TY - JOUR
T1 - Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification
T2 - A review
AU - Mathai, Prince P.
AU - Staley, Christopher
AU - Sadowsky, Michael J.
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10
Y1 - 2020/10
N2 - The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the environment, 3) inability of traditional microbiological methods to attribute source, 4) lack of correspondence between numbers of fecal indicator bacteria in waterways and many human pathogens, and 5) source allocation requirements and load determinations needed for total maximum daily loads. The MST tools have changed over time, evolving from culture-dependent to culture-independent molecular analyses. More recently, MST tools based on microbial community analyses, mainly DNA sequencing-based approaches, have been developed in an attempt to overcome some of these issues. These approaches generate large data sets and require the use of sophisticated machine learning algorithms to allocate potential host sources to contaminated waterways. In this review we discuss the origins and needs for community-based MST methods, as well as elaborate on the Bayesian algorithm-based program SourceTracker, which is increasingly being used for the determination of sources of fecal contamination of waterways.
AB - The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the environment, 3) inability of traditional microbiological methods to attribute source, 4) lack of correspondence between numbers of fecal indicator bacteria in waterways and many human pathogens, and 5) source allocation requirements and load determinations needed for total maximum daily loads. The MST tools have changed over time, evolving from culture-dependent to culture-independent molecular analyses. More recently, MST tools based on microbial community analyses, mainly DNA sequencing-based approaches, have been developed in an attempt to overcome some of these issues. These approaches generate large data sets and require the use of sophisticated machine learning algorithms to allocate potential host sources to contaminated waterways. In this review we discuss the origins and needs for community-based MST methods, as well as elaborate on the Bayesian algorithm-based program SourceTracker, which is increasingly being used for the determination of sources of fecal contamination of waterways.
KW - Fecal contamination
KW - High-throughput DNA sequencing
KW - Microbial source tracking
KW - SourceTracker
UR - http://www.scopus.com/inward/record.url?scp=85090548522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090548522&partnerID=8YFLogxK
U2 - 10.1016/j.mimet.2020.106050
DO - 10.1016/j.mimet.2020.106050
M3 - Review article
C2 - 32891632
AN - SCOPUS:85090548522
SN - 0167-7012
VL - 177
JO - Journal of Microbiological Methods
JF - Journal of Microbiological Methods
M1 - 106050
ER -