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ARS Home » Pacific West Area » Albany, California » Plant Gene Expression Center » Research » Publications at this Location » Publication #334063
Title: MetaCoMET: a web platform for discovery and visualization of the core microbiome

Author
item WANG, YI - University Of California
item XU, LING - University Of California
item Gu, Yong
item Coleman-Derr, Devin

Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2016
Publication Date: 3/9/2016
Citation: Wang, Y., Xu, L., Gu, Y.Q., Coleman-Derr, D.A. 2016. MetaCoMET: a web platform for discovery and visualization of the core microbiome. Bioinformatics. 32(22):3469-3470. doi:10.1093/bioinformatics/btw507.

Interpretive Summary: To compare the microbial communities associated with different environments, it is often useful to identify the core microbes shared between them. Current microbial datasets are often large and complex and computational tools are helpful for performing a core microbiome analysis. We have developed a web-based tool that is easy to use for analyzing the core microbiome. As the definition of the core microbiome remains largely subjective, we have included multiple methods for defining the core into our tool, along with a variety of user-customizable options for the analysis. The tool generates ready to use figures that are publication quality.

Technical Abstract: A key component of the analysis of microbiome datasets is the identification of OTUs shared between multiple experimental conditions, commonly referred to as the core microbiome. Results: We present a web platform named MetaCoMET that enables the discovery and visualization of the core microbiome and provides a comparison of the relative abundance and diversity patterns between subsets of samples within a microbiome dataset. MetaCoMET provides an efficient and interactive graphical interface for analyzing each subset defined by the union or disjunction of groups within the Venn diagram, and includes a graphical taxonomy summary, alpha diversity metrics, Principal Coordinate analysis, abundance-based heatmaps, and a chart indicating the geographic distribution of each sample.