A New Approach to Primer Selection in Polymerase Chain Reaction Experiments

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Contents

We address the problem of primer selection in polymerase chain reaction (PCR) experiments. We prove that the problem of minimizing the number of primers required to amplify a set of DNA sequences is NP-complete, and show that even approximating solutions to this problem to within a constant factor times optimal is intractable. On the practical side, we give a simple branch-and-bound algorithm that solves the primers minimization problem within reasonable time for typical instances. We present an efficient approximation scheme for this problem, and prove that our heuristic always produces solutions no worse than a logarithmic factor times the optimal, this being the best approximation possible within polynomial time. Finally, we analyze a weighted variant, where both the number of primers as well as the sum of their costs is optimized simultaneously. We conclude by presenting the empirical performance of our methods on biological data.