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      Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities

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          Abstract

          The class Deltaproteobacteria comprises an ecologically and metabolically diverse group of bacteria best known for dissimilatory sulphate reduction and predatory behaviour. Although this lineage is the fourth described class of the phylum Proteobacteria , it rarely affiliates with other proteobacterial classes and is frequently not recovered as a monophyletic unit in phylogenetic analyses. Indeed, one branch of the class Deltaproteobacteria encompassing Bdellovibrio-like predators was recently reclassified into a separate proteobacterial class, the Oligoflexia . Here we systematically explore the phylogeny of taxa currently assigned to these classes using 120 conserved single-copy marker genes as well as rRNA genes. The overwhelming majority of markers reject the inclusion of the classes Deltaproteobacteria and Oligoflexia in the phylum Proteobacteria . Instead, the great majority of currently recognized members of the class Deltaproteobacteria are better classified into four novel phylum-level lineages. We propose the names Desulfobacterota phyl. nov. and Myxococcota phyl. nov. for two of these phyla, based on the oldest validly published names in each lineage, and retain the placeholder name SAR324 for the third phylum pending formal description of type material. Members of the class Oligoflexia represent a separate phylum for which we propose the name Bdellovibrionota phyl. nov. based on priority in the literature and general recognition of the genus Bdellovibrio. Desulfobacterota phyl. nov. includes the taxa previously classified in the phylum Thermodesulfobacteria , and these reclassifications imply that the ability of sulphate reduction was vertically inherited in the Thermodesulfobacteria rather than laterally acquired as previously inferred. Our analysis also indicates the independent acquisition of predatory behaviour in the phyla Myxococcota and Bdellovibrionota, which is consistent with their distinct modes of action. This work represents a stable reclassification of one of the most taxonomically challenging areas of the bacterial tree and provides a robust framework for future ecological and systematic studies.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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

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                Journal
                International Journal of Systematic and Evolutionary Microbiology
                Microbiology Society
                1466-5026
                1466-5034
                November 05 2020
                Affiliations
                [1 ] School of Biological Sciences, University of Auckland, Auckland, New Zealand
                [2 ] Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
                [3 ] University of Vienna, Centre for Microbiology and Environmental Systems Science, Division of Microbial Ecology, Vienna, Austria
                [4 ] b.experts GmbH Dortmund, Germany
                [5 ] Hiroshima University, Hiroshima, Japan
                [6 ] Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Hokkaido, Japan
                [7 ] Department of Microbiology, University of Georgia, Athens, Georgia, USA
                [8 ] Research Department for Limnology, University of Innsbruck, Mondsee, Austria
                [9 ] Department of Microbiology, Bremen Institute for Materials Testing, Bremen, Germany
                Article
                10.1099/ijsem.0.004213
                33151140
                65a55e6c-d04a-4212-862c-cf28e2bad76c
                © 2020
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