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      Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

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          Abstract

          Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes.

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          Most cited references101

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Is Open Access

            Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

            Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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              Quorum sensing: cell-to-cell communication in bacteria.

              Bacteria communicate with one another using chemical signal molecules. As in higher organisms, the information supplied by these molecules is critical for synchronizing the activities of large groups of cells. In bacteria, chemical communication involves producing, releasing, detecting, and responding to small hormone-like molecules termed autoinducers . This process, termed quorum sensing, allows bacteria to monitor the environment for other bacteria and to alter behavior on a population-wide scale in response to changes in the number and/or species present in a community. Most quorum-sensing-controlled processes are unproductive when undertaken by an individual bacterium acting alone but become beneficial when carried out simultaneously by a large number of cells. Thus, quorum sensing confuses the distinction between prokaryotes and eukaryotes because it enables bacteria to act as multicellular organisms. This review focuses on the architectures of bacterial chemical communication networks; how chemical information is integrated, processed, and transduced to control gene expression; how intra- and interspecies cell-cell communication is accomplished; and the intriguing possibility of prokaryote-eukaryote cross-communication.
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                Author and article information

                Journal
                Evol Bioinform Online
                Evol. Bioinform. Online
                Evolutionary Bioinformatics
                Evolutionary Bioinformatics Online
                Libertas Academica
                1176-9343
                2016
                12 May 2016
                : 12
                : Suppl 1
                : 5-16
                Affiliations
                [1 ]Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.
                [2 ]Biomolecular Sciences Institute, Florida International University, Miami, FL, USA.
                [3 ]Chromatin Structure and Evolution Group (Chromevol), Department of Biological Sciences, Florida International University, Miami, FL, USA.
                [4 ]Department of Computer Science, Eckerd College, St. Petersburg, FL, USA.
                [5 ]Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA.
                [6 ]Global Health Consortium, Florida International University, Miami, FL, USA.
                Author notes
                Article
                ebo-suppl.1-2016-005
                10.4137/EBO.S36436
                4869604
                27199545
                72367838-c51a-4b8b-94cb-fb6e9cd707e9
                © 2016 the authors, publisher and licensee Libertas Academica Limited

                This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

                History
                : 01 December 2015
                : 26 January 2016
                : 31 January 2016
                Categories
                Review

                Bioinformatics & Computational biology
                microbiome,metagenomics,metatranscriptomics,metabolomics,networks

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