HPV DNA Forward and Reverse Primer Identification

Abstract Certain synthetic DNA sequences of the Human Papilloma Virus (HPV) are manually amplified to facilitate the detection and discrimination of multiple HPV types, after the identification of the Forward and Reverse Primer. We present a software model that automatically finds appropriate primer binding sites using user-selected parameters. It decreases the time and error involved in finding a primer pair that can identify various HPV types in the context of HPV detection. This model uses a rule-based system to calculate metrics for each possible forward and reverse primer pair, where this resulted in approximately 170 million possible combinations of Forward and Reverse Primer candidates. To accomplish such a computationally intensive task parallel computing was employed. Finally, a machine learning approach (clustering) was employed to group suitable Forward and Reverse Primer pairs, thus researchers can examine these groups for suitable pairs for the manually next step, reducing their search space and the possibility of suitable pairs not being investigated. This work is of value as up until this model, researchers trying to identify suitable Forward and Reverse Primer pairs conducted that work manually. Given the time involved in the following amplification steps and the cost, this model will aid researchers to identify the most appropriate Forward and Reverse Primer pairs perhaps improving the outcomes of their valuable research space.