference value is an arbitrarily determined value; the normalization coefficient is provided by determining the relationship between the signal value at the cycle of the data set corresponding to the reference cycle and the reference value; and,

[0145] (ii) A step of providing a standardized data set by applying the above standardization coefficients to the signal values ​​of the data set to obtain corrected signal values.

[0146] A reference cycle is a cycle selected to determine a specific signal value used to provide a standardized coefficient with a reference value. The reference cycle used to provide the standardized coefficient can be arbitrarily selected from the cycles of the data set.

[0147] According to one embodiment, the reference cycle can be selected from cycles within the background area.

[0148] A background signal is a signal generated by the target analyte itself within the sample, or by a signal generation means that is not involved with the target analyte but is not caused by the target analyte within the sample. A background zone is an area where almost no signal is generated by the target analyte and only background signals are generated.

[0149] The background region refers to the initial stage of the signal generation process before sufficient amplification of the signal is detected. Specifically, the reference cycle can be determined from 1-30 cycles, 2-30 cycles, 2-20 cycles, 2-15 cycles, 2-10 cycles, 2-8 cycles, 3-30 cycles, 3-20 cycles, 3-15 cycles, 3-10 cycles, 3-9, 3-8, 4-8, or 5-8 cycles within the background region.

[0150] A reference value is a value used to provide a standardized coefficient. The reference value of the present invention refers to any value applied to a reference cycle number to correct signal values ​​of a data set. The reference value may be an arbitrarily determined value. Preferably, the reference value is a value arbitrarily determined from a real number other than zero. Preferably, the reference value may be a value of the same type as the value of the data set to be corrected and may have the same unit or dimension as the data set to be corrected.

[0151] When a standardized coefficient is provided by a reference value and a signal value in a cycle corresponding to a reference cycle of the data set, the standardized coefficient may be provided by the ratio of the signal value to the reference value in the cycle corresponding to the reference cycle of the data set.

[0152] According to a more specific embodiment of the present invention, the method additionally comprises the following step between steps (a-1) and (a-2):

[0153] (a-1-1) A step of applying a threshold value to the first data set; and

[0154] (a-1-2) If the first data set has a signal value exceeding the threshold value, proceed to the next step (a-2).

[0155] This embodiment illustrates a process for filtering a first data set.

[0156] Generally, for negative samples that do not contain the target nucleic acid sequence, the first data set obtained in step (a-1) as well as the second data set obtained in step (a-2) will have significantly low signal values ​​at all cycle numbers, and in particular, the second data set from the negative sample will not exhibit a pattern such as an amplification curve or a growth curve, that is, the slope value will not increase as the cycle number increases. This can make it difficult to obtain an S-shaped function that approximates a selected portion of the second data set. Therefore, it would be desirable not to apply the method of the present invention to negative samples.

[0157] In this regard, the method of the present invention includes the step of filtering a data set, that is, removing a data set expected to be derived from a negative sample to obtain only a data set expected to be derived from a positive sample.

[0158] In step (a-1-1), a threshold value is applied to the first data set of step (a-1) to distinguish between a data set from positive samples and a data set from negative samples. This distinction can be easily achieved by applying a threshold. After applying a threshold to the first data set of step (a-1), a data set having signal values ​​exceeding the threshold value is determined to be a data set originating from positive samples, and a data set having all signal values ​​below the threshold is determined to be a data set originating from negative samples.

[0159] Subsequently, in step (a-1-2), if the first data set has a signal value exceeding the threshold value, the next step (a-2) is performed. Since the data set having a signal value exceeding the threshold value is expected to originate from a positive sample, it is expected to generate the best-fitting S-shaped function according to the method of the present invention.

[0160] On the other hand, if the above data set has all signal values ​​below the threshold value, the next step (a-2) is not performed. Since a data set having all signal values ​​below the threshold value is expected to be derived from voice samples, it may not be possible to generate the best-fitting S-shaped function according to the method of the present invention. A data set having all signal values ​​below the threshold value may indicate the absence of the target analyte without performing step (a-2).

[0161] (a-2) Obtain the second data set

[0162] Next, according to the present embodiment, (a-2) a second data set is obtained by calculating a slope value at each cycle number for the first data set. The second data set includes a plurality of data points, each having a cycle number and a slope value at the cycle number.

[0163] The above second data set can be used interchangeably with the "slope data set".

[0164] As used herein, the term "slope value" refers to the change in signal value at each data point and may be used interchangeably with "change value." Since the slope value or the change value may include a rate of change, the slope data set may include a rate of change data set.

[0165] According to one embodiment of the present invention, the slope value of step (a-2) is obtained by differentiation, difference, difference, ratio, or linear regression analysis of the signal value.

[0166] Calculation of slope values ​​by differentiation generally includes the step of calculating a function that approximates a first data set (e.g., raw data set), the step of calculating a derivative from said approximated function, and the step of obtaining slope values ​​at each cycle number using said derivative.

[0167] Calculation of a slope value by difference generally includes the step of calculating the difference between a signal value at a cycle number for which a slope value is to be calculated (target signal value) and a signal value at another cycle number (reference signal value), and the step of taking said difference as the slope value at the target cycle number. The reference signal value may, for example, be a signal value at a cycle number immediately preceding or immediately following the target cycle number, or a signal value at two or more cycle numbers before or after the target cycle number. Alternatively, the reference signal value may be the average of signal values ​​at several cycle numbers before or after the target cycle number. Typically, the reference signal value may be a signal value at a cycle number immediately preceding the target cycle number.

[0168] The calculation of the slope value of a signal value by the difference generally includes the step of calculating the difference between a signal value at a cycle number (target cycle number) for which the slope value is to be calculated and a signal value at another cycle number (reference signal value); the step of dividing the calculated difference by the difference between the target cycle number and the reference cycle number; and the step of taking the result as the slope value at the target cycle. The reference signal value may, for example, be a signal value at a cycle number immediately preceding or immediately following the target cycle number, or a signal value at two or more cycle numbers before or after the target cycle number. The slope value determined by the difference corresponds to the amount of change in the signal value per cycle number.

[0169] Calculation of the slope value by ratio generally involves calculating the ratio between two signal values ​​at two cycle numbers. In one embodiment, the ratio at the target cycle number is calculated by dividing the signal value at the target cycle by the signal value at the immediately preceding cycle.

[0170] Calculation of slope values ​​by linear regression analysis generally involves obtaining a linear function that fits several data points including a data point (target data point) for which slope values ​​are to be calculated, and determining the increase in signal value per cycle number in the fitted function. The number of data points used to obtain the linear function may be two or more data points, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or fewer.

[0171] Examples of linear regression analysis include, but are not limited to, the least squares method.

[0172] The above least squares method can be expressed by the following mathematical formula 6.

[0173] Mathematical formula 6

[0174]

[0175] In the above equation, I is the cycle of the data point for which the slope is to be calculated, m is the slope of the data point in the I-th cycle, and x i is the cycle of the i-th cycle, and y i is the signal value measured in the i-th cycle, and S i is the slope value in the i-th cycle.

[0176] The above "n" or "a+b+1" is the number of data points used to calculate the slope in the Ith cycle and is referred to as the Linear Squares Method Range (LSMR). "a" is a value for calculating the minimum cycle among the set of data points used to calculate the slope in the Ith cycle. "b" is a value for calculating the maximum cycle. "a" and "b" independently represent integers from 0 to 10, preferably from 1 to 5, more preferably from 1 to 3. It is advantageous for the values ​​of "a" and "b" to be the same, but they may differ depending on the measurement target, the measurement environment, and the cycle in which the slope is to be measured.

[0177] According to one embodiment, the calculation of slope values ​​at a specific cycle number(s) may differ from the calculation of slope values ​​at the remaining cycle numbers. For example, when slope values ​​are obtained by a difference method, the slope values ​​at the first cycle number or the last cycle number may be obtained by a method different from the difference method; and when slope values ​​are obtained by linear regression analysis, the slope values ​​at the initial cycle numbers (e.g., the first cycle number and the second cycle number) or the end cycle numbers (e.g., the last cycle number and the cycle number immediately preceding it) may be obtained by a method different from linear regression analysis.

[0178] For example, the slope value at the first cycle number or the end cycle number can be determined as 0. Alternatively, the slope value at the first cycle number can be determined as the signal value at the first cycle number or the second cycle number. Alternatively, the slope value at the first cycle number or the end cycle number can be determined as a predetermined value.

[0179] According to one embodiment, the method may further include a step of correcting slope values ​​of one or more data points in the slope data set between steps (a-1) and (a-3).

[0180] Examples of such correction include correcting abnormal signals within a slope data set, which includes detecting cycle numbers having abnormal signal values ​​within the slope data set and then correcting the slope values ​​corresponding to the detected abnormal cycle numbers.

[0181] According to a more specific embodiment of the present invention, the method additionally comprises the following step between steps (a-2) and (a-3):

[0182] (a-2-1) A step of applying a threshold value to the second data set; and

[0183] (a-2-2) If the second data set has a slope value exceeding the threshold value, proceed to the next step (a-3).

[0184] This embodiment illustrates a process for filtering a second data set.

[0185] Since this embodiment is identical to the process of filtering the first data set except that a threshold value is applied to the second data set rather than the first data set, the common details between them are omitted to avoid excessive complexity in the specification due to repetition.

[0186] However, the threshold value applied during the process of filtering the second data set may be different from the threshold applied during the process of filtering the first data set.

[0187] (a-3) Calculate an S-shaped function that approximates a selected portion of the second data set

[0188] Next, according to the present embodiment, an S-shaped function that approximates a selected portion of the second data set is calculated.

[0189] According to one embodiment, step (a-3) may include the following substeps:

[0190] (a-3-1) A step of selecting a portion that represents the pattern of the amplification curve or growth curve in the second data set and can be fitted to an S-shaped function; and

[0191] (a-3-2) A step of calculating an S-shaped function that approximates the selected part above.

[0192] In one embodiment, the selected portion within the second data set (slope data set) of step (a-3) is a cycle interval representing the pattern of the amplification curve or growth curve in the presence of the target analyte. That is, the selected portion is within the cycle interval representing the shape of the amplification curve or growth curve in the presence of the target analyte.

[0193] Generally, in the presence of the target analyte, a slope data set such as the second data set exhibits a "bell-shaped curve," also known as a normal distribution. With respect to the axis of symmetry (central axis), the left side of the bell-shaped curve shows an increasing pattern, such as an amplification curve or a growth curve, that is, a pattern in which the slope value increases as the cycle number increases, whereas the right side of the bell-shaped curve shows a pattern in which the slope value decreases as the cycle number increases.

[0194] The start and end cycles defining the limits (metes and bounds) of the selected portion within the second data set of step (a-3) above are as follows:

[0195] (i) Start cycle

[0196] The starting cycle of the selected portion above can be selected by considering the goodness of fit of an S-shaped function that approximates or fits the selected portion.

[0197] In one embodiment, the starting cycle of the selected portion may be any one of the 1st to 30th cycle numbers; any one of the 1st to 25th cycle numbers; any one of the 1st to 20th cycle numbers; any one of the 1st to 15th cycle numbers; any one of the 1st to 10th cycle numbers; or any one of the 1st to 5th cycle numbers.

[0198] In a specific embodiment, the start cycle of the selected portion is any one of the 1st to 10th cycle numbers in the second data set.

[0199] (ii) Last cycle

[0200] The last cycle of the above-mentioned selected part can be selected by considering the fitting goodness of an S-shaped function that approximates or fits the above-mentioned selected part.

[0201] In one embodiment, the last cycle of the selected portion is any one of the last cycle number in the second data set or five consecutive cycle numbers immediately preceding the last cycle number; any one of the last cycle number in the second data set or four consecutive cycle numbers immediately preceding the last cycle number; any one of the last cycle number in the second data set or three consecutive cycle numbers immediately preceding the last cycle number; any one of the last cycle number in the second data set or two consecutive cycle numbers immediately preceding the last cycle number; or the last cycle number in the second data set or the cycle number immediately preceding the last cycle number.

[0202] In one embodiment, the last cycle of the selected portion is the last cycle number in the second data set. For example, if the last cycle number in the slope data set is the 50th cycle number, the last cycle of the selected portion is the 50th cycle number in the second data set.

[0203] According to a more specific embodiment of the present invention, the selected portion of the second data set in step (a-3) is within the range from the first cycle number to the last cycle number in the second data set. In other words, the start cycle and the last cycle are each selected within the range from the first cycle number to the last cycle number in the second data set.

[0204] A selected portion of the second data set can be selected directly by the user or automatically by a computer system, taking into account the requirements mentioned above.

[0205] A selected portion of the second data set may include additional data points that were not originally present in the gradient data set.

[0206] Subsequently, an S-shaped function that approximates the selected part is calculated.

[0207] As used herein, the phrase "calculating an S-shaped function" refers to the process of calculating an S-shaped function that best fits the data points of the selected portion. The process of calculating the S-shaped function may be referred to herein as S-shaped regression analysis. The S-shaped function may be calculated using fitting methods known in the art.

[0208] According to one embodiment, calculating an S-shaped function in step (a-3) comprises fitting the data points of the selected portion to a conventional S-shaped function known in the art; and determining the best-fitting function and its parameters.

[0209] According to another embodiment, calculating an S-shaped function in step (a-3) includes determining parameters of a specific S-shaped function. Specifically, calculating an S-shaped regression function in step (a-3) includes fitting data points of the selected portion to a specific S-shaped function; and determining parameters of the best-fitted function.

[0210] According to one embodiment, a computer system is used to calculate a function that fits data points of a selected portion. The computer system includes a computer-readable storage medium in which a computer program capable of performing regression analysis is stored, and a processor capable of running said computer program.

[0211] As described above, the S-shaped function may be a predetermined function, and the regression analysis may be a process of determining the parameters of the S-shaped function.

[0212] Specifically, when a cycle at a data point is set as the independent variable x and a signal value at the cycle is set as the dependent variable y, the dependent variable is expressed as a polynomial function of the independent variable.

[0213] Step (b): Obtain the n-th derivative function and the n+1-th derivative function from the S-shaped function ( 120 )

[0214] Next, the method of the present invention comprises the step of (b) obtaining an n-th derivative function and an n+1-th derivative function from the S-shaped function. n is a natural number.

[0215] This step includes the process of differentiating the S-shaped function to calculate the first, second, n-th, or n+1-th order derivative function (derivative).

[0216] As can be seen in Example 3 described below, n is a natural number.

[0217] According to one embodiment of the present invention, n is a natural number selected from 1 to 10. More specifically, n is a natural number selected from 1 to 7, even more specifically, n is a natural number selected from 1 to 5, and even more specifically, n is a natural number selected from 1 to 4.

[0218] Step (c): i) obtain a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the said (n+1)-th derivative function 130 )

[0219] Next, the method of the present invention comprises the step of (c) obtaining a linear function connecting the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function.

[0220] As used in this specification, the term "extreme point" refers to a point where a function changes from increasing to decreasing (maximum point) and a point where a function changes from decreasing to increasing (minimum point), and is also referred to as a local or relative maximum point and a local or relative minimum point.

[0221] As used herein, the term "global maximum point" refers to the point in a function where the function value is maximum, and the term "global minimum point" refers to the point in a function where the function value is minimum.

[0222] FIG. 13 is a diagram showing an S-shaped function (a) and its first (b), second (c), and fourth (d) derivative functions.

[0223] In the first derivative function of FIG. 13, point (1) is a pole (specifically a maximum point) and a maximum point, in the second derivative function, point (2) is a pole (specifically a maximum point) and a maximum point, point (3) is a pole (specifically a minimum point) and a minimum point, in the fourth derivative function, point (4) is a pole (specifically a maximum point) but not a maximum point, point (5) is a pole (specifically a minimum point) and a minimum point, and point (6) is a pole (specifically a maximum point) and a maximum point.

[0224] As can be seen in Figure 13, the poles and the maximum (or minimum) points may be the same, but they may also be different.

[0225] In this regard, it is necessary to clearly distinguish the difference between the peak and the peak (or minimum) when detecting the target analyte within the sample.

[0226] One of the features of the present invention is that a linear function connecting the second pole of the n+1th derivative function and the first pole of the nth derivative function or the n+1th derivative function is used. Accordingly, according to the present invention, a linear function connecting the second pole (minimum point) and the third pole (maximum point) of the 4th derivative function in FIG. 13 is not used.

[0227] Step (d): Detecting target analytes in a sample using the intersection of an S-shaped function and a linear function ( 140 )

[0228] Finally, the method of the present invention includes (d) a step of detecting a target analyte in a sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c).

[0229] The most significant feature of the present invention is that, in detecting a target analyte in a sample, instead of simply using the maximum or minimum point of the nth derivative of the S-shaped function, it uses the intersection point between the first derivative of the nth derivative of the S-shaped function and the second derivative of the n+1st derivative of the S-shaped function, thereby having suitability for quantification and excellent quantitative accuracy and precision despite the inter-experimental variation of the real-time PCR reaction, as confirmed in the examples described below.

[0230] As used herein, the terms "to detect," "detecting," or "detecting" include both "qualitative" and "quantitative" of the target analyte. As used herein, the terms "qualitative" or "qualitative analysis" refer to determining the presence of the target analyte in a sample using the method of the present invention; whereas as used herein, the terms "quantitative" or "quantitative analysis" refer to determining the initial amount of the target analyte in a sample using the method of the present invention.

[0231] According to one embodiment of the present invention, using an intersection point in step (d) includes determining the intersection point as a Quantification Cycle (Cq) value. The Cq value is used for qualitative or quantitative analysis of a target analyte in a sample.

[0232] Although Cq is used substantially interchangeably with Ct, this specification distinguishes between Ct determined using a threshold and Cq determined according to the method of the present invention.

[0233] According to a more specific embodiment of the present invention, determining the intersection point as a Quantification Cycle (Cq) value includes determining the cycle number of the intersection point as a Cq value.

[0234] The above intersection point, more specifically the determined Cq (Quantification Cycle) value, can be used for qualitative or quantitative analysis of a target analyte in a sample as follows.

[0235] a. Qualitative Analysis

[0236] In qualitative analysis, a sample that does not have the above-mentioned intersection (specifically, a determined Cq value) or a sample that has an intersection (specifically, a determined Cq value) greater than the last cycle in the data set of step (a-1) or (a-2) is determined to not contain the target analyte (i.e., a negative sample); and a sample that has an intersection (specifically, a determined Cq value) less than or equal to the last cycle in the data set of step (a-1) or (a-2) is determined to contain the target analyte (i.e., a positive sample).

[0237] For example, according to the method of the present invention, if the crossover point (specifically, the determined Cq value) from an unknown sample is 35 and the last cycle in the data set of step (a-1) or step (a-2) is 50, the sample is determined to be positive; whereas if the crossover point (specifically, the determined Cq value) is 70 and the last cycle in the data set of step (a-1) or step (a-2) is 50, the sample is determined to be negative.

[0238] b. Quantitative Analysis

[0239] In quantitative analysis, a series of dilutions of a standard target analyte are each applied to an amplification reaction to obtain multiple amplification curves, and then intersection points (specifically, Cq values) are determined from the amplification curves according to the method of the present invention described above. Subsequently, the intersection points (specifically, Cq values) are plotted against the log-transformed concentration of the standard analyte to obtain a standard curve for quantification. Then, an unknown sample (a sample with an unknown initial amount) is applied to an amplification reaction to obtain intersection points (specifically, Cq values), and then these are applied to the standard curve to determine the initial amount of the unknown sample.

[0240] Qualitative or quantitative analysis using these intersection points (specifically, Cq values) can be performed according to various methods known in the art.

[0241] Recording media, devices, and programs

[0242] According to another aspect of the present invention, the present invention is a computer-readable recording medium comprising instructions for implementing a processor for executing a method for detecting a target analyte in a sample, wherein the method comprises the following steps:

[0243] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0244] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0245] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0246] (d) A step of detecting a target analyte in a sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c).

[0247] According to another aspect of the present invention, the present invention provides a computer program stored in a computer-readable recording medium that implements a processor for executing a method for detecting a target analyte in a sample, the method comprising the following steps:

[0248] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0249] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0250] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0251] (d) A step of detecting a target analyte in a sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c).

[0252] According to another aspect of the present invention, the present invention provides an apparatus for detecting a target analyte in a sample, comprising (a) a computer processor and (b) a computer-readable recording medium of the present invention coupled to said computer processor.

[0253] According to one embodiment of the present invention, the device of the present invention may additionally include a reaction vessel capable of accommodating a sample and a signal generating means, a temperature control means for controlling the temperature of the reaction vessel, and / or a detector for detecting a signal in an amplification cycle.

[0254] The recording medium, device, and computer program of the present invention enable the method of the present invention described above to be carried out on a computer, and common details among them are omitted to avoid excessive complexity in this specification due to repetitive descriptions.

[0255] Program instructions, when executed by a processor, cause the processor to execute the method of the present invention described above. Program instructions for executing a method for detecting a target analyte in a sample may include the following instructions: (i) an instruction to obtain an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (ii) an instruction to obtain an n-th derivative function and an n+1-th derivative function from the S-shaped function; (iii) an instruction to obtain a linear function connecting i) the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function; and (iv) an instruction to detect the target analyte in the sample (e.g., display it on an output device) using the intersection point of the S-shaped function and the linear function.

[0256] The method of the present invention is executed in a processor, and the processor may be a processor in a data acquisition device such as a stand-alone computer, a network-attached computer, or a real-time PCR device.

[0257] According to one embodiment of the present invention, the computer-readable recording medium is a non-transitory computer-readable recording medium.

[0258] Computer-readable recording media include, but are not limited to, various storage media known in the art, such as CD-R, CD-ROM, DVD, flash memory, floppy disk, hard drive, portable HDD, USB, magnetic tape, MINIDISC, non-volatile memory card, EEPROM, optical disc, optical storage media, RAM, ROM, system memory, and web server.

[0259] The detection process and results of a target analyte within a sample can be provided in various ways. For example, the detection process and results of a target analyte within a sample can be provided to a separate system, such as a desktop computer system, via a network connection (e.g., LAN, VPN, Internet, and intranet) or a direct connection (e.g., USB or other direct wired or wireless connection), or can be provided on portable media such as CDs, DVDs, floppy disks, and portable HDDs. Similarly, the detection process and results of a target analyte within a sample can be provided to a server system via a network connection (e.g., LAN, VPN, Internet, intranet, and wireless communication networks) to a client, such as a laptop or desktop computer system.

[0260] Instructions for implementing a processor that executes the present invention may be included in a logic system. Although said instructions may be provided on a software recording medium (e.g., portable HDD, USB, floppy disk, CD, and DVD), they may be downloadable and stored in a memory module (e.g., a hard drive or other memory such as local or attached RAM or ROM). Computer code that executes the present invention may be executed in various coding languages ​​such as C, C++, Java, Visual Basic, VBScript, JavaScript, Perl, and XML. Additionally, various languages ​​and protocols may be used for the external and internal storage and transmission of signals and commands according to the present invention.

[0261] A computer processor can be constructed so that a single processor performs all of the aforementioned performances. Alternatively, a processor unit can be constructed so that multiple processors execute each performance.

[0262] According to one embodiment of the present invention, a processor can be implemented by installing software on a conventional device for detecting a target nucleic acid sequence (e.g., a real-time PCR device).

[0263] II. Method for determining the Quantification Cycle (Cq) value for a target analyte in a sample (Second embodiment)

[0264] According to another aspect of the present invention, the present invention provides a method for determining a quantification cycle (Cq) value for a target analyte in a sample, comprising the following steps:

[0265] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0266] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0267] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0268] (d) A step of determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value.

[0269] Since the present invention is a method for determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as a Cq value using the method of the first embodiment of the present invention described above, the description of the common content between them is omitted to avoid excessive complexity in the present specification due to repetitive description.

[0270] According to one embodiment, the method is a computer-implemented method.

[0271] FIG. 3 is a flowchart of the processes for carrying out the method of the present invention according to one embodiment of the present invention. The method of the present invention will be described with reference to FIG. 3 as follows:

[0272] Step (a): Obtain an S-shaped function representing the growth curve ( 210 )

[0273] First, the method of the present invention comprises the step of (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte.

[0274] According to one embodiment of the present invention, the target analyte is a target nucleic acid molecule.

[0275] According to one embodiment of the present invention, the S-shaped function is selected from the group consisting of a sigmoid function, a logistic function, a Gompertz function, a Chapman function, and a Richard function.

[0276] According to one embodiment of the present invention, the step of obtaining an S-shaped function of step (a) comprises the following steps:

[0277] (a-1) A step of obtaining a first data set representing a growth curve by an amplification reaction for a target analyte; said first data set includes a plurality of data points each having a cycle number and a signal value at said cycle number;

[0278] (a-2) a step of obtaining a second data set by calculating a slope value at each cycle number for the first data set; the second data set includes a plurality of data points each having a cycle number and a slope value at said cycle number; and

[0279] (a-3) A step of calculating an S-shaped function that approximates a selected portion of the second data set.

[0280] According to a more specific embodiment of the present invention, the first data set of step (a-1) is a raw data set, a mathematically modified data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically modified data set.

[0281] According to a more specific embodiment of the present invention, the method additionally comprises the following step between steps (a-1) and (a-2):

[0282] (a-1-1) A step of applying a threshold value to the first data set; and

[0283] (a-1-2) If the first data set has a signal value exceeding the threshold value, proceed to the next step (a-2).

[0284] According to a more specific embodiment of the present invention, the slope value of step (a-2) is obtained by differentiation, difference, difference, ratio, or linear regression analysis of the signal value.

[0285] According to a more specific embodiment of the present invention, the method additionally comprises the following step between steps (a-2) and (a-3):

[0286] (a-2-1) A step of applying a threshold value to the second data set; and

[0287] (a-2-2) If the second data set has a slope value exceeding the threshold value, proceed to the next step (a-3).

[0288] According to one embodiment, step (a-3) may include the following substeps:

[0289] (a-3-1) A step of selecting a portion that represents the pattern of the amplification curve or growth curve in the second data set and can be fitted to an S-shaped function; and

[0290] (a-3-2) A step of calculating an S-shaped function that approximates the selected part above.

[0291] According to a more specific embodiment of the present invention, a selected portion of the second data set of step (a-3) is within the range from the first cycle number to the last cycle number in the second data set.

[0292] According to one embodiment, calculating an S-shaped function in step (a-3) comprises fitting the data points of the selected portion to a conventional S-shaped function known in the art; and determining the best-fitting function and its parameters.

[0293] According to another embodiment, calculating an S-shaped function in step (a-3) includes determining parameters of a specific S-shaped function. Specifically, calculating an S-shaped regression function in step (a-3) includes fitting data points of the selected portion to a specific S-shaped function; and determining parameters of the best-fitted function.

[0294] Step (b): Obtain the n-th derivative function and the (n+1)-th derivative function from the S-shaped function (220)

[0295] Next, the method of the present invention comprises the step of (b) obtaining an n-th derivative function and an n+1-th derivative function from the S-shaped function. n is a natural number.

[0296] According to one embodiment of the present invention, n is a natural number selected from 1 to 10.

[0297] Step (c): i) obtain a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the said (n+1)-th derivative function 230 )

[0298] Next, the method of the present invention comprises the step of (c) obtaining a linear function connecting the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function.

[0299] Step (d): Determine the intersection point of the S-shaped function and the linear function as the Cq value ( 240 )

[0300] Finally, the method of the present invention includes (d) a step of determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as a Cq value.

[0301] Recording media, devices, and programs

[0302] According to another aspect of the present invention, the present invention is a computer-readable recording medium comprising instructions for implementing a processor for executing a method for determining a quantification cycle (Cq) value for a target analyte in a sample, wherein the method comprises the following steps:

[0303] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0304] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0305] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0306] (d) A step of determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value.

[0307] According to another aspect of the present invention, the present invention provides a computer program stored in a computer-readable recording medium, which implements a processor for executing a method for determining a quantification cycle (Cq) value for a target analyte in a sample, said method comprising the following steps:

[0308] (a) A step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte;

[0309] (b) a step of obtaining an n-th order derivative function and an (n+1)-th order derivative function from the above S-shaped function; wherein n is a natural number, and

[0310] (c) i) obtaining a linear function connecting the first extreme point of the n-th derivative function or the (n+1)-th derivative function and ii) the second extreme point of the (n+1)-th derivative function; and

[0311] (d) A step of determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value.

[0312] According to another aspect of the present invention, the present invention provides an apparatus for determining a quantification cycle (Cq) value for a target analyte in a sample, comprising (a) a computer processor and (b) a computer-readable recording medium of the present invention coupled to said computer processor.

[0313] Program instructions, when executed by a processor, cause the processor to execute the method of the present invention described above. Program instructions for executing a method for determining a Quantification Cycle (Cq) value for a target analyte in a sample may include the following instructions: (i) an instruction to obtain an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (ii) an instruction to obtain an n-th derivative function and an n+1-th derivative function from the S-shaped function; (iii) an instruction to obtain a linear function connecting i) the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function; and (iv) an instruction to determine the intersection point of the S-shaped function and the linear function as the Cq value (e.g., display on an output device).

[0314] According to one embodiment, the computer-readable recording medium is a non-transitory computer-readable recording medium. Effects of the invention

[0315] The features and advantages of the present invention are summarized as follows:

[0316] (a) The present invention utilizes the intersection point of a linear function connecting the first pole of an n-th derivative function or an n+1-th derivative function and the second pole of an n+1-th derivative function, and a function representing an amplification curve for the target analyte, for the detection of the target analyte.

[0317] (b) The present invention has superior quantitative accuracy and precision compared to conventional methods, despite inter-experimental variation in real-time PCR reactions.

[0318] (c) The present invention has excellent quantitative accuracy and can be applied to complex samples where experimental variation may occur, and can be usefully utilized when accurate measurement of DNA amount is required in molecular biology research. Brief explanation of the drawing

[0319] FIG. 1 is a flowchart of the processes for carrying out the method of the present invention according to one embodiment of the present invention. FIG. 2 is a flowchart of the processes for carrying out the method of the present invention according to a more specific embodiment of the present invention. FIG. 3 is a flowchart of the processes for carrying out the method of the present invention according to another embodiment of the present invention. FIG. 4 shows raw data sets obtained from samples containing target analytes at various concentrations and samples not containing target analytes according to one embodiment of the present invention (solid line: NG 100 pg; -○-: NG 10 pg; -□-: NG 1 pg; and -△-: 100 fg; -Ⅹ-: NTC). FIG. 5 shows a dataset of change values ​​obtained from samples containing various concentrations of a target analyte and a sample not containing the target analyte according to one embodiment of the present invention ((a): NG 100 pg; (b): NG 10 pg; (c) NG 1 pg; and (d): NG 100 fg). FIG. 6 shows curves representing the Richard function obtained from samples containing Neisseria gonoroae (NG) genomic DNA at various concentrations as target analytes according to one embodiment of the present invention ((a): real-time PCR raw data of NG without baseline subtraction, (b): result of Richard function fitting, (c): Richard function fitting data with baseline subtraction). FIG. 7 illustrates a process for detecting a target analyte by determining the cycle number of the intersection point of the curve represented by the Richard function obtained according to one embodiment of the present invention and the first-order function as the Cq (Cycle quantification) value. The first-order function is a first-order function that passes through the first extreme point of the first derivative function and the second extreme point of the second derivative function by applying differentiation to the curve represented by the Richard function. dev1 represents the first derivative function, EP 1 represents the first extreme point, EP 2 represents the second extreme point, and ITF represents the intersection of the two functions. FIG. 8 illustrates a process for detecting a target analyte by determining the cycle number of the intersection point between the curve represented by the Richard function obtained according to one embodiment of the present invention and the first-order function as the Cq (Cycle quantification) value. The first-order function is a first-order function that passes through the first pole of the second-order derivative function and the second pole of the second-order derivative function by applying differentiation to the curve represented by the Richard function. dev2 represents the second-order derivative function. Figure 9 shows the result of determining the Cq value using a linear function connecting the first pole of the n-th derivative function or the n+1-th derivative function and the second pole of the n+1-th derivative function, and the curve represented by the Richard function obtained from the raw data set obtained from a sample containing NG 100 pg of the target analyte (n is 1 to 3). Figure 10 shows a raw data set obtained by 6 replicate experiments from samples containing Neisseria gonoroae (NG) genomic DNA at various concentrations as target analytes. Figure 11 shows a standard curve generated by using a sample containing four concentrations of target analytes, determining the cycle number of the intersection point of a linear function passing through the first pole of the first derivative function and the second pole of the second derivative function, and then using an S-shaped regression function. Figure 12 shows a standard curve generated by using a sample containing four concentrations of target analytes, determining the cycle number of the intersection point of a linear function passing through the first pole of the second derivative function and the second pole of the S-shaped regression function. FIG. 13 is a diagram showing an S-shaped function (a) and its first (b), second (c), and fourth (d) derivative functions. Specific details for implementing the invention

[0320] The present invention will be described in more detail below through examples. These embodiments are intended solely to explain the present invention more specifically, and it will be obvious to those skilled in the art that the scope of the present invention is not limited by these embodiments according to the gist of the present invention.

[0321] Examples

[0322] Example 1: Detection of a target analyte using a linear function connecting the first pole of the n-th order (n=1) derivative of an S-shaped regression function obtained from a raw data set and the second pole of the n+1-th order (n=1) derivative.

[0323] We investigated whether the target analyte could be detected using a linear function connecting the first pole of the first derivative of the S-shaped regression function obtained from the raw data set and the second pole of the second derivative.

[0324] Example 1-1: Execution of real-time polymerase chain reaction and acquisition of raw data set

[0325] Neiceria gonoroae as a target analyte ( Neisseria gonorrhoeae ; NG) Genomic DNA (Source: Coram Deo Lab Co., Ltd.; Deposit No. ATCC 700825) was used. To amplify the target analyte and generate a signal from the amplification product, real-time polymerase chain reaction was performed using the primers and TaqMan probes listed in Table 1 below.

[0326]

[0327] In Table 1 above, BHQ represents a Black Hole Quencher.

[0328] The above real-time polymerase chain reaction used 10 pmole forward and reverse primers (SEQ Nos. 1 and 2), 5 pmole TaqMan probe (SEQ No. 3), Neisseria gonoroe genomic DNA as the target analyte (100 pg, 10 pg, 1 pg, or 100 fg), sterile distilled water (NTC) as a negative control, and 10 µl of 2X Master Mix (final, 200 µM dNTPs, 2.5 mM MgCl2, 1.6 U of Taq 20 µl of a sample containing DNA polymerase (Solgent, Korea) was placed in a real-time polymerase chain reaction (CFX96 Real-time Cycler, Bio-Rad) and reacted at 95°C for 15 minutes, followed by 50 cycles of reaction at 95°C for 30 seconds, 60°C for 60 seconds, and 72°C for 30 seconds. Fluorescence was detected at 60°C during each cycle.

[0329] A raw dataset (baseline not deducted) for the target analyte was obtained from the instrument through the above real-time polymerase chain reaction.

[0330] Raw data sets obtained from samples containing the target analytes at various concentrations and samples not containing the target analytes are shown in FIG. 4 (solid line: NG 100 pg; -○-: NG 10 pg; -□-: NG 1 pg; and -△-: NG 100 fg; -Ⅹ-: NTC).

[0331] Example 1-2: Obtaining a change value data set

[0332] A change value data set was obtained by calculating the change value of the signal value from the signal value at each cycle number in the raw data set obtained in Example 1-1 above.

[0333] The change value from the signal value was calculated using the difference method as shown in Equation 7 below.

[0334] Mathematical formula 7

[0335]

[0336] In the above mathematical formula, i is the cycle number of the data set; and y i is the signal value of the i-th cycle number; △y i is the change in signal value at the i-th cycle number.

[0337] Data sets of change values ​​obtained from samples containing the target analyte at various concentrations and samples not containing the target analyte are shown in FIG. 5 ((a): NG 100 pg; (b): NG 10 pg; (c) NG 1 pg; and (d): NG 100 fg).

[0338] Examples 1-3: Selection of raw data sets for obtaining an S-shaped regression function

[0339] Among the raw data sets obtained in Examples 1-2 above, a raw data set for obtaining an S-shaped regression function was selected.

[0340] As a method for selecting raw data sets, a screening threshold was applied to each raw data set. Subsequently, data sets having values ​​above the applied screening threshold were determined as raw data sets for obtaining an S-shaped regression function and proceeded to the next step, whereas data sets having only values ​​below the applied screening threshold were determined as negative without obtaining an S-shaped regression function.

[0341] In this embodiment, a screening threshold of 100 (unit: △RFU (relative fluorescence unit)) was used.

[0342] As a result of the screening above, the dataset obtained from samples containing target analytes at various concentrations contained values ​​above the screening threshold and was determined to be a dataset for obtaining an S-shaped regression function; therefore, the following steps were performed. On the other hand, the dataset obtained from samples not containing target analytes contained only values ​​below the screening threshold and was determined to be a negative sample without performing the following steps.

[0343] Examples 1-4: Obtaining an S-shaped regression function for an interval within a raw data set

[0344] In the above Example 1-3, a cycle interval was selected to obtain an S-shaped regression function from the raw data set for which it was decided to proceed with the following step (Example 1-4). The cycle interval was set to a range from the first cycle number to the last cycle number within the change value data set.

[0345] Subsequently, an S-shaped regression function was determined to approximate the selected interval. The Richard function of Equation 5 below was used for the S-shaped regression function.

[0346] Mathematical formula 5

[0347]

[0348] In the above mathematical formula, x is the cycle number; F(x) is the reaction fluorescence in the cycle; and F max is the maximum response fluorescence; b is the slope of the curve; c is the fractional period at which the response fluorescence is reached; d is the Richards coefficient; F b is background fluorescence.

[0349] Fitting of selected intervals within the raw data set to a Richards function was performed using a conventionally known LM algorithm (Levenberg-Marquardt algorithm; Christian Kanzow et al., JCAM, 172(2):375(2004)). A Richards function approximating the interval was obtained using the above LM algorithm.

[0350] Figure 6 shows curves representing Richard functions obtained from samples containing Neisseria gonoroa (NG) genomic DNA at various concentrations as target analytes, and the values ​​of the parameters of each Richard function are shown in Table 2 below.

[0351]

[0352] Examples 1-5: Detection of target analytes using an S-shaped regression function and the intersection point of a linear function connecting the first pole of the first derivative of the S-shaped regression function and the second pole of the second derivative.

[0353] Differentiation was applied to the curve represented by the Richard function obtained in Examples 1-4 above to calculate the first pole of the first derivative function and the second pole of the second derivative function, and a linear function passing through the first pole of the first derivative function and the second pole of the second derivative function was calculated.

[0354] Afterwards, the point of tangency between the curve represented by the Richard function and the above linear function was calculated.

[0355] As shown in Fig. 7, the cycle number of the intersection point of the curve represented by the Richard function obtained in Examples 1-4 and the linear function was determined as the detection quantification cycle Cq (Quantification Cycle) to detect the target analyte.

[0356] Subsequently, samples with a cycle number less than or equal to the 50th cycle, which is the last cycle of the dataset, were determined to be positive, and samples with a cycle number greater than the 50th cycle were determined to be negative. The results are shown in Table 3 below.

[0357] As shown in Table 3 below, the cycle numbers obtained from samples containing target analytes at various concentrations were found to be 28.71, 32.08, 35.58, and 39.57, and since all cycle numbers were less than 50, all samples were determined to be positive.

[0358]

[0359] Example 2: Detection of a target analyte using a linear function connecting the first pole of the n+1th (n=1) derivative of an S-shaped regression function obtained from a raw data set and the second pole of the n+1th (n=1) derivative.

[0360] In Example 1, instead of determining the cycle number Cq of the intersection point of the first derivative function passing through the first pole of the first derivative function and the second pole of the second derivative function by applying differentiation to the curve represented by the Richard function obtained from the raw data set, the first derivative function passing through the first pole of the second derivative function and the second pole of the second derivative function was calculated by applying differentiation to the curve represented by the Richard function.

[0361] Subsequently, as shown in FIG. 8, the target analyte was detected by determining the cycle number Cq of the intersection point of the first-order function passing through the first pole of the second-order derivative function and the second pole of the second-order derivative function, and the curve represented by the Richard function obtained in Examples 1-4.

[0362] Subsequently, samples with a cycle number less than or equal to the 50th cycle, which is the last cycle of the dataset, were determined to be positive, and samples with a cycle number greater than the 50th cycle were determined to be negative. The results are shown in Table 4 below.

[0363] As shown in Table 4 below, the cycle numbers obtained from samples containing target analytes at various concentrations were found to be 26.05, 29.36, 32.48, and 36.29, and since all cycle numbers were less than 50, all samples were determined to be positive.

[0364]

[0365] Example 3: Determining Cq using a linear function (n is 1 to 3) for the first extremum of the n-th derivative or n+1-th derivative of an S-shaped regression function obtained from a raw data set and the second extremum of the n+1-th derivative.

[0366] The procedure was carried out in the same manner as in Example 1, except that a raw data set obtained from a sample containing NG 100 pg of the target analyte was used, and the curve represented by the Richard function obtained therefrom and 1) a linear function passing through the first pole of the first derivative function and the second pole of the second derivative function, or a linear function passing through the first pole of the second derivative function and the second pole of the second derivative function (n=1), 2) a linear function passing through the first pole of the second derivative function and the second pole of the third derivative function, or a linear function passing through the first pole of the third derivative function and the second pole of the third derivative function (n=2), and 3) a linear function passing through the first pole of the third derivative function and the second pole of the fourth derivative function, or a linear function passing through the first pole of the fourth derivative function and the second pole of the fourth derivative function The cycle number of the intersection point of the function (n=3) was determined as Cq, and the result is summarized in Fig. 9.

[0367] As can be seen in Fig. 9, it was confirmed that Cq can be determined using the extrema up to the fourth derivative of the S-shaped regression function. In addition, looking at the first to fourth derivative of the S-shaped regression function, the graph patterns all show an increasing and then decreasing pattern, so it was found that Cq according to the present embodiment can be determined even by using extrema of the fifth derivative or higher.

[0368] Comparative Example 1: Detection of a target analyte using the maximum point of the first derivative of an S-shaped regression function obtained from a raw data set

[0369] Instead of determining the cycle number Cq as the intersection point of the first derivative function passing through the first pole of the first derivative function and the second pole of the second derivative function with the curve represented by the Richard function obtained in Example 1, the cycle number Cq was determined as the first derivative maximum (FDM) of the first derivative function.

[0370] Subsequently, samples with an FDM cycle number less than or equal to the 50th cycle, which is the last cycle of the dataset, were determined to be positive, and samples with a cycle number greater than the 50th cycle were determined to be negative. The results are shown in Table 5 below.

[0371] As shown in Table 5, the FDM cycle numbers obtained from the raw data set of samples containing target analytes at various concentrations were found to be 30.70, 34.20, 38.06, and 42.06, and since all cycle numbers were less than 50, all samples were determined to be positive.

[0372]

[0373] Comparative Example 2: Detection of a target analyte using the maximum point of the second derivative of an S-shaped regression function obtained from a raw data set

[0374] Instead of determining the cycle number Cq as the intersection point of the first derivative function passing through the first pole of the first derivative function and the second pole of the second derivative function with the curve represented by the Richard function obtained in Example 1, the cycle number Cq was determined as the second derivative maximum (SDM) of the second derivative function.

[0375] Subsequently, samples with an SDM cycle number less than or equal to the 50th cycle, which is the last cycle of the dataset, were determined to be positive, and samples with a cycle number greater than the 50th cycle were determined to be negative. The results are shown in Table 6 below.

[0376] As shown in Table 6, the SDM cycle numbers obtained from the raw data set of samples containing target analytes at various concentrations were found to be 27.55, 30.88, 34.50, and 38.65, and since all cycle numbers were less than 50, all of the samples were determined to be positive.

[0377]

[0378] Test Example 1: Comparison of Measured Values ​​and Reading Results

[0379] The results of Examples 1 and 2 and the results of Comparative Examples 1 and 2 according to the conventional method are summarized in Table 7 below.

[0380]

[0381] As shown in Table 7 above, it was confirmed that Examples 1 and 2 can be used to detect the presence of target analytes on par with Comparative Examples 1 and 2.

[0382] Test Example 2: Verification of the suitability, quantitative accuracy, and precision of the quantification of the target analyte

[0383] It was investigated whether the method according to Examples 1 and 2 could be used for the quantification of target analytes (determination of initial content).

[0384] The above quantification was based on a known absolute quantification method utilizing a standard curve of a standard substance. A standard curve was obtained through a real-time polymerase chain reaction using standard substances of various concentrations, and the coefficient of determination (R²) of the standard curve was calculated. 2 It was confirmed whether the method according to Examples 1 and 2 is suitable for quantifying target analytes through the PCR efficiency. The PCR efficiency was calculated using the following Equation 8. In this test example, the cycle number of the intersection point of the linear function passing through the first pole of the first derivative function and the second pole of the second derivative function described in Example 1 and the S-shaped regression function was used, and the cycle number of the intersection point of the linear function passing through the first pole of the second derivative function and the second pole of the second derivative function described in Example 2 and the S-shaped regression function was used.

[0385] Mathematical formula 8

[0386]

[0387] In the above mathematical formula, slope is the slope of the standard curve.

[0388] Test Example 2-1: Obtaining a standard curve using a standard substance

[0389] According to Test Examples 2-1-1 to 2-1-4 below, a standard curve for the absolute quantification of a target analyte was obtained.

[0390] Test Example 2-1-1: Execution of real-time polymerase chain reaction and acquisition of raw data set

[0391] A raw data set was obtained by performing 6 replicate experiments on the four concentrations of target analytes used in Example 1-1 above.

[0392] The raw data set obtained by 6 replicate experiments from samples containing Neisseria gonoroae (NG) genomic DNA at various concentrations as target analytes is shown in Fig. 10.

[0393] Test Example 2-1-2: Data set selection to obtain an S-shaped regression function

[0394] As a result of selecting data sets using a threshold of 100 △RFU in the same manner as in Examples 1-3 above, all data sets were determined to be data sets for obtaining an S-shaped regression function.

[0395] Test Example 2-1-3: Obtaining an S-shaped regression function for an interval within a dataset

[0396] As in Examples 1-4 above, an S-shaped regression function for a selected interval within each data set was obtained, and a Richards function approximating the selected interval was determined. The values ​​of the parameters of the determined Richards function are shown in Table 8 below.

[0397]

[0398]

[0399] Test Example 2-1-4: Generation of Standard Curve

[0400] (1) In the same manner as in Example 1 above, the cycle number of the intersection point of the linear function passing through the first pole of the first derivative function and the second pole of the second derivative function was determined for each Richard function, along with the S-shaped regression function described therein. Then, a standard curve for quantification was generated by plotting the log-converted concentration of the target analyte on the x-axis and the intersection point value of the target analyte's linear function on the y-axis.

[0401] Standard curves generated using samples containing four concentrations of target analytes are shown in Fig. 11, and the coefficient of determination (R²) of each standard curve 2 ) and PCR efficiency are shown in Table 9 below.

[0402]

[0403] As shown in Table 9 above, the R of the generated standard curve 2 It was confirmed that the average value was 0.9979 and the average PCR efficiency was 92%.

[0404] (2) In the same manner as in Example 2 above, the cycle number of the intersection point of the linear function passing through the first and second poles of the S-shaped regression function and the second derivative function described for each Richard function was determined. Then, a standard curve for quantification was generated by plotting the log-converted concentration of the target analyte on the x-axis and the intersection point value of the target analyte's linear function on the y-axis.

[0405] Standard curves generated using samples containing four concentrations of target analytes are shown in Fig. 12, and the coefficient of determination (R²) of each standard curve 2 ) and PCR efficiency are shown in Table 10 below.

[0406]

[0407] As shown in Table 10 above, the R of the generated standard curve 2 It was confirmed that the average value was 0.9984 and the average PCR efficiency was 100%.

[0408] Test Example 2-2: Verification of Quantitative Suitability

[0409] Whether the method according to Examples 1 and 2 is suitable for quantifying the target analyte is determined by the R of the standard curve 2 It was determined based on the value and PCR efficiency.

[0410] According to the literature [2006 Bio-Rad Laboratories, Inc. Real-Time PCR Applications Guide], the suitability criterion for a quantitative method is R, which corresponds to the linearity of the standard curve. 2 The value must exceed 0.980, and theoretically 2 n The PCR amplification efficiency increasing by this must be within the 90~105% range.

[0411] As can be seen in Tables 9 and 10 above, the average R of the standard curve obtained by the method according to Examples 1 and 2 2 The values ​​were 0.9979 and 0.9984, exceeding the standard of 0.980 in the aforementioned literature, and the PCR amplification efficiency was confirmed to be within the 90~105% range of the standard in the aforementioned literature.

[0412] Through this, it was confirmed that the method according to Examples 1 and 2 is suitable for quantifying target analytes.

[0413] Test Example 2-3: Verification of accuracy and precision of quantification

[0414] The accuracy and precision when quantifying the target analyte using the methods according to Examples 1 and 2 were determined based on the relative error (RE) value and the coefficient of variation (CV).

[0415] According to the literature [2008 Introduction to Probability and Statistics], relative error is expressed as a percentage of how different a predicted value is from an actual value; the closer it is to 0, the smaller the difference between the predicted value and the actual value. The coefficient of variation is an indicator of the degree of relative variation, and the lower the value, the closer the data is clustered to the mean.

[0416] To compare the relative error according to each method, six replicate standard curves for the quantification of FDM and SDM in Comparative Example 1 and Comparative Example 2 were generated in the same manner used in Test Example 2-1-4 above.

[0417] The relative error was calculated by using the number of copies of Neisseria gonoroae genome DNA (100 pg, 10 pg, 1 pg, or 100 fg) used as the target analyte in the following mathematical formula 9 as the actual value, and using the value inversely calculated through the slope and y-intercept of the standard curve as the predicted value to generate and compare quantification cycles (Cq) for 4 repeated detections at 4 different concentrations in the examples and comparative examples, respectively.

[0418] To compare the coefficient of variation according to each method, a quantification cycle (Cq) for four repeated detections of four concentrations of Neisseria gonoroae genomic DNA (100 pg, 10 pg, 1 pg, or 100 fg) used as the target analyte was generated in the same manner as used in Examples 1 and 2 and Comparative Examples 1 and 2.

[0419] Using the Cq generated above and the following mathematical formula 10, the coefficients of variation of the examples and comparative examples were calculated and compared, respectively, and the results are summarized in Table 11 below.

[0420] Mathematical formula 9

[0421]

[0422] Mathematical formula 10

[0423]

[0424]

[0425] As can be seen in Table 11 above, when comparing the relative errors of the examples and comparative examples, it was confirmed that the accuracy of Examples 1 and 2 is higher because the relative error values ​​of Examples 1 (20.28) and 2 (5.29) are smaller than those of Comparative Example 1 (45.95) and Comparative Example 2 (20.65). In terms of the coefficient of variation, it was confirmed that the precision of Examples 1 and 2 is higher because the relative error values ​​of Examples 1 (35.27) and 2 (37.32) are smaller than those of Comparative Example 1 (38.32) and Comparative Example 2 (38.47).

[0426] In summary, it was confirmed that the methods of Examples 1 and 2 improved the accuracy and precision in quantifying target analytes compared to Comparative Examples 1 and 2.

[0427] Specific parts of the present invention have been described in detail above. It is evident to those skilled in the art that such specific descriptions are merely preferred embodiments and do not limit the scope of the invention. Accordingly, the actual scope of the invention is defined by the appended claims and their equivalents.

Claims

Claim 1 A method for detecting a target analyte in a sample comprising the following steps: (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (b) obtaining an n-th order derivative function and an n+1-th order derivative function from the S-shaped function; wherein n is a natural number, and (c) obtaining a linear function connecting i) the first extreme point of the n-th order derivative function or the n+1-th order derivative function and ii) the second extreme point of the n+1-th order derivative function; and (d) detecting the target analyte in the sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c). Claim 2 A method for determining a Quantification Cycle (Cq) value for a target analyte in a sample, comprising the following steps: (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (b) obtaining an n-th derivative function and an n+1-th derivative function from the S-shaped function; wherein n is a natural number; and (c) obtaining a linear function connecting i) the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function; and (d) determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value. Claim 3 A method according to claim 1 or 2, wherein the step of obtaining an S-shaped function of step (a) comprises the following steps: (a-1) obtaining a first data set representing a growth curve by an amplification reaction for a target analyte; said first data set comprising a plurality of data points each having a cycle number and a signal value at said cycle number; (a-2) obtaining a second data set by calculating a slope value at each cycle number for said first data set; said second data set comprising a plurality of data points each having a cycle number and a slope value at said cycle number; and (a-3) calculating an S-shaped function approximating a selected portion of said second data set. Claim 4 A method according to claim 3, wherein the first data set of step (a-1) is a raw data set, a mathematically modified data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically modified data set. Claim 5 A method according to claim 3, wherein the slope value of step (a-2) is obtained by differentiation, difference, difference, ratio, or linear regression analysis of the signal value. Claim 6 A method according to claim 3, characterized in that the selected portion of the second data set in step (a-3) is within the range from the first cycle number to the last cycle number in the second data set. Claim 7 A method according to claim 3, wherein calculating an S-shaped function in step (a-3) includes determining the parameters of the S-shaped function. Claim 8 A method according to claim 1 or 2, wherein in step (a), the S-shaped function is selected from the group consisting of a sigmoid function, a logistic function, a Gompertz function, a Chapman function, and a Richard function. Claim 9 A method according to claim 1 or 2, wherein n is a natural number selected from 1 to 10. Claim 10 A method according to claim 1, wherein using an intersection point in step (d) includes determining the intersection point as a Quantification Cycle (Cq) value. Claim 11 A method according to claim 2 or 10, wherein determining the intersection point as a Quantification Cycle (Cq) value includes determining the cycle number of the intersection point as a Cq value. Claim 12 In claim 3, the method is characterized by additionally including the following step between steps (a-1) and (a-2): (a-1-1) applying a threshold value to a first data set; and (a-1-2) proceeding to the next step (a-2) if the first data set has a signal value exceeding the threshold value. Claim 13 In claim 3, the method is characterized by additionally including the following step between steps (a-2) and (a-3): (a-2-1) applying a threshold value to a second data set; and (a-2-2) proceeding to the next step (a-3) if the second data set has a slope value exceeding the threshold value. Claim 14 A method according to claim 1 or 2, characterized in that the target analyte is a target nucleic acid molecule. Claim 15 A computer-readable recording medium comprising instructions for implementing a processor for executing a method for detecting a target analyte in a sample, wherein the method comprises the following steps: (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (b) obtaining an n-th derivative function and an n+1-th derivative function from the S-shaped function; wherein n is a natural number, and (c) obtaining a linear function connecting i) the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function; and (d) detecting the target analyte in a sample using the intersection point of the S-shaped function of step (a) and the linear function of step (c). Claim 16 A computer-readable recording medium comprising instructions for implementing a processor for executing a method for determining a Quantification Cycle (Cq) value for a target nucleic acid material in a sample, wherein the method comprises the following steps: (a) obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte; (b) obtaining an n-th derivative function and an n+1-th derivative function from the S-shaped function, wherein n is a natural number; and (c) obtaining a linear function connecting i) the first extreme point of the n-th derivative function or the n+1-th derivative function and ii) the second extreme point of the n+1-th derivative function; and (d) determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value.

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