MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data

Abstract Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly b...

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Bibliographic Details
Published in:Nucleic acids research 2023-07, Vol.51 (W1), p.W310-W318
Main Authors: Lu, Yao, Zhou, Guangyan, Ewald, Jessica, Pang, Zhiqiang, Shiri, Tanisha, Xia, Jianguo
Format: Article
Language:English
Subjects:
Biomarkers
Computational Biology - methods
Internet
Metabolomics - methods
Microbiological Techniques - instrumentation
Microbiological Techniques - methods
Microbiota
User-Computer Interface
Web Server Issue
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Summary:Abstract Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis and interactive visualizations for popular graphical outputs, updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca. Graphical Abstract Graphical Abstract
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkad407