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Microbiome. 2018 Oct 18;6(1):185. doi: 10.1186/s40168-018-0568-3.

Fecal source identification using random forest.

Author information

1
School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
2
Department of Medicine, University of Chicago, Chicago, IL, USA.
3
School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. mclellan@uwm.edu.

Abstract

BACKGROUND:

Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species. As a result, members of Clostridiales and Bacteroidales are particularly suitable for identifying sources of fecal contamination in environmental samples. However, a comprehensive evaluation of their predictive power and development of computational approaches is lacking. Given the global public health concern for waterborne disease, accurate identification of fecal pollution sources is essential for effective risk assessment and management. Here, we use random forest algorithm and 16S rRNA gene amplicon sequences assigned to Clostridiales and Bacteroidales to identify common fecal pollution sources. We benchmarked the accuracy, consistency, and sensitivity of our classification approach using fecal, environmental, and artificial in silico generated samples.

RESULTS:

Clostridiales and Bacteroidales classifiers were composed mainly of sequences that displayed differential distributions (host-preferred) among sewage, cow, deer, pig, cat, and dog sources. Each classifier correctly identified human and individual animal sources in approximately 90% of the fecal and environmental samples tested. Misclassifications resulted mostly from false-positive identification of cat and dog fecal signatures in host animals not used to build the classifiers, suggesting characterization of additional animals would improve accuracy. Random forest predictions were highly reproducible, reflecting the consistency of the bacterial signatures within each of the animal and sewage sources. Using in silico generated samples, we could detect fecal bacterial signatures when the source dataset accounted for as little as ~ 0.5% of the assemblage, with ~ 0.04% of the sequences matching the classifiers. Finally, we developed a proxy to estimate proportions among sources, which allowed us to determine which sources contribute the most to observed fecal pollution.

CONCLUSION:

Random forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.

KEYWORDS:

16S rRNA gene; Bacteroidales; Clostridiales; High-throughput sequencing; Microbial source tracking; Random forest classification

PMID:
30336775
PMCID:
PMC6194674
DOI:
10.1186/s40168-018-0568-3
[Indexed for MEDLINE]
Free PMC Article

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