ABSTRACT
Global outbreaks and epidemics caused by emerging or re-emerging mosquito-borne viruses are becoming more common. These viruses belong to multiple genera including Flavivirus and Alphavirus and often cause non-specific or asymptomatic infection, which can confound viral prevalence studies. In addition, many acute phase diagnostic tests rely on the detection of viral components such as RNA or antigen. Standard serological tests are often not reliable for diagnosis after the acute phase due to cross-reactivity among viruses (e.g. flaviviruses). In order to contribute to development efforts for mosquito-borne serodiagnostics, we incubated 137 human sera on individual custom peptide arrays that consisted of over 866 unique peptides in quadruplicate. Our bioinformatics workflow to analyze these data incorporated machine learning, statistics, and B-cell epitope prediction. The unprocessed array data can be useful in separate meta-analyses that can be applicable to diverse efforts including the development of new pan-flavivirus antibodies, more accurate epitope mapping, and vaccine development against these viral pathogens.