A Fast-and-Robust Profiler for Improving Polymerase Chain Reaction Diagnostics

Polymerase chain reaction (PCR) is an in vitro technology in molecular genetics that progressively amplifies minimal copies of short DNA sequences in a fast and inexpensive manner. However, PCR performance is sensitive to suboptimal processing conditions. Compromised PCR conditions lead to artifacts and bias that downgrade the discriminatory power and reproducibility of the results. Promising attempts to resolve the PCR performance optimization issue have been guided by quality improvement tactics adopted in the past for industrial trials. Thus, orthogonal arrays (OAs) have been employed to program quick-and-easy structured experiments. Profiling of influences facilitates the quantification of effects that may counteract the detectability of amplified DNA fragments. Nevertheless, the attractive feature of reducing greatly the amount of work and expenditures by planning trials with saturated-unreplicated OA schemes is known to be relinquished in the subsequent analysis phase. This is because of an inherent incompatibility of ordinary multi-factorial comparison techniques to convert small yet dense datasets. Treating unreplicated-saturated data with either the analysis of variance (ANOVA) or regression models destroys the information extraction process. Both of those mentioned approaches are rendered blind to error since the examined effects absorb all available degrees of freedom. Therefore, in lack of approximating an experimental uncertainty, any outcome interpretation is rendered subjective. We propose a profiling method that permits the non-linear maximization of amplicon resolution by eliminating the necessity for direct error estimation. Our approach is distribution-free, calibration-free, simulation-free and sparsity-free with well-known power properties. It is also user-friendly by promoting rudimentary analytics. Testing our method on published amplicon count data, we found that the preponderant effect is the concentration of MgCl2 (p 0.1). Comparison of the proposed method with other stochastic approaches is also discussed. Our technique is expected to have extensive applications in genetics and biotechnology where there is a demand for cheap, expedient, and robust information.