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

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 bioinforma...

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Detalles Bibliográficos
Autores principales: Lu, Yao, Zhou, Guangyan, Ewald, Jessica, Pang, Zhiqiang, Shiri, Tanisha, Xia, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320150/
https://www.ncbi.nlm.nih.gov/pubmed/37166960
http://dx.doi.org/10.1093/nar/gkad407
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author Lu, Yao
Zhou, Guangyan
Ewald, Jessica
Pang, Zhiqiang
Shiri, Tanisha
Xia, Jianguo
author_facet Lu, Yao
Zhou, Guangyan
Ewald, Jessica
Pang, Zhiqiang
Shiri, Tanisha
Xia, Jianguo
author_sort Lu, Yao
collection PubMed
description 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.
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spelling pubmed-103201502023-07-06 MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data Lu, Yao Zhou, Guangyan Ewald, Jessica Pang, Zhiqiang Shiri, Tanisha Xia, Jianguo Nucleic Acids Res Web Server Issue 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. Oxford University Press 2023-05-11 /pmc/articles/PMC10320150/ /pubmed/37166960 http://dx.doi.org/10.1093/nar/gkad407 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Lu, Yao
Zhou, Guangyan
Ewald, Jessica
Pang, Zhiqiang
Shiri, Tanisha
Xia, Jianguo
MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title_full MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title_fullStr MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title_full_unstemmed MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title_short MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
title_sort microbiomeanalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320150/
https://www.ncbi.nlm.nih.gov/pubmed/37166960
http://dx.doi.org/10.1093/nar/gkad407
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