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      Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms

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          Abstract

          Marine algae annually sequester petagrams of carbon dioxide into polysaccharides, which are a central metabolic fuel for marine carbon cycling. Diatom microalgae produce sulfated polysaccharides containing methyl pentoses that are challenging to degrade for bacteria compared to other monomers, implicating these sugars as a potential carbon sink. Free-living bacteria occurring in phytoplankton blooms that specialise on consuming microalgal sugars, containing fucose and rhamnose remain unknown. Here, genomic and proteomic data indicate that small, coccoid, free-living Verrucomicrobiota specialise in fucose and rhamnose consumption during spring algal blooms in the North Sea. Verrucomicrobiota cell abundance was coupled with the algae bloom onset and accounted for up to 8% of the bacterioplankton. Glycoside hydrolases, sulfatases, and bacterial microcompartments, critical proteins for the consumption of fucosylated and sulfated polysaccharides, were actively expressed during consecutive spring bloom events. These specialised pathways were assigned to novel and discrete candidate species of the Akkermansiaceae and Puniceicoccaceae families, which we here describe as Candidatus Mariakkermansia forsetii and Candidatus Fucivorax forsetii. Moreover, our results suggest specialised metabolic pathways could determine the fate of complex polysaccharides consumed during algae blooms. Thus the sequestration of phytoplankton organic matter via methyl pentose sugars likely depend on the activity of specialised Verrucomicrobiota populations.

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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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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                lorellan@mpi-bremen.de
                ramann@mpi-bremen.de
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                7 September 2021
                7 September 2021
                March 2022
                : 16
                : 3
                : 630-641
                Affiliations
                [1 ]GRID grid.419529.2, ISNI 0000 0004 0491 3210, Department of Molecular Ecology, , Max Planck Institute for Marine Microbiology, ; Bremen, Germany
                [2 ]GRID grid.7704.4, ISNI 0000 0001 2297 4381, Center for Marine Environmental Sciences, , MARUM, University of Bremen, ; Bremen, Germany
                Author information
                http://orcid.org/0000-0002-2766-5243
                http://orcid.org/0000-0001-6530-0125
                http://orcid.org/0000-0002-8700-2564
                http://orcid.org/0000-0001-9828-1290
                http://orcid.org/0000-0002-0846-7372
                Article
                1105
                10.1038/s41396-021-01105-7
                8857213
                34493810
                0bbfc169-8dbe-4954-922f-0461ad4f1258
                © The Author(s) 2021

                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/.

                History
                : 30 March 2021
                : 19 August 2021
                : 23 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004189, Max-Planck-Gesellschaft (Max Planck Society);
                Categories
                Article
                Custom metadata
                © International Society for Microbial Ecology 2022

                Microbiology & Virology
                water microbiology,metagenomics,microbial ecology
                Microbiology & Virology
                water microbiology, metagenomics, microbial ecology

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