Method for detecting target analyte in 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 uses, in detecting a target analyte, an intersection point between a function representing an amplification curve for the target analyte and a first-order function obtained by connecting a first pole of an n-th order differential function or (n+1)-th order differential function for the function representing the amplification curve for the target analyte to a second pole of the (n+1)-th order differential function, thereby having excellent quantitative accuracy and precision in comparison to an existing method in spite of deviations between real-time PCR reaction experiments.

Description

Method for detecting target analytes in a sample

[0001] Cross-reference to related applications

[0002] This patent application claims priority to Republic of Korea Patent Application No. 2023-0191631, filed with the Korean Intellectual Property Office on December 26, 2023, the disclosures of which 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.

[0005] Nucleic acid amplification and detection have become core technologies in modern molecular biology research. The polymerase chain reaction (PCR), the most widely used method for nucleic acid amplification, involves repeated cycles of denaturation of double-stranded DNA, annealing of oligonucleotide primers to a DNA template, and extension of the primers by DNA polymerase (Mullis et al., U.S. Patent Nos. 4,683,195, 4,683,202, and 4,800,159; Saiki et al., (1985) Science 230, 1350-1354).

[0006] Real-time PCR (RT-PCR) is a PCR-based technology for detecting target nucleic acids in a sample in real time. To detect a specific target nucleic acid, RT-PCR utilizes a signal generation device capable of generating a detectable fluorescent signal proportional to the amount of target nucleic acid. This fluorescent signal can be generated using an intercalator, which generates a signal when intercalated between double-stranded DNA, or an oligonucleotide containing a fluorescent reporter and quencher molecule. A fluorescent signal with an intensity proportional to the amount of target molecule is detected at each amplification cycle and plotted against the amplification cycle to obtain an amplification curve or amplification profile curve.

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

[0008] The baseline phase refers to the region where the fluorescence signal changes little during the initial stages of PCR. In the baseline region, the level of PCR amplicon is not sufficient to be detected, so the signal detected in this region may be due to background signals, including fluorescence signals from the reaction reagents and the measuring device. The exponential phase indicates an increase in fluorescence signal proportional to the increase in amplification product. The plateau phase refers to the region where the fluorescence signal barely increases due to saturation of the PCR amplicon and fluorescence signal levels.

[0009] In particular, real-time PCR (RT-PCR) is being utilized in many fields due to its sensitivity and specificity. For example, it can be used for qualitative or quantitative analysis of target nucleic acids through amplification. This is based on various analytical methods developed to date for detecting target analytes in samples using data sets obtained through real-time PCR.

[0010] One method uses a predetermined threshold value to determine whether the signal value in the data set reaches or exceeds that threshold. This requires collecting as many real-world response data sets as possible to determine the threshold value.

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

[0012] However, the above methods had the problem that they could be relatively greatly affected by experimental deviations in experimental PCR reactions.

[0013] Accordingly, the inventors of the present invention recognized the need for developing a technology that can overcome quantitative inaccuracies arising from experimental deviations in real-time PCR reactions.

[0014] Numerous references and patents are cited and cited throughout this specification. The disclosures of these references and patents are incorporated herein by reference in their entirety to further clarify the state of the art and the scope of the present invention.

[0015] The present inventors have endeavored to develop a technique that can reduce the influence of experimental variation in real-time PCR reactions and improve the accuracy of quantitative PCR. As a result, unlike the conventional method of detecting a target analyte in a sample by using the maximum or minimum point of the first, second, or nth-order differential function for a function representing an amplification curve for the target analyte, the present inventors have completed the present invention by using the intersection point of the linear function connecting the first extreme point of the nth-order differential function or the n+1st-order differential function for the function representing the amplification curve for the target analyte and the second extreme point of the n+1st-order differential function for the detection of the target analyte, and confirming that the quantitative accuracy and precision are superior to the conventional method despite the inter-experimental variation in real-time PCR reactions.

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

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

[0018] Another object of the present invention is to provide a computer-readable recording medium including 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 examples, claims and drawings.

[0020] I. Method for detecting target analyte in a sample (first aspect)

[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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0024] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

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

[0026] The present inventors have endeavored to develop a technique that can reduce the influence of experimental variation in real-time PCR reactions and improve the accuracy of quantitative PCR. As a result, unlike the conventional method of detecting a target analyte in a sample by using the maximum or minimum point of the first, second, or nth-order differential function of a function representing an amplification curve for the target analyte, the present inventors have utilized the intersection point of the linear function connecting the first extreme point of the nth-order differential function or the n+1st-order differential function of the function representing the amplification curve for the target analyte and the second extreme point of the n+1st-order differential function and the function representing the amplification curve for the target analyte for detection of the target analyte. As a result, it was confirmed that the quantitative accuracy and precision are superior to the conventional method despite the inter-experimental variation in real-time PCR reactions.

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

[0028] Figure 1 is a flowchart of processes for implementing the method of the present invention according to one embodiment of the present invention. The method of the present invention is described with reference to Figure 1 as follows:

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

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

[0031] The term "sample" as used herein refers to any material containing or suspected of containing a nucleic acid of interest, or any material that is itself a nucleic acid containing or suspected of containing a 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 washings, milk, urine, feces, eye fluid, saliva, semen, brain extracts, spinal fluid (SCF), cecum, spleen, and tonsil tissue extracts, ascites, and amniotic fluid. Additionally, the sample may include naturally occurring nucleic acid molecules isolated from biological sources, and synthetic nucleic acid molecules.

[0032] The term "target analyte" as used herein may include various substances (e.g., biological substances and non-biological substances, such as chemicals). Specifically, target analytes may include biological substances, such as nucleic acid molecules (e.g., DNA and RNA), proteins, peptides, carbohydrates, lipids, amino acids, biological compounds, hormones, antibodies, antigens, metabolites, and cells. More specifically, target analytes may include nucleic acid molecules. Target analytes are present within a 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. Target nucleic acid sequences include single-stranded and double-stranded sequences. Target nucleic acid sequences include sequences initially present in a sample as well as sequences newly generated in a reaction.

[0034] The target nucleic acid sequence includes any DNA (gDNA and cDNA), RNA molecules, and hybrids thereof (chimeric nucleic acids). The sequence may be double-stranded or single-stranded. If the starting nucleic acid is double-stranded, it is desirable to convert the double strand into a single-stranded or partially single-stranded form. Known methods for separating the 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 this 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 such as mammals and humans), viral nucleic acid (e.g., herpes virus, HIV, influenza virus, Epstein-Barr virus, hepatitis virus, poliovirus, etc.), or viroid nucleic acid. The nucleic acid molecule may also be any nucleic acid molecule that is or can be produced recombinantly or is or can be synthesized chemically. Thus, the target nucleic acid sequence may or may not be found in nature.

[0036] The target nucleic acid sequence should not be construed as limited to sequences known at a given point in time or available from a given point in time, but rather to include sequences that are available or may become known at any time, either now or in the future. That is, the target nucleic acid sequence may or may not be known at the time the method of the present invention is practiced. For unknown target nucleic acid sequences, their sequences may be determined by one of the conventional sequencing methods prior to practicing 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 sample type. If the extracted nucleic acid is RNA, an additional reverse transcription process is performed to synthesize cDNA from the extracted RNA (reference: Sambrook, J. et al., Molecular Cloning. A Laboratory Manual, 3rd ed. Cold Spring Harbor Press (2001)).

[0038] In one embodiment, the target nucleic acid sequence comprises a nucleotide mutation.

[0039] The term "nucleotide variation" as used herein refers to a substitution, deletion, or insertion of a single or multiple nucleotides in a DNA sequence at a specific location among contiguous DNA segments of similar sequence. These contiguous DNA segments comprise a gene or any other region of a chromosome. Such nucleotide variations may be mutations or polymorphic allelic variations. For example, the nucleotide variations detected in the present invention include single nucleotide polymorphisms (SNPs), mutations, deletions, insertions, substitutions, and translocations. Exemplary nucleotide variations include various variations in the human genome (e.g., variations in methylenetetrahydrofolate reductase (MTHFR)), variations associated with drug resistance in pathogens, and mutations that cause tumorigenesis. The term "nucleotide variation" as used herein 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, including but 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), US Patent 5,556,751), SDA (strand displacement amplification, see GT Walker et al., Nucleic Acids Res. 20(7):16911696(1992), EP 0497272 (ref.), 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); U.S. Patent No. 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 to approximate a growth curve, particularly an amplification curve, and more particularly a real-time PCR curve, for a target analyte.

[0042] The term "growth curve" as used herein includes (1) a curve composed 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 modification to an amplification curve after n-th differentiation and (ii) a modification to an amplification curve after n-th difference (e.g., slope regression)).

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

[0044] A baseline-subtracted data set can be obtained using various methods known in the art (see U.S. Patent No. 8,560,247 and WO 2016 / 052991). Furthermore, a data set, including both pre-normalized and post-normalized data sets, can be baselined using a quadratic function with a symmetric axis. Baselining a data set using a quadratic function means correcting the data set by subtracting the quadratic function from the data set.

[0045] As used herein, 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. Smoothing the data set improves the visual representation of the data set.

[0046] In data analysis, smoothing a data set creates a rough data set that follows the underlying patterns. Smoothing a data set removes noise, other fine-grained structures, or abrupt phenomena. Through the smoothing process, the signals at each data point are processed, reducing the characteristics of individual data points and reducing the signal differences between adjacent data points. This makes it easier to discern information about macroscopic changes in the signal in the data set.

[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 implementation example, the sigmoid function is expressed by the following mathematical expression 1:

[0049] Mathematical formula 1

[0050]

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

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

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

[0054] According to a more specific implementation example, the logistic function is expressed by the following mathematical expression 2:

[0055] Mathematical formula 2

[0056]

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

[0058] In the above mathematical formula, α1 may correspond to a background value in a curve represented by the function; α2 may correspond to a maximum value in a curve represented by the function; α3 may correspond to a cycle number having a median value in a curve represented by the function; and α4 may correspond to a slope of a 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 implementation example, the Gompertz function is expressed by the following mathematical expression 3:

[0061] Mathematical formula 3

[0062]

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

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

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

[0066] According to a more specific implementation example, the Chapman function is expressed by the following mathematical expression 4:

[0067] Mathematical formula 4

[0068]

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

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

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

[0072] According to a more specific implementation example, the Richard function is expressed by the following mathematical expression 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; 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 Richard coefficient; and Fb is the background fluorescence.

[0076] For further discussion of the S-shaped function, see [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 in step (a) includes 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; wherein the first data set includes a plurality of data points each having a cycle number and a signal value at the cycle number;

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

[0083] The above first data set may be used interchangeably with “amplified data set”.

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

[0085] The term "signal generating process" as used herein refers to any process capable of generating a signal dependent on the properties of a target analyte in a sample, i.e., the activity, amount, or presence (or absence) of the target analyte, particularly the presence (or absence) of the analyte in the sample. The signal generating process herein includes biological reactions and chemical reactions. Such biological reactions include genetic assays such as PCR, real-time PCR, and microarray analysis, immunological assays, and bacterial growth assays. In one embodiment, the signal generating process includes analyzing the production, change, or destruction of a chemical substance.

[0086] The signal generation process is accompanied by a signal change. The signal change can serve as an indicator, either qualitatively or quantitatively, of 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 present 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, particularly a signal amplification process.

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

[0090] Specifically, the signal generation process is a process that accompanies the amplification of a target nucleic acid molecule. More specifically, the signal generation process is a process that accompanies the amplification of a target nucleic acid molecule and can increase or decrease the signal (specifically, can increase the signal) during the amplification of the target nucleic acid molecule.

[0091] The term "signal generation" as used herein includes the appearance or disappearance of a signal and the increase or decrease of a signal.

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

[0093] The signal generation process can be performed using various methods known to those skilled in the art. Examples of such methods can be found in the description of the signal generation means below.

[0094] According to one implementation example, the signal generation process can be performed as a process involving signal amplification together with target amplification.

[0095] In one embodiment, the amplification reaction as a signal generation process is performed in a manner in which 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 performed in a manner in which the signal is amplified without amplification of the target nucleic acid molecule (e.g., the CPT method (Duck P, et al., Biotechniques, 9:142-148 (1990)), the Invader assay (U.S. Pat. Nos. 6,358,691 and 6,194,149)).

[0096] The term "signal" as used herein refers to a measurable output. The term "signal value" as used herein refers to a quantitative expression of a signal.

[0097] The intensity of a signal or a change in signal can serve as an indicator, either qualitatively or quantitatively, of 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. The signal change may include both a signal increase and a signal decrease. In one embodiment, the signal generation process is a process of amplifying a signal value.

[0099] The signal includes various signal characteristics from signal detection, such as signal intensity (e.g., relative fluorescence unit (RFU) value or, when performing an amplification reaction, RFU value at a specific cycle, selected cycle, or end-point), 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 implementation example, when an amplification curve is obtained by real-time PCR, various signal values ​​(or characteristics) can be selected from the amplification curve and used to determine the presence of a target (intensity, Ct value, Cq value, or amplification curve data).

[0102] The signal (especially the signal intensity) may vary depending on its detection temperature as well as the signal generation means used.

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

[0104] The term "signal generating means" as used herein refers to any substance used to generate a signal indicating the presence of a target nucleic acid sequence, including, for example, oligonucleotides, labels, and enzymes. Alternatively, the term "signal generating means" as used herein may be used to refer to any method that utilizes a substance for signal generation.

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

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

[0107] a. A signal generating means for generating a signal by duplex formation 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 that is involved in the generation of a signal to be detected. In one embodiment, the detection oligonucleotide comprises an oligonucleotide that is involved in the actual signal generation. For example, signal generation depends on hybridization or non-hybridization of the detection oligonucleotide with another oligonucleotide (e.g., an oligonucleotide comprising a nucleotide sequence complementary to a target nucleic acid sequence or the detection oligonucleotide). In one embodiment, the detection oligonucleotide comprises at least one label.

[0109] Signal generation by dimer formation between target nucleic acid sequence and detection oligonucleotide can be achieved by the Scorpion method (Whitcombe et al., Nature Biotechnology 17:804-807 (1999)), Sunrise or Amplifluor method (Nazarenko et al., Nucleic Acids Research, 25(12):2516-2521 (1997), and U.S. Patent No. 6,117,635), Lux method (U.S. Patent No. 7,537,886), Plexor method (Sherrill CB, et al., Journal of the American Chemical Society, 126:4550-4556 (2004)), Molecular Beacon method (Tyagi et al., Nature Biotechnology v.14 MARCH 1996), Hybeacon method (French DJ et al., Mol. Cell Probes, 15(6):363-374 (2001)), and adjacent This can be achieved by a variety of methods, including the hybridization probe 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 that is specifically hybridized to a target nucleic acid sequence.

[0111] The term "mediating oligonucleotide" as used herein refers to an oligonucleotide that mediates the formation of a duplex that does not include a target nucleic acid sequence.

[0112] In one embodiment, the cleavage of the intermediate oligonucleotide itself does not generate a signal, but rather the fragment formed by the cleavage participates in a sequential reaction for signal generation.

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

[0114] c. A signal generating means for generating a signal by cleavage of the detection oligonucleotide after the detection oligonucleotide is hybridized to the target nucleic acid sequence.

[0115] After the detection oligonucleotide is hybridized to the 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 for generating a signal by cleavage of a detection oligonucleotide in a manner dependent on cleavage of an intermediate oligonucleotide specifically hybridized to a target nucleic acid sequence.

[0117] In one embodiment, when an intermediate oligonucleotide hybridized to a target nucleic acid sequence is cleaved to release a fragment, the fragment specifically hybridizes to a detector oligonucleotide, thereby inducing cleavage of the detector oligonucleotide.

[0118] Signal generation by cleavage of the detection oligonucleotide in a manner dependent on cleavage of the above-described intermediate oligonucleotide can be achieved by a variety of methods, including the Invader assay (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] The term "signal amplification reaction" as used herein means a reaction that increases or decreases a signal generated by a signal generating means.

[0120] According to one embodiment, a signal amplification reaction refers to 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 reaction may or may not be accompanied by amplification of the target analyte (e.g., a target nucleic acid molecule). More specifically, the signal amplification reaction refers to signal amplification accompanied by amplification of the target analyte.

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

[0122] The term "cycle number" or "cycle," as used herein, refers to a unit of change in conditions in multiple measurements involving changes in conditions. For example, changes in conditions include changes in temperature, reaction time, reaction frequency, concentration, pH, and / or the number of copies of a target nucleic acid molecule sequence. Accordingly, a cycle may include a time or process cycle, a unit operation cycle, and a reproducibility cycle.

[0123] For example, when studying enzyme kinetics, the enzyme reaction rate is measured repeatedly while the substrate concentration is regularly increased. In this reaction, each increase in substrate concentration may correspond to a change in conditions, and each increase in substrate concentration may correspond to a cycle.

[0124] As another example, isothermal amplification involves measuring a sample multiple times during a reaction time under isothermal conditions, where the reaction time can correspond to a change in conditions, and the unit of the reaction time can correspond to a cycle. For example, if a 5-minute interval, such as 5, 10, or 15 minutes, is set as one reaction time, the cycle can be expressed as a 5-minute cycle, a 10-minute cycle, a 15-minute cycle, etc. Alternatively, if a 5-minute interval is considered a unit, a 5-minute cycle can be expressed as the first cycle, a 10-minute cycle as the second cycle, a 15-minute cycle as the third cycle, etc.

[0125] As another example, in the case of melting analysis or hybridization analysis, the change in signal can be measured while changing the temperature within a certain temperature range, and the temperature can correspond to the change in condition, and the temperature unit (e.g., measurement temperature) can correspond to the cycle. For example, if 0.5℃ intervals are set as one reaction time, such as 40℃, 40.5℃, 50℃, 50.5℃, etc., the cycles can be expressed as 40℃ cycle, 40.5℃ cycle, 50℃ cycle, 50.5℃ cycle, etc. Alternatively, if 0.5℃ intervals are regarded as one unit, the 40℃ cycle can be expressed as the 1st cycle, the 40.5℃ cycle as the 2nd cycle, the 50℃ cycle as the 3rd cycle, etc.

[0126] Specifically, when repeating a series of reactions or repeating a reaction at regular time intervals, the term "cycle" means one unit of said repetition.

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

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

[0129] The term "signal value" as used herein means a numerical value of a signal level (e.g., signal intensity) actually measured in each cycle of a signal generation process, or a modification thereof. The modification may include a mathematically processed value of the measured signal value. Examples of mathematically processed values ​​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] The term "data point" as used herein refers to a single coordinate value that includes a cycle and a signal value within said cycle. The term "data" as used herein refers to all information that constitutes a data set. For example, each cycle and signal value of an amplification reaction is data.

[0131] Data points obtained from a signal generation reaction, particularly a signal amplification reaction, can be plotted as coordinate values ​​within a Cartesian coordinate system. In the Cartesian coordinate system, the X-axis represents the cycles of the amplification reaction, and the Y-axis represents the signal values ​​or their variations measured at each cycle.

[0132] The term "data set" as used herein refers to a set of data points. For example, the data set may include a raw data set, which is a set of data points obtained directly from an amplification reaction using a signal generation means. Alternatively, the data set may be a modified data set obtained by modifying a data set that includes a set of data points obtained directly from a signal generation process. The data set may include all or a portion of the set of data points obtained from the signal generation process. The data set includes a plurality of data points. The data set may include at least two data points. Specifically, the number of data points in the data set may be at least 2, 3, 4, 5, 10, or 20. Furthermore, the number of data points in the data set may be 1000 or less. Specifically, the number of data points in the data set may be 1000, 500, 300, 200, 100, 90, 80, 70, or 60 or less. The number of data points in a data set may be 3-1000. Specifically, the number may be 3-1000, 10-500, 1-100, 20-100, 20-80, 20-70, or 20-60. In one implementation example, the number of data points in a data set may be 20 to 60.

[0133] In one embodiment, the first data set can be obtained by processing multiple data sets. When two target analytes are detected in a single reaction, data sets for each of the two target analytes can be provided by processing the data sets obtained by the single reaction. For example, data sets for each of the two target analytes can 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 transformed data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically transformed data set.

[0137] In a more specific implementation example, the first data set of step (a-1) is a raw data set.

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

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

[0140] The term "mathematically processed data set" as used herein 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. The baseline-subtracted data set can be obtained by various methods known in the art (e.g., U.S. Patent No. 8,560,247).

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

[0142] The term "normalization" as used herein refers to the process of reducing or eliminating signal deviations between data sets for multiple responses. The terms "correction" or "adjustment" as used herein refer to correcting or transforming data (particularly signal values) in a data set for analysis purposes. Standardization 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 transformed data set of the raw data set; the data set is provided from a signal generated by a signal generation reaction using a signal generation means; the data set includes a plurality of data points each having a cycle number and a signal value at the 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 a relationship between a signal value in a cycle of the data set corresponding to the reference cycle and the reference value; and,

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

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

[0147] According to one implementation example, the reference cycle can be selected from the cycles within the background region.

[0148] Background signals are signals generated by the target analyte itself within the sample, or by the signal generation means itself that is not involved in the target analyte within the sample. The background region is an area where only background signals are generated, with little signal generated by the target analyte.

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

[0150] A reference value is a value used to provide a standardization coefficient. The reference value of the present invention refers to an arbitrary 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 real numbers other than 0. 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 units or dimensions as the data set to be corrected.

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

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

[0153] (a-1-1) 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, the step of proceeding to the next step (a-2).

[0155] This implementation example illustrates the process of filtering the first data set.

[0156] In general, for negative samples that do not contain the target nucleic acid sequence, both the first data set obtained in step (a-1) and the second data set obtained in step (a-2) will have significantly lower 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, i.e., the slope value will not increase as the cycle number increases. This may make it difficult to obtain an S-shaped function that approximates a selected portion of the second data set. Therefore, it would be preferable not to apply the method of the present invention to negative samples.

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

[0158] In the above step (a-1-1), a threshold value is applied to the first data set of the 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 value. After applying a threshold value to the first data set of the step (a-1), a data set having a signal value exceeding the threshold value is determined to be a data set derived from positive samples, and a data set having all signal values ​​below the threshold value is determined to be a data set derived from negative samples.

[0159] Thereafter, 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 be derived from a positive sample, it is expected to generate a best-fitting S-shaped function according to the method of the present invention.

[0160] On the other hand, if the data set has all signal values ​​below the threshold value, the next step (a-2) is not performed. Since the data set having all signal values ​​below the threshold value is expected to be derived from negative samples, the method of the present invention may not be able to generate the best-fitting S-shaped function. The 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 implementation example, (a-2) a slope value is calculated at each cycle number for the first data set to obtain a second 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 may be used interchangeably with the “slope data set”.

[0164] The term "slope value" as used herein refers to the change in signal value at each data point, and may be used interchangeably with "change value." Since a slope value or change value may include a rate of change, a 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] Computing a slope value by differentiation generally involves the steps of computing a function that approximates a first data set (e.g., a raw data set), computing a derivative from the approximated function, and using the derivative to obtain a slope value at each cycle number.

[0167] Calculating a slope value by difference generally includes the steps of calculating a 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 taking the difference as the slope value at the target cycle number. The reference signal value may be, for example, a signal value at a cycle number immediately before or after the target cycle number, or may be a signal value at two or more cycle numbers prior to or after the target cycle number. Alternatively, the reference signal value may be an average of signal values ​​at several cycle numbers prior to or after the target cycle number. Typically, the reference signal value may be a signal value at a cycle number immediately before the target cycle number.

[0168] Calculation of a slope value of a signal value by a difference generally includes the steps of calculating a difference between a signal value at a cycle number for which a slope value is to be calculated (target cycle number) and a signal value at another cycle number (reference signal value), dividing the calculated difference by the difference between the target cycle number and the reference cycle number, and taking the result as the slope value at the target cycle. The reference signal value may be, for example, a signal value at a cycle number immediately before or after the target cycle number, or may be 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] Computing a slope value by ratio typically involves calculating a ratio between two signal values ​​at two cycle numbers. In one implementation, the ratio at a target cycle number is calculated by dividing the signal value at the target cycle by the signal value at the immediately preceding cycle.

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

[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 ith cycle, and S i is the slope value in the ith 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 called the least 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 1 to 5, and more preferably 1 to 3. The values ​​of "a" and "b" are advantageously the same, but may be different depending on the measurement target, measurement environment, and cycle for which the slope is to be measured.

[0177] According to one implementation example, the calculation of the slope value at a particular cycle number(s) may be different from the calculation of the slope values ​​at the remaining cycle numbers. For example, when the slope value is obtained by a difference method, the slope value at the first cycle number or the last cycle number may be obtained by a method different from the difference method; and when the slope value is obtained by linear regression analysis, the slope value at the early cycle number (e.g., the first cycle number and the second cycle number) or the late cycle number (e.g., the last cycle number and the immediately preceding cycle number) may be obtained by a method different from the linear regression analysis.

[0178] For example, the slope value at the first cycle number or the last cycle number may be determined as 0. Alternatively, the slope value at the first cycle number may 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 last cycle number may be determined as a predetermined value.

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

[0180] An example of such correction includes correcting an abnormal signal within a slope data set, which includes detecting a cycle number having an abnormal signal value within the slope data set and then correcting the slope value corresponding to the detected abnormal cycle number.

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

[0182] (a-2-1) 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, the next step (a-3) is performed.

[0184] This implementation example illustrates the process of filtering a second data set.

[0185] Since this implementation example is identical to the process of filtering the first data set except that the threshold value is applied to the second data set instead of the first data set, description of common content between them is omitted to avoid excessive complexity of this specification due to repeated description.

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

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

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

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

[0190] (a-3-1) a step of selecting a portion that represents a pattern of an amplification curve or a 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 portion.

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

[0193] Typically, a slope data set, such as the second data set, in the presence of a target analyte exhibits a "bell-shaped curve," also known as a normal distribution. The left side of the bell-shaped curve relative to the axis of symmetry (the central axis) exhibits an increasing pattern, such as an amplification or growth curve, in which the slope value increases with increasing cycle number, while the right side of the bell-shaped curve exhibits a decreasing pattern in which the slope value decreases with increasing cycle number.

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

[0195] (i) Start cycle

[0196] The starting cycle of the selected portion may 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 certain embodiments, the starting 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 portion may be selected by considering the fitting goodness-of-fit of an S-shaped function that approximates or fits the above-mentioned selected portion.

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

[0202] In one implementation, 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, each of the start cycle and the last cycle is selected within the range from the first cycle number to the last cycle number in the second data set.

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

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

[0206] Afterwards, an S-shaped function approximating the selected portion is calculated.

[0207] The phrase "calculating an S-shaped function" as used herein refers to the process of producing 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 a fitting method known in the art.

[0208] According to one embodiment, calculating the S-shaped function in step (a-3) includes 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 the S-shaped function in step (a-3) includes determining parameters of a specific S-shaped function. Specifically, calculating the S-shaped regression function in step (a-3) includes fitting the data points of the selected portion to a specific S-shaped function; and determining parameters of the best-fitting function.

[0210] According to one embodiment, a computer system is used to calculate a function fitting data points of a selected portion. The computer system includes a computer-readable storage medium having stored therein a computer program capable of performing regression analysis, and a processor capable of executing the 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 parameters of the S-shaped function.

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

[0213] Step (b): Obtaining the nth-order and n+1st-order differential functions from the S-shaped function (120)

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

[0215] This step includes the process of differentiating the S-shaped function and calculating the first, second, nth, or n+1th differential function (derivative).

[0216] As can be confirmed 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): Obtain a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function (130).

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

[0220] The term "extreme point" as used herein means 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), also called a local or relative maximum point and a local or relative minimum point.

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

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

[0223] In the first-order differential function of Fig. 13, point (1) is both a pole (specifically a maximum point) and a maximum point; in the second-order differential function, point (2) is both a pole (specifically a maximum point) and a maximum point; point (3) is both a pole (specifically a minimum point) and a minimum point; and in the fourth-order differential function, point (4) is both a pole (specifically a maximum point) and a maximum point; point (5) is both a pole (specifically a minimum point) and a minimum point; and point (6) is both a pole (specifically a maximum point) and a maximum point.

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

[0225] In this respect, it is necessary to clearly distinguish between the extreme points and the maximum (or minimum) points in detecting the target analyte in the sample.

[0226] One of the features of the present invention is that it utilizes a linear function connecting the second pole of the n+1-order differential function and the first pole of the n-order differential function or the n+1-order differential function. Therefore, according to the present invention, the linear function connecting the second pole (minimum point) and the third pole (maximum point) of the fourth-order differential function in FIG. 13 is not utilized.

[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 a step of (d) detecting a target analyte in a sample using the intersection 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, the present invention does not simply use the maximum or minimum point of the nth-order differential function of the S-shaped function, but rather uses the intersection point of the first function connecting the first extreme point of the nth-order differential function or the n+1st-order differential function with the second extreme point of the n+1st-order differential function and the S-shaped function, thereby providing suitability for quantitation and excellent quantitative accuracy and precision despite variations between experiments in real-time PCR reactions, as confirmed in the examples described below.

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

[0231] According to one embodiment of the present invention, utilizing the 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 determination of the target analyte in the sample.

[0232] Although Cq is used with a substantially identical meaning to Ct, in this specification, Ct determined using a threshold and Cq determined according to the method of the present invention are used to distinguish between them.

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

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

[0235] a. Qualitative analysis

[0236] In qualitative analysis, a sample having no intersection point (specifically, a determined Cq value) or a sample having an intersection point (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 having an intersection point (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, if the crossover point (specifically, the determined Cq value) from an unknown sample is 35 according to the method of the present invention, 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 dilution series of a standard target analyte is applied to each 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. Thereafter, the intersection points (specifically, Cq values) are plotted against the log-transformed concentration of the standard analyte to obtain a standard curve for quantification. Thereafter, an unknown sample (a sample of which the initial amount is unknown) is applied to the amplification reaction to obtain intersection points (specifically, Cq values), which are then 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, a computer-readable recording medium comprising instructions for implementing a processor to execute a method for detecting a target analyte in a sample, the method comprising 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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0245] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

[0246] (d) A step of detecting a target analyte in a sample using the intersection 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 on a computer-readable recording medium, which 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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0250] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

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

[0252] According to another aspect of the present invention, there is provided a device for detecting a target analyte in a sample, the device comprising (a) a computer processor, and (b) a computer-readable recording medium of the present invention coupled to the 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 implemented on a computer. The description of common contents among them is omitted to avoid excessive complexity of this specification due to repeated description.

[0255] The program instructions, when executed by a processor, cause the processor to perform the method of the present invention described above. The program instructions for performing the method of detecting a target analyte in a sample may include the following instructions: (i) instructions for obtaining an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (ii) instructions for obtaining an nth-order differential function and an n+1st-order differential function from the S-shaped function; (iii) instructions for obtaining a linear function connecting i) a first extreme point of the nth-order differential function or the n+1st-order differential function and ii) a second extreme point of the n+1st-order differential function; and (iv) instructions for detecting (e.g., displaying on an output device) a target analyte in the sample using the intersection of the S-shaped function and the linear function.

[0256] The method of the present invention is executed in a processor, which may be a stand alone computer, a network attached computer, or a processor in a data acquisition device such as 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 disk, optical storage medium, RAM, ROM, system memory, and web server.

[0259] The detection process and results of target analytes in a sample can be provided in various ways. For example, the detection process and results of target analytes in 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 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 target analytes in a sample can be provided to a server system via a network connection (e.g., LAN, VPN, Internet, Intranet, and wireless communication network) to a client, such as a laptop or desktop computer system.

[0260] Instructions for implementing a processor executing the present invention may be included in a logic system. These instructions may be provided on software storage media (e.g., portable hard drives, USB drives, floppy disks, CDs, and DVDs), but may also 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 for executing the present invention may be implemented in various coding languages, such as C, C++, Java, Visual Basic, VBScript, JavaScript, Perl, and XML. Furthermore, 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 may be configured to perform all of the above-described performances on a single processor. Alternatively, the processor unit may be configured to have multiple processors each performing a specific function.

[0262] According to one embodiment of the present invention, the processor can be implemented by installing software in 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 the target analyte in the sample (second aspect)

[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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0267] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

[0268] (d) A step of determining the intersection point of the S-shaped function of the above step (a) and the linear function of the above 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 the Cq value using the method of the first aspect of the present invention described above, description of common contents between them is omitted to avoid excessive complexity of the present specification due to repeated description.

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

[0271] Figure 3 is a flowchart of processes for implementing the method of the present invention according to one embodiment of the present invention. The method of the present invention is described with reference to Figure 3 as follows:

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

[0273] First, the method of the present invention includes 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 in step (a) includes 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; wherein the first data set includes a plurality of data points each having a cycle number and a signal value at the cycle number;

[0278] (a-2) obtaining a second data set by calculating a slope value at each cycle number for the first data set; wherein the second data set includes a plurality of data points each having a cycle number and a slope value at the 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 transformed data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically transformed data set.

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

[0282] (a-1-1) 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, the step of proceeding 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-in-difference, ratio or linear regression analysis of the signal value.

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

[0286] (a-2-1) 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, the next step (a-3) is performed.

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

[0289] (a-3-1) a step of selecting a portion that represents a pattern of an amplification curve or a 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 portion.

[0291] 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.

[0292] According to one embodiment, calculating the S-shaped function in step (a-3) includes 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 the S-shaped function in step (a-3) includes determining parameters of a specific S-shaped function. Specifically, calculating the S-shaped regression function in step (a-3) includes fitting the data points of the selected portion to a specific S-shaped function; and determining parameters of the best-fitting function.

[0294] Step (b): Obtaining the nth-order and n+1st-order differential functions from the S-shaped function (220)

[0295] Next, the method of the present invention includes the step of (b) obtaining an nth-order differential function and an n+1th-order differential function from the S-shaped function, where 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): Obtain a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function (230).

[0298] Next, the method of the present invention includes the step of (c) obtaining a linear function connecting i) a first extreme point of the n-th order differential function or the n+1-th order differential function and ii) a second extreme point of the n+1-th order differential 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 the step of (d) determining the intersection point of the S-shaped function of step (a) and the linear function of step (c) as the Cq value.

[0301] Recording media, devices and programs

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

[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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0305] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

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

[0307] According to another aspect of the present invention, a computer program stored on a computer-readable recording medium embodying a processor for executing a method for determining a quantification cycle (Cq) value for a target analyte in a sample, the 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 nth-order differential function and an n+1th-order differential function from the S-shaped function; wherein n is a natural number,

[0310] (c) a step of obtaining a linear function connecting i) the first extreme point of the nth or n+1st order differential function and ii) the second extreme point of the n+1st order differential function; and

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

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

[0313] The program instructions, when executed by a processor, cause the processor to perform the method of the present invention described above. The program instructions for performing the method of determining a Quantification Cycle (Cq) value for a target analyte in a sample may include the following instructions: (i) instructions for obtaining an S-shaped function representing a growth curve by an amplification reaction for the target analyte; (ii) instructions for obtaining an nth-order differential function and an n+1st-order differential function from the S-shaped function; (iii) instructions for obtaining a linear function connecting i) a first extreme point of the nth-order differential function or the n+1st-order differential function and ii) a second extreme point of the n+1st-order differential function; and (iv) instructions for determining (e.g., displaying on an output device) an intersection point of the S-shaped function and the linear function as a Cq value.

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

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

[0316] (a) The present invention utilizes the intersection of a first-order function connecting the first pole of an nth-order differential function or an n+1st-order differential function representing an amplification curve for the target analyte and the second pole of the n+1st-order differential function with the function representing an amplification curve for the target analyte to detect the target analyte.

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

[0318] (c) The present invention has excellent quantitative accuracy and can be applied to complex samples that may have experimental deviations, and can be usefully utilized in molecular biology research when accurate measurement of DNA quantity is required.

[0319] Figure 1 is a flowchart of processes for implementing the method of the present invention according to one embodiment of the present invention.

[0320] Figure 2 is a flowchart of processes for implementing the method of the present invention according to a more specific implementation example of the present invention.

[0321] Figure 3 is a flowchart of processes for implementing the method of the present invention according to another embodiment of the present invention.

[0322] FIG. 4 shows a raw data set obtained from samples containing various concentrations of a target analyte and samples not containing the target analyte according to one embodiment of the present invention (solid line: NG 100 pg; -○-: NG 10 pg; -□-: NG 1 pg; and -△-: 100 fg; -Ⅹ-: NTC).

[0323] FIG. 5 shows a data set of change values ​​obtained from samples containing various concentrations of a target analyte and samples 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).

[0324] FIG. 6 shows curves representing Richard functions obtained from samples containing various concentrations of Neisseria gonorrhoeae (NG) genomic DNA as a target analyte according to one embodiment of the present invention ((a): raw data of real-time PCR of NG without baseline subtraction, (b): results of Richard function fitting, (c): data of Richard function fitting with baseline subtraction).

[0325] Figure 7 shows a process for detecting a target analyte by determining the cycle number of the intersection of a curve represented by a Richard function obtained according to one embodiment of the present invention and a first-order function as a Cq (Cycle quantification) value. The first-order function is a first-order function that passes through the first extreme point of the first-order differential function and the second extreme point of the second-order differential function by applying differentiation to the curve represented by the Richard function. dev1 represents the first-order differential function, EP 1 represents the first extreme point, EP 2 represents the second extreme point, and ITF represents the intersection of two functions.

[0326] Figure 8 illustrates a process for detecting a target analyte by determining the cycle number of the intersection of a curve represented by a Richard function obtained according to one embodiment of the present invention and a linear function as a Cq (Cycle quantification) value. The linear function is a linear function that passes through the first extreme of a second-order differential function and the second extreme of a second-order differential function by applying differentiation to the curve represented by the Richard function. dev2 represents the second-order differential function.

[0327] Figure 9 shows the results of determining the Cq value using a curve represented by a Richard function obtained from a raw data set obtained from a sample containing NG 100 pg of a target analyte and a linear function connecting the first pole of the nth order differential function or the n+1st order differential function and the second pole of the n+1st order differential function (n is 1 to 3).

[0328] Figure 10 shows a raw data set obtained by six replicate experiments from samples containing various concentrations of Neisseria gonorrhoeae (NG) genomic DNA as a target analyte.

[0329] Figure 11 shows a standard curve generated by using a sample containing four concentrations of a target analyte, and determining the cycle number of the intersection of the linear function passing through the first pole of the S-shaped regression function and the first differential function and the second pole of the second differential function.

[0330] Figure 12 shows a standard curve generated by using a sample containing four concentrations of a target analyte, and determining the cycle number of the intersection of the first-order function passing through the first pole of the S-shaped regression function and the second-order differential function and the second pole of the second-order differential function.

[0331] Figure 13 is a diagram showing an S-shaped function (a) and its first-order (b), second-order (c), and fourth-order (d) differential functions.

[0332] Hereinafter, the present invention will be described in more detail through examples. These examples are intended solely to illustrate the present invention more specifically. It will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples, in accordance with the gist of the present invention.

[0333] Example

[0334] Example 1: Detection of target analytes using a linear function connecting the first pole of the nth order (n=1) differential function of the sigmoid regression function obtained from a raw data set and the second pole of the n+1st order (n=1) differential function.

[0335] We investigated whether target analytes could be detected using a linear function connecting the first pole of the first derivative function and the second pole of the second derivative function of the S-shaped regression function obtained from the raw data set.

[0336] Example 1-1: Implementation of real-time polymerase chain reaction and acquisition of raw data sets

[0337] Neisseria gonorrhoeae (NG) genomic DNA (obtained from Coramdeo Lab Co., Ltd.; Accession No. ATCC 700825) was used as the target analyte. To amplify the target analyte and generate a signal from the amplified product, real-time polymerase chain reaction was performed using the primers and TaqMan probe listed in Table 1 below.

[0338]

[0339] In Table 1 above, BHQ stands for Black Hole Quencher.

[0340] The real-time polymerase chain reaction was performed by adding 20 μl of a sample containing 10 pmoles of forward and reverse primers (SEQ ID NOs: 1 and 2), 5 pmoles of TaqMan probe (SEQ ID NO: 3), Neisseria gonorrhoeae genomic DNA (100 pg, 10 pg, 1 pg, or 100 fg) as a target analyte, sterile distilled water (NTC) as a negative control, and 10 μl of 2X Master Mix (final, 200 uM dNTPs, 2.5 mM MgCl2, 1.6 U of TaqDNA polymerase) (Solgent, Korea) to a real-time polymerase chain reaction device (CFX96 Real-time Cycler, Bio-Rad), reacting at 95°C for 15 minutes, and then performing 30 cycles of 95°C for 30 seconds, 60°C for 60 seconds, and 72°C for 30 seconds. The experiment was repeated 50 times. Fluorescence detection was measured at 60°C for each cycle.

[0341] A raw dataset (baseline not subtracted) for the target analyte was obtained from the device through the above real-time polymerase chain reaction.

[0342] The raw data sets obtained from samples containing the target analyte at various concentrations and samples not containing the target analyte are shown in Figure 4 (solid line: NG 100 pg; -○-: NG 10 pg; -□-: NG 1 pg; and -△-: NG 100 fg; -Ⅹ-: NTC).

[0343] Example 1-2: Obtaining a change value data set

[0344] In the raw data set obtained in Example 1-1 above, a change value data set was obtained by calculating the change value of the signal value from the signal value at each cycle number.

[0345] The calculation of the change value from the signal value was performed using the difference method as shown in the following mathematical formula 7.

[0346] Mathematical formula 7

[0347]

[0348] In the above mathematical formula, i is the cycle number of the data set; y i is the signal value of the ith cycle number; △y i is the change in signal value at the i-th cycle number.

[0349] A data set of change values ​​obtained from samples containing the target analyte at various concentrations and samples not containing the target analyte is shown in Figure 5 ((a): NG 100 pg; (b): NG 10 pg; (c) NG 1 pg; and (d): NG 100 fg).

[0350] Example 1-3: Selecting a raw data set to obtain an S-shaped regression function.

[0351] Among the raw data sets obtained in the above Example 1-2, a raw data set was selected to obtain an S-shaped regression function.

[0352] As a method for selecting raw data sets, a screening threshold was applied to each raw data set. Data sets with values ​​above the applied screening threshold were determined to be raw data sets for obtaining an S-shaped regression function and were then moved to the next step. On the other hand, data sets with values ​​below the applied screening threshold were determined to be negative without obtaining an S-shaped regression function.

[0353] In this example, a screening threshold of 100 (unit: △RFU (relative fluorescence unit)) was used.

[0354] As a result of the above screening, the data set obtained from samples containing various concentrations of the target analyte contained values ​​above the screening threshold and was thus determined to be a data set for obtaining an S-shaped regression function, and thus the following steps were performed. On the other hand, the data set obtained from samples not containing the target analyte contained only values ​​below the screening threshold and was thus determined to be a negative sample without performing the following steps.

[0355] Example 1-4: Obtaining an S-shaped regression function for an interval within a raw data set

[0356] 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 steps (Example 1-4). The cycle interval was set to the range from the first cycle number to the last cycle number within the change value data set.

[0357] Afterwards, an S-shaped regression function approximating the selected section was determined. The S-shaped regression function used the Richard function of the following mathematical formula 5.

[0358] Mathematical Formula 5

[0359]

[0360] In the above mathematical formula, x is the cycle number; F(x) is the reaction fluorescence in the cycle; 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 Richard coefficient; F b is the background fluorescence.

[0361] Fitting of a selected interval within the raw data set to a Richard function was performed using a conventionally known LM algorithm (Levenberg-Marquardt algorithm; Christian Kanzow et al., JCAM, 172(2):375(2004)). Using the LM algorithm, a Richard function approximating the interval was obtained.

[0362] Curves representing Richard functions obtained from samples containing various concentrations of Neisseria gonorrhoeae (NG) genomic DNA as a target analyte are shown in Fig. 6, and the values ​​of the parameters of each Richard function are shown in Table 2 below.

[0363]

[0364] Example 1-5: Detection of target analytes using the intersection 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 of the S-shaped regression function.

[0365] By applying differentiation to the curve represented by the Richard function obtained in the above Example 1-4, the first pole of the first-order differential function and the second pole of the second-order differential function were calculated, and a linear function passing through the first pole of the first-order differential function and the second pole of the second-order differential function was calculated.

[0366] Afterwards, the point of contact between the curve represented by the Richard function and the first-order function was calculated.

[0367] As shown in Fig. 7, the cycle number of the intersection point of the curve indicated by the Richard function obtained in Example 1-4 and the first-order function was determined as the quantitative cycle Cq (Quantification Cycle) for detection, and the target analyte was detected.

[0368] Thereafter, samples whose cycle number was less than or equal to the 50th cycle, which is the last cycle of the data set, were judged as positive, and samples whose cycle number was greater than the 50th cycle were judged as negative. The results are shown in Table 3 below.

[0369] As shown in Table 3 below, the cycle numbers obtained from samples containing various concentrations of target analytes were 28.71, 32.08, 35.58, and 39.57, and since all of the cycle numbers were less than 50, all of the samples were determined to be positive.

[0370]

[0371] Example 2: Detection of target analytes using a linear function connecting the first pole of the n+1st order (n=1) differential function of the sigmoid regression function obtained from a raw data set and the second pole of the n+1st order (n=1) differential function.

[0372] In Example 1, instead of applying differentiation to the curve represented by the Richard function obtained from the raw data set and determining the cycle number of the intersection of the linear function passing through the first pole of the first-order differential function and the second pole of the second-order differential function as Cq, the linear function passing through the first pole of the second-order differential function and the second pole of the second-order differential function was calculated by applying differentiation to the curve represented by the Richard function.

[0373] Thereafter, as shown in Fig. 8, the cycle number of the intersection of the curve represented by the Richard function obtained in the above Example 1-4 and the first pole of the second-order differential function and the second pole of the second-order differential function was determined as Cq to detect the target analyte.

[0374] Thereafter, samples whose cycle number was less than or equal to the 50th cycle, the last cycle of the data set, were judged as positive, and samples whose cycle number was greater than the 50th cycle were judged as negative. The results are shown in Table 4 below.

[0375] As shown in Table 4 below, the cycle numbers obtained from samples containing various concentrations of target analytes were 26.05, 29.36, 32.48, and 36.29, and since all of the cycle numbers were less than 50, all of the samples were determined to be positive.

[0376]

[0377] Example 3: Determining Cq using the first function of the nth or n+1st differential function of the sigmoid regression function obtained from the raw data set and the second extremum of the n+1st differential function (n is 1 to 3)

[0378] The same method as Example 1 was used, but 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 differential function and the second pole of the second differential function or a linear function passing through the first pole of the second differential function and the second pole of the second differential function (n=1), 2) a linear function passing through the first pole of the second differential function and the second pole of the third differential function or a linear function passing through the first pole of the third differential function and the second pole of the third differential function (n=2), and 3) a linear function passing through the first pole of the third differential function and the second pole of the fourth differential function or a linear function passing through the first pole of the fourth differential function and the second pole of the fourth differential function The cycle number of the intersection of the function (n=3) was determined as Cq, and the results are summarized in Fig. 9.

[0379] As can be seen in Fig. 9, it was confirmed that Cq can be determined using the extreme points up to the 4th differential function of the S-shaped regression function. In addition, when looking at the 1st differential function to the 4th differential function of the S-shaped regression function, the graph patterns all show an increasing and then decreasing pattern, so it was confirmed that Cq according to this embodiment can be determined using the extreme points of the 5th differential function or higher.

[0380] Comparative Example 1: Detection of target analytes using the maximum point of the first derivative of the S-shaped regression function obtained from the raw data set.

[0381] Instead of determining the cycle number of the intersection of the curve represented by the Richard function obtained in Example 1 and the first pole of the first-order differential function and the second pole of the second-order differential function as Cq, the cycle number having the first derivative maximum (FDM) of the first-order differential function was determined as Cq.

[0382] Thereafter, samples whose FDM cycle number was less than or equal to the 50th cycle, which is the last cycle of the data set, were judged as positive, and samples whose FDM cycle number was greater than the 50th cycle were judged as negative. The results are shown in Table 5 below.

[0383] As shown in Table 5, the FDM cycle numbers obtained from the raw data set of samples containing various concentrations of target analytes were found to be 30.70, 34.20, 38.06, and 42.06, and since all of the cycle numbers were less than 50, all of the samples were determined to be positive.

[0384]

[0385] Comparative Example 2: Detection of target analytes using the maximum point of the second derivative of the S-shaped regression function obtained from the raw data set.

[0386] Instead of determining the cycle number of the intersection of the curve represented by the Richard function obtained in Example 1 and the first pole of the first-order differential function and the second pole of the second-order differential function as Cq, the cycle number having the maximum value (Second Derivative Maximum, SDM) of the second-order differential function was determined as Cq.

[0387] Thereafter, samples whose SDM cycle number was less than or equal to the 50th cycle, which is the last cycle of the data set, were judged as positive, and samples whose SDM cycle number was greater than the 50th cycle were judged as negative. The results are shown in Table 6 below.

[0388] As shown in Table 6, the SDM cycle numbers obtained from the raw data set of samples containing various concentrations of target analytes were 27.55, 30.88, 34.50, and 38.65, and since all of the cycle numbers were less than 50, all of the samples were determined to be positive.

[0389]

[0390] Test Example 1: Comparison of measured values ​​and reading results

[0391] 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.

[0392]

[0393] As shown in Table 7 above, it was confirmed that Examples 1 and 2 could be used to detect the presence or absence of a target analyte equally well as Comparative Examples 1 and 2.

[0394] Test Example 2: Confirmation of suitability for quantification of target analytes and quantitative accuracy and precision.

[0395] It was investigated whether the method according to Examples 1 and 2 could be used for the quantification (determination of initial content) of the target analyte.

[0396] The above quantification was based on a known absolute quantification method using a standard curve of standard substances. A standard curve was obtained through real-time polymerase chain reaction using standard substances of various concentrations, and the coefficient of determination (R) of the standard curve 2 ) and PCR efficiency, it was confirmed whether the methods according to Examples 1 and 2 were suitable for quantifying target analytes. The PCR efficiency was obtained using the following mathematical formula 8. In this test example, the cycle number of the intersection of the linear function passing through the first extreme point of the first differential function and the second extreme point of the second differential function described in Example 1 and the S-shaped regression function was used, and the cycle number of the intersection of the linear function passing through the first extreme point of the second differential function and the second extreme point of the second differential function described in Example 2 and the S-shaped regression function was used.

[0397] Mathematical formula 8

[0398]

[0399] In the above mathematical equation, slope is the slope of the standard curve.

[0400] Test Example 2-1: Obtaining a standard curve using standard substances

[0401] According to the following test examples 2-1-1 to 2-1-4, a standard curve for absolute quantification of the target analyte was obtained.

[0402] Test Example 2-1-1: Implementation of real-time polymerase chain reaction and acquisition of raw data set

[0403] Six replicate experiments were performed for the four concentrations of target analytes used in Example 1-1 above to obtain a raw data set.

[0404] The raw data set obtained by six replicate experiments from samples containing various concentrations of Neisseria gonorrhoeae (NG) genomic DNA as the target analyte is shown in Figure 10.

[0405] Test Example 2-1-2: Selecting a Data Set to Obtain an S-Shaped Regression Function

[0406] As a result of selecting data sets using a threshold of 100 △RFU in the same manner as in the above Example 1-3, all data sets were determined to be data sets for obtaining an S-shaped regression function.

[0407] Test Example 2-1-3: Obtaining an S-shaped regression function for an interval within a data set

[0408] As in Example 1-4 above, an S-shaped regression function was obtained for the selected interval within each data set, and a Richard function approximating the selected interval was determined. The parameter values ​​of the determined Richard function are shown in Table 8 below.

[0409]

[0410]

[0411] Test Example 2-1-4: Generation of a Standard Curve

[0412] (1) In the same manner as in Example 1 above, the cycle number of the intersection of the linear function passing through the first pole of the S-shaped regression function and the first pole of the first-order differential function and the second pole of the second-order differential function described for each Richard function was determined. Thereafter, a standard curve for application to quantitation was generated by plotting the log-converted concentration of the target analyte on the x-axis and the intersection point value of the linear function of the target analyte on the y-axis.

[0413] The standard curves generated using samples containing four concentrations of target analytes are shown in Figure 11, and the coefficient of determination (R) of each standard curve 2 ) and PCR efficiency are shown in Table 9 below.

[0414]

[0415] As shown in Table 9 above, the R of the generated standard curve 2 The average value was 0.9979, and the average PCR efficiency was confirmed to be 92%.

[0416] (2) In the same manner as in Example 2 above, the cycle number of the intersection of the linear function passing through the first and second extrema of the S-shaped regression function and the second-order differential function described for each Richard function was determined. Thereafter, the log-converted concentration of the target analyte was plotted on the x-axis, and the intersection point value of the linear function of the target analyte was plotted on the y-axis to generate a standard curve for application to quantitation.

[0417] The standard curves generated using samples containing four concentrations of target analytes are shown in Figure 12, and the coefficient of determination (R) of each standard curve 2 ) and PCR efficiency are shown in Table 10 below.

[0418]

[0419] As shown in Table 10 above, the R of the generated standard curve 2 The average value was 0.9984, and the average PCR efficiency was confirmed to be 100%.

[0420] Test Example 2-2: Quantitative Suitability Verification

[0421] 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 judged based on the value and PCR efficiency.

[0422] According to the literature [2006 Bio-Rad Laboratories, Inc. Real-Time PCR Applications Guide], the criterion for the suitability of a quantitative method is R corresponding to the linearity of the standard curve. 2 The value must exceed 0.980, and theoretically 2 n The PCR amplification efficiency should be within the range of 90-105%.

[0423] As can be seen in Tables 9 and 10 above, the average R of the standard curves obtained by the methods according to Examples 1 and 2 2 The values ​​were 0.9979 and 0.9984, which exceeded the standard of 0.980 in the above literature, and the PCR amplification efficiency was confirmed to be within the range of 90-105% of the standard of the above literature.

[0424] Through this, it was confirmed that the methods according to Examples 1 and 2 were suitable for quantifying target analytes.

[0425] Test Example 2-3: Confirming Quantitative Accuracy and Precision

[0426] The accuracy and precision of quantifying target analytes using the methods according to Examples 1 and 2 were determined based on the relative error (RE) value and coefficient of variation (CV).

[0427] According to the literature [2008 Introduction to Probability and Statistics], the relative error is a percentage that indicates how much the predicted value differs from the 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. The lower the value, the closer the data is to the mean.

[0428] In order to compare the relative errors according to each method, six replicate standard curves for the quantification of FDM and SDM of Comparative Examples 1 and 2 were generated using the same method used in Test Example 2-1-4 above.

[0429] The calculation of the relative error was performed by using the number of copies of Neisseria gonorrhoeae genomic 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 the value calculated backward through the slope and y-intercept of the standard curve as the predicted value, and comparing the quantitative cycle Cq (Quantification Cycle) for 4 repeated detections of 4 concentrations in each of the examples and comparative examples.

[0430] To compare the coefficient of variation according to each method, four replicate detection quantification cycles Cq (Quantification Cycle) of four concentrations of Neisseria gonorrhoeae genomic DNA (100 pg, 10 pg, 1 pg or 100 fg) used as the target analyte were generated in the same manner as in Examples 1 and 2 and Comparative Examples 1 and 2.

[0431] The coefficients of variation of the examples and comparative examples were calculated and compared using the Cq generated above and the following mathematical formula 10, and the results are summarized in Table 11 below.

[0432] Mathematical Formula 9

[0433]

[0434] Mathematical formula 10

[0435]

[0436]

[0437] As can be seen in Table 11 above, the results of comparing the relative errors of the examples and comparative examples show that the numerical values ​​of the relative errors of Example 1 (20.28) and Example 2 (5.29) are smaller than those of Comparative Example 1 (45.95) and Comparative Example 2 (20.65), confirming that the accuracy of Examples 1 and 2 is higher. In terms of the coefficient of variation, the numerical values ​​of the relative errors of Example 1 (35.27) and Example 2 (37.32) are smaller than those of Comparative Example 1 (38.32) and Comparative Example 2 (38.47), confirming that the precision of Examples 1 and 2 is higher.

[0438] In summary, it was confirmed that the methods of Examples 1 and 2 had improved accuracy and precision in quantifying target analytes compared to Comparative Examples 1 and 2.

[0439] While specific aspects of the present invention have been described in detail above, it should be apparent to those skilled in the art that these specific descriptions are merely preferred implementation examples and are not intended to limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting a target analyte in a sample comprising the following steps: (a) a step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte; (b) a step of obtaining an nth order differential function and an n+1th order differential function from the S-shaped function; wherein n is a natural number, (c) a step of obtaining a first-order function connecting i) the first extreme point of the nth-order differential function or the n+1st-order differential function and ii) the second extreme point of the n+1st-order differential function; and (d) A step of detecting a target analyte in a sample by using the intersection of the S-shaped function of step (a) and the linear function of step (c).

2. A method for determining the quantification cycle (Cq) value for a target analyte in a sample, comprising the following steps: (a) a step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte; (b) a step of obtaining an nth order differential function and an n+1th order differential function from the S-shaped function; wherein n is a natural number, (c) a step of obtaining a first-order function connecting i) the first extreme point of the nth-order differential function or the n+1st-order differential function and ii) the second extreme point of the n+1st-order differential function; and (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.

3. A method according to claim 1 or 2, characterized in that the step of obtaining an S-shaped function in step (a) comprises the following steps: (a-1) a step of obtaining a first data set representing a growth curve by an amplification reaction for a target analyte; wherein the first data set includes a plurality of data points each having a cycle number and a signal value at the cycle number; (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 the cycle number; and (a-3) A step of calculating an S-shaped function approximating a selected portion of the second data set.

4. A method according to claim 3, characterized in that the first data set of step (a-1) is a raw data set, a mathematically transformed data set of the raw data set, a normalized data set of the raw data set, or a normalized data set of the mathematically transformed data set.

5. A method according to claim 3, characterized in that the slope value of step (a-2) is obtained by differentiation, difference, difference, ratio or linear regression analysis of signal values.

6. A method according to claim 3, characterized in that the selected portion of the second data set in step (a-3) is within a range from the first cycle number to the last cycle number in the second data set.

7. A method according to claim 3, characterized in that calculating an S-shaped function in step (a-3) includes determining parameters of the S-shaped function.

8. A method according to claim 1 or 2, characterized in that the S-shaped function in step (a) is selected from the group consisting of a sigmoid function, a logistic function, a Gompertz function, a Chapman function, and a Richard function.

9. A method according to claim 1 or 2, characterized in that n is a natural number selected from 1 to 10.

10. A method according to claim 1, characterized in that using the intersection in step (d) includes determining the intersection as a quantification cycle (Cq) value.

11. A method according to claim 2 or claim 10, characterized in that determining the intersection point as a quantification cycle (Cq) value includes determining the cycle number of the intersection point as a Cq value.

12. In the third paragraph, the method is characterized in that it additionally includes the following steps between steps (a-1) and (a-2): (a-1-1) a step of applying a threshold value to the first data set; and (a-1-2) If the first data set has a signal value exceeding the threshold value, the step of proceeding to the next step (a-2).

13. In the third paragraph, the method is characterized in that it additionally includes the following steps between steps (a-2) and (a-3): (a-2-1) a step of applying a threshold value to the second data set; and (a-2-2) If the second data set has a slope value exceeding the threshold value, the next step (a-3) is performed.

14. A method according to claim 1 or 2, characterized in that the target analyte is a target nucleic acid molecule.

15. A computer-readable recording medium comprising instructions for implementing a processor for executing a method for detecting a target analyte in a sample, the method comprising the following steps: (a) a step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte; (b) a step of obtaining an nth order differential function and an n+1th order differential function from the S-shaped function; wherein n is a natural number, (c) a step of obtaining a first-order function connecting i) the first extreme point of the nth-order differential function or the n+1st-order differential function and ii) the second extreme point of the n+1st-order differential function; and (d) A step of detecting a target analyte in a sample by using the intersection of the S-shaped function of step (a) and the linear function of step (c).

16. A computer-readable recording medium including 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, the method comprising the following steps: (a) a step of obtaining an S-shaped function representing a growth curve by an amplification reaction for a target analyte; (b) a step of obtaining an nth order differential function and an n+1th order differential function from the S-shaped function; wherein n is a natural number, (c) a step of obtaining a first-order function connecting i) the first extreme point of the nth-order differential function or the n+1st-order differential function and ii) the second extreme point of the n+1st-order differential function; and (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.

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