Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community
As is well known, soil is a complex ecosystem harboring the most prokaryotic biodiversity on the Earth. In recent years, the advent of high-throughput sequencing techniques has greatly facilitated the progress of soil ecological studies. However, how to effectively understand the underlying biologic...
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ftpubmed:oai:pubmedcentral.nih.gov:3972102 2023-05-15T15:05:14+02:00 Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community Xu, Zhuofei Hansen, Martin Asser Hansen, Lars H. Jacquiod, Samuel Sørensen, Søren J. 2014-04-01 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972102 http://www.ncbi.nlm.nih.gov/pubmed/24691166 https://doi.org/10.1371/journal.pone.0093445 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972102 http://www.ncbi.nlm.nih.gov/pubmed/24691166 http://dx.doi.org/10.1371/journal.pone.0093445 This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2014 ftpubmed https://doi.org/10.1371/journal.pone.0093445 2014-04-06T01:20:34Z As is well known, soil is a complex ecosystem harboring the most prokaryotic biodiversity on the Earth. In recent years, the advent of high-throughput sequencing techniques has greatly facilitated the progress of soil ecological studies. However, how to effectively understand the underlying biological features of large-scale sequencing data is a new challenge. In the present study, we used 33 publicly available metagenomes from diverse soil sites (i.e. grassland, forest soil, desert, Arctic soil, and mangrove sediment) and integrated some state-of-the-art computational tools to explore the phylogenetic and functional characterizations of the microbial communities in soil. Microbial composition and metabolic potential in soils were comprehensively illustrated at the metagenomic level. A spectrum of metagenomic biomarkers containing 46 taxa and 33 metabolic modules were detected to be significantly differential that could be used as indicators to distinguish at least one of five soil communities. The co-occurrence associations between complex microbial compositions and functions were inferred by network-based approaches. Our results together with the established bioinformatic pipelines should provide a foundation for future research into the relation between soil biodiversity and ecosystem function. Text Arctic PubMed Central (PMC) Arctic PLoS ONE 9 4 e93445 |
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Research Article Xu, Zhuofei Hansen, Martin Asser Hansen, Lars H. Jacquiod, Samuel Sørensen, Søren J. Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
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Research Article |
description |
As is well known, soil is a complex ecosystem harboring the most prokaryotic biodiversity on the Earth. In recent years, the advent of high-throughput sequencing techniques has greatly facilitated the progress of soil ecological studies. However, how to effectively understand the underlying biological features of large-scale sequencing data is a new challenge. In the present study, we used 33 publicly available metagenomes from diverse soil sites (i.e. grassland, forest soil, desert, Arctic soil, and mangrove sediment) and integrated some state-of-the-art computational tools to explore the phylogenetic and functional characterizations of the microbial communities in soil. Microbial composition and metabolic potential in soils were comprehensively illustrated at the metagenomic level. A spectrum of metagenomic biomarkers containing 46 taxa and 33 metabolic modules were detected to be significantly differential that could be used as indicators to distinguish at least one of five soil communities. The co-occurrence associations between complex microbial compositions and functions were inferred by network-based approaches. Our results together with the established bioinformatic pipelines should provide a foundation for future research into the relation between soil biodiversity and ecosystem function. |
format |
Text |
author |
Xu, Zhuofei Hansen, Martin Asser Hansen, Lars H. Jacquiod, Samuel Sørensen, Søren J. |
author_facet |
Xu, Zhuofei Hansen, Martin Asser Hansen, Lars H. Jacquiod, Samuel Sørensen, Søren J. |
author_sort |
Xu, Zhuofei |
title |
Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
title_short |
Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
title_full |
Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
title_fullStr |
Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
title_full_unstemmed |
Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community |
title_sort |
bioinformatic approaches reveal metagenomic characterization of soil microbial community |
publisher |
Public Library of Science |
publishDate |
2014 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972102 http://www.ncbi.nlm.nih.gov/pubmed/24691166 https://doi.org/10.1371/journal.pone.0093445 |
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Arctic |
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Arctic |
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Arctic |
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Arctic |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972102 http://www.ncbi.nlm.nih.gov/pubmed/24691166 http://dx.doi.org/10.1371/journal.pone.0093445 |
op_rights |
This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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CC-BY |
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https://doi.org/10.1371/journal.pone.0093445 |
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