Influence of Library Composition on SourceTracker Predictions for Community-Based Microbial Source Tracking

Clairessa M. Brown, Prince P. Mathai, Tina Loesekann, Christopher Staley, Michael J. Sadowsky

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Community-based microbial source tracking (MST) utilizes high-throughput DNA sequencing to profile and compare the microbial communities in different fecal sources and environmental samples. SourceTracker, a program that compares a library of OTUs from fecal sources (i.e., sources) to those in environmental samples (i.e., sinks) in order to determine sources of fecal contamination, is an emerging tool for community-based MST studies. In this study, we investigated the ability of SourceTracker to determine sources of known fecal contamination in spiked, in situ mesocosms containing different source contributors. We also evaluated how SourceTracker results were impacted by accounting for autochthonous taxa present in the sink environment. While SourceTracker was able to predict most sources present in the in situ mesocosms, fecal source library composition substantially influenced the program's ability to predict source contributions. Moreover, prediction results were most reliable when the library contained only known sources, autochthonous taxa were accounted for and when source profiles had low intragroup variability. Although SourceTracker struggled to differentiate between sources with similar bacterial community structures, it was able to consistently identify abundant and expected sources, suggesting that the SourceTracker program can be a useful tool for community-based MST studies.

Original languageEnglish (US)
Pages (from-to)60-68
Number of pages9
JournalEnvironmental Science and Technology
Volume53
Issue number1
DOIs
StatePublished - Jan 2 2019

Bibliographical note

Funding Information:
This work is the result of research sponsored by the Minnesota Sea Grant College Program supported by the NOAA Office of Sea Grants, United States Department of Commerce, under grant no. NA14OAR4170080. This work was also supported by the University of Minnesota NIH Biotechnology Training Grant (grant no. 2T32GM0083-21A1). The U.S. Government is authorized to reproduce and distribute reprints for government purposes, notwithstanding any copyright notation that may appear hereon. This paper is journal reprint no. JR657 of the Minnesota Sea Grant College Program. We thank the Minnesota Supercomputing Institute (MSI) for providing resources that contributed to the research.

Publisher Copyright:
© 2018 American Chemical Society.

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