| Summary: | <p>Abstract</p> <p>Background</p> <p>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).</p> <p>Results</p> <p>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 <url>http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html</url>and <url>http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html</url></p> <p>Conclusions</p> <p>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.</p>
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