Multiplex primer prediction software for divergent targets

Gardner, Shea N.; Hiddessen, Amy L.; Williams, Peter L.; Hara, Christine; Wagner, Mark C.; Colston, Bill W.

The algorithm seeks to minimize the number of primers to amplify all targets, although because the algorithm is heuristic and not an exhaustive search of all combinations of primers, the smallest primer sets may not always be selected. This is an NP complete problem (38) , so an exhaustive search for the global optimum is only practical for very small target sets. It is also beyond the scope of the current software to predict priming with mismatches between target and primer, which may occur under nonstringent hybridization conditions or for mismatches at the 5 0 end of a primer. While allowing for such priming would reduce the number of primers needed to amplify all targets, it would substantially slow the algorithm and increase memory requirements. Nor do we claim to have taken all steps to optimize (e.g. through parallelization, see Supplementary Methods) for speed or memory, but instead present this software as a simple embodiment of one alternative to MSA for multiplex primer design.