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      The microbiomes on the roots of wheat (Triticum aestivum L.) and rice (Oryza sativa L.) exhibit significant differences in structure between root types and along root axes

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          Abstract

          There is increasing interest in understanding how the microbial communities on roots can be manipulated to improve plant productivity. Root systems are not homogeneous organs but are comprised of different root types of various ages and anatomies that perform different functions. Relatively little is known about how this variation influences the distribution and abundance of microorganisms on roots and in the rhizosphere. Such information is important for understanding how root–microbe interactions might affect root function and prevent diseases. This study tested specific hypotheses related to the spatial variation of bacterial and fungal communities on wheat (Triticum aestivum L.) and rice (Oryza sativa L.) roots grown in contrasting soils. We demonstrate that microbial communities differed significantly between soil type, between host species, between root types, and with position along the root axes. The magnitude of variation between different root types and along individual roots was comparable with the variation detected between different plant species. We discuss the general patterns that emerged in this variation and identify bacterial and fungal taxa that were consistently more abundant on specific regions of the root system. We argue that these patterns should be measured more routinely so that localised root–microbe interactions can be better linked with root system design, plant health and performance.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            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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              Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Functional Plant Biology
                Functional Plant Biol.
                CSIRO Publishing
                1445-4408
                2021
                2021
                : 48
                : 9
                : 871
                Article
                10.1071/FP20351
                33934748
                e5f86b33-23f8-499b-98ff-30ffddbd5968
                © 2021
                History

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