Kinetic Outlier Detection (KOD) in real-time PCR.
Journal article, 2003
Real-time PCR is becoming the method of choice for precise quantification of minute amounts of nucleic acids. For proper comparison of samples, almost all quantification methods assume similar PCR efficiencies in the exponential phase of the reaction. However, inhibition of PCR is common when working with biological samples and may invalidate the assumed similarity of PCR efficiencies. Here we present a statistical method, Kinetic Outlier Detection (KOD), to detect samples with dissimilar efficiencies. KOD is based on a comparison of PCR efficiency, estimated from the amplification curve of a test sample, with the mean PCR efficiency of samples in a training set. KOD is demonstrated and validated on samples with the same initial number of template molecules, where PCR is inhibited to various degrees by elevated concentrations of dNTP; and in detection of cDNA samples with an aberrant ratio of two genes. Translating the dissimilarity in efficiency to quantity, KOD identifies outliers that differ by 1.3-1.9-fold in their quantity from normal samples with a P-value of 0.05. This precision is higher than the minimal 2-fold difference in number of DNA molecules that real-time PCR usually aims to detect. Thus, KOD may be a useful tool for outlier detection in real-time PCR.
Polymerase Chain Reaction
Complementary
18S
Algorithms
Rats
Brain
genetics
Gene Expression Profiling
DNA
RNA
Male
metabolism
genetics
Sprague-Dawley
statistics & numerical data
Animals
Rats
genetics
Cyclophilins
Ribosomal
methods
standards
metabolism
Author
Tzachi Bar
Chalmers, Department of Chemistry and Bioscience
Anders Ståhlberg
Chalmers, Department of Chemistry and Bioscience, Molecular Biotechnology
Anders Muszta
Chalmers, Department of Mathematics
University of Gothenburg
Mikael Kubista
Chalmers, Department of Chemistry and Bioscience, Molecular Biotechnology
Nucleic Acids Research
0305-1048 (ISSN) 1362-4962 (eISSN)
Vol. 31 17 e105-Subject Categories
Cell and Molecular Biology
PubMed
12930979