A MapReduce-based method for the thorough and rapid design of high-quality primers for qPCR experiments

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 re-quire a great deal of manual effort to generate feasible and valid primers, including homol-ogy 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 strin-gent and allele-invariant constraints. In this dissertation, we propose an entirely new meth-od that overcomes these drawbacks. In the first part of this dissertation, we propose the 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 specifici-ty. 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 computation-ally 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. Existing web servers for primer design have major drawbacks, including requiring the use of BLAST-like tools for homology tests, lack of support for ranking of primers, TaqMan probes, and simultaneous design of primers against multiple targets. Due to the large-scale computational overhead, the few web servers supporting homology tests use heuristic approaches or perform homology tests within a limited scope. The primer pairs designed by MRPrimer are very stable and effective in qPCR experiments. However, alt-hough MRPrimer can design very high-quality primers, routine use is inconvenient because it runs on a cluster of computers and requires several hours of runtime when the filtering constraints are adjusted. In the second part of this dissertation, we propose MRPrimerW, the online version of MRPrimer, allows users to design the best primers quickly in a web interface, without requiring a MapReduce cluster or a long computation, as in Google’s search system. It per-forms complete homology testing, supports batch design of primers for multi-target qPCR experiments, supports design of TaqMan probes, and ranks the resulting primers to return the top-1 best primers to the user. To ensure high accuracy, we adopted the core algorithm of MRPrimer, but completely redesigned it to allow users to receive query results quickly in a web interface, without requiring a MapReduce cluster or a long computation. MRPrimerW provides primer design services and a complete set of 341,963,135 in-silico validated primers covering 99% of human and mouse genes. In summary, we have proposed a new method for primer design that overcomes most of drawbacks of existing methods. For an entire DNA database, we have proposed MRPrimer that can design all possible feasible and valid primer pairs through simultane-ously checking a multitude of filtering constraints and validating primer specificity. For user query from web interface, we have proposed MRPrimerW that performs complete homology tests, supports batch designing for qPCR, supports TaqMan probe design, and supports ranking of primers. We believe that the proposed methods will be contribute to increasing the efficiency and specificity of experiments involving PCR. ⓒ 2016 DGIST