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Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere

In environmental metagenomic experiments, a very high proportion of the microbial sequencing data (> 70%) remains largely unexploited because rare and closely related genomes are missed in short-read assemblies. The identity and the potential metabolisms of a large fraction of natural microbial c...

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Autores principales: Debroas, Didier, Hochart, Corentin, Galand, Pierre E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723795/
https://www.ncbi.nlm.nih.gov/pubmed/37938749
http://dx.doi.org/10.1038/s43705-022-00167-8
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author Debroas, Didier
Hochart, Corentin
Galand, Pierre E.
author_facet Debroas, Didier
Hochart, Corentin
Galand, Pierre E.
author_sort Debroas, Didier
collection PubMed
description In environmental metagenomic experiments, a very high proportion of the microbial sequencing data (> 70%) remains largely unexploited because rare and closely related genomes are missed in short-read assemblies. The identity and the potential metabolisms of a large fraction of natural microbial communities thus remain inaccessible to researchers. The purpose of this study was to explore the genomic content of unassembled metagenomic data and test their level of novelty. We used data from a three-year microbial metagenomic time series of the NW Mediterranean Sea, and conducted reference-free and database-guided analysis. The results revealed a significant genomic difference between the assembled and unassembled reads. The unassembled reads had a lower mean identity against public databases, and fewer metabolic pathways could be reconstructed. In addition, the unassembled fraction presented a clear temporal pattern, unlike the assembled ones, and a specific community composition that was similar to the rare communities defined by metabarcoding using the 16S rRNA gene. The rare gene pool was characterised by keystone bacterial taxa, and the presence of viruses, suggesting that viral lysis could maintain some taxa in a state of rarity. Our study demonstrates that unassembled metagenomic data can provide important information on the structure and functioning of microbial communities.
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spelling pubmed-97237952023-01-04 Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere Debroas, Didier Hochart, Corentin Galand, Pierre E. ISME Commun Article In environmental metagenomic experiments, a very high proportion of the microbial sequencing data (> 70%) remains largely unexploited because rare and closely related genomes are missed in short-read assemblies. The identity and the potential metabolisms of a large fraction of natural microbial communities thus remain inaccessible to researchers. The purpose of this study was to explore the genomic content of unassembled metagenomic data and test their level of novelty. We used data from a three-year microbial metagenomic time series of the NW Mediterranean Sea, and conducted reference-free and database-guided analysis. The results revealed a significant genomic difference between the assembled and unassembled reads. The unassembled reads had a lower mean identity against public databases, and fewer metabolic pathways could be reconstructed. In addition, the unassembled fraction presented a clear temporal pattern, unlike the assembled ones, and a specific community composition that was similar to the rare communities defined by metabarcoding using the 16S rRNA gene. The rare gene pool was characterised by keystone bacterial taxa, and the presence of viruses, suggesting that viral lysis could maintain some taxa in a state of rarity. Our study demonstrates that unassembled metagenomic data can provide important information on the structure and functioning of microbial communities. Nature Publishing Group UK 2022-09-21 /pmc/articles/PMC9723795/ /pubmed/37938749 http://dx.doi.org/10.1038/s43705-022-00167-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Debroas, Didier
Hochart, Corentin
Galand, Pierre E.
Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title_full Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title_fullStr Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title_full_unstemmed Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title_short Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
title_sort seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723795/
https://www.ncbi.nlm.nih.gov/pubmed/37938749
http://dx.doi.org/10.1038/s43705-022-00167-8
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