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      Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem

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          Abstract

          Ecological theory suggests that habitat disturbance differentially influences distributions of habitat generalist and specialist species. While well-established for macroorganisms, this theory has rarely been explored for microorganisms. Here we tested these principles in permeable (sandy) sediments, ecosystems with much spatiotemporal variation in resource availability and physicochemical conditions. Microbial community composition and function were profiled in intertidal and subtidal sediments using 16S rRNA gene amplicon sequencing and metagenomics, yielding 135 metagenome-assembled genomes. Community composition and metabolic traits modestly varied with sediment depth and sampling date. Several taxa were highly abundant and prevalent in all samples, including within the orders Woeseiales and Flavobacteriales, and classified as habitat generalists; genome reconstructions indicate these taxa are highly metabolically flexible facultative anaerobes and adapt to resource variability by using different electron donors and acceptors. In contrast, obligately anaerobic taxa such as sulfate reducers and candidate lineage MBNT15 were less abundant overall and only thrived in more stable deeper sediments. We substantiated these findings by measuring three metabolic processes in these sediments; whereas the habitat generalist-associated processes of sulfide oxidation and fermentation occurred rapidly at all depths, the specialist-associated process of sulfate reduction was restricted to deeper sediments. A manipulative experiment also confirmed habitat generalists outcompete specialist taxa during simulated habitat disturbance. Together, these findings show metabolically flexible habitat generalists become dominant in highly dynamic environments, whereas metabolically constrained specialists are restricted to narrower niches. Thus, an ecological theory describing distribution patterns for macroorganisms likely extends to microorganisms. Such findings have broad ecological and biogeochemical ramifications.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Contributors
                perran.cook@monash.edu
                chris.greening@monash.edu
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                3 May 2021
                3 May 2021
                October 2021
                : 15
                : 10
                : 2986-3004
                Affiliations
                [1 ]Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC Australia
                [2 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Biological Sciences, , Monash University, ; Clayton, VIC Australia
                [3 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Natural Resources Sciences, , McGill University, ; Sainte-Anne-de-Bellevue, QC Canada
                [4 ]GRID grid.1018.8, ISNI 0000 0001 2342 0938, Department of Physiology, Anatomy and Microbiology, , La Trobe University, ; Bundoora, VIC Australia
                [5 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, , The University of Queensland, ; St Lucia, QLD Australia
                [6 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Water Studies Centre, School of Chemistry, , Monash University, ; Clayton, VIC Australia
                [7 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Earth, Atmosphere and Environment, , Monash University, ; Clayton, VIC Australia
                [8 ]GRID grid.9654.e, ISNI 0000 0004 0372 3343, School of Biological Sciences, , University of Auckland, ; Auckland, New Zealand
                Author information
                http://orcid.org/0000-0002-5382-827X
                http://orcid.org/0000-0003-4833-3106
                http://orcid.org/0000-0001-5386-7925
                http://orcid.org/0000-0003-4753-9292
                http://orcid.org/0000-0003-1664-6060
                http://orcid.org/0000-0002-0444-3488
                http://orcid.org/0000-0001-7616-0594
                Article
                988
                10.1038/s41396-021-00988-w
                8443593
                33941890
                2ce83090-36a5-4032-b9f7-a89d3fc3c6d0
                © 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
                : 6 January 2021
                : 25 March 2021
                : 9 April 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000923, Department of Education and Training | Australian Research Council (ARC);
                Award ID: DP180101762
                Award ID: DE170100310
                Award ID: FL150100038
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: APP1178715
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                microbial ecology,microbial communities
                Microbiology & Virology
                microbial ecology, microbial communities

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