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

James R. Perkins, John M. Dawes, Stephen McMahon, David L. H. Bennett, Christine Orengo, Matthias Kohl

Research output: Contribution to journalArticlepeer-review

157 Citations (Scopus)

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 and http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html

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.

Original languageEnglish
Article number296
Pages (from-to)-
Number of pages8
JournalBMC GENOMICS
Volume13
Issue number1
DOIs
Publication statusPublished - 2 Jul 2012

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