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      Digestive tract microbiota of beef cattle that differed in feed efficiency

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          Abstract

          We hypothesized cattle that differed in BW gain had different digestive tract microbiota. Two experiments were conducted. In both experiments, steers received a diet that consisted of 8.0% chopped alfalfa hay, 20% wet distillers grain with solubles, 67.75% dry-rolled corn, and 4.25% vitamin/mineral mix (including monensin) on a dry matter basis. Steers had ad libitum access to feed and water. In experiment 1, 144 steers (age = 310 ± 1.5 d; BW = 503 ± 37.2 kg) were individually fed for 105 d. Ruminal digesta samples were collected from eight steers with the greatest (1.96 ± 0.02 kg/d) and eight steers with the least ADG (1.57 ± 0.02 kg/d) that were within ±0.32 SD of the mean (10.1 ± 0.05 kg/d) dry matter. In experiment 2, 66 steers (age = 396 ± 1 d; BW = 456 ± 5 kg) were individually fed for 84 d. Rumen, duodenum, jejunum, ileum, cecum, and colon digesta samples were collected from eight steers with the greatest (2.39 ± 0.06 kg/d) and eight steers with the least ADG (1.85 ± 0.06 kg/d) that were within ±0.55 SD of the mean dry matter intake (11.9 ± 0.1 kg/d). In both studies, DNA was isolated and the V1 to V3 regions of the 16S rRNA gene were sequenced. Operational taxonomic units were classified using 0.03 dissimilarity and identified using the Greengenes 16S rRNA gene database. In experiment 1, there were no differences in the Chao1, Shannon, Simpson, and InvSimpson diversity indexes or the permutation multivariate analysis of variance (PERMANOVA; P = 0.57). The hierarchical test returned six clades as being differentially abundant between steer classifications (P < 0.05). In experiment 2, Chao1, Shannon, Simpson, and InvSimpson diversity indexes and PERMANOVA between steer classified as less or greater ADG did not differ (P > 0.05) for the rumen, duodenum, ileum, cecum, and colon. In the jejunum, there tended to be a difference in the Chao1 (P = 0.09) and Simpson diversity (P = 0.09) indexes between steer classifications, but there was no difference in the Shannon (P = 0.14) and InvSimpson (P = 0.14) diversity indexes. Classification groups for the jejunum differed (P = 0.006) in the PERMANOVA. The hierarchical dependence false discovery rate procedure returned 11 clades as being differentially abundant between steer classifications in the jejunum (P < 0.05). The majority of the OTU were in the Families Corynebacteriaceae and Coriobacteriaceae. This study suggests that intestinal differences in the microbiota of ruminants may be associated with animal performance.

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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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            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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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Journal
                Journal of Animal Science
                Oxford University Press (OUP)
                0021-8812
                1525-3163
                February 2020
                February 01 2020
                January 13 2020
                February 2020
                February 01 2020
                January 13 2020
                : 98
                : 2
                Affiliations
                [1 ]U.S. Department of Agriculture, Agricultural Research Service, U.S. Meat Animal Research Center, Clay Center, NE
                Article
                10.1093/jas/skaa008
                7297442
                31930312
                2a03f037-ff15-4fb7-80b7-651e3d246976
                © 2020
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