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Title: Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring
Authors: Cordier, Tristan; Lanzen, Anders; Apotheloz-Perret-Gentil, Laure and Stoeck, Thorsten; Pawlowski, Jan
Citation: TRENDS IN MICROBIOLOGY, 2019, 27, 387-397
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.
Issue Date: 2019
Type: Review
DOI: 10.1016/j.tim.2018.10.012
ISSN: 0966-842X
E-ISSN: 1878-4380
Funder: Swiss Network for International Studies
Swiss National Science FoundationSwiss National Science Foundation (SNSF) [31003A\_179125]
European Cross-Border Cooperation Program (Interreg France-Switzerland 2014-2020, SYNAQUA project)
IKERBASQUE The Basque Foundation for Science
Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [STO414/15-1]
European UnionEuropean Union (EU) [CA15219]
Appears in Publication types:Artículos científicos

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