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

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...

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Bibliographic Details
Published in:BMC genomics 2012-07, Vol.13 (1), p.296-296
Main Authors: Perkins, James R, Dawes, John M, McMahon, Steve B, Bennett, David L H, Orengo, Christine, Kohl, Matthias
Format: Article
Language:English
Subjects:
Abundance
Algorithms
Analysis
Colleges & universities
Computational biology
Data processing
Experiments
Gene expression
Genes
Genetic transcription
Information management
Language
Methods
Molecular biology
Polymerase chain reaction
Programming languages
Real-Time Polymerase Chain Reaction - methods
Reverse transcription
Software
Statistical methods
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Summary: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). 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.htmland http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html 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.
ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-13-296