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      Insights into the Vertical Stratification of Microbial Ecological Roles across the Deepest Seawater Column on Earth

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          Abstract

          The Earth’s oceans are a huge body of water with physicochemical properties and microbial community profiles that change with depth, which in turn influences their biogeochemical cycling potential. The differences between microbial communities and their functional potential in surface to hadopelagic water samples are only beginning to be explored. Here, we used metagenomics to investigate the microbial communities and their potential to drive biogeochemical cycling in seven different water layers down the vertical profile of the Challenger Deep (0–10,500 m) in the Mariana Trench, the deepest natural point in the Earth’s oceans. We recovered 726 metagenome-assembled genomes (MAGs) affiliated to 27 phyla. Overall, biodiversity increased in line with increased depth. In addition, the genome size of MAGs at ≥4000 m layers was slightly larger compared to those at 0–2000 m. As expected, surface waters were the main source of primary production, predominantly from Cyanobacteria. Intriguingly, microbes conducting an unusual form of nitrogen metabolism were identified in the deepest waters (>10,000 m), as demonstrated by an enrichment of genes encoding proteins involved in dissimilatory nitrate to ammonia conversion (DNRA), nitrogen fixation and urea transport. These likely facilitate the survival of ammonia-oxidizing archaea α lineage, which are typically present in environments with a high ammonia concentration. In addition, the microbial potential for oxidative phosphorylation and the glyoxylate shunt was enhanced in >10,000 m waters. This study provides novel insights into how microbial communities and their genetic potential for biogeochemical cycling differs through the Challenger deep water column, and into the unique adaptive lifestyle of microbes in the Earth’s deepest seawater.

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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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            CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

            Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
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              trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses

              Summary: Multiple sequence alignments are central to many areas of bioinformatics. It has been shown that the removal of poorly aligned regions from an alignment increases the quality of subsequent analyses. Such an alignment trimming phase is complicated in large-scale phylogenetic analyses that deal with thousands of alignments. Here, we present trimAl, a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses. trimAl can consider several parameters, alone or in multiple combinations, for selecting the most reliable positions in the alignment. These include the proportion of sequences with a gap, the level of amino acid similarity and, if several alignments for the same set of sequences are provided, the level of consistency across different alignments. Moreover, trimAl can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized. Availability: trimAl has been written in C++, it is portable to all platforms. trimAl is freely available for download (http://trimal.cgenomics.org) and can be used online through the Phylemon web server (http://phylemon2.bioinfo.cipf.es/). Supplementary Material is available at http://trimal.cgenomics.org/publications. Contact: tgabaldon@crg.es
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                Author and article information

                Journal
                Microorganisms
                Microorganisms
                microorganisms
                Microorganisms
                MDPI
                2076-2607
                27 August 2020
                September 2020
                : 8
                : 9
                : 1309
                Affiliations
                [1 ]College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; xuechunxu@ 123456outlook.com (C.-X.X.); liujiwen@ 123456ouc.edu.cn (J.L.); zhengyf90@ 123456126.com (Y.Z.); xiaoyuzhu321@ 123456126.com (X.-Y.Z.); liangjinchangluyao@ 123456163.com (J.L.); waqaryousufxai@ 123456outlook.com (W.A.)
                [2 ]Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China
                [3 ]Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
                [4 ]School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; D.Lea-Smith@ 123456uea.ac.uk (D.J.L.-S.); G.Rowley@ 123456uea.ac.uk (G.R.); Jonathan.Todd@ 123456uea.ac.uk (J.D.T.)
                [5 ]School of Earth Sciences, University of Melbourne, Parkville, VIC 3010, Australia; linheyu@ 123456outlook.com
                [6 ]Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
                Author notes
                [* ]Correspondence: xhzhang@ 123456ouc.edu.cn
                Author information
                https://orcid.org/0000-0003-2463-406X
                https://orcid.org/0000-0001-7573-5480
                https://orcid.org/0000-0002-7428-7775
                Article
                microorganisms-08-01309
                10.3390/microorganisms8091309
                7565560
                32867361
                54febd5d-1d77-43fc-8204-5e5aa8dbfcb7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 July 2020
                : 26 August 2020
                Categories
                Article

                mariana trench,hadal water,metagenomics,microbial community,function,metagenome-assembled genomes

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