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      Replicated life-history patterns and subsurface origins of the bacterial sister phyla Nitrospirota and Nitrospinota

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          Abstract

          The phyla Nitrospirota and Nitrospinota have received significant research attention due to their unique nitrogen metabolisms important to biogeochemical and industrial processes. These phyla are common inhabitants of marine and terrestrial subsurface environments and contain members capable of diverse physiologies in addition to nitrite oxidation and complete ammonia oxidation. Here, we use phylogenomics and gene-based analysis with ancestral state reconstruction and gene-tree–species-tree reconciliation methods to investigate the life histories of these two phyla. We find that basal clades of both phyla primarily inhabit marine and terrestrial subsurface environments. The genomes of basal clades in both phyla appear smaller and more densely coded than the later-branching clades. The extant basal clades of both phyla share many traits inferred to be present in their respective common ancestors, including hydrogen, one-carbon, and sulfur-based metabolisms. Later-branching groups, namely the more frequently studied classes Nitrospiria and Nitrospinia, are both characterized by genome expansions driven by either de novo origination or laterally transferred genes that encode functions expanding their metabolic repertoire. These expansions include gene clusters that perform the unique nitrogen metabolisms that both phyla are most well known for. Our analyses support replicated evolutionary histories of these two bacterial phyla, with modern subsurface environments representing a genomic repository for the coding potential of ancestral metabolic traits.

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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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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
                borcutt@bigelow.org
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                3 April 2023
                3 April 2023
                June 2023
                : 17
                : 6
                : 891-902
                Affiliations
                [1 ]GRID grid.296275.d, ISNI 0000 0000 9516 4913, Bigelow Laboratory for Ocean Sciences, ; 60 Bigelow Drive, East Boothbay, ME 04544 USA
                [2 ]GRID grid.34429.38, ISNI 0000 0004 1936 8198, University of Guelph, School of Environmental Sciences, ; 50 Stone Road East, Guelph, ON N1G 2W1 Canada
                [3 ]Basepaws Pet Genetics, 1820 W. Carson Street, Suite 202-351, Torrance, CA 90501 USA
                [4 ]GRID grid.474431.1, ISNI 0000 0004 0525 4843, Desert Research Institute, ; 755 East Flamingo Road, Las Vegas, NV 89119 USA
                Author information
                http://orcid.org/0000-0001-5005-673X
                http://orcid.org/0000-0001-9526-070X
                http://orcid.org/0000-0003-2825-5753
                http://orcid.org/0000-0003-4458-3108
                http://orcid.org/0000-0002-6233-3578
                Article
                1397
                10.1038/s41396-023-01397-x
                10203281
                37012337
                e5fc29b5-ab38-44f6-806b-53eb8beb4d17
                © The Author(s) 2023

                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
                : 15 July 2022
                : 13 March 2023
                : 17 March 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: OCE-1031808
                Award ID: OIA-1826734
                Award ID: OCE-173017
                Award ID: OIA-1826734
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012627, NASA | NASA Astrobiology Institute (NAI);
                Award ID: 80NSSC19K0466
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100013505, Center for Dark Energy Biosphere Investigations (C-DEBI);
                Award ID: OCE-0939654
                Award Recipient :
                Categories
                Article
                Custom metadata
                © International Society for Microbial Ecology 2023

                Microbiology & Virology
                environmental sciences,microbial ecology,bacterial genetics
                Microbiology & Virology
                environmental sciences, microbial ecology, bacterial genetics

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