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Bioinformatics Advance Access published online on October 6, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp578
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© The Author(s) 2009. Published by Oxford University Press.
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.

HTqPCR: High-throughput analysis and visualization of quantitative real-time PCR data in R

Heidi Dvinge 1 and Paul Bertone 1,*

1EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB101SD, United Kingdom

*to whom correspondence should be addressed. Paul Bertone, E-mail: bertone{at}ebi.ac.uk


  Abstract

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.

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

Contact: bertone{at}ebi.ac.uk

Associate Editor: Prof. Martin Bishop

Received on August 10, 2009; revised on September 28, 2009; accepted on September 30, 2009

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