2 research outputs found
Approximation Algorithms for Minimum PCR Primer Set Selection with Amplification Length and Uniqueness Constraints
A critical problem in the emerging high-throughput genotyping protocols is to
minimize the number of polymerase chain reaction (PCR) primers required to
amplify the single nucleotide polymorphism loci of interest. In this paper we
study PCR primer set selection with amplification length and uniqueness
constraints from both theoretical and practical perspectives. We give a greedy
algorithm that achieves a logarithmic approximation factor for the problem of
minimizing the number of primers subject to a given upperbound on the length of
PCR amplification products. We also give, using randomized rounding, the first
non-trivial approximation algorithm for a version of the problem that requires
unique amplification of each amplification target. Empirical results on
randomly generated testcases as well as testcases extracted from the from the
National Center for Biotechnology Information's genomic databases show that our
algorithms are highly scalable and produce better results compared to previous
heuristics
Approximation algorithms for minimum PCR primer set selection with amplification length and uniqueness constraints
A critical problem in the emerging high-throughput genotyping protocols is to minimize the number of polymerase chain reaction (PCR) primers required to amplify the single nucleotide polymorphism loci of interest. In this paper we study PCR primer set selection with amplification length and uniqueness constraints from both theoretical and practical perspectives. We give a greedy algorithm that achieves a logarithmic approximation factor for the problem of minimizing the number of primers subject to a given upperbound on the length of PCR amplification products. We also give, using randomized rounding, the first non-trivial approximation algorithm for a version of the problem that requires unique amplification of each amplification target. Empirical results on randomly generated testcases as well as testcases extracted from the from the National Center for Biotechnology Information’s genomic databases show that our algorithms are highly scalable and produce better results compared to previous heuristics.