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      Large-scale phylogenomics of aquatic bacteria reveal molecular mechanisms for adaptation to salinity

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          Abstract

          The crossing of environmental barriers poses major adaptive challenges. Rareness of freshwater-marine transitions separates the bacterial communities, but how these are related to brackish counterparts remains elusive, as do the molecular adaptations facilitating cross-biome transitions. We conducted large-scale phylogenomic analysis of freshwater, brackish, and marine quality-filtered metagenome-assembled genomes (11,248). Average nucleotide identity analyses showed that bacterial species rarely existed in multiple biomes. In contrast, distinct brackish basins cohosted numerous species, but their intraspecific population structures displayed clear signs of geographic separation. We further identified the most recent cross-biome transitions, which were rare, ancient, and most commonly directed toward the brackish biome. Transitions were accompanied by systematic changes in amino acid composition and isoelectric point distributions of inferred proteomes, which evolved over millions of years, as well as convergent gains or losses of specific gene functions. Therefore, adaptive challenges entailing proteome reorganization and specific changes in gene content constrains the cross-biome transitions, resulting in species-level separation between aquatic biomes.

          Abstract

          Wide-scale comparative genomics reveals how aquatic bacteria have adapted to salinity throughout evolutionary history.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draft
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                May 2023
                26 May 2023
                : 9
                : 21
                : eadg2059
                Affiliations
                [ 1 ]Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
                [ 2 ]Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden.
                Author notes
                [* ]Corresponding author. Email: anders.andersson@ 123456scilifelab.se
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-9544-5755
                https://orcid.org/0000-0002-1108-6888
                https://orcid.org/0000-0001-7850-5285
                https://orcid.org/0000-0002-6970-6453
                https://orcid.org/0000-0002-4265-1835
                https://orcid.org/0000-0002-3627-6899
                Article
                adg2059
                10.1126/sciadv.adg2059
                10219603
                37235649
                b6702a4a-be93-400a-848a-d4c89c117c85
                Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 December 2022
                : 21 April 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001862, Swedish Research Council Formas;
                Award ID: 2021-00546
                Funded by: FundRef http://dx.doi.org/10.13039/501100001862, Swedish Research Council Formas;
                Award ID: 2022-01515
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 2021-05563
                Categories
                Research Article
                Earth, Environmental, Ecological, and Space Sciences
                SciAdv r-articles
                Evolutionary Biology
                Microbiology
                Microbiology
                Custom metadata
                Fritzie Benzon

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