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Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
Leibniz Inst Balt Sea Res, Warnemunde, Germany..
Leibniz Inst Balt Sea Res, Warnemunde, Germany.;Sorbonne Univ, CNRS, Lab Oceanog Microbienne, LOMIC, Banyuls Sur Mer, France..
Linnaeus Univ, Ctr Ecol & Evolut Microbial Model Syst, Kalmar, Sweden.;Carl von Ossietzky Univ Oldenburg, HIFMB, Oldenburg, Germany.;Helmholtz Zentrum Polar & Meeresforsch, Alfred Wegener Inst, Bremerhaven, Germany..
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2020 (English)In: COMMUNICATIONS BIOLOGY, ISSN 2399-3642, Vol. 3, no 1, article id 119Article in journal (Refereed) Published
Abstract [en]

Alneberg et al. conduct metagenomics binning of water samples collected over major environmental gradients in the Baltic Sea. They use machine-learning to predict the placement of genome clusters along niche gradients based on the content of functional genes. The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenome-assembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, size-fraction) could be predicted based solely on its functional genes. The same approach predicted the genomes' placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2020. Vol. 3, no 1, article id 119
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Biological Sciences
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URN: urn:nbn:se:kth:diva-271933DOI: 10.1038/s42003-020-0856-xISI: 000521060500003PubMedID: 32170201Scopus ID: 2-s2.0-85081916375OAI: oai:DiVA.org:kth-271933DiVA, id: diva2:1424917
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QC 20200420

Available from: 2020-04-20 Created: 2020-04-20 Last updated: 2020-04-20Bibliographically approved

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Alneberg, JohannesAndersson, Anders F.

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