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      Mock community as an in situ positive control for amplicon sequencing of microbiotas from the same ecosystem

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          Abstract

          Metataxonomy has become the standard for characterizing the diversity and composition of microbial communities associated with multicellular organisms and their environment. Currently available protocols for metataxonomy assume a uniform DNA extraction, amplification and sequencing efficiency for all sample types and taxa. It has been suggested that the addition of a mock community (MC) to biological samples before the DNA extraction step could aid identification of technical biases during processing and support direct comparisons of microbiota composition, but the impact of MC on diversity estimates of samples is unknown. Here, large and small aliquots of pulverized bovine fecal samples were extracted with no, low or high doses of MC, characterized using standard Illumina technology for metataxonomics, and analysed with custom bioinformatic pipelines. We demonstrated that sample diversity estimates were distorted only if MC dose was high compared to sample mass (i.e. when MC > 10% of sample reads). We also showed that MC was an informative in situ positive control, permitting an estimation of the sample 16S copy number, and detecting sample outliers. We tested this approach on a range of sample types from a terrestrial ecosystem, including rhizosphere soil, whole invertebrates, and wild vertebrate fecal samples, and discuss possible clinical applications.

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          Fitting Linear Mixed-Effects Models Usinglme4

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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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              Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

              The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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                Author and article information

                Contributors
                giulio.galla@fmach.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 March 2023
                11 March 2023
                2023
                : 13
                : 4056
                Affiliations
                [1 ]GRID grid.424414.3, ISNI 0000 0004 1755 6224, Conservation Genomics Research Unit, Research and Innovation Centre, , Fondazione Edmund Mach, ; San Michele all’Adige, Italy
                [2 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Microbiology, , Universität Innsbruck, ; Innsbruck, Austria
                [3 ]GRID grid.418908.c, ISNI 0000 0001 1089 6435, Institute for Alpine Environment, , EURAC Research, ; Bozen, Italy
                [4 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Ecology, , Universität Innsbruck, ; Innsbruck, Austria
                Article
                30916
                10.1038/s41598-023-30916-1
                10008532
                36906688
                fa1b0721-c87c-4f7e-89c1-bed2de3af4de
                © The Author(s) 2023

                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
                : 13 June 2022
                : 3 March 2023
                Funding
                Funded by: Interregional Project Network, Euregio Tirolo-Alto Adige-Trentino
                Award ID: IPN94
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                conservation biology,microbial ecology,environmental microbiology
                Uncategorized
                conservation biology, microbial ecology, environmental microbiology

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