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      Contamination Sources and Transmission Routes for Campylobacter on (Mixed) Broiler Farms in Belgium, and Comparison of the Gut Microbiota of Flocks Colonized and Uncolonized with Campylobacter

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          Abstract

          Biosecurity seems to be the most promising tool for Campylobacter control on poultry farms. A longitudinal molecular epidemiological study was performed during two production cycles, in which the broilers, the poultry house, and the environment of 10 (mixed) broiler farms were monitored weekly. Cecal droppings from the second production cycle were also used for 16S metabarcoding to study the differences in the microbiota of colonized and uncolonized flocks. Results showed that 3 out of 10 farms were positive for Campylobacter in the first production cycle, and 4 out of 10 were positive in the second. Broilers became colonized at the earliest when they were four weeks old. The majority of the flocks (57%) became colonized after partial depopulation. Before colonization of the flocks, Campylobacter was rarely detected in the environment, but it was frequently isolated from cattle and swine. Although these animals appeared to be consistent carriers of Campylobacter, molecular typing revealed that they were not the source of flock colonization. In accordance with previous reports, this study suggests that partial depopulation appears to be an important risk factor for Campylobacter introduction into the broiler house. Metabarcoding indicated that two Campylobacter-free flocks carried high relative abundances of Megamonas in their ceca, suggesting potential competition with Campylobacter.

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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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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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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

                Journal
                Pathogens
                Pathogens
                pathogens
                Pathogens
                MDPI
                2076-0817
                13 January 2021
                January 2021
                : 10
                : 1
                : 66
                Affiliations
                [1 ]Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9820 Merelbeke, Belgium; karolien.hertogs@ 123456ugent.be (K.H.); annelies.haegeman@ 123456ilvo.vlaanderen.be (A.H.); dries.schaumont@ 123456ilvo.vlaanderen.be (D.S.); marc.heyndrickx@ 123456ilvo.vlaanderen.be (M.H.)
                [2 ]Department of Reproduction, Obstetrics and Herd health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium; jeroen.dewulf@ 123456ugent.be
                [3 ]Animal Health Care Flanders (DGZ), 8820 Torhout, Belgium; gelaudephilippe@ 123456gmail.com
                [4 ]Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium; lieven.dezutter@ 123456ugent.be
                [5 ]Department of Pathology, Bacteriology and Poultry Diseases, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium
                Author notes
                [†]

                Formerly employed by 1 and 2.

                [‡]

                Formerly employed by 4.

                Author information
                https://orcid.org/0000-0002-8192-5368
                https://orcid.org/0000-0002-4389-0440
                https://orcid.org/0000-0003-2675-6271
                https://orcid.org/0000-0001-9843-5990
                https://orcid.org/0000-0002-2913-2686
                https://orcid.org/0000-0002-7200-5869
                Article
                pathogens-10-00066
                10.3390/pathogens10010066
                7828549
                33451094
                e0b1107c-358e-46f5-a05d-945fce998d99
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 November 2020
                : 11 January 2021
                Categories
                Article

                campylobacter,broilers,farm,partial thinning,metabarcoding
                campylobacter, broilers, farm, partial thinning, metabarcoding

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