Data Analysis and
BioInformatics in
real-time qPCR
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Bioinformatics
is a multidisciplinary approach to discribe, model and understand
biological processes on basis of information on genes, proteins and
metabolism. It uses computers, data bases and algorythms
to link information and translate it back into biology, physiology or
pathophysiology.
BioInformatics
=> Database Management Systems, Data Mining, Sample Tracking, Information Management, Data
Acquisition, Data
Analysis, Statistics,
Pattern
Recognition &
Classification, Simulation & Modeling
Bioinformatics initially
centered on sequence and genome analysis but
now the extensive use of microarrays, mass spectrometry, qPCR and qRT-PCR, has
stimulated bioinformatic work in data acquisition, signal processing,
and data mining. Also, simulation and modeling are becoming
increasingly important areas of focus in bioinformatics which finally
will lead to a new level of understanding the networks in the
metabolism: Genomics, Transcriptomics, Splicomics, Proteomics,
Metabolomics, etc.
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BioInformatics Page content:
<> <> SSC
- A data-driven clustering method for
time course gene expression data.
Distribution-insensitive
cluster analysis in SAS on real-time PCR gene expression data of
steadily expressed genes.
FREE - Molecular Biology
Freeware for Windows
qPCR-DAMS
FastPCR
Power
Calculator
Calculator
for determining the number of copies of a template
NAR 2004 vol 32
(Web Server
issue)
NAR 2005 vol 33
(Database issue)
ePCR
= electronic PCR
iPCR =
Virtual PCR tool
in silico PCR = in silico simulation of molecular
biology experiments - NEW
BioInformatics analysis of alternative
splicing
Statistical
analysis of real-time PCR data.
Yuan
JS, Reed A, Chen F, Stewart CN Jr. BMC
Bioinformatics. 2006 (7): 85.
Department of Plant Sciences, University of Tennessee, Knoxville, TN
37996, USA.
<> BACKGROUND:
Even though real-time PCR has been broadly applied in biomedical sciences,
data processing procedures for the analysis of quantitative real-time PCR
are still lacking; specifically in the realm of appropriate statistical
treatment. Confidence interval and statistical
significance considerations are not explicit in many of
the current data analysis approaches. Based on the standard
curve method and other useful data analysis methods, we present and compare
four statistical approaches and models for the analysis of real-time
PCR data.
RESULTS: In the first approach, a
multiple regression analysis model was developed to
derive DeltaDeltaCt from estimation of interaction of gene and treatment
effects. In the second approach, an ANCOVA (analysis of covariance) model
was proposed, and the DeltaDeltaCt can be derived from analysis of
effects of variables. The other two models involve
calculation DeltaCt followed by a two group t-test and
non-parametric analogous Wilcoxon test. SAS programs were developed
for all four models and data output for analysis of a sample set are presented.
In addition, a data quality control model was developed and implemented
using SAS.
CONCLUSION: Practical
statistical solutions with SAS programs
were developed for real-time PCR data and a sample dataset was analyzed
with the SAS programs. The analysis using the
various models and programs yielded similar results.
Data quality control and analysis procedures presented here
provide statistical elements for the estimation of the relative
expression of genes using real-time PCR.
Data
Analysis Methods
There are
two methods, both equally valid, for analyzing data obtained from real
time PCR: Relative Standard Curve Method and Comparative CT Method.
The first, relative standard curve method, is useful for investigators
that have a limited number of cDNA samples and a large number of genes
of interest. The comparative CT method is useful for investigators
who have a lage number of cDNA samples and a limited number of genes
of interest (RRC Core Genomics Facility, University of
Illinois
at Chicago)
<> Data Analysis Methods
Relative
Standard Curve Method
Comparative
CT Method
qPCR
Bioinformatik: Neue Entwicklungen in der post-qPCR
Datenanalyse (in German)
Michael W. Pfaffl (2006), Laborwelt (1): 10-13, ISSN 1611–0854
(Editor: T. Gabrielczyk)
Die Entwicklung der Polymerase Ketten Reaktion (PCR) in den 80er Jahren
gehört zweifelsohne zu den größten Errungenschaften in
der Molekularbiologie. Mittels der klassischen PCR lassen sich
hochsensitiv Genabschnitte oder DNA Fragmente qualitativ sowie
semi-quantitativ nachweisen. Um spezifische mRNA zu quantifizieren,
stellt man der PCR die Reverse Transkription (RT) vor. Die Anwendung
der RT-PCR zur Quantifizierung spezifischen mRNA ist heute zum
Routinewerkzeug in der Expressionsanalytik geworden. Die gewonnenen
Ergebnisse sind von überproportionalen Nutzen in der
molekularbiologischen Forschung und molekularen Diagnostik, in der
vergleichenden Expressionsanalytik sowie zur Aufklärung der
„Functional Genomics“.
Der Nachweis kann qualitativ in klassischen Thermocyclern oder in
„real-time“ quantitativ mittels Echtzeit PCR (qPCR) durchgeführt
werden. Die Ergebnisse sind direkt verfügbar, so dass der Einsatz
der qPCR eine deutliche Zeitersparnis mit sich bringt. Da die Zunahme
der Fluoreszenz und die Menge an neusynthetisierten PCR-Produkten
über einen weiten Bereich proportional zueinander sind, kann aus
den gewonnenen Fluoreszenzdaten die eingesetzte Ausgangsmenge der DNA
respektive RNA bestimmt werden. Vorraussetzung für einen
zuverlässigen quantitativen Nachweis ist eine funktionierende
Analytik und Datenauswertung, die exakte Quantifizierungsergebnisse bei
ausreichender Genauigkeit und hoher Wiederholbarkeit liefert.
Real-Time
PCR: A Review of Approaches to Data Analysis.
The registration of
the accumulation of polymerase chain reaction (PCR) products in the
course of amplification
(real-time PCR) requires specific equipment, i.e., detecting amplifiers
capable of recording the level of fluorescence
in the reaction tube during amplicon formation. When the time of the
reaction is complete, researchers are able
to obtain DNA accumulation graphs. This review discusses the most
promising algorithms of the analysis of
real-time PCR curves and possible errors, caused by the software used
or by operators' mistakes. The data included
will assist researchers in understanding the features of a method to
obtain more reliable results.
Data Analysis - Tools and Technologies for
Real-Time
PCR
Biocompare's qPCR Tutorial presents researchers with an overview
of real-time qPCR, identifies the advantages and disadvantages of the
various detection technologies, outlines the key issues for optimizing
experimental design and offers a brief description of the various
methods used for data analysis.
Evaluation of real-time PCR data.
Vaerman JL, Saussoy P, Ingargiola I. J Biol
Regul Homeost Agents. 2004 18(2): 212-214.
UCL, Cliniques Saint Luc, Bruxelles, Belgium.
If real-time PCR is to
be of
much worth to its user, some idea regarding the reliability of its data
is essential. We discuss here some of the problems associated
with interpreting numerical real-time PCR data that lend themselves to
analytical evaluation. We translate into the language of molecular
biology some of the criteria which are used to evaluate
the performance of any new method (linearity,
precision, specificity, limit of detection and quantification).
Real-time PCR gene expression profiling
Mikael Kubista, Björn Sjögreen, Amin Forootan, Radek Sindelka
and Jiri Jonák, and José Manuel Andrade
Real-time PCR has rapidly become the preferred technique for
quantitative analysis of nucleic acids. Its superior sensitivity,
reproducibility and dynamic range make it the preferred choice for
expression profiling in scientific, as well as routine,
applications. => Link to GenEx software
Statistical
practice in high-throughput screening data analysis.
Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R.
Nat Biotechnol. 2006 24(2): 167-75.
McGill University and Genome Quebec Innovation Centre,
740 avenue du Docteur Penfield, Montreal, Quebec, Canada
High-throughput screening is an early critical step in drug discovery.
Its aim is to screen a large number of diverse chemical compounds to
identify candidate 'hits' rapidly and accurately. Few statistical tools
are currently available, however, to detect quality hits with a high
degree of confidence. We examine statistical aspects of data
preprocessing and hit identification for primary screens. We focus on
concerns related to positional effects of wells within plates, choice
of hit threshold and the importance of minimizing false-positive and
false-negative rates. We argue that replicate measurements are needed
to verify assumptions of current methods and to suggest data analysis
strategies when assumptions are not met. The integration of replicates
with robust statistical methods in primary screens will facilitate the
discovery of reliable hits, ultimately improving the sensitivity and
specificity of the screening process.
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