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

Autor:	Lu Y; Department of Microbiology and Immunology, McGill University, Quebec, Canada., Zhou G; Institute of Parasitology, McGill University, Quebec, Canada., Ewald J; Institute of Parasitology, McGill University, Quebec, Canada., Pang Z; Institute of Parasitology, McGill University, Quebec, Canada., Shiri T; Institute of Parasitology, McGill University, Quebec, Canada., Xia J; Department of Microbiology and Immunology, McGill University, Quebec, Canada.; Institute of Parasitology, McGill University, Quebec, Canada.; Department of Animal Science, McGill University, Quebec, Canada.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2023 Jul 05; Vol. 51 (W1), pp. W310-W318.
DOI:	10.1093/nar/gkad407
Abstrakt:	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. (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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