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ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization

SUMMARY: Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflam...

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Autores principales: Yang, Chen, Mai, Jiahao, Cao, Xuan, Burberry, Aaron, Cominelli, Fabio, Zhang, Liangliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425198/
https://www.ncbi.nlm.nih.gov/pubmed/37527009
http://dx.doi.org/10.1093/bioinformatics/btad470
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author Yang, Chen
Mai, Jiahao
Cao, Xuan
Burberry, Aaron
Cominelli, Fabio
Zhang, Liangliang
author_facet Yang, Chen
Mai, Jiahao
Cao, Xuan
Burberry, Aaron
Cominelli, Fabio
Zhang, Liangliang
author_sort Yang, Chen
collection PubMed
description SUMMARY: Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, for analyzing functional profiles derived from 16S rRNA sequencing. This powerful tool enables researchers to conduct extensive differential abundance analyses and generate visually appealing visualizations that effectively highlight functional signals. With ggpicrust2, users can obtain publishable results and gain deeper insights into the functional composition of their microbial communities. AVAILABILITY AND IMPLEMENTATION: The package is open-source under the MIT and file license and is available at CRAN and https://github.com/cafferychen777/ggpicrust2. Its shiny web is available at https://a95dps-caffery-chen.shinyapps.io/ggpicrust2_shiny/.
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spelling pubmed-104251982023-08-15 ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization Yang, Chen Mai, Jiahao Cao, Xuan Burberry, Aaron Cominelli, Fabio Zhang, Liangliang Bioinformatics Applications Note SUMMARY: Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, for analyzing functional profiles derived from 16S rRNA sequencing. This powerful tool enables researchers to conduct extensive differential abundance analyses and generate visually appealing visualizations that effectively highlight functional signals. With ggpicrust2, users can obtain publishable results and gain deeper insights into the functional composition of their microbial communities. AVAILABILITY AND IMPLEMENTATION: The package is open-source under the MIT and file license and is available at CRAN and https://github.com/cafferychen777/ggpicrust2. Its shiny web is available at https://a95dps-caffery-chen.shinyapps.io/ggpicrust2_shiny/. Oxford University Press 2023-08-01 /pmc/articles/PMC10425198/ /pubmed/37527009 http://dx.doi.org/10.1093/bioinformatics/btad470 Text en © The Author(s) 2023. Published by Oxford University Press. 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 Applications Note
Yang, Chen
Mai, Jiahao
Cao, Xuan
Burberry, Aaron
Cominelli, Fabio
Zhang, Liangliang
ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title_full ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title_fullStr ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title_full_unstemmed ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title_short ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
title_sort ggpicrust2: an r package for picrust2 predicted functional profile analysis and visualization
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425198/
https://www.ncbi.nlm.nih.gov/pubmed/37527009
http://dx.doi.org/10.1093/bioinformatics/btad470
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