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ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data

James R Perkins1*, John M Dawes2, Steve B McMahon2, David LH Bennett2, Christine Orengo1 and Matthias Kohl3

Author Affiliations

1 Institute of Structural and Molecular Biology, University College of London, Gower Street, London WC1E 6BT, UK

2 Wolfson Centre for Age-Related Diseases, King’s College London, London SE1 1UL, UK

3 Department of Mechanical and Process Engineering, Furtwangen University, Jakob-Kienzle-Str. 17, 78054 Villingen-Schwenningen, Germany

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BMC Genomics 2012, 13:296  doi:10.1186/1471-2164-13-296

Published: 2 July 2012

Abstract

Background

Measuring gene transcription using real-time reverse transcription polymerase chain reaction (RT-qPCR) technology is a mainstay of molecular biology. Technologies now exist to measure the abundance of many transcripts in parallel. The selection of the optimal reference gene for the normalisation of this data is a recurring problem, and several algorithms have been developed in order to solve it. So far nothing in R exists to unite these methods, together with other functions to read in and normalise the data using the chosen reference gene(s).

Results

We have developed two R/Bioconductor packages, ReadqPCR and NormqPCR, intended for a user with some experience with high-throughput data analysis using R, who wishes to use R to analyse RT-qPCR data. We illustrate their potential use in a workflow analysing a generic RT-qPCR experiment, and apply this to a real dataset. Packages are available from http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html webciteand http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html webcite

Conclusions

These packages increase the repetoire of RT-qPCR analysis tools available to the R user and allow them to (amongst other things) read their data into R, hold it in an ExpressionSet compatible R object, choose appropriate reference genes, normalise the data and look for differential expression between samples.