Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1785068389688934400 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10320150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT luyao microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata AT zhouguangyan microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata AT ewaldjessica microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata AT pangzhiqiang microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata AT shiritanisha microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata AT xiajianguo microbiomeanalyst20comprehensivestatisticalfunctionalandintegrativeanalysisofmicrobiomedata |