Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

Abstract The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the co...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yan Kou, Xiaomin Xu, Zhengnong Zhu, Lei Dai, Yan Tan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b1d69e8f0d84339b2a81184576c9576
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3b1d69e8f0d84339b2a81184576c9576
record_format	dspace
spelling	oai:doaj.org-article:3b1d69e8f0d84339b2a81184576c95762021-12-02T12:33:15ZMicrobe-set enrichment analysis facilitates functional interpretation of microbiome profiling data10.1038/s41598-020-78511-y2045-2322https://doaj.org/article/3b1d69e8f0d84339b2a81184576c95762020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78511-yhttps://doaj.org/toc/2045-2322Abstract The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.Yan KouXiaomin XuZhengnong ZhuLei DaiYan TanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Kou Xiaomin Xu Zhengnong Zhu Lei Dai Yan Tan Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
description	Abstract The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.
format	article
author	Yan Kou Xiaomin Xu Zhengnong Zhu Lei Dai Yan Tan
author_facet	Yan Kou Xiaomin Xu Zhengnong Zhu Lei Dai Yan Tan
author_sort	Yan Kou
title	Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
title_short	Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
title_full	Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
title_fullStr	Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
title_full_unstemmed	Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
title_sort	microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/3b1d69e8f0d84339b2a81184576c9576
work_keys_str_mv	AT yankou microbesetenrichmentanalysisfacilitatesfunctionalinterpretationofmicrobiomeprofilingdata AT xiaominxu microbesetenrichmentanalysisfacilitatesfunctionalinterpretationofmicrobiomeprofilingdata AT zhengnongzhu microbesetenrichmentanalysisfacilitatesfunctionalinterpretationofmicrobiomeprofilingdata AT leidai microbesetenrichmentanalysisfacilitatesfunctionalinterpretationofmicrobiomeprofilingdata AT yantan microbesetenrichmentanalysisfacilitatesfunctionalinterpretationofmicrobiomeprofilingdata
_version_	1718393852762521600

Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

Ejemplares similares