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      Genome-resolved metagenomics suggests a mutualistic relationship between Mycoplasma and salmonid hosts

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          Abstract

          Salmonids are important sources of protein for a large proportion of the human population. Mycoplasma species are a major constituent of the gut microbiota of salmonids, often representing the majority of microbiota. Despite the frequent reported dominance of salmonid-related Mycoplasma species, little is known about the phylogenomic placement, functions and potential evolutionary relationships with their salmonid hosts. In this study, we utilise 2.9 billion metagenomic reads generated from 12 samples from three different salmonid host species to I) characterise and curate the first metagenome-assembled genomes (MAGs) of Mycoplasma dominating the intestines of three different salmonid species, II) establish the phylogeny of these salmonid candidate Mycoplasma species, III) perform a comprehensive pangenomic analysis of Mycoplasma, IV) decipher the putative functionalities of the salmonid MAGs and reveal specific functions expected to benefit the host. Our data provide a basis for future studies examining the composition and function of the salmonid microbiota.

          Abstract

          Jacob Rasmussen et al. use metagenomic analyses to examine the diversity of Mycoplasma species among three commonly-fished species of salmonids. Their results establish a phylogeny of candidate salmonid related  Mycoplasma species and suggest a mutualistic relationship between these microbial species and salmon hosts.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
                jacob.rasmussen@bio.ku.dk
                morten.limborg@sund.ku.dk
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                14 May 2021
                14 May 2021
                2021
                : 4
                : 579
                Affiliations
                [1 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Laboratory of Genomics and Molecular Medicine, Department of Biology, , University of Copenhagen, ; Copenhagen, Denmark
                [2 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Center for Evolutionary Hologenomics, GLOBE institute, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [3 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Veterinary and Animal Sciences, , University of Copenhagen, Veterinary Clinical Microbiology, ; Copenhagen, Denmark
                [4 ]GRID grid.460789.4, ISNI 0000 0004 4910 6535, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, , Université Paris-Saclay, ; Evry, France
                [5 ]GRID grid.458267.a, Lerøy Seafood Group ASA, ; Bergen, Norway
                [6 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Veterinary and Animal Sciences, , University of Copenhagen, Parasitology and Aquatic Pathobiology, ; Copenhagen, Denmark
                [7 ]GRID grid.10919.30, ISNI 0000000122595234, Norwegian College of Fishery Science, , UiT the Arctic University of Norway, ; Tromsø, Norway
                [8 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, Department of Natural History, NTNU University Museum, , Norwegian University of Science and Technology (NTNU), ; Trondheim, Norway
                [9 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, Institute of Metagenomics, BGI-Shenzhen, ; Shenzhen, China
                Author information
                http://orcid.org/0000-0002-7710-8912
                http://orcid.org/0000-0002-5805-7195
                http://orcid.org/0000-0002-6024-0917
                http://orcid.org/0000-0002-7718-6531
                Article
                2105
                10.1038/s42003-021-02105-1
                8121932
                33990699
                f4c6d804-7f4e-43e7-9c5a-36188ebe6291
                © 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
                : 23 October 2020
                : 14 April 2021
                Funding
                Funded by: The research was funded by The Independent Research Fund Denmark (“HappyFish”, grant No. 8022-00005B), GUDP (“Præ-Pro-Fisk”, grant no. 34009-17-1218) and the FHF – Norwegian Seafood Research Fund (“HoloFish”, grant No. 901436).
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                © The Author(s) 2021

                bacterial host response,applied microbiology,bacterial genomics,environmental microbiology

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