31
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 4

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          METAGENassist: a comprehensive web server for comparative metagenomics

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes.

            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
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Impact of Experimental Hookworm Infection on the Human Gut Microbiota

              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.
                Bookmark

                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 March 2017
                13 December 2016
                13 December 2016
                : 33
                : 5
                : 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
                btw725
                10.1093/bioinformatics/btw725
                5408814
                28025202
                © The Author 2016. Published by Oxford University Press.

                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.

                Page count
                Pages: 2
                Product
                Funding
                Funded by: Australian Government
                Funded by: NHMRC Fellowship
                Categories
                Applications Notes
                Data and Text Mining

                Bioinformatics & Computational biology

                Comments

                Comment on this article