Major developments in molecular biology, coupled with strides in genomics and proteomics, have led to an explosive growth in biological data. To obtain usable, relevant information from these vast databases (e.g. through data mining) it is mandatory to use advanced computational methods and hardware. The rapidly expanding field of bioinformatics, where biology, computer science, and information technology merge to form a single discipline, has the potential to serve as a bridge between medical informatics and experimental science. In this proposal we focus on a unique cohort of HIV-1 infected subjects, long term non-progressors (LTNP), who can remain asymptomatic for >15 yr without antiretroviral therapy. Many factors underlie the LTNP state. This project, which expands our current bioinformatics studies in a more translational direction, will develop a complex clinical and experimental database that will be analyzed by powerful informatics tools to identify key genes and proteins that contribute to the LTNP phenotype. We hypothesize that using innovative informatics technology that we specifically develop, we shall prepare a publicly available, large, interactive database containing medical, genomic and proteomic information on HIV-1 infected patients that can be queried to guide rational, evidence-based, decision making at both the clinical and public health levels. Our proposal represents a productive partnership between 3 specialized research groups, with extensive expertise in: a) clinical management of HIV-1 disease, b) molecular and cellular immunovirology, and c) bioinformatics and computational sciences. The goal of this biomedical informatics proposal is the characterization and efficient utilization of data obtained from basic biomedical research and integrate it with clinical outcome. The following specific aims are proposed. Specific Aim 1: To identify functional genes and proteins that are unique to our specific HIV-1 infected patient cohorts using state of the art genomic and proteomic technologies. Specific Aim 2: To evaluate the role of human allelic variants in influencing the rate of HIV-1 disease progression using SNP analysis and real time, quantitative PCR. Specific Aim 3: To develop new computational tools to analyze and integrate genomic, proteomic, and clinical data from our HIV-1 patient cohorts and convert them into clinically useful information relating to the pathogenesis, transmission and therapeutic response of HIV-1 infected patients. This will involve the design of: data mining-oriented schemas;advanced algorithms for integrating genomic, proteomic, and clinical data in a data warehouse;and metrics and methods to explore the association between host genetic variations and HIV disease. Ultimately this database will be provided on the web as a publicly accessible resource. This study will contribute to our fundamental understanding of the pathogenesis of HIV infections and identify new biomarkers of disease progression and potential molecular targets for therapy.