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      Exposure to airborne bacteria depends upon vertical stratification and vegetation complexity

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          Abstract

          Exposure to biodiverse aerobiomes supports human health, but it is unclear which ecological factors influence exposure. Few studies have investigated near-surface green space aerobiome dynamics, and no studies have reported aerobiome vertical stratification in different urban green spaces. We used columnar sampling and next generation sequencing of the bacterial 16S rRNA gene, combined with geospatial and network analyses to investigate urban green space aerobiome spatio-compositional dynamics. We show a strong effect of habitat on bacterial diversity and network complexity. We observed aerobiome vertical stratification and network complexity that was contingent on habitat type. Tree density, closer proximity, and canopy coverage associated with greater aerobiome alpha diversity. Grassland aerobiomes exhibited greater proportions of putative pathogens compared to scrub, and also stratified vertically. We provide novel insights into the urban ecosystem with potential importance for public health, whereby the possibility of differential aerobiome exposures appears to depend on habitat type and height in the airspace. This has important implications for managing urban landscapes for the regulation of aerobiome exposure.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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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
                jmrobinson3@sheffield.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 May 2021
                4 May 2021
                2021
                : 11
                : 9516
                Affiliations
                [1 ]GRID grid.11835.3e, ISNI 0000 0004 1936 9262, Department of Landscape Architecture, , The University of Sheffield, ; Sheffield, S10 2TN UK
                [2 ]inVIVO Planetary Health of the Worldwide Universities Network, NJ, 10704 USA
                [3 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, College of Science and Engineering, , Flinders University, ; Bedford Park, SA 5042 Australia
                [4 ]The Healthy Urban Microbiome Initiative (HUMI), Adelaide, Australia
                [5 ]GRID grid.8752.8, ISNI 0000 0004 0460 5971, School of Science, Engineering and Environment, , University of Salford, ; Salford, M5 4WX UK
                [6 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, School of Public Health and the Environment Institute, , University of Adelaide, ; Adelaide, SA 5005 Australia
                [7 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Microbiology and Cell Science, , University of Florida, ; Gainesville, FL 32603 USA
                Article
                89065
                10.1038/s41598-021-89065-y
                8096821
                33947905
                f051ff93-b02e-423b-83bd-db9c068ddcde
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 January 2021
                : 20 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
                Award ID: ES/J500215/1
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,biodiversity
                Uncategorized
                ecology, biodiversity

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