Application of an entropy-based computational strategy to identify genomic markers for molecular detection and typing of human papillomavirus

Human papillomavirus (HPV) is a diverse group of double-stranded DNA viruses that present high tropism for the epithelium and infect keratinocytes. Currently, over 200 viral types have been identified, and almost 40 types preferentially infect the epithelial cells of the genital tract. Infections caused by HPV are the most prevalent viral infections that are sexually transmitted in the world. Given how HPV infection is one of the key factors in the development of cervical cancer, we need to develop more effective diagnostic methods to correctly diagnose patients. The significance of our research is that we have developed and applied a novel computational approach based on entropy to identify phylogenetically informative genomic regions that could be used as markers for the detection and typing of HPV. We have demonstrated that our strategy is capable of finding phylogenetically informative L1 regions to design a primer set that can be used to accurately detect and genotype HPV isolates. •Accurate HPV detection and typing is important in cervical cancer prevention.•We developed a novel molecular marker for the detection and typing of HPV.•High specificity and sensitivity of EntroA primers for detection of HPV.•EntroA primers showed high detection power for different HPV types.