MRPrimer: a MapReduce-based method for the thorough design of valid and ranked primers for PCR

Abstract

Primer design is a fundamental technique that is widely used for polymerase chain reaction (PCR). Although many methods have been proposed for primer design, they require a great deal of manual effort to generate feasible and valid primers, including homology tests on off-target sequences using BLAST-like tools. That approach is inconvenient for many target sequences of quantitative PCR (qPCR) due to considering the same stringent and allele-invariant constraints. To address this issue, we propose an entirely new method called MRPrimer that can design all feasible and valid primer pairs existing in a DNA database at once, while simultaneously checking a multitude of filtering constraints and validating primer specificity. Furthermore, MRPrimer suggests the best primer pair for each target sequence, based on a ranking method. Through qPCR analysis using 343 primer pairs and the corresponding sequencing and comparative analyses, we showed that the primer pairs designed by MRPrimer are very stable and effective for qPCR. In addition, MRPrimer is computationally efficient and scalable and therefore useful for quickly constructing an entire collection of feasible and valid primers for frequently updated databases like RefSeq. Furthermore, we suggest that MRPrimer can be utilized conveniently for experiments requiring primer design, especially real-time qPCR.