Faster greedy algorithms for Multiple Degenerate Primer Selection

To achieve increased throughput, amplification of multiple DNA sequences is often performed using a single Polymerase Chain Reaction (PCR) called Multiplex PCR (MP-PCR). Successful MP-PCR requires efficient methods for selecting sets of synthetic oligonucleotides called primers that collectively amplify all DNA loci of interest. Since the potential for forming primer-dimer pairs and unintended amplification products increases with the number of primers, a common optimization objective is to minimize the number of primers required to amplify all targets. Significant reductions in the number of required primers can be achieved by using primers with degenerate bases, or degenerate primers. The problem of selecting the minimum number of degenerate primers that amplify a given set of loci, referred to as the Multiple Degenerate Primer Selection Problem (MDPSP) has received much attention from researchers in the past few years. Since several variants of the problem have been proved to be NP-Complete, research has focused on heuristic algorithms that perform well on real biological data. In this paper, we present two new greedy algorithms for MDPSP, analyze their time and space complexities and compare their performance on random and real biological data with that of two previously reported algorithms. Our results show that the execution time and memory requirement of proposed algorithms is less than of existing algorithms, thus enabling the processing of larger input sets. Also, the new algorithms eliminate the dependency of the previous algorithms on an empirical input parameter that affects the runtime and quality of output. The software is downloadable at http://www.engr.uconn.edu/˜sub02005/software.html