APPROACH TO ENABLE AN AUTOMATIC PRE-PROCESSING
OF qRT-PCR
Analysis
Marco Franke
1
, Klaus-Dieter Thoben
1
and Rainer Söller
2
1
BIBA - Bremer Institut für Produktion und Logistik GmbH, University Bremen
Hochschulring 20, 28359, Bremen, Germany
2
Qiagen Hamburg, Königstraße 4a, 22767, Hamburg, Germany
Keywords: Polymerase Chain Reaction, Automated Pre-processing of qRT-PCR Results, Noise Removal for Sigmoid
Curves.
Abstract: A fully automated analysis method for real-time PCR samples can be achieved through an automated noise
removal. This results in a guaranteed robustness against the factors that repeatedly impact the analysis of
real-time PCR data and usually lead to a manual analysis by skilled experts. The presented approach attains
a degree of robustness which allows an automated analysis for real-time PCR. This new automated pre-
processing method improves the real-time PCR data for the analysis in such a way that accepted qualitative
analysis methods can be used in an automated way. Furthermore, the article shows an evaluation of an
implemented demonstrator for an automated analysis which combines the developed pre-processing method
with the second derivation method for the qualitative analysis.
1 INTRODUCTION
The process of Real-time quantitative polymerase
chain reaction (qRT-PCR) has been widely used for
over ten years and has entered into the domain of
medicine, for example in Leukemia Research. The
determination of molecular targets by using
qualitative and quantitative qRT-PCR analysis has
become a common method. The qRT-PCR method
can also be used for the detection of microorganisms
or other organic media containing a genome.
The detection of microorganisms in industrial
environment is realized with the methods of
microbiology. Such methods are standardized and
accepted as solid proof. Its major disadvantage lies
in the considerable time it requires. A large
proportion of time is spent on the stage in which the
microorganisms grow on the medium. The duration
of this phase depends on the type of microorganisms
and environmental conditions and can be very long.
The quality check of a raw material, interstage
product or end product can take a few hours or a few
days. The needed time for a quality analysis is
longer than the time for producing the product. This
means that the quality check can not work in real
time and the usage of preventive methods for
reducing an occurrence of biological contamination
is needed. This issue is not resolved but improved by
the qRT-PCR method. The qRT-PCR method
requires a constant time for the detection of any
micro-organism, which takes less than 12 hours. In
the project ProDIAP(BIBA,2010) the qRT-PCR-
analysis required six hours.
Since the establishment of the qRT-PCR method,
different approaches have been developed in the
field of qualitative and quantitative analyses which
also include the fit points and second derivation as a
common method. For example, the softwares of
LightCycler (Roche Applied Science, 2010) and
RotorGene (Qiagen, 2010) use such methods. One of
the methods which are explored currently is the
maxRatio (Shain and Clemens, 2008) method. One
feature of the maxRatio method is to support a much
more aggressive noise filter without losing
significant signal information. All these developed
methods need an additional pre-processing to reduce
the wide range of noise within measurement data.
The currently used noise removal methods require
the experience of experts to estimate the parameter
values and to evaluate the result of noise removal.
The chosen parameter values describe e.g. the
281
Franke M., Thoben K. and Söller R..
APPROACH TO ENABLE AN AUTOMATIC PRE-PROCESSING OF qRT-PCR - Analysis.
DOI: 10.5220/0003734102810285
In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2012), pages 281-285
ISBN: 978-989-8425-90-4
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
specific characteristics of the used qRT-PCR-
analysis and the used qRT-PCR-KIT and have to
adapt to them each time. This adaption/calibration
can only be executed by a manual process. This fact
prevents the usage of the qRT-PCR-analysis in an
automated industrial environment or at the
customer`s side as an easy-to-use analysis product.
Therefore, the weakness of parameter based noise
removal methods and the research of the usage of
methods without parameters for noise removal are
the focus of this article.
This article presents a new approach for an
automatic pre-processing method for common qRT-
PCR-analysis. The objective is to achieve a pre-
processing method without the required intervention
of experts. If a pre-processing without parameters is
possible, it enables an automatic qRT-PCR-analysis
with common analysis methods. Such a solution has
been established under the project ProDiap (Bremer
Institut für Produktion und Logistik, 2010).The
solution is presented in chapter 4.
In this article, the problem and its solution,
which lies in an approach for the development of an
automated pre-processing method for a qRT-PCR-
analysis are presented. In the next section, the
common noise removal methods are discussed. After
this, the approach will be described and finally, the
evaluation by using the demonstrator will be shown.
2 STATE OF THE ART WITH
REGARD TO
PRE-PROCESSING
The influence of the noise within the analysis
process implies the need for pre-processing. As
minimal information, a qRT-PCR measurement
contains a set of tuples, which represent a time-
stamp and a measured fluorescence. The magnitude
of the fluorescence correlates with the concentration
of the qRT-PCR-analysis result product. The
influence on this correlation is declared in this
article as noise.
The interpretation of a measurement result is
based on the mapping of a measurement result in a
Cartesian coordinate system. The qualitative and the
quantitative analyses are grounded on such a curve.
In figure 1, an example curve is shown, which
represents a positive result of a qRT-PCR-analysis.
It is characterized by four phases (Wong and
Medrano, 2005).
In such a case, a specific micro-organism would
be detected.
Figure 1: Four phases of qRT-PCR curve.
The quality of a curve decreases with the
decreasing quality of these four phases. In real
measurements, the noise changes the expression of
the four phases of the curve. This complicates the
classification of such curves compared to the
classification of noise-free curves significantly. The
effects on the curve are described below with regard
to different noise types.
2.1 Noise
Generally, in test results, two different types of noise
can occur (Wilhelm, 2003). As pointed out by
Wilhelm (2003) and Larinonov, Krause and Miller
(2005) a background noise is caused by properties of
materials and other external influences. Here,
Larinonov et al. (2005) a correlation between the
qRT-PCR systems and the occurrence of
background noise is mentioned. The expression of
noise ranges from a constant shift to a linear increase
of noise over cycles (Larinonov et al., 2005). The
second type of noise is defined as signal trend. The
possible causes of the occurrence of this are not yet
resolved. According to Wilhelm` s (2003) opinion,
the product accumulation is no reason for the signal
trend. The signal trend can influence the curve of a
test result in two ways. It can increase or decrease
the measured fluorescence of a test result. Without
additional information, an expert can not know the
resulting expression.
The effects of background noise and signal trend
for a repeated application of a sample analysis may
lead to varying results. In general, a background
noise always occurs, which leads to a measurable
fluorescence from the first cycle, although the
fluorescence in the linear ground phase would have
the value zero. The influence of noise is shown in
figure 2 and 3 by way of example.
The curve in figure 3 is representative for a test
result which would be classified as positive and
containing just a little noise. Figure 2 shows a
complement curve, which should be evaluated as
positive, although stronger effects of noise have
occurred.
BIOINFORMATICS 2012 - International Conference on Bioinformatics Models, Methods and Algorithms
282
In the following the influence of noise on the two
curves, as well as the possibilities of its detection
and elimination are presented.
2.2 Noise Removal
The curves in figure 2 and 3 include some noise of
the type background noise and noise of the type
signal trend.
Figure 2: Test result with intense noise.
Figure 3: Test result with some noise.
The background noise in figure 3 can be
described as a straight line with the gradient zero in
the linear ground phase. In contrast, the background
noise in figure 2 cannot be described as a straight
line in the linear ground phase. One possibility is to
use n-degree polynomial or cubic splines to define
such a background noise. The cross-over between
the linear ground phase and the early exponential
phase need to be defined for a downstream analysis.
This separation is not possible, which is shown in
figure 2. The signal trend has a similar influence on
the curve which is shown in figure 2.
The noise removal cannot generally detect and
mathematically formulate the background noise and
the signal trend of a curve. For that reason, the noise
cannot be eliminated without losing information of
the signal. Existing methods smooth the curves by
using parameters to control the intensity of noise
removal. If an expert chooses the wrong values for
parameters, the method removes the noise and the
underlying signal.
2.3 Noise Removal by Smoothing
In the following, the smoothing on the basis of
existing procedures will be presented shortly. Based
on the weaknesses of these procedures, the
requirements for the presented smoothing operator
will be presented. A smoothing is generally defined
for two areas of a curve, the so-called local and
global scope.
The local scope is defined by the range between
two points of a curve. There is no information from
the perspective qRT-PCR-analysis between them.
Thus, between two points, different courses of
curves could occur, such as a line, a polynomial or
something else. Polynomials are presumed as
possible curve progression to the technical base of
qRT-PCR systems. The intervals are interpolated in
accordance with polynomial smoothing in
corresponding software. For example, the software
Sofar (Wilhelm, 2003) uses splines (Gutenberg,
2004) for smoothing in local scope.
The global scope is defined as an interval of the
whole curve. Different smoothing operators are
currently used in the field of Simple Moving
Average (SMA) (Savitzky and Golay, 1964). The
methods smooth the course of curve by using an
averaging over fluorescence values. Thus, the
smoothing operator in the field of SMA needs two
parameters. The first parameter controls the choice
of a window function and the other one controls the
window size. The approximation to the original
curve progression, which contains no noise, can be
solved by an expert by adapting the two parameters.
This manual smoothing needs a lot of experience to
guarantee the success of the pre-processing. For an
automated pre-processing method, it would be
necessary that the used method contains no
parameters.
3 APPROACH OF AN
AUTOMATED
PRE-PROCESSING USING THE
BÉZIER CURVE
The presented approach uses no parameters for
smoothing. The objective is to restore a course of
curve which is similar to figure 1. This means that
the resulting curve progression contains the four
phases and the second derivation method can easily
detect them. In the following, the usage of the Bézier
curve for an automated pre-processing is described.
APPROACH TO ENABLE AN AUTOMATIC PRE-PROCESSING OF qRT-PCR - Analysis
283
3.1 Smoothing in Global Scope by the
Bézier Curve
A Bézier curve is a polynomial which represents a
smoothed curve of the test result and can have any
degree. The degree of Bézier curves corresponds to
the number of cycles in a test result.
The points of a curve will be defined as convex
hulle by the calculation of the Bézier curve. The
resulting curve only contains the first und the last
original point. The other points will be calculated by
the Bernstein polynomial. This method is
advantageous compared to the classical average
method because a set of original points of the test
result determines the coordinates of a calculated
point through the application of the Bernstein
polynomials.
An example of a calculation with a Bézier curve,
which is calculated with a java applet (Koegler,
2000), is given in figure 4 and in figure 5.
Figure 4: Calculation of a Bézier curve.
Figure 5: Smoothing with Bézier curve.
4 APPLICATION SCENARIO
As part of the project ProDIAP, the detection of
biological contamination in the manufacture of
emulsion paints was one task. The detection was
realised by the Real-Time qRT-PCR. The following
describes an evaluation about the quality of the
combination between the developed pre-processing
and the application of second derivation method for
qualitative analysis.
5 EVALUATION
The project ProDIAP used 640 samples for the
evaluation of a qualitative analysis. The automated
qualitative analysis was applied by the software,
which has been developed in this project. These
results were compared to a manual interpretation by
an expert from Qiagen Hamburg (Qiagen, 2010).
Figure 6 illustrates the results of the set of 640
samples.
Figure 6: Overview of the evaluation result.
6 SUMMARY
The evaluation has shown that an automated
qualitative analysis of qRT-PCR data is possible and
has reached a high quality. This quality could be
reached by the application of smoothing with Bézier
curves as a noise removal method.
The influence of smoothing with Bézier curves
on the results of a quantitative analysis is not
estimated.
ACKNOWLEDGEMENTS
This article was sponsored by the German
Bundesministerium für Bildung und Forschung
(BMBF), Ref# 01RI0709A-C.
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