Journal of microbiology

1225-8873

59

SCIE

[논문]Microbial source tracking using metagenomics and other new technologies

Shahbaz Raza, Tatsuya Unno

Jungman Kim (김정만)

Michael J. Sadowsky

The environment is under siege from a variety of pollution
sources. Fecal pollution is especially harmful as it disperses
pathogenic bacteria into waterways. Unraveling origins of
mixed sources of fecal bacteria is difficult and microbial
source tracking (MST) in complex environments is still a
daunting task. Despite the challenges, the need for answers
far outweighs the difficulties experienced. Advancements in
qPCR and next generation sequencing (NGS) technologies
have shifted the traditional culture-based MST approaches
towards culture independent technologies, where communitybased
MST is becoming a method of choice. Metagenomic
tools may be useful to overcome some of the limitations of
community-based MST methods as they can give deep insight
into identifying host specific fecal markers and their association
with different environments. Adoption of machine
learning (ML) algorithms, along with the metagenomic based
MST approaches, will also provide a statistically robust and
automated platform. To compliment that, ML-based approaches
provide accurate optimization of resources. With the
successful application of ML based models in disease prediction,
outbreak investigation and medicine prescription,
it would be possible that these methods would serve as a
better surrogate of traditional MST approaches in future.

2021-02-10

https://doi.org/10.1007/s12275-021-0668-9