Myth 1: PCR Nearly Always Works and Design Is Not that Important
It might come as a surprise to many that despite the wide use and large investment, PCR in fact is still subject to many artifacts and environmental factors and is not as robust as would be desirable. Many of these artifacts can be avoided by careful oligonucleotide design. Over the last 10 years (1996–2006), I have informally polled scientists who are experts in PCR and asked: “What percentage of the time does a casually designed PCR reaction ‘work’ without any experimental optimization?” In this context, “work” means that the desired amplification product is made in good yield with a minimum of artifact products such as primer dimers, wrong amplicons, or inefficient amplification. By “casually designed,” I mean that typical software tools are used by an experienced molecular biologist. The consensus answer is 70–75%. If one allows for optimization of the annealing temperature in the thermocy- cling protocol (e.g., by using temperature gradient optimization), magnesium concentration optimization, and primer concentration optimization, then the consensus percentage increases to 90–95%. What is a user to do, however, in the 5–10% of cases where single-target PCR fails? Typically, they redesign the primers (without knowledge of what caused the original failure), resyn- thesize the oligonucleotides, and retest the PCR. Such a strategy works fine for laboratories that perform only a few PCRs. Once a particular PCR protocol is tested, it is usually quite reproducible, and this leads to the feeling that PCR is reliable. Even the 90–95% of single-target PCRs that “work” can be improved by using good design principles, which increases the sensitivity, decreases the background amplifications, and requires less experimental optimization. In a high-throughput industrial-scale environment, however, individual optimization of each PCR, redesigning failures, performing individualized thermocycling and buffer conditions, and tracking all these is a nightmare logistically and leads to non-uniform success. In multiplex PCR, all the targets are obviously amplified under the same solution and temperature cycling conditions, so there is no possibility of doing individual optimizations. Instead, it is desirable to have the capability to automatically design PCRs that work under a single general set of conditions without any optimization, which would enable parallel PCRs (e.g., in 384-well format) to be performed under the same buffer conditions and thermocycling protocol. Such robustness would further improve reliability of PCR in all applications but particularly in non-laboratory settings such as hospital clinics or field-testing applications.
Discovery of PCR
Shortly after the discovery of PCR, software for designing oligonucleotides was developed (12). Some examples of widely used primer design software (some of which are described in this book) include VectorNTI, OLIGO (12), Wisconsin GCG, Primer3 (13), PRIMO (14), PRIDE (15), PRIMERFINDER (http://arep.med.harvard.edu/PrimerFinder/PrimerFinderOverview.html), OSP (16), PRIMERMASTER (17), HybSIMULATOR (18), and PrimerPremiere. Many of these programs do incorporate novel features such as accounting for template quality (14) and providing primer predictions that are completely automated (14,15). Each software package has certain advantages and disadvantages, but all are not equal. They widely differ in their ease- of-use, computational efficiency, and underlying theoretical and conceptual framework. These differences result in varying PCR design quality. In addition, there are standalone Web servers that allow for individual parts of PCR to be predicted, notably DNA-MFOLD by Michael Zuker (http://www.bioinfo.rpi.edu/applications/mfold/old/dna/) and HYTHER by my laboratory (http://ozone3.chem.wayne.edu).
Why Is There a Need for Primer Design Software?
DNA hybridization experiments often require optimization because DNA hybridization does not strictly follow the Watson–Crick pairing rules. Instead, a DNA oligonucleotide can potentially pair with many sites on the genome with perhaps only one or a few mismatches, leading to false-positive results. In addition, the desired target sites of single-stranded genomic DNA or mRNA are often folded into stable secondary structures that must be unfolded to allow an oligonucleotide to bind. Sometimes, the target folding is so stable that very little probe DNA binds to the target, leading to a false-negative test. Various other artifacts include probe folding and probe dimerization. Thus, for DNA-based diagnostics to be successful, there is a need to fully understand the science underlying DNA folding and match versus mismatch hybridization. Achieving this goal has been a central activity of my academic laboratory as well as DNA Software, Inc.