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Trends Microbiol. 2019 May;27(5):387-397. doi: 10.1016/j.tim.2018.10.012. Epub 2018 Dec 13.

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Author information

1
University of Geneva, Department of Genetics and Evolution, 1211 Geneva, Switzerland. Electronic address: tristan.cordier@gmail.com.
2
AZTI, Marine Research Division, Herrera Kaia, Portualdea z/g, 20110 Pasaia, Basque Country, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
3
University of Geneva, Department of Genetics and Evolution, 1211 Geneva, Switzerland.
4
University of Kaiserslautern, Ecology Group, D-67663 Kaiserslautern, Germany.
5
University of Geneva, Department of Genetics and Evolution, 1211 Geneva, Switzerland; ID-Gene ecodiagnostics, Campus Biotech, Avenue Sécheron 15, 1202 Geneva, Switzerland.

Abstract

Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

KEYWORDS:

big data; biomonitoring; environmental genomics; machine learning

PMID:
30554770
DOI:
10.1016/j.tim.2018.10.012

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