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      Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions

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          Abstract

          Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page.

          Availability and Implementation: The web-interface is accessible via http://cgenome.net/calypso/. The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931). The software is freely available for non-commercial users.

          Contact: l.krause@ 123456uq.edu.au

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          METAGENassist: a comprehensive web server for comparative metagenomics

          David L. Arndt, Jianguo Xia, Yifeng Liu … (2012)
          With recent improvements in DNA sequencing and sample extraction techniques, the quantity and quality of metagenomic data are now growing exponentially. This abundance of richly annotated metagenomic data and bacterial census information has spawned a new branch of microbiology called comparative metagenomics. Comparative metagenomics involves the comparison of bacterial populations between different environmental samples, different culture conditions or different microbial hosts. However, in order to do comparative metagenomics, one typically requires a sophisticated knowledge of multivariate statistics and/or advanced software programming skills. To make comparative metagenomics more accessible to microbiologists, we have developed a freely accessible, easy-to-use web server for comparative metagenomic analysis called METAGENassist. Users can upload their bacterial census data from a wide variety of common formats, using either amplified 16S rRNA data or shotgun metagenomic data. Metadata concerning environmental, culture, or host conditions can also be uploaded. During the data upload process, METAGENassist also performs an automated taxonomic-to-phenotypic mapping. Phenotypic information covering nearly 20 functional categories such as GC content, genome size, oxygen requirements, energy sources and preferred temperature range is automatically generated from the taxonomic input data. Using this phenotypically enriched data, users can then perform a variety of multivariate and univariate data analyses including fold change analysis, t-tests, PCA, PLS-DA, clustering and classification. To facilitate data processing, users are guided through a step-by-step analysis workflow using a variety of menus, information hyperlinks and check boxes. METAGENassist also generates colorful, publication quality tables and graphs that can be downloaded and used directly in the preparation of scientific papers. METAGENassist is available at http://www.metagenassist.ca.
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            Impact of Experimental Hookworm Infection on the Human Gut Microbiota

            Cinzia Cantacessi, Paul Giacomin, John Croese … (2014)
            The interactions between gastrointestinal parasitic helminths and commensal bacteria are likely to play a pivotal role in the establishment of host-parasite cross-talk, ultimately shaping the development of the intestinal immune system. However, little information is available on the impact of infections by gastrointestinal helminths on the bacterial communities inhabiting the human gut. We used 16S rRNA gene amplification and pyrosequencing to characterize, for the first time to our knowledge, the differences in composition and relative abundance of fecal microbial communities in human subjects prior to and following experimental infection with the blood-feeding intestinal hookworm, Necator americanus. Our data show that, although hookworm infection leads to a minor increase in microbial species richness, no detectable effect is observed on community structure, diversity or relative abundance of individual bacterial species.
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              ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes.

              Philip Hugenholtz, Erik Kristiansson, Daniel Dalevi (2009)
              Microorganisms are ubiquitous in nature and constitute intrinsic parts of almost every ecosystem. A culture-independent and powerful way to study microbial communities is metagenomics. In such studies, functional analysis is performed on fragmented genetic material from multiple species in the community. The recent advances in high-throughput sequencing have greatly increased the amount of data in metagenomic projects. At present, there is an urgent need for efficient statistical tools to analyse these data. We have created ShotgunFunctionalizeR, an R-package for functional comparison of metagenomes. The package contains tools for importing, annotating and visualizing metagenomic data produced by shotgun high-throughput sequencing. ShotgunFunctionalizeR contains several statistical procedures for assessing functional differences between samples, both for individual genes and for entire pathways. In addition to standard and previously published methods, we have developed and implemented a novel approach based on a Poisson model. This procedure is highly flexible and thus applicable to a wide range of different experimental designs. We demonstrate the potential of ShotgunFunctionalizeR by performing a regression analysis on metagenomes sampled at multiple depths in the Pacific Ocean. http://shotgun.zool.gu.se
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 March 2017
                Publication date (Electronic): 13 December 2016
                Publication date PMC-release: 13 December 2016
                Volume: 33
                Issue: 5
                Pages: 782-783
                Affiliations
                [1 ]QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
                [2 ]The University of Queensland Diamantina Institute, Brisbane, QLD 4102, Australia
                [3 ]Nestle Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland
                Author notes
                [* ]To whom correspondence should be addressed.
                Article
                Publisher ID: btw725
                DOI: 10.1093/bioinformatics/btw725
                PMC ID: 5408814
                PubMed ID: 28025202
                SO-VID: e5246ab9-e99f-428c-b122-f2301a43f51b
                Copyright © © The Author 2016. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 10 August 2016
                Date revision received : 09 November 2016
                Date accepted : 16 November 2016
                Page count
                Pages: 2
                Funding
                Funded by: Australian Government
                Funded by: NHMRC Fellowship
                Categories
                Subject: Applications Notes
                Subject: Data and Text Mining

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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