Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.

Hempel, Christopher A; Buchner, Dominik; Mack, Leoni; Brasseur, Marie V; Tulpan, Dan; Leese, Florian; Steinke, Dirk

Hempel, Christopher A; Buchner, Dominik; Mack, Leoni; Brasseur, Marie V; Tulpan, Dan; Leese, Florian; Steinke, Dirk.

Afiliação

Hempel CA; Department of Integrative Biology, University of Guelph, Guelph, ON, Canada.
Buchner D; Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada.
Mack L; Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
Brasseur MV; Faculty of Aquatic Ecology, University of Duisburg-Essen, Essen, Germany.
Tulpan D; Leibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum A. Koenig, Bonn, Germany.
Leese F; School of Computer Science, University of Guelph, Guelph, ON, Canada.
Steinke D; Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.

Front Microbiol ; 14: 1217750, 2023.

Article em En | MEDLINE | ID: mdl-38075934

Palavras-chave

ExStream; bioinformatics; environmental assessment; freshwater; mesocosm; metabarcoding; metatranscriptomics; stressor prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Microbiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Canadá

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