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• W2076799537 abstract "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." @default.
• W2076799537 created "2016-06-24" @default.
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• W2076799537 date "2014-04-01" @default.
• W2076799537 modified "2023-10-16" @default.
• W2076799537 title "Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community" @default.
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• W2076799537 doi "https://doi.org/10.1371/journal.pone.0093445" @default.
• W2076799537 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3972102" @default.
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• W2076799537 hasPublicationYear "2014" @default.
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