METAGENassist: a comprehensive web server for comparative metagenomics

Abstract

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.

Rhizosphere microbiome assemblage is affected by plant development

“…Sequences were also assigned to phylotypes using the phylotype command in Mothur. A multivariate data analysis of the OTUs was performed using METAGENassist (Arndt et al, 2012), followed by normalization based on interquantile range (IQR) (Hackstadt and Hess, 2009) and log 2 -transformation. IQR normalization allows one to increase statistical power by removing sequences that do not fall within the middle 50% of observations and thus reducing the number of statistical tests one has to perform.…”

“…IQR normalization allows one to increase statistical power by removing sequences that do not fall within the middle 50% of observations and thus reducing the number of statistical tests one has to perform. Principal component analysis (PCA) and significant features were identified for all treatments using METAGENassist (Arndt et al, 2012). The Vegan package (Oksanen et al, 2012) for R was used for community dissimilarity calculations (Bray-Curtis index) and principal coordinate analysis (PCoA).…”

Exploiting Natural Variation to Uncover an Alkene Biosynthetic Enzyme in Poplar

“…Analysis of metabolite data was conducted in R and METAGENassist (Arndt et al, 2012). For hierarchical clustering, samples were normalized (sum of 1) to make them comparable and peak concentrations normalized with a Pareto scaling.…”

A Comparison of Three Bioinformatics Pipelines for the Analysis of Preterm Gut Microbiota using 16S rRNA Gene Sequencing Data

“…Analysis of such communities requires the use of bioinformatics tools to efficiently and reproducibly process the large amount of data generated from amplicon sequencing to derive a taxonomic overview. There are various tools available to analyse 16S rRNA gene sequencing data including QIIME (Quantitative Insights Into Microbial Ecology) [2], mothur [3], MG-RAST (Metagenomics -Rapid Annotation using Subsystems Technology) [4], Genboree [5], EzTaxon [6], Pheonix2 [7], METAGENassist [8], MEGAN [9], VAMPS [10], SnoWMan [11], CloVR-16S [12], the RDPipeline (Ribosomal Database Project Pipeline) [13], Vegan [14], ade4 [15], and ape [16]. These tools can be categorised into those that are self-contained analysis pipelines i.e.…”