In this minireview, we expand upon traditional microbial source tracking (MST) methods by discussing two recently developed, next-generation-sequencing (NGS)-based MST approaches to identify sources of fecal pollution in recreational waters. One method defines operational taxonomic units (OTUs) that are specific to a fecal source, e.g., humans and animals or shared among multiple fecal sources to determine the magnitude and likely source association of fecal pollution. The other method uses SourceTracker, a program using a Bayesian algorithm, to determine which OTUs have contributed to an environmental community based on the composition of microbial communities in multiple fecal sources. Contemporary NGS-based MST tools offer a promising avenue to rapidly characterize fecal source contributions for water monitoring and remediation efforts at a broader and more efficient scale than previous molecular MST methods. However, both NGS methods require optimized sequence processing methodologies (e.g. quality filtering and clustering algorithms) and are influenced by primer selection for amplicon sequencing. Therefore, care must be taken when extrapolating data or combining datasets. Furthermore, traditional limitations of library-dependent MST methods, including differential decay of source material in environmental waters and spatiotemporal variation in source communities, remain to be fully understood. Nevertheless, increasing use of these methods, as well as expanding fecal taxon libraries representative of source communities, will help improve the accuracy of these methods and provide promising tools for future MST investigations.
Bibliographical noteFunding Information:
This work was supported by The Korea Ministry of Environment (MOE) as ?the Environmental Health Action Program? (2016001350006), by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A6A1A03012862), by the GIST Research Institute (GRI) in 2018, by the Minnesota Sea Grant College Program supported by the NOAA Office of Sea Grants, United States Department of Commerce, under grant no. NA14OAR4170080, by the University of Minnesota NIH Biotechnology Training Grant (grant no. 2T32GM0083-21A1) and by the University of Minnesota Agricultural Experiment Station. Sequence data processing and analysis was performed, in part, using the resources of the Minnesota Supercomputing Institute.
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