KR20120097215A - Method for determining a baseline in amplification profile curve of real-time pcr - Google Patents
Method for determining a baseline in amplification profile curve of real-time pcr Download PDFInfo
- Publication number
- KR20120097215A KR20120097215A KR1020110016650A KR20110016650A KR20120097215A KR 20120097215 A KR20120097215 A KR 20120097215A KR 1020110016650 A KR1020110016650 A KR 1020110016650A KR 20110016650 A KR20110016650 A KR 20110016650A KR 20120097215 A KR20120097215 A KR 20120097215A
- Authority
- KR
- South Korea
- Prior art keywords
- baseline
- straight line
- value
- real
- data
- Prior art date
Links
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6844—Nucleic acid amplification reactions
- C12Q1/6851—Quantitative amplification
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B99/00—Subject matter not provided for in other groups of this subclass
Abstract
The present invention relates to a method for determining a baseline in an amplification profile curve obtained by performing a real time polymerase chain reaction. When the background fluorescence signal intensity based on the baseline according to the method of the present invention is subtracted from the observed fluorescence signal intensity, an amplification profile curve that accurately reflects the amount of the actual amplification product can be obtained, thereby enabling more accurate polynucleotide quantification. .
Description
The present invention relates to a method for determining a baseline in an amplification profile curve obtained by performing a real time polymerase chain reaction.
Real-time polynucleotide chain reaction (real-time PCR) is a technology that can quantify polynucleotides by monitoring the increase of PCR amplification products in real time. It is widely used in biological research and clinical analysis such as monitoring, genomic-level gene quantification and pathogen detection.
In real-time PCR, the amount of PCR amplification product can be detected by fluorescence signal. Intercalating method using a reagent that shows fluorescence by binding to double-stranded DNA as a detection method, a method of using oligonucleotides labeled with a fluorescent material at the 5 'end and a quencher material at the 3' end, etc. There is this. When the above methods are used, the intensity of the fluorescence signal increases according to the amount of polynucleotide that increases with real-time PCR, and the user generates an amplification profile curve indicating the intensity of the fluorescence signal according to the number of amplification cycles. You get
The amplification profile curve is typically a baseline region where the background fluorescence signal intensity does not reflect the actual amount of polynucleotides, an exponential region where the increase in fluorescence signal intensity is reflected by the increase in the amount of polynucleotides, And the PCR reaction is saturated and divided into a plateau region where no further increase in fluorescence signal intensity appears (FIG. 1).
The baseline region means a section in which the fluorescence signal intensity is maintained at the beginning of the PCR reaction in the amplification profile curve. The baseline region may have a slight increase or decrease in fluorescence intensity in part, but the rapid increase with the amount of amplification product is such that most of the fluorescence intensity remains within a certain range or increases or decreases very slowly regardless of the amount of amplification product. It is distinguished from the exponential domain showing. The fluorescence signal intensity in this section is not a reflection of the amount of polynucleotides, but a noise value generated from the reaction sample used or the machine itself, and is called a background signal intensity. At the beginning of the PCR reaction, since the amount of PCR amplification products has not yet reached a detectable amount by fluorescence, a baseline region showing only a background signal appears, and when the amount of amplification products reaches a detectable amount, the fluorescence signal intensity according to the polynucleotide amount Will appear.
Since the signal intensity of the baseline region does not reflect the amount of polynucleotides, in real-time PCR data analysis, it is usually subtracted from the actual observed data signal intensity. By subtracting the signal strength of the baseline region, it is possible to measure the amount of the actual amplification product more accurately, and to compare the amplification profile curves with different background signal strengths. In the prior art, the baseline region is previously determined as an initial constant cycle of the real-time polymerase amplification reaction, for example, 3 to 15 amplification cycles, or the user bases the interval before the exponential region starts by looking at the amplification profile curve. Determined by line area. Then, the signal strength of the baseline region was determined by performing linear regression analysis or LMS (least mean square) algorithm based on the signal strengths of these regions.
If the baseline region is predetermined, using samples with different initial polynucleotide amounts results in different cycles of exponential regions appearing in the amplification profile curve, which cannot be reflected and allow the user to determine the baseline region arbitrarily. In this case, different analysis results may be generated by different users analyzing at the same experiment result or at different time points. Depending on how the baseline region signal strength is determined, the actual amount of amplification products appearing in the amplification profile curve formed by subtracting the baseline region signal strength from the observed signal strength may appear different, thus providing more accurate results for accurate polynucleotide quantification. There is a need to determine the baseline region signal strength.
The present invention provides a method for determining a baseline from an amplification profile curve obtained by performing a real-time polymerase chain reaction, and further, a method for obtaining an amplification profile curve representing an actual amount of amplification product using the same.
The present invention provides a method for more accurate polynucleotide quantification.
The first aspect of the present invention is a method for determining the baseline in the amplification profile curve obtained by performing a real-time polymerase chain reaction,
(a) performing a real-time polymerase chain reaction, wherein the polymerase chain reaction includes n amplification cycles (n is an integer of 2 or more) and is detectable capable of providing a signal according to the amount of polynucleotides Performing in the presence of a probe;
(b) obtaining a data value of a signal strength value provided by the probe according to the number of amplification cycles;
(c) selecting p values (p is an integer of 2 or more and n or less) differently from each other n C p times;
(d) calculating a linear straight line from the selected p data values; And
(e) determining the baseline of the first linear straight line, the straight line of which the absolute value of the slope of the straight line is closest to zero.
The method relates to a method for determining the baseline in the amplification profile curve obtained by performing a real time polymerase chain reaction.
The term "real-time polymerase chain reaction (real-time PCR)" is an improvement on the polymerase chain reaction (PCR) that amplifies polynucleotides using polymerase. Means a technique that can be monitored in real time by the intensity of the fluorescence emitted by the fluorescent material to increase the amount of the polynucleotide amplified by the addition of a fluorescent material capable of binding to the polynucleotide when performing. The polymerase chain reaction amplifies the polynucleotide by repeatedly performing three steps of varying the temperature of denaturation, annealing, and elongation with the polynucleotide as dNTP, primer, and polymerase. Means. The real-time polymerase chain reaction may use an intercalating method, a TaqMan probe method, or a cycling probe method, depending on the fluorescent material used, but is not limited thereto.
The term "amplification profile curve" means a curve in which the fluorescence signal detected by performing a real-time polymerase chain reaction is represented by the fluorescence signal intensity value according to the number of amplification cycles. That is, the function of the fluorescence signal intensity value according to the number of amplification cycles is the amplification profile curve. In general, the amplification profile curve is a graph obtained by dividing the detected signal strength by the signal strength of a passive reference dye (Rx), which is a standardized value, on the y-axis and the number of amplification cycles on the x-axis.
The term "baseline" means a straight line representing the fluorescence signal intensity appearing in the baseline region in the amplification profile curve obtained by performing a real-time polymerase chain reaction. The baseline is determined based on signal strength data values present in the baseline region, and is generally determined by linear regression analysis based on data values present in the baseline region. The fluorescence signal intensity indicated by the baseline is the background fluorescence signal intensity independent of the amplification product, which is usually subtracted from the observed fluorescence signal intensity to produce a calibrated amplification profile curve that reflects the actual amount of amplification of the polynucleotide. do. For example, the background fluorescence signal intensity or baseline is generated to generate an amplification profile curve in which the ΔRn is the y-axis and the number of amplification cycles is the x-axis, ΔRn minus the background fluorescence signal Rn. Decide
The method comprises the steps of (a) performing a real-time polymerase chain reaction, wherein the polymerase chain reaction includes n amplification cycles (n is an integer of 2 or more) and can provide a signal according to the amount of polynucleotides. Performing in the presence of a detectable probe.
N is an integer of 2 or more and may generally be 40 to 50, but is not limited thereto. The detectable probe is an intercalating dye such as SYBR Green I, Ethidium bromide, YO-PRO-1 BOXTO, labeled with donor fluorephore (FITC) at the 3 'end and acceptor fluorophore at the 5' end. (acceptor fluorophore) labeled fluorogenic hybridization oligoprobe, TaqMan prob, Hairpin oligoprobe, self-fluorescing amplicon (sunrise primer & scorpion) primers)), but is not limited thereto.
The method includes (b) obtaining a data value for a signal strength value provided by the probe according to the number of amplification cycles.
Since it is said to include n amplification cycles in step (a), n data values can be obtained after the real-time polymerase chain reaction of step (a).
The method includes (c) selecting p values (p is an integer of 2 or more and n or less) of the data values differently from each other n C p times.
Selecting p values differently from each other n c p times among the data values means that p values are selected several times by repeating the number of times that all the p methods among n data values can be selected. This means that the values are chosen to be different each time they are selected. In this case, even if only one of the p selected data values is different, it is considered to be different from each other. The n C p represents a combination symbol used in probability or statistics, and means the number of all methods for selecting p differently among n variables ( n C p = n! / P! (Np)!) .
The method includes (d) calculating a first order straight line from the p data values selected.
The selected p data values are signal strength values according to the number of amplification cycles, and thus a linear first line can be calculated from them. P is the number of data n C p times selected hayeoteumeuro selected p number one when calculating a primary linear C p n first-order straight line after performing the step (d) for each data in the step (c) is calculated. If p is 2, one straight line is determined by two points, so the first-order straight line can be calculated directly. If p is 2 or higher, the first-order can be calculated using mathematical or statistical means to predict a constant model from the observed data. A straight line can be calculated. For example, linear regression analysis or least mean square (LMS) algorithm may be used, but is not limited thereto. According to an embodiment of the present invention, step (d) provides a method of calculating a one-dimensional straight line by linear regression analysis from the selected p data values.
The method includes (e) determining, as a baseline, a straight line of which the absolute value of the slope of the straight line is closest to zero among the calculated primary straight lines. Step (e) is a step of determining a baseline among the n C p linear straight lines calculated after performing step (d).
Usually, the background fluorescence signal intensity is calculated by linear regression analysis from the fluorescence signal intensity of the data of the baseline region determined by determining the baseline region in the amplification profile curve. In the method of the present invention, a first linear straight line is calculated from some data values on the amplification profile curve, and the first fluorescent straight line calculated from the data values of the baseline region is selected from the first linear straight lines. Determined by the baseline indicating the intensity. In step (e), the absolute value of the slope of the straight line is 0 to select the first straight line calculated from the data values of the baseline region among the first straight lines calculated from the data randomly selected in the step (d). Select the straight line nearest to. This is because the background fluorescence signal intensity of the baseline region will remain in a constant range without sudden change in fluorescence intensity, so the slope of the first linear line calculated from the data value of the baseline region will be close to zero.
Since the method of the present invention does not determine the baseline region in advance, different baseline regions may be determined for the amplification profile curves having different shapes, and are not arbitrarily determined by the user, thereby obtaining the same analysis result regardless of the user or viewpoint. Can be. In addition, since the first-order curve reflecting only some data values without calculating data values of the entire baseline region may be calculated, the influence of noise values may be excluded. When the real-time polymerase chain reaction is performed, theoretically, the background fluorescence signal intensity should appear at a constant level at the baseline, but in reality, a baseline drift in which the intensity of the fluorescence signal varies. Therefore, if the background fluorescence signal strength is calculated based on all signal strength values of the base line region, noise values having a large deviation from the majority of the signal strengths may be affected, thereby producing incorrect values. In the present invention, in order to exclude the influence of the noise value, the first straight line is calculated by selecting only some values from the entire data values, and the first straight line determined to be calculated from some data values rather than the noise of the baseline region (the slope is 0). Determine the background signal strength. This is because the first-order straight line calculated from some data values including noise values that deviate significantly from most signal strengths in the baseline region is unlikely to have a slope close to zero. When the baseline is determined, a standardized amplification profile curve representing a substantial increase in the amplification product may be obtained by subtracting the signal strength value according to the baseline from the signal strength value of the data value obtained in step (b).
According to an embodiment of the present invention, in the method, in step (e), a straight line having a slope of a straight line of 0 or more and an absolute value of the slope of the straight line closest to zero is calculated as the baseline. It provides a method of determining.
If the polymerase chain reaction occurs normally, the amount of polynucleotide increases as the number of amplification cycles increases. Therefore, as the number of amplification cycles increases, the intensity of the fluorescence signal also increases theoretically. Therefore, when determining the baseline in step (e), it is more accurate to select the first straight line except for the negative linear slope. This may help determine the fluorescence signal strength.
According to an embodiment of the present invention, in the method (e), the baseline is a straight line whose y-intercept of the straight line is greater than or equal to zero and the absolute value of the slope of the straight line is closest to zero. It provides a method to determine. If the y-intercept of the baseline determined in step (e) is less than zero, a portion where the intensity of the background fluorescence signal is negative appears in a portion of the straight line, which is a data value that cannot be obtained when a normal reaction occurs. Except to choose.
According to one embodiment of the invention, in the method, when determining the baseline in the step (e), the signal strength value of the data value obtained in the step (b) calculated in the step (d) The present invention provides a method of calculating a sum of values obtained by subtracting a signal strength value according to a first straight line to determine a first straight line whose sum is less than or equal to zero. By excluding the first straight line whose sum is equal to or less than 0, the first straight line calculated from data values existing in the congestion state region may be excluded.
In general, the amplification profile curve is a baseline region where the background fluorescence signal is constant, an exponential region where the increase in the intensity of the fluorescence signal is reflected by the increase in the amount of polynucleotides, and the PCR reaction is saturated. It has a plateau region which no longer exhibits an increase in fluorescence signal intensity (FIG. 1). In other words, the fluorescence signal intensity does not increase rapidly in the stationary state region as well as in the baseline region within the amplification profile curve and remains constant within a certain range. Therefore, in step (e), if the absolute value of the slope of the straight line is selected to be closest to 0, the first order calculated from the data values existing in the congestion state region as well as the first straight line calculated from the data values existing in the baseline region Straight lines can also be selected. Calculate the sum of the values of the observed fluorescence signal intensities minus the signal intensities along the first straight line calculated in step (d) to prevent the selection of the first straight line calculated from the data values present in the congestion state region. When the sum is less than the first linear line can be excluded from the selection. Since the signal strength values along the first-order straight line calculated from the data values present in the stationary state region will be greater than the fluorescence signal strength values appearing throughout the amplification profile curve, the sum of the values subtracted from the fluorescence signal strength values of the amplification profile curve is Will be zero or less.
A second aspect of the present invention is a method of determining the baseline in the amplification profile curve obtained by performing a real-time polymerase chain reaction,
(a) performing a real-time polymerase chain reaction, wherein the polymerase chain reaction includes n amplification cycles (n is an integer of 2 or more) and is detectable capable of providing a signal according to the amount of polynucleotides Performing in the presence of a probe;
(b) obtaining a data value of a signal strength value provided by the probe according to the number of amplification cycles;
(c) selecting p values (p is an integer of 2 or more and n or less) differently from each other n C p times among the data values;
(d) calculating a linear straight line from the selected p data values;
(e) selecting a straight line whose absolute value of the slope of the straight line is closest to 0 among the calculated primary straight lines;
(f) The data value of step (b), wherein the absolute value of the signal strength value of the data value obtained in step (b) is subtracted from the signal strength value according to the straight line selected in step (e). Selecting a; And
(g) determining a first-order straight line calculated by linear regression analysis based on the data value selected in step (f) as a baseline.
In the second aspect of the present invention, the steps (a) to (d) are the same as the first embodiment, but the steps (e) to (g) are different. This is a process for selecting a first-order straight line calculated from the data values of the baseline area and then calculating a baseline corresponding to more inlier data values. The term "inlier data value" means a data value that follows the tendency of most fluorescence signal intensity values present in the baseline region. In determining the baseline, based on more inlier data values, one may determine a baseline that more closely matches the actual observed data values than based on some randomly selected data.
In one embodiment of the present invention, step (e) may be a step of selecting a red line in which the slope of the straight line is zero or more and the absolute value of the slope is closest to zero among the calculated primary straight lines. In another embodiment of the present invention, step (e) may be a step of selecting a straight line whose y-intercept of the straight line is greater than or equal to zero and the absolute value of the slope is closest to zero among the calculated primary straight lines. In another embodiment of the present invention, when selecting a straight line in the step (e), the signal according to the primary straight line calculated in the step (d) from the signal strength value of the data value obtained in the step (b) The sum of values obtained by subtracting the intensity value may be calculated to exclude the first linear line whose sum is less than or equal to zero. In another embodiment of the present invention, step (d) may be a step of calculating a one-dimensional straight line by linear regression analysis with the selected p data values.
In the step (f) of the second aspect of the present invention, in the case of a data value whose error of the signal strength value with respect to the straight line selected in the step (e) is not large, the data value follows the tendency of most data values of the base line region. It is a data value to be considered in calculating the background fluorescence signal, and a data value having a large error corresponds to a noise value different from the trend of most data values even in the baseline region, or a data value in another region. Therefore, in step (f), in order to select an inlier data value excluding a noise value, a data value whose absolute value of the error with the straight line selected in step (e) is less than or equal to a predetermined value is selected. The “constant value” is a value that can be arbitrarily determined by the user and means an error small enough to be regarded as a signal strength of a baseline region that does not correspond to baseline drift or noise. The constant value may be 0.0005 to 0.2, more preferably 0.0005 to 0.05 based on ΔRn, but is not limited thereto.
In the step (g), the first straight line is calculated based on the inlier data values of the baseline area selected in the step (f), and is determined as the baseline.
According to an embodiment of the present invention, there is provided a method wherein p of step (c) of the above aspects is 2.
When subtracting the background signal intensity of the baseline obtained using the method of the present invention from the amplification profile curve obtained by performing the real-time polymerase chain reaction, an amplification profile curve that more accurately reflects the amount of the actual amplification product can be obtained. Polynucleotide quantification can be made.
Figure 1 shows a general amplification profile curve obtained by performing real-time PCR.
2 is a curve before subtracting the background signal intensity of the baseline by the amplification profile curve obtained by performing Example 1. FIG.
3A shows the 37 th sample and FIG. 3B shows the amplification profile curve of the 47 th sample and the baseline determined using the method of the present invention. Filled circles are baselines determined using the present invention, and unfilled circles are baselines determined using Applied Biosystems' StepOnePlus Real-Time PCR System machine.
4A is a curve obtained by subtracting the background signal strength of the baseline determined by the method of the present invention from the amplification profile curve of FIG. 2.
4B is a curve obtained by subtracting the background signal intensity of the baseline determined using the StepOnePlus Real-Time PCR System machine of Applied Biosystems from the amplification profile curve of FIG. 2.
Figure 5 is a graph comparing the slope of each baseline (filled circles) determined using the present invention and the sample-based slope (unfilled circles) determined using a StepOnePlus Real-Time PCR System machine of Applied Biosystems to be.
Hereinafter, one or more embodiments will be described in more detail with reference to Examples. However, these examples are provided to illustrate one or more embodiments by way of example, but the scope of the invention is not limited to these examples.
Example 1: How to determine the baseline from the amplification profile curve
Initial nucleic acid amount of 18S is 10 8 , 10 7 , 10 6 , 10 5 , 10 4 , 10 3 , 10 2 , 10, 1, 0.1, 0.01 copies, 8 identical samples for each nucleic acid amount Real time polymerase chain reaction of 88 samples was made. Applied Biosystems' StepOnePlus Real-Time PCR System machine was used and the number of amplification cycles was 40 and the fluorescent probe used was a TaqMan probe. An amplification profile curve based on the acquired fluorescence signal intensity data values is shown in FIG. 2.
The method of determining the baseline described below was performed for each of the 88 samples. Since there are 40 amplification cycles, each amplification profile curve has 40 data values. Two of the 40 data values were selected 40 C 2 times (ie 780 times) to be different each time the selected data values were selected. The primary straight line was calculated from the selected two data values, thereby calculating 780 primary straight lines. Of the calculated 780 primary straight lines, the absolute value of the slope is closest to zero, the slope value is zero or more, the y-intercept value is zero or more, and the obtained fluorescence signal intensity data value (data value shown in FIG. 2). A straight line having a sum of zero or more of the values of the fluorescence signal data along the straight line was selected. The final baseline was determined by performing a linear regression analysis on the basis of the data values within 0.005 of the error of the selected straight line fluorescence signal data.
Figure 3a 37th sample (10 5 copies), Figure 3b shows the 47th sample the baseline determined by using the amplification profile curves and the method of the invention of (10 6 copies). Baselines by the method of the present invention are shown as filled circles, and those by the StepOnePlus Real-Time PCR System machine from Applied Biosystems are shown as unfilled circles. As shown in the figure, it can be seen that the baseline according to the present invention has a slope value closer to zero. 4A and 4B show an amplification profile curve obtained by subtracting the background fluorescence signal intensity of the baseline from the amplification profile curve of FIG. 2. This is the result of applying the baseline determined for each sample to each amplification profile curve of the corresponding sample. Figure 4a is using the method of the present invention and Figure 4b is a StepOnePlus Real-Time PCR System machine from Applied Biosystems. FIG. 5 shows the slope of the baseline determined per sample using the invention (filled circles) and Applied Biosystems' StepOnePlus Real-Time PCR System machine (unfilled circles). While baselines determined using the present invention have almost zero slopes, many of the baseline slopes determined using Applied Biosystems' StepOnePlus Real-Time PCR System machines are found to deviate from zero.
[Matlab code used to perform Example 1]
function [baseline, deltaRn] = ComputeBaseline (Rn, DEBUG)
%% Description
% ComputeBaseline finds the background fluoresence level, which is called
% baseline.
%
%
% INPUT Rn: normalize Reporter [numCycles x numWells]
%
% OUPUT baseline: the background fluoresence for each well [numCycles x numWells]
%
% OUPUT deltaRn: the difference between Rn and baseline, Rn-baseline
% [numCycles x numWells]
% num Cycles: the total number of cycles
% numWells: the number of well used
if nargin <2
DEBUG = 0;
end
numCycles = size (Rn, 1);
numWells = size (Rn, 2);
baseline = zeros (numCycles, numWells);
%% Generate all possible sets of two points
% pts contains the set, [numCycles * (numCycles-1) / 2 x 2]
pts = zeros (numCycles * (numCycles-1) / 2,2);
count = 1;
for i = 1: (numCycles-1)
for j = (i + 1): numCycles
pts (count, 1) = i;
pts (count, 2) = j;
count = count + 1;
end
end
%% Find the baseline for each well
for index = 1: numWells
%% Estimate initial baselines
% x: cycle number, y: fluoresence, a: slope, b: offset
x2 = pts (:, 2);
x1 = pts (:, 1);
y2 = Rn (sub2ind ([numCycles, numWells], x2, repmat (index, size (x2))));
y1 = Rn (sub2ind ([numCycles, numWells], x1, repmat (index, size (x1))));
a = (y2-y1) ./ (x2-x1);
b = (y1. * x2-y2. * x1) ./ (x2-x1);
% considers baselines with positive slopes
positiveSlope = find (y2> = y1);
a_positiveslope = a (positiveSlope);
b_positiveslope = b (positiveSlope);
% res: all the potential baselines with positive slopes = ax + b
% diff: Rn-baseline
res = repmat (a_positiveslope ', numCycles, 1). * repmat ((1: numCycles)', 1, length (a_positiveslope)) + repmat (b_positiveslope ', numCycles, 1);
diff = repmat (Rn (:, index), 1, length (a_positiveslope))-res;
% considers baselines that do not have negative values and that the
% average deltaRn is positive
positives = find (min (res)> = 0 & sum (diff)> 0);
if (size (positives, 2) == 0)
positives = find (min (res)> = 0);
end
res_positivebase = res (:, positives);
a_positivebase = a_positiveslope (positives);
if (size (positives, 2) == 0)
res_positivebase = res;
a_positivebase = a_positiveslope;
end
% chooses the baseline with the minimum slope
[X I] = min (a_positivebase);
if (DEBUG)
figure; hold on; plot (res_positivebase (:, I)); plot (Rn (:, index), 'r');
figure; plot (abs (res_positivebase (:, I) -Rn (:, index)))
end
%% Estimate the final baseline
% finds the inlier cycles that lie close to the baseline
inliers = find (abs (res_positivebase (:, I) -Rn (:, index)) <0.0005); % 0.05);
% st: the start cycle for the final baseline segment
% end: the end cycle for the final baseline segment
% b: the average Rn value for the baseline segment
b = mean (Rn (inliers, index));
% for a short baseline segment, assume the slope is zero
% for a long enough baseline segment, perform the linear regression
if length (inliers) <4
baseline (:, index) = repmat (b, 1, numCycles);
else
st = inliers (1);
en = inliers (end);
md = (en + st) / 2;
sz = length (inliers);
y = Rn (inliers, index) -repmat (b, sz, 1);
A = inliers-md;
slope = A \ y;
baseline (:, index) = ((1: numCycles) '-md) * slope + repmat (b, numCycles, 1);
end
end
deltaRn = Rn-baseline;
Claims (7)
(a) performing a real-time polymerase chain reaction, wherein the polymerase chain reaction includes n amplification cycles (n is an integer of 2 or more) and is detectable capable of providing a signal according to the amount of polynucleotides Performing in the presence of a probe;
(b) obtaining a data value of a signal strength value provided by the probe according to the number of amplification cycles;
(c) selecting p values (p is an integer of 2 or more and n or less) differently from each other n C p times among the data values;
(d) calculating a linear straight line from the selected p data values; And
(e) determining the baseline of the first linear straight line, the baseline of which the absolute value of the slope of the straight line is closest to zero.
(a) performing a real-time polymerase chain reaction, wherein the polymerase chain reaction includes n amplification cycles (n is an integer of 2 or more) and is detectable capable of providing a signal according to the amount of polynucleotides Performing in the presence of a probe;
(b) obtaining a data value of a signal strength value provided by the probe according to the number of amplification cycles;
(c) selecting p values (p is an integer of 2 or more and n or less) differently from each other n C p times among the data values;
(d) calculating a linear straight line from the selected p data values;
(e) selecting a straight line whose absolute value of the slope of the straight line is closest to 0 among the calculated primary straight lines;
(f) The data value of step (b), wherein the absolute value of the signal strength value of the data value obtained in step (b) is subtracted from the signal strength value according to the straight line selected in step (e). Selecting a; And
(g) determining a baseline of the first straight line calculated by linear regression analysis using the data value selected in step (f).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020110016650A KR20120097215A (en) | 2011-02-24 | 2011-02-24 | Method for determining a baseline in amplification profile curve of real-time pcr |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020110016650A KR20120097215A (en) | 2011-02-24 | 2011-02-24 | Method for determining a baseline in amplification profile curve of real-time pcr |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| KR20120097215A true KR20120097215A (en) | 2012-09-03 |
Family
ID=47108608
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020110016650A KR20120097215A (en) | 2011-02-24 | 2011-02-24 | Method for determining a baseline in amplification profile curve of real-time pcr |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR20120097215A (en) |
-
2011
- 2011-02-24 KR KR1020110016650A patent/KR20120097215A/en not_active Application Discontinuation
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11615863B2 (en) | Universal method to determine real-time PCR cycle threshold values | |
| US7680868B2 (en) | PCR elbow determination by use of a double sigmoid function curve fit with the Levenburg-Marquardt algorithm and normalization | |
| JP6431076B2 (en) | Jump detection and correction in real-time PCR signals | |
| EP2107470B1 (en) | PCR elbow determination using quadratic test for curvature analysis of a double sigmoid | |
| US7991562B2 (en) | PCR elbow determination using curvature analysis of a double sigmoid | |
| JP5599986B2 (en) | System and method for stepped discontinuity removal in real-time PCR fluorescence data | |
| EP1804172A2 (en) | PCR elbow determination using curvature analysis of a double sigmoid | |
| KR20120103496A (en) | Method for acquiring a standard curve and a threshold in real-time pcr | |
| KR101771402B1 (en) | Methods for nucleic acid quantification | |
| KR20120097215A (en) | Method for determining a baseline in amplification profile curve of real-time pcr | |
| TWI615474B (en) | Measuring method for nucleic acid samples | |
| US20210292817A1 (en) | Methods for quantitation of analytes in multiplexed biochemical reactions | |
| US20210350874A1 (en) | Methods for Analyzing Real Time Digital PCR Data | |
| US20140273254A1 (en) | Method, product, and system for quantifying the methylation status of a nucleic acid in a sample | |
| EP3218516B1 (en) | Method for determining a property of a starting sample | |
| KR20120107717A (en) | Method for selecting a threshold in amplification profile curve of real-time pcr |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| WITN | Withdrawal due to no request for examination |