An epidemiological river metagenome based on MinION

River waters worldwide are impacted by disease-causing agents including bacteria, protists, flatworms, viruses, and harmful algae that derive from domestic sewage and farm runoff, and/or are emergent due to nutrient pollution and climate change. We are determining the ability of MinION long read metagenomic data to identify pathogens that otherwise would require numerous individual assays to detect. We also are assessing the utility of long read data to circumvent false positive matches from shared conserved sequences in short read data (the customary method used to generate metagenomic data), and the extent to which the phylogenetic diversity inferred from the long reads mirrors the profiles obtained by the sequencing of rDNA amplicons. Our preliminary results suggest that MinIONTM data can accurately predict the composition of synthetic metagenomes. Presently, we are assessing how the diversity detected by MinION^TM from environmental metagenomes compares to individual PCR-based detection of pathogens and we are testing ways to use this platform to monitor the distribution of organisms over space and time. This metagenomic approach is a novel application of MinION^TM that can improve public health associated with environmental exposure to biological agents and that has the potential for worldwide application, especially in developing nations where public health genomics is urgently needed.