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      Elucidation of the rhizosphere microbiome linked to Spartina alterniflora phenotype in a salt marsh on Skidaway Island, Georgia, USA

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          ABSTRACT

          Smooth cordgrass, Spartina alterniflora, dominates salt marshes on the east coast of the United States. While the physicochemical cues affecting S. alterniflora productivity have been studied intensively, the role of plant–microbe interactions in ecosystem functioning remains poorly understood. Thus, in this study, the effects of S. alterniflora phenotype on the composition of archaeal, bacterial, diazotrophic and fungal communities were investigated. Overall, prokaryotic communities were more diverse and bacteria were more abundant in the areas colonized by the tall plant phenotype in comparison to those of short plant phenotype. Diazotrophic methanogens (Methanomicrobia) preferentially colonized the area of the short plant phenotype. Putative iron-oxidizing Zetaproteobacteria and sulfur-oxidizing Campylobacteria were identified as indicator species in the rhizosphere of tall and short plant phenotypes, respectively. Finally, while diazotrophic populations shaped microbial interactions in the areas colonized by the tall plant phenotype, fungal populations filled this role in the areas occupied by the short plant phenotype. The results here demonstrate that S. alterniflora phenotype and proximity to the root zone are selective forces dictating microbial community assembly. Results further reveal that reduction–oxidation chemistry is a major factor driving the selection of belowground microbial populations in salt marsh habitats.

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              Is Open Access

              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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                Author and article information

                Contributors
                Journal
                FEMS Microbiology Ecology
                Oxford University Press (OUP)
                0168-6496
                1574-6941
                April 01 2020
                April 2020
                March 30 2020
                April 01 2020
                April 2020
                : 96
                : 4
                Affiliations
                [1 ]School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
                [2 ]School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
                Article
                10.1093/femsec/fiaa026
                32227167
                e3909af8-0d32-4e89-b7aa-ba96146068aa
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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