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Short description:
Please summarise the application in a few sentences. Avoid links here. User-friendly, web-based analytical pipeline for comparative metagenomic studies. Input can be derived from either 16S rRNA data or NextGen shotgun sequencing.
Software version:
Biological application domain(s) (Phylogenetics, Genomics, ...):
Metagenomics, Machine learning,
Principal bioinformatics method(s) (Assembly, Mapping, ...):
Visualisation, Statistical calculation, Sequence clustering,
Technology (Sanger, Illumina, 454, SOLiD, Ion Torrent, ...):
Interface (Command line, Web UI, Desktop GUI, SOAP WS, HTTP WS, API, QL):
Resource type (Command-line tool, Web application, Desktop application, Script, Suite, Workbench, Database portal, Workflow, Plug-in, Library, Web API, Web service, SPARQL endpoint):
University of Alberta
== Description == <!-- Describe the application in the space below --> METAGENassist allows users to compare metagenomic samples using a variety of statistical methods via an easy-to-use web interface. === Data input formats === Researchers make their initial taxonomic assignments in other programs and then upload the resulting analysis file(s) to METAGENassist. Files from commonly used programs including mothur, QIIME, MEGAN, MG-RAST and STAMP can be read directly, or a taxonomic profile can be input in a generic Comma Separated Value (CSV) format. Data deriving from either 16S rRNA experiments or shotgun metagenomic sequencing can be used. === Data processing === Users a guided through data processing steps that include data integrity/quality checks, data filtering and normalization, and automated taxonomic-to-phenotypic mapping. === Statistical analysis === METAGENassist offers a wide array of commonly used statistical and machine learning methods including: * '''univariate statistics''' - fold change analysis, t-tests, volcano plots, one-way ANOVA, correlation analysis; * '''multivariate statistics''' - principal component analysis (PCA) and partial least squares - discriminant analysis (PLS-DA); * '''clustering''' - dendrograms, heatmaps, K-means clustering, self organizing feature maps (SOM); * '''supervised classification''' - random forests and support vector machine (SVM). === Data output === Upon completion, METAGENassist generates a variety of well-annotated tables and colorful, labeled graphs in an anti-aliased PNG format. PDF versions for some plots are also available. The processed data and images are available for download. <!-- -->
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