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MCMC-ODPR : primer design optimization using Markov Chain Monte Carlo sampling
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Kitchen, James, Moore, Jonathan D., Palmer, Sarah A. and Allaby, Robin G.. (2012) MCMC-ODPR : primer design optimization using Markov Chain Monte Carlo sampling. BMC Bioinformatics, Vol.13 (No.1). p. 287. ISSN 1471-2105
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Official URL: http://dx.doi.org/10.1186/1471-2105-13-287
Abstract
Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly. We aimed to develop a system that would implement primer reuse to design degenerate primers that could be designed around SNPs, thus find the fewest necessary primers and the lowest cost whilst maintaining an acceptable coverage and provide a cost effective solution. We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse. We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reuse (MCMC-ODPR) algorithm. Results After repeating the program 1020 times to assess the variance, an average of 17.14% fewer primers were found to be necessary using MCMC-ODPR for an equivalent coverage without implementing primer reuse. The algorithm was able to reuse primers up to five times. We compared MCMC-ODPR with single sequence primer design programs Primer3 and Primer-BLAST and achieved a lower primer cost per amplicon base covered of 0.21 and 0.19 and 0.18 primer nucleotides on three separate gene sequences, respectively. With multiple sequences, MCMC-ODPR achieved a lower cost per base covered of 0.19 than programs BatchPrimer3 and PAMPS, which achieved 0.25 and 0.64 primer nucleotides, respectively. Conclusions MCMC-ODPR is a useful tool for designing primers at various melting temperatures at good target coverage. By combining degeneracy with optimal primer reuse the user may increase coverage of sequences amplified by the designed primers at significantly lower costs. Our analyses showed that overall MCMC-ODPR outperformed the other primer-design programs in our study in terms of cost per covered base.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology |
| Divisions: | Faculty of Science > Life Sciences (2010- ) |
| Library of Congress Subject Headings (LCSH): | Polymerase chain reaction, Nucleotide sequence, Markov processes, Monte Carlo method |
| Journal or Publication Title: | BMC Bioinformatics |
| Publisher: | Bio Med Central |
| ISSN: | 1471-2105 |
| Date: | 2012 |
| Volume: | Vol.13 |
| Number: | No.1 |
| Page Range: | p. 287 |
| Identification Number: | 10.1186/1471-2105-13-287 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| Funder: | Natural Environment Research Council (Great Britain) (NERC) |
| Grant number: | NE/G005974/1 (NERC) |
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| URI: | http://wrap.warwick.ac.uk/id/eprint/52044 |
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