Nucleic Acids Research Advance Access originally published online on May 20, 2008
Nucleic Acids Research 2008 36(11):e66; doi:10.1093/nar/gkn290
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Nucleic Acids Research, 2008, Vol. 36, No. 11 e66
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Predicting failure rate of PCR in large genomes
1Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu and 2Estonian Biocentre, Tartu, Estonia
*To whom correspondence should be addressed. Tel: +372 7375001; Fax: +372 7420286; Email: maido.remm{at}ut.ee
Received March 12, 2008. Revised April 24, 2008. Accepted April 28, 2008.
We have developed statistical models for estimating the failure rate of polymerase chain reaction (PCR) primers using 236 primer sequence-related factors. The model involved 1314 primer pairs and is based on more than 80 000 PCR experiments. We found that the most important factor in determining PCR failure is the number of predicted primer-binding sites in the genomic DNA. We also compared different ways of defining primer-binding sites (fixed length word versus thermodynamic model; exact match versus matches including 1–2 mismatches). We found that the most efficient prediction of PCR failure rates can be achieved using a combination of four factors (number of primer-binding sites counted in different ways plus GC% of the primer) combined into single statistical model GM1. According to our estimations from experimental data, the GM1 model can reduce the average failure rate of PCR primers nearly 3-fold (from 17% to 6%). The GM1 model can easily be implemented in software to premask genome sequences for potentially failing PCR primers, thus improving large-scale PCR-primer design.
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