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      The influence of caging, bedding, and diet on the composition of the microbiota in different regions of the mouse gut

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          Abstract

          Countless studies have identified differences between the gut microbiota of humans affected with myriad conditions and healthy individuals, and animal models are commonly used to determine whether those differences are causative or correlative. Recently, concerns have arisen regarding the reproducibility of animal models between institutions and across time. To determine the influence of three common husbandry-associated factors that vary between institutions, groups of weanling mice were placed in either static or ventilated microisolator caging, with either aspen or paperchip bedding, and with one of three commonly used rodent chows, in a fully crossed study design. After thirteen weeks, samples were collected from multiple regions of the gastrointestinal tract and characterized using culture-independent sequencing methods. Results demonstrated that seemingly benign husbandry factors can interact to induce profound changes in the composition of the microbiota present in certain regions of the gut, most notably the cecum, and that those changes are muted during colonic transit. These findings indicate that differences in factors such as caging and bedding can interact to modulate the gut microbiota that in turn may affect reproducibility of some animal models, and that cecal samples might be optimal when screening environmental effects on the gut microbiota.

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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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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              MetaboAnalyst: a web server for metabolomic data analysis and interpretation

              Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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                Author and article information

                Contributors
                franklinc@missouri.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 March 2018
                6 March 2018
                2018
                : 8
                : 4065
                Affiliations
                [1 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, University of Missouri Mutant Mouse Resource and Research Center, ; Columbia, USA
                [2 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, University of Missouri Metagenomics Center, ; Columbia, USA
                [3 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, University of Missouri, College of Veterinary Medicine, Department of Veterinary Pathobiology, ; Columbia, USA
                [4 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, University of Missouri, Informatics Research Core Facility, ; Columbia, USA
                Author information
                http://orcid.org/0000-0002-0630-8589
                Article
                21986
                10.1038/s41598-018-21986-7
                5840362
                29511208
                c96dfd28-da61-40a8-b6ce-07275923c7f0
                © The Author(s) 2018

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 May 2017
                : 14 February 2018
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