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Daniel Pick, Nao Hisakawa, Forest Rohwer (Computational Science Research Center, San Diego State University, San Diego, CA 92182)In recent years biologists have collected and sequenced large amounts of genetic material from environmental samples. The study of such material is known as metagenomics, and metagenomic data sets have become publicly available at archives such as MG_RAST, http://metagenomics/nmpdr.org, and CAMERA, http://camera.calit2.net. In Dinsdale et al2, it was shown that metagenomic analysis can explain the variance among and within environmental samples, and in Willner et al.3it was shown that dinucleotide composition defines a metagenomic signature which can explain up to 80% of the variance between biomes. Many metagenomic data sets have been annotated with the geographical coordinates where the samples were taken, enabling them to be integrated with digital map data using geographic information systems (GIS). We hypothesize that there are previously undiscovered correlations between such geographical features as sea surface temperature, UV light, organic pollution, and calculated metagenomic characteristics such as dinucleotide ratios and hits to metabolic subsystems.