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Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data

Nature Protocols volume 15, pages 799–821 (2020)Cite this article

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Abstract

MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min.

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Fig. 1: Overview of the MicrobiomeAnalyst workflow.
Fig. 2: Comprehensive data analysis and report generation.
Fig. 3: Interactive 3D PCoA plot for beta-diversity analysis.
Fig. 4: Heat tree visualization of taxonomic differences.
Fig. 5: Correlation network analysis.
Fig. 6: Graphical summary of LEfSe analysis.
Fig. 7: Visualization of the ‘Random Forests’ results.
Fig. 8: Visualization of enriched pathways in the KEGG global metabolic network.
Fig. 9: TSEA results.

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Data availability

All example datasets used in the protocol are integrated as example datasets in their respective modules and are also available for download from the ‘Resources’ page of MicrobiomeAnalyst (https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/docs/Resources.xhtml). There are no restrictions on their use.

Code availability

MicrobiomeAnalyst is freely accessible as a web-based application. The underlying R code is freely available at GitHub (https://github.com/xia-lab/MicrobiomeAnalystR) under a GNU General Public License v.2 or later. The code in this protocol has been peer-reviewed.

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Acknowledgements

The authors thank Genome Canada, Génome Québec, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canada Research Chairs (CRC) Program for funding support.

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Authors and Affiliations

  1. Institute of Parasitology, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada

    Jasmine Chong, Peng Liu, Guangyan Zhou & Jianguo Xia

  2. Department of Animal Science, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada

    Jianguo Xia

  3. Department of Microbiology & Immunology, Montreal, Quebec, Canada

    Jianguo Xia

  4. Department of Human Genetics, Montreal, Quebec, Canada

    Jianguo Xia

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  1. Jasmine Chong
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Contributions

J.C. and J.X. prepared the manuscript. J.C., P.L., G.Z., and J.X. contributed to the development of MicrobiomeAnalyst. All authors read and approved the final manuscript.

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Correspondence to Jianguo Xia.

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Peer review information Nature Protocols thanks Tiffany Weir and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Khan, N. et al. Mucosal Immunol. 12, 772–783 (2019): https://doi.org/10.1038/s41385-019-0147-3

Stinson, L. F., Boyce, M. C., Payne, M. S. & Keelan, J. A. Front. Microbiol. 10, 1124 (2019): https://doi.org/10.3389/fmicb.2019.01124

Amrane, S. et al. Sci. Rep. 9, 12807 (2019): https://doi.org/10.1038/s41598-019-49189-8

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Chong, J., Liu, P., Zhou, G. et al. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc 15, 799–821 (2020). https://doi.org/10.1038/s41596-019-0264-1

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