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      SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree

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          Abstract

          Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons.

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          Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach.

          The most commonly used method in evolutionary biology for combining information across multiple tests of the same null hypothesis is Fisher's combined probability test. This note shows that an alternative method called the weighted Z-test has more power and more precision than does Fisher's test. Furthermore, in contrast to some statements in the literature, the weighted Z-method is superior to the unweighted Z-transform approach. The results in this note show that, when combining P-values from multiple tests of the same hypothesis, the weighted Z-method should be preferred.
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            A stagewise rejective multiple test procedure based on a modified Bonferroni test

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              Balance Trees Reveal Microbial Niche Differentiation

              By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss.
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                06 July 2017
                2017
                06 July 2017
                : 15
                : 372-378
                Affiliations
                [a ]Department of Mathematics and Statistics, Utah State University, Logan, UT, USA
                [b ]Department of Economics, Cornell University, Ithaca, NY, USA
                [c ]USTAR Applied Nutrition Research Team, Department of Nutrition, Dietetics, and Food Science, Utah State University, Logan, UT, USA
                [d ]Western Dairy Center, Department of Nutrition, Dietetics, and Food Science, Utah State University, Logan, UT, USA
                [e ]School of Veterinary Medicine, University of California, Davis, CA, USA
                Author notes
                [* ]Corresponding author at: 3900 Old Main Hill, Utah State University, Logan, UT 84322-3900, USA.Utah State University3900 Old Main HillLoganUT84322-3900USA john.r.stevens@ 123456usu.edu
                Article
                S2001-0370(17)30013-2
                10.1016/j.csbj.2017.06.002
                5512180
                e1aa4098-b509-4ceb-876b-e580a6c62fdf
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 7 February 2017
                : 2 June 2017
                : 28 June 2017
                Categories
                Short Survey

                microbial informatics,phylogenetic tree,microbial community analysis,microbiome

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