Bioinformatics Advance Access published online on August 30, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm390
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Optimization of primer design for the detection of variable genomic lesions in cancer
1Bioinformatics Program, University of California, San Dego, 2Department of Computer Science and Engineering, University of California, San Dego, 3Moores Cancer Center, University of California, San Diego, 4Department of Computer Science & Center for Computational Molecular Biology, Brown University
*To whom correspondence should be addressed. Ali Bashir, E-mail: abashir{at}ucsd.edu
| Abstract |
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Primer approximation multiplex PCR (PAMP) is a new experimental protocol for efficiently assaying structural variation in genomes. PAMP is particularly suited to cancer genomes where the precise breakpoints of alterations such as deletions or translocations vary between patients. The design of PCR primer sets for PAMP is challenging because a large number of primer pairs are required to detect alterations in the hundreds of kilobases range that can occur in cancer. These sets of primers must achieve high coverage of the region of interest, while avoiding primer dimers and satisfying the physicochemical constraints of good PCR primers. We describe a natural formulation of these constraints as a combinatorial optimization problem. We show that the PAMP primer design problem is NP-hard, and design algorithms based on simulated annealing and integer programming, that provide good solutions to this problem in practice.
The algorithms are applied to a test region around the known CDKN2A deletion, which show excellent results even in a 1:49 mixture of mutated:wild-type cells. We use these test results to help set design parameters for larger problems. We can achieve near-optimal designs for regions close to 1Mb.
Associate Editor: Dr. Chris Stoeckert
Received on April 26, 2007; revised on July 24, 2007; accepted on July 24, 2007