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      Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep

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          Abstract

          At marine cold seeps, gaseous and liquid hydrocarbons migrate from deep subsurface origins to the sediment-water interface. Cold seep sediments are known to host taxonomically diverse microorganisms, but little is known about their metabolic potential and depth distribution in relation to hydrocarbon and electron acceptor availability. Here we combined geophysical, geochemical, metagenomic and metabolomic measurements to profile microbial activities at a newly discovered cold seep in the deep sea. Metagenomic profiling revealed compositional and functional differentiation between near-surface sediments and deeper subsurface layers. In both sulfate-rich and sulfate-depleted depths, various archaeal and bacterial community members are actively oxidizing thermogenic hydrocarbons anaerobically. Depth distributions of hydrocarbon-oxidizing archaea revealed that they are not necessarily associated with sulfate reduction, which is especially surprising for anaerobic ethane and butane oxidizers. Overall, these findings link subseafloor microbiomes to various biochemical mechanisms for the anaerobic degradation of deeply-sourced thermogenic hydrocarbons.

          Abstract

          Describing anaerobic short chain alkane degrading archaea at a newly discovered cold seep, the authors here suggest that these organisms play much more important roles in submarine carbon cycling globally than previously thought.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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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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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Author and article information

                Contributors
                dongxy23@mail.sysu.edu.cn
                chubert@ucalgary.ca
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 November 2020
                17 November 2020
                2020
                : 11
                : 5825
                Affiliations
                [1 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, School of Marine Sciences, , Sun Yat-Sen University, ; Zhuhai, 519082 China
                [2 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Biological Sciences, , University of Calgary, ; Calgary, AB T2N 1N4 Canada
                [3 ]Geological Survey of Canada-Atlantic, Dartmouth, NS B3B 1A6 Canada
                [4 ]Applied Petroleum Technology (Canada), Calgary, AB T2N 1Z6 Canada
                [5 ]GRID grid.453335.7, Nova Scotia Department of Energy and Mines, ; Halifax, NS B2Y 4A2 Canada
                [6 ]GRID grid.466781.a, ISNI 0000 0001 2222 3430, Institute for Geo-Resources and Environment, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), ; 1-1-1 Higashi, Tsukuba, 305-8567 Japan
                [7 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Biological Sciences, , Monash University, ; Clayton, VIC 3800 Australia
                [8 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Microbiology, Biomedicine Discovery Institute, , Monash University, ; Clayton, VIC 3800 Australia
                Author information
                http://orcid.org/0000-0002-9224-5923
                http://orcid.org/0000-0001-7086-2237
                http://orcid.org/0000-0002-6319-6536
                http://orcid.org/0000-0002-1109-3797
                http://orcid.org/0000-0001-5429-9958
                http://orcid.org/0000-0001-7616-0594
                http://orcid.org/0000-0002-8691-8116
                Article
                19648
                10.1038/s41467-020-19648-2
                7673041
                33203858
                3ebcaa4f-a42d-4139-b35d-32359f593c70
                © The Author(s) 2020

                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
                : 26 February 2020
                : 6 October 2020
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                © The Author(s) 2020

                Uncategorized
                marine microbiology,carbon cycle,microbial ecology
                Uncategorized
                marine microbiology, carbon cycle, microbial ecology

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