HTqPCR

high-throughput analysis and visualization of quantitative real-time PCR data in R

Dvinge, Heidi
Bertone, Paul

EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

Received on August 10, 2009; revised on September 28, 2009; accepted on September 30, 2009; Advance Access publication October 6, 2009

Associate Editor: Martin Bishop

Availability: HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software.

Contact: [email protected]

^*To whom correspondence should be addressed.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Bioinformatics 25(24):p 3325-3326, December 15, 2009.

Motivation

Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility.

Results

We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates.