Journal Article

<i>HTqPCR</i>: high-throughput analysis and visualization of quantitative real-time PCR data in R

Heidi Dvinge and Paul Bertone

in Bioinformatics

Volume 25, issue 24, pages 3325-3326
Published in print December 2009 | ISSN: 1367-4803
Published online October 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp578

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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 through Bioconductor or at http://www.ebi.ac.uk/bertone/software.

Contact: bertone@ebi.ac.uk

Journal Article.  993 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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