Method and Device for Carrying out a qPCR Process

Abstract

The disclosure relates to a computer-implemented method for carrying out a quantitative polymerase chain reaction (qPCR) process, comprising the following steps: —cyclically carrying out qPCR cycles; —measuring an intensity value of a fluorescence relating to each qPCR cycle to obtain a qPCR curve from intensity values; —analyzing the shape of the qPCR curve using a data-based classification model trained to provide a classification result depending on the shape of the qPCR curve; and—carrying out the qPCR process depending on the classification result of the analysis of the shape of the qPCR curve.

Claims

1. A method, which is computer implemented, for conducting a quantitative polymerase chain reaction (qPCR) process, the method comprising: cyclically executing qPCR cycles; measuring an intensity value of a fluorescence at each qPCR cycle to obtain a qPCR curve composed of intensity values; evaluating a shape of the qPCR curve with a data-based classification model which has been trained to provide a classification result depending on the shape of the qPCR curve; and conducting the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve.

2. The method as claimed in claim 1, wherein the data-based classification model comprises one of a neural network and a support vector machine model.

3. The method as claimed in claim 1, wherein the data-based classification model has been trained to provide, depending on the qPCR curve, the classification result which indicates one of a presence and a nonpresence of a DNA strand segment to be detected.

4. The method as claimed in claim 1 further comprising: determining residual error plots between the qPCR curve and a parameterized presence function and a parameterized nonpresence function, the data-based classification model having been trained to provide, depending on at least one of the residual error plots, the classification result which indicates one of a presence and a nonpresence of a DNA strand segment to be detected.

5. The method as claimed in claim 4 further comprising: establishing the presence of the DNA strand segment to be detected in response to the classification result based on the residual error plot from the parameterized presence function indicating the presence of the DNA strand segment to be detected.

6. The method as claimed in claim 4 further comprising: establishing the nonpresence of the DNA strand segment to be detected in response to the classification result based on the residual error plot from the parameterized nonpresence function indicating the nonpresence of the DNA strand segment to be detected.

7. The method as claimed in claim 1, the conducting the qPCR process further comprising: signaling that a ct value is determinable; and determining the ct value from a parameterized presence function in response to a presence of the DNA strand segment to be detected being established.

8. A device for conducting a quantitative polymerase chain reaction process, the device being configured to: cyclically execute qPCR cycles; measure an intensity value of a fluorescence at each qPCR cycle to obtain a qPCR curve composed of intensity values; evaluate a shape of the qPCR curve with a data-based classification model which has been trained to provide a classification result depending on the shape of the qPCR curve; and conduct the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve.

9. The method as claimed in claim 1, wherein the method is carried out by executing a computer program.

10. A non-transitory electronic storage medium storing a computer program for conducting a quantitative polymerase chain reaction process, the computer program being configured to, when executed by a computer, cause the computer to: cyclically execute qPCR cycles; measure an intensity value of a fluorescence at each qPCR cycle to obtain a qPCR curve composed of intensity values; evaluate a shape of the qPCR curve with a data-based classification model which has been trained to provide a classification result depending on the shape of the qPCR curve; and conduct the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve.

11. The method as claimed in claim 2, wherein the data-based classification model comprises a deep neural network.

12. The method as claimed in claim 2, wherein the data-based classification model comprises a recurrent neural network.

13. The method as claimed in claim 12, wherein the recurrent neural network comprises an LSTM.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Embodiments will be more particularly elucidated below on the basis of the accompanying drawings, where:

[0034] FIG. 1 shows a schematic depiction of a cycle of a PCR method;

[0035] FIG. 2 shows a schematic depiction of a typical qPCR curve comprising a plot of intensity values;

[0036] FIG. 3 shows a measured plot of a qPCR curve;

[0037] FIGS. 4a and 4b show ideal plots of the qPCR curve in the case of a nondetectable substance and a detectable substance, respectively; and

[0038] FIG. 5 shows a flowchart to illustrate a method for conducting a qPCR measurement; and

[0039] FIG. 6 shows a flowchart to illustrate a further method for conducting a qPCR measurement.

DESCRIPTION OF EMBODIMENTS

[0040] FIG. 1 shows a schematic depiction of a PCR method known per se, comprising the steps of denaturation, annealing and elongation.

[0041] In the annealing step S1, the double-stranded DNA in a substance is broken up into two individual strands at a high temperature of, for example, above 90° C. In a subsequent annealing step S2, a so-called primer is bound to the individual strands at a particular DNA position marking the start of a DNA strand segment to be detected. Said primer represents the starting point of an amplification of the DNA strand segment. In an elongation step S3, the complementary DNA strand segment is synthesized on the individual strands from free nucleotides added to the substance, starting at the position marked by the primer, with the result that the previously split individual strands have been completed to form complete double strands at the end of the elongation step.

[0042] By providing the free nucleotides or the primer with fluorescent molecules which exhibit fluorescence properties only when bound to the DNA strand segment, it is possible, by determining an intensity of a fluorescence following the elongation step S3, to obtain an intensity value of the fluorescence through an appropriate measurement. What is assigned to the measured intensity of the fluorescent light is an intensity value.

[0043] The method comprising steps S1 to S3 is executed cyclically and the intensity values are recorded in order to obtain a plot of intensity values as a qPCR curve.

[0044] The plot of intensity values ideally has the shape depicted in FIG. 2. FIG. 2 shows a plot of normalized intensity I against the cycle index z. Said plot is divided into three sections, namely a baseline section B, in which the fluorescence of the incorporated fluorescent molecules is still indistinguishable from a background fluorescence, an exponential section E, in which the intensity values are visible and rise exponentially, and in a plateau section P, in which there is flattening of the rise in intensity values, since the reagents used (solution containing nucleotides) have been consumed and no further binding to broken-up individual strands is taking place.

[0045] FIG. 3 depicts, by way of example, a plot of the intensity values obtained in a real measurement as a qPCR curve. Strong fluctuations are evident, and these may result from background fluorescence, thermal noise, fluctuations in the reagent concentrations, and bubbles and artifacts in the fluorescence volume. It is evident that it is not readily possible to determine the baseline section, exponential section and plateau section of the qPCR curve.

[0046] FIGS. 4a and 4b show ideal plots of a qPCR curve without the presence of a DNA strand segment to be detected and with the presence of a DNA strand segment to be detected, respectively.

[0047] FIG. 5 depicts a flowchart to illustrate a method for conducting a qPCR measurement process. The method can be executed on a data processing device which controls a qPCR process on a PCR system and which provides from a PCR system in each cycle an intensity value indicating the intensity of a fluorescence depending on a DNA strand segment amplified during the qPCR. In the data processing device, the below-described method can be implemented in software and/or hardware.

[0048] In step S11, the qPCR measurement is carried out in order to receive intensity values in consecutive cycles of a qPCR measurement. The number of cycles for the qPCR measurement is about 30 to 60 cycles, preferably 40 cycles. A qPCR curve showing the intensity values (or values derived therefrom) against a cycle index is obtained.

[0049] In step S12, the intensity values of the qPCR curve are supplied to a trained classification model. The classification model is in the form of a data-based model, such as, for example, a SVM (support vector machine) or a deep neural network. Alternatively, the data-based classification model can also be formed with a neural network composed of temporal convolutional layers.

[0050] The classification model can have been trained with data sets of actually measured qPCR plots, each of which has been assigned a label indicating whether or not the data set (qPCR curve) corresponds to a measurement of a substance containing the DNA strand segment to be detected.

[0051] In step S13, it is determined, according to the result of the classification by the classification model, whether the qPCR curve corresponds to a presence or a nonpresence of the DNA strand segment to be detected, i.e., specified whether the DNA strand segment to be detected is present in the substance or not.

[0052] In step S14, the PCR method is executed according to the classification result. In particular, the qPCR method can be conducted by signaling that a ct value is determinable and determining the ct value from the parameterized presence function, if a presence of the DNA strand segment to be detected is established.

[0053] FIG. 6 depicts a flowchart to illustrate an alternative method for conducting the PCR measurement and for evaluating a measured qPCR curve.

[0054] In step S21, a qPCR method is used to carry out a qPCR measurement and to determine a qPCR curve through consecutive measurement of intensity values.

[0055] In step S22, a specified parametric nonpresence function is first parameterized by fitting the measured qPCR curve to the nonpresence function. For example, the nonpresence function can be a linear function, as depicted in FIG. 4a, which substantially corresponds to the plot characteristics of a baseline. Fitting to the nonpresence function can, for example, be done with the aid of least squares minimization.

[0056] In step S23, the measured qPCR curve is fitted to a presence function. The presence function corresponds to a parameterized function which substantially corresponds to plot characteristics as depicted in FIG. 4b. The presence function can have a sigmoid function. Parameterization of the presence function makes it possible to fit the measured qPCR curve to the presence function.

[0057] In step S24, residual error plots of the measured qPCR curve in relation to the parameterized presence function and in relation to the parameterized nonpresence function are determined.

[0058] In step S25, the residual error plots are supplied to a data-based classification model. The classification model has been trained on the basis of training data which assign residual error plots to the corresponding presence function or nonpresence function. This means that the training data indicate whether or not a residual error plot from the parameterized presence function confirms the presence of the strand segment to be detected. Furthermore, the training data indicate whether or not the residual error plot from the parameterized nonpresence function confirms the nonpresence of the strand segment to be detected.

[0059] Accordingly, it is established in step S25 with the aid of the residual error plot that a measured qPCR curve indicates a presence of the DNA strand segment to be detected in the substance if the classification model confirms the residual error plot from the presence function. Analogously, it is established with the aid of the residual error plot that a measured qPCR curve indicates a nonpresence of the DNA strand segment to be detected in the substance if the classification model confirms the residual error plot from the nonpresence function. This means that, if what generally arises from the residual error plot based on the parameterized presence function or the parameterized nonpresence function is that the classification result the residual error plot of the corresponding parameterized presence function or parameterized nonpresence function underlying the residual error plot, it is established that the DNA strand segment to be detected is present or is not present.

[0060] If the classification result with regard to the residual error plot gives rise to a result contrary to parameterized presence or nonpresence function underlying the residual error plot, it can be decided to discard the qPCR measurement, since it is not possible to make a clear decision about a presence or nonpresence of the DNA strand segment to be detected.