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      Iron Flocs and the Three Domains: Microbial Interactions in Freshwater Iron Mats

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          Abstract

          Freshwater iron mats are dynamic geochemical environments with broad ecological diversity, primarily formed by the iron-oxidizing bacteria. The community features functional groups involved in biogeochemical cycles for iron, sulfur, carbon, and nitrogen.

          ABSTRACT

          Freshwater iron mats are dynamic geochemical environments with broad ecological diversity, primarily formed by the iron-oxidizing bacteria. The community features functional groups involved in biogeochemical cycles for iron, sulfur, carbon, and nitrogen. Despite this complexity, iron mat communities provide an excellent model system for exploring microbial ecological interactions and ecological theories in situ. Syntrophies and competition between the functional groups in iron mats, how they connect cycles, and the maintenance of these communities by taxons outside bacteria (the eukaryota, archaea, and viruses) have been largely unstudied. Here, we review what is currently known about freshwater iron mat communities, the taxa that reside there, and the interactions between these organisms, and we propose ways in which future studies may uncover exciting new discoveries. For example, the archaea in these mats may play a greater role than previously thought as they are diverse and widespread in iron mats based on 16S rRNA genes and include methanogenic taxa. Studies with a holistic view of the iron mat community members focusing on their diverse interactions will expand our understanding of community functions, such as those involved in pollution removal. To begin addressing questions regarding the fundamental interactions and to identify the conditions in which they occur, more laboratory culturing techniques and coculture studies, more network and keystone species analyses, and the expansion of studies to more freshwater iron mat systems are necessary. Increasingly accessible bioinformatic, geochemical, and culturing tools now open avenues to address the questions that we pose herein.

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          Most cited references117

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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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            Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

            mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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              Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.

              Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina's MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                15 December 2020
                Nov-Dec 2020
                : 11
                : 6
                : e02720-20
                Affiliations
                [a ]Department of Biology, East Carolina University, Greenville, North Carolina, USA
                University of Texas Health Science Center at Houston
                Author notes
                Address correspondence to Erin K. Field, fielde14@ 123456ecu.edu .
                Author information
                https://orcid.org/0000-0002-2121-1355
                Article
                mBio02720-20
                10.1128/mBio.02720-20
                7773991
                33323508
                35f05e1f-4b13-4d82-8bbb-f01ea09a854b
                Copyright © 2020 Brooks and Field.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                Page count
                supplementary-material: 0, Figures: 3, Tables: 0, Equations: 0, References: 117, Pages: 16, Words: 10913
                Funding
                Funded by: Graduate Women in Science (GWIS), https://doi.org/10.13039/100014816;
                Award Recipient :
                Categories
                Minireview
                Applied and Environmental Science
                Custom metadata
                November/December 2020

                Life sciences
                freshwater,iron mat,iron-oxidizing bacteria,microbial ecology,microbial interactions
                Life sciences
                freshwater, iron mat, iron-oxidizing bacteria, microbial ecology, microbial interactions

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