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      Characterization of the bacterial microbiome of Rhipicephalus ( Boophilus) microplus collected from Pecari tajacu “Sajino” Madre de Dios, Peru

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          Abstract

          Ticks are arthropods that can host and transmit pathogens to wild animals, domestic animals, and even humans. The bacterial microbiome of adult (males and females) and nymph Rhipicephalus microplus ticks collected from a collared peccary, Pecari tajacu, captured in the rural area of Botijón Village in the Amazon region of Madre de Dios, Peru, was evaluated using metagenomics. The Chao1 and Shannon–Weaver analyses indicated greater bacterial richness and diversity in female ticks (GARH; 375–4.15) and nymph ticks (GARN; 332–4.75) compared to that in male ticks (GARM; 215–3.20). Taxonomic analyses identified 185 operational taxonomic units representing 147 bacterial genera. Of the 25 most prevalent genera, Salmonella (17.5%) and Vibrio (15.0%) showed the highest relative abundance followed by several other potentially pathogenic genera, such as Paracoccus (7.8%), Staphylococcus (6.8%), Pseudomonas (6.6%), Corynebacterium (5.0%), Cloacibacterium (3.6%), and Acinetobacter (2.5%). In total, 19.7% of the detected genera are shared by GARH, GARM, and GARN, and they can be considered as the core microbiome of R. microplus. To the best of our knowledge, this study is the first to characterize the microbiome of ticks collected from P. tajacu and to report the presence of Salmonella and Vibrio in R. microplus. The pathogenic potential and the role of these bacteria in the physiology of R. microplus should be further investigated due to the possible implications for public health and animal health in populations neighboring the habitat of P. tajacu.

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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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              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                jesus.rojas.jaimes@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 March 2021
                23 March 2021
                2021
                : 11
                : 6661
                Affiliations
                [1 ]GRID grid.441984.4, ISNI 0000 0000 9092 8486, Facultad de Ciencias de la Salud, , Universidad Privada del Norte, ; Av. El Sol 461, San Juan de Lurigancho, 15434 Lima, Peru
                [2 ]DATAOMICS E.I.R.L., Lima, Peru
                [3 ]GRID grid.440598.4, ISNI 0000 0004 4648 8611, Departamento Académico de Ciencias Básicas, , Universidad Nacional Amazónica de Madre de Dios, ; Puerto Maldonado, Peru
                [4 ]Incabiotec SAC, Tumbes, Peru
                Article
                86177
                10.1038/s41598-021-86177-3
                7988070
                33758359
                992475ed-8a98-4dcb-9d7b-83c4e8bd2f85
                © 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
                : 4 November 2020
                : 8 March 2021
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                © The Author(s) 2021

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                animal migration,biodiversity,ecological epidemiology,ecosystem ecology,microbial ecology,molecular ecology

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