Kinetic Outlier Detection (KOD) in real-time PCR.
Artikel i vetenskaplig tidskrift, 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

Författare

Tzachi Bar

Chalmers, Institutionen för kemi och biovetenskap

Anders Ståhlberg

Chalmers, Institutionen för kemi och biovetenskap, Molekylär bioteknik

Anders Muszta

Chalmers, Institutionen för matematik

Göteborgs universitet

Mikael Kubista

Chalmers, Institutionen för kemi och biovetenskap, Molekylär bioteknik

Nucleic Acids Research

0305-1048 (ISSN) 1362-4962 (eISSN)

Vol. 31 17 e105-

Ämneskategorier

Cell- och molekylärbiologi

PubMed

12930979

Mer information

Skapat

2017-10-07