Primer Set Selection in Multiple PCR Experiments

The selection of a suitable set of primers is crucial to the multiple PCR (polymerase chain reaction) experiment, which is one of the most important techniques in molecular biology. The minimum primer set (MPS) problem is to minimize the number of primers required to amplify a set of DNA sequences, so that the experimental costs and time will be reduced. However, the MPS problem has been proved to be NP-complete. In this paper, we propose an efficient heuristic algorithm for solving the MPS problem. In our algorithm, the kernel procedure is to choose a set of possible good primer candidates, each may be able to amplify more than one target DNA sequence. The kernel procedure is accomplished by the local motif finding method, which is based on the combination of the Gibbs sampler method, the ant colony optimization (ACO) strategy, and Liao’s algorithm for finding motifs. We add a new weight parameter to the method, which can guide us to find local motifs with the local view. Then, the complementary sequences of those local motifs (possible primer candidates) are input into the binary integer programming for getting the near optimal set. With the concept of possible primer candidates, the size of the solution space in the binary integer programming can be reduced drastically. We perform experiments on some artificial domains and two gene families. The experimental results show that the time required for our algorithm is reduced drastically, while the performance of our algorithm is comparable to that obtained by the exhaustive search. This research work was partially supported by the National Science Council of the Republic of China under NSC93-2213-E-110-004. To whom all correspondence should be sent: [email protected]