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      Global landscape of phenazine biosynthesis and biodegradation reveals species-specific colonization patterns in agricultural soils and crop microbiomes

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          Abstract

          Phenazines are natural bacterial antibiotics that can protect crops from disease. However, for most crops it is unknown which producers and specific phenazines are ecologically relevant, and whether phenazine biodegradation can counter their effects. To better understand their ecology, we developed and environmentally-validated a quantitative metagenomic approach to mine for phenazine biosynthesis and biodegradation genes, applying it to >800 soil and plant-associated shotgun-metagenomes. We discover novel producer-crop associations and demonstrate that phenazine biosynthesis is prevalent across habitats and preferentially enriched in rhizospheres, whereas biodegrading bacteria are rare. We validate an association between maize and Dyella japonica, a putative producer abundant in crop microbiomes. D. japonica upregulates phenazine biosynthesis during phosphate limitation and robustly colonizes maize seedling roots. This work provides a global picture of phenazines in natural environments and highlights plant-microbe associations of agricultural potential. Our metagenomic approach may be extended to other metabolites and functional traits in diverse ecosystems.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Fast and sensitive protein alignment using DIAMOND.

            The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                15 September 2020
                2020
                : 9
                : e59726
                Affiliations
                [1 ]Division of Geological and Planetary Sciences, California Institute of Technology PasadenaUnited States
                [2 ]Division of Biology and Biological Engineering, California Institute of Technology PasadenaUnited States
                [3 ]Wheat Health, Genetics and Quality Research Unit, USDA Agricultural Research Service PullmanUnited States
                CorpoGen Colombia
                National Institute of Child Health and Human Development United States
                CorpoGen Colombia
                Author information
                https://orcid.org/0000-0002-6650-5488
                https://orcid.org/0000-0003-1647-1918
                Article
                59726
                10.7554/eLife.59726
                7591250
                32930660
                f9894113-7deb-45ed-b096-7514449b2ae9
                © 2020, Dar et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 05 June 2020
                : 02 September 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100005237, Helen Hay Whitney Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000183, Army Research Office;
                Award ID: W911NF-17-1-0024
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1R01AI127850-01A1
                Award Recipient :
                Funded by: Rothschild Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006961, California Institute of Technology;
                Award ID: GPS Division Geobiology Postdoctoral Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003043, EMBO;
                Award ID: Long-Term Postdoctoral Fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Ecology
                Microbiology and Infectious Disease
                Custom metadata
                Computational approach quantifies the abundance of phenazine-antibiotic producing and biodegrading bacteria in diverse soil and plant-associated habitats.

                Life sciences
                phenazines,metagenomics,ecology,dyella japonica,other
                Life sciences
                phenazines, metagenomics, ecology, dyella japonica, other

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