M2IA: a web server for microbiome and metabolome integrative analysis

Abstract Motivation Microbiome–metabolome association studies have experienced exponential growth for an in-depth understanding of the impact of microbiota on human health over the last decade. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge d...

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Veröffentlicht in:Bioinformatics 2020-06, Vol.36 (11), p.3493-3498
Hauptverfasser: Ni, Yan, Yu, Gang, Chen, Huan, Deng, Yongqiong, Wells, Philippa M, Steves, Claire J, Ju, Feng, Fu, Junfen
Format: Artikel
Sprache:eng
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Zusammenfassung:Abstract Motivation Microbiome–metabolome association studies have experienced exponential growth for an in-depth understanding of the impact of microbiota on human health over the last decade. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge due to the lack of a comprehensive computational tool that can facilitate data integration and interpretation. In this study, an automated microbiome and metabolome integrative analysis pipeline (M2IA) has been developed to meet the urgent needs for tools that can effectively integrate microbiome and metabolome data to derive biological insights. Results M2IA streamlines the integrative data analysis between metabolome and microbiome, from data preprocessing, univariate and multivariate statistical analyses, advanced functional analysis for biological interpretation, to a summary report. The functionality of M2IA was demonstrated using TwinsUK cohort datasets consisting of 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. Moreover, two important metabolic pathways, i.e. benzoate degradation and phosphotransferase system, were identified to be closely associated with obesity. Availability and implementation M2IA is public available at http://m2ia.met-bioinformatics.cn. Contact yanni617@zju.edu.cn or fjf68@zju.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btaa188