Coccidioides immitis and C. posadasii are two species of soil fungi that cause the disease coccidioidomycosis, (or Valley fever), whose incidence has increased 587% over the last 20 years and now causes over 150,000 new infections in the United States annually. The Centers for Disease Control and Prevention (CDC) report that average cost for treating Valley fever is over $50,000 per patient and delays or misdiagnosis of Valley fever result in an average of $590,000 in extra healthcare costs, including extensive hospital stays and a lifetime of treatment. Current Valley fever diagnostic tests are based on proprietary antibodies or antigens, which may not be easily accessed. We propose that advanced genomic analyses of these fungal pathogens can support the identification of new biomarkers and embody the foundational framework that is necessary for development of new diagnostic tools. We aim to improve the diagnostics available for detection of Coccidioides spp. by analyzing existing genomic data and using in silico predictions to identify several putative antigens that are highly expressed during host infection and would likely interact with the host. We will express these novel antigens along with standard control CF and TP antigens from Coccidioides in a model organism (Escherichia coli) and use the protein products to create an electrochemical sensor Molecularly Imprinted Polymer (MIP) diagnostic assay. MIPs are a polymer that has been processed using a molecular imprinting technique which leaves cavities in the polymer matrix with high affinity for a chosen ‘template’ molecule (in our case the antigens expressed by E. coli). When paired with an electrochemical biosensor, MIPs can be the basis for a low-cost diagnostic tool because they mimic biological recognition well enough to be called ‘synthetic antibodies.’ In total, this proposal represents a unique pairing of advanced genomic data analysis and novel diagnostic development in an important, yet understudied, fungal pathogen.