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Nucleic Acids Research 2006 34(7):e56; doi:10.1093/nar/gkl185
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Published online 14 April 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Methods Online

Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data

A. Callegaro1, R. Spinelli2,3, L. Beltrame2,3, S. Bicciato1, L. Caristina3, S. Censuales4, G. De Bellis3,5 and C. Battaglia2,3,*

1 Department of Chemical Process Engineering, University of Padua Padua, Italy 2 Department of Sciences and Biomedical Technologies, University of Milan Milan, Italy 3 Array Technology Group, Interdisciplinary Center for Biomolecular Studies and Industrial Applications (CISI) University of Milan, Milan, Italy 4 Centro Trasfusionale, Sacco Hospital, University of Milan Milan, Italy 5 Institute for Biomedical Technologies, National Research Council (CNR) Milan, Italy

*To whom correspondence should be addressed at Array Technology Group (ATG), Department of Biomedical Sciences and Technologies and CISI, University of Milan, Milan, Italy. Tel: +39 02 50330425; Fax: +39 02 50330414: Email: cristina.battaglia{at}unimi.it

Received October 1, 2005. Revised November 30, 2005. Accepted March 24, 2006.

Single nucleotide polymorphisms (SNPs) are often determined using TaqMan real-time PCR assays (Applied Biosystems) and commercial software that assigns genotypes based on reporter probe signals at the end of amplification. Limitations to the large-scale application of this approach include the need for positive controls or operator intervention to set signal thresholds when one allele is rare. In the interest of optimizing real-time PCR genotyping, we developed an algorithm for automatic genotype calling based on the full course of real-time PCR data. Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay. The algorithm is unique in that it classifies samples according to the behavior of blanks (no DNA samples), which cluster with heterozygous samples. This method of classification eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare. Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing.


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