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...
Gespeichert in:
Veröffentlicht in: | Bioinformatics 2020-06, Vol.36 (11), p.3493-3498 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 3498 |
---|---|
container_issue | 11 |
container_start_page | 3493 |
container_title | Bioinformatics |
container_volume | 36 |
creator | Ni, Yan Yu, Gang Chen, Huan Deng, Yongqiong Wells, Philippa M Steves, Claire J Ju, Feng Fu, Junfen |
description | 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. |
doi_str_mv | 10.1093/bioinformatics/btaa188 |
format | Article |
fullrecord | <record><control><sourceid>proquest_TOX</sourceid><recordid>TN_cdi_proquest_miscellaneous_2377685731</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/bioinformatics/btaa188</oup_id><sourcerecordid>2377685731</sourcerecordid><originalsourceid>FETCH-LOGICAL-c331t-32f7356718d5fa96afc6a0b76ae03639d8fb7d6d1700e620d109cfad086885763</originalsourceid><addsrcrecordid>eNqNkDFPwzAQhS0EoqXwF6qMLKF2nNgOW1UVqFTEAnN0jm1klMTFTor673HUgsTGdHe6797TPYTmBN8RXNKFtM52xvkWeluHhewBiBBnaEpyhtMMF-V57CnjaS4wnaCrED4wLkie55doQjPCWVaIKVo_Z5vlfQLJl5ZJ0H6vfRJlk9bW3kWTVifQqaTVPUjXjKPtev3uo-1-XEFzCDZcowsDTdA3pzpDbw_r19VTun153KyW27SmlPQpzQynBeNEqMJAycDUDLDkDDSmjJZKGMkVU4RjrFmGVXy1NqCwYEIUnNEZuj3q7rz7HHToq9aGWjcNdNoNocoo5yySlESUHdH4Rwhem2rnbQv-UBFcjRFWfyOsThHGw_nJY5CtVr9nP5lFgBwBN-z-K_oNJ4qDug</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2377685731</pqid></control><display><type>article</type><title>M2IA: a web server for microbiome and metabolome integrative analysis</title><source>Oxford Journals Open Access Collection</source><creator>Ni, Yan ; Yu, Gang ; Chen, Huan ; Deng, Yongqiong ; Wells, Philippa M ; Steves, Claire J ; Ju, Feng ; Fu, Junfen</creator><creatorcontrib>Ni, Yan ; Yu, Gang ; Chen, Huan ; Deng, Yongqiong ; Wells, Philippa M ; Steves, Claire J ; Ju, Feng ; Fu, Junfen</creatorcontrib><description>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.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btaa188</identifier><identifier>PMID: 32176258</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><ispartof>Bioinformatics, 2020-06, Vol.36 (11), p.3493-3498</ispartof><rights>The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2020</rights><rights>The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-32f7356718d5fa96afc6a0b76ae03639d8fb7d6d1700e620d109cfad086885763</citedby><cites>FETCH-LOGICAL-c331t-32f7356718d5fa96afc6a0b76ae03639d8fb7d6d1700e620d109cfad086885763</cites><orcidid>0000-0003-1779-7266</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1598,27901,27902</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bioinformatics/btaa188$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32176258$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ni, Yan</creatorcontrib><creatorcontrib>Yu, Gang</creatorcontrib><creatorcontrib>Chen, Huan</creatorcontrib><creatorcontrib>Deng, Yongqiong</creatorcontrib><creatorcontrib>Wells, Philippa M</creatorcontrib><creatorcontrib>Steves, Claire J</creatorcontrib><creatorcontrib>Ju, Feng</creatorcontrib><creatorcontrib>Fu, Junfen</creatorcontrib><title>M2IA: a web server for microbiome and metabolome integrative analysis</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>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.</description><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqNkDFPwzAQhS0EoqXwF6qMLKF2nNgOW1UVqFTEAnN0jm1klMTFTor673HUgsTGdHe6797TPYTmBN8RXNKFtM52xvkWeluHhewBiBBnaEpyhtMMF-V57CnjaS4wnaCrED4wLkie55doQjPCWVaIKVo_Z5vlfQLJl5ZJ0H6vfRJlk9bW3kWTVifQqaTVPUjXjKPtev3uo-1-XEFzCDZcowsDTdA3pzpDbw_r19VTun153KyW27SmlPQpzQynBeNEqMJAycDUDLDkDDSmjJZKGMkVU4RjrFmGVXy1NqCwYEIUnNEZuj3q7rz7HHToq9aGWjcNdNoNocoo5yySlESUHdH4Rwhem2rnbQv-UBFcjRFWfyOsThHGw_nJY5CtVr9nP5lFgBwBN-z-K_oNJ4qDug</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Ni, Yan</creator><creator>Yu, Gang</creator><creator>Chen, Huan</creator><creator>Deng, Yongqiong</creator><creator>Wells, Philippa M</creator><creator>Steves, Claire J</creator><creator>Ju, Feng</creator><creator>Fu, Junfen</creator><general>Oxford University Press</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1779-7266</orcidid></search><sort><creationdate>20200601</creationdate><title>M2IA: a web server for microbiome and metabolome integrative analysis</title><author>Ni, Yan ; Yu, Gang ; Chen, Huan ; Deng, Yongqiong ; Wells, Philippa M ; Steves, Claire J ; Ju, Feng ; Fu, Junfen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-32f7356718d5fa96afc6a0b76ae03639d8fb7d6d1700e620d109cfad086885763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ni, Yan</creatorcontrib><creatorcontrib>Yu, Gang</creatorcontrib><creatorcontrib>Chen, Huan</creatorcontrib><creatorcontrib>Deng, Yongqiong</creatorcontrib><creatorcontrib>Wells, Philippa M</creatorcontrib><creatorcontrib>Steves, Claire J</creatorcontrib><creatorcontrib>Ju, Feng</creatorcontrib><creatorcontrib>Fu, Junfen</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ni, Yan</au><au>Yu, Gang</au><au>Chen, Huan</au><au>Deng, Yongqiong</au><au>Wells, Philippa M</au><au>Steves, Claire J</au><au>Ju, Feng</au><au>Fu, Junfen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>M2IA: a web server for microbiome and metabolome integrative analysis</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>36</volume><issue>11</issue><spage>3493</spage><epage>3498</epage><pages>3493-3498</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>32176258</pmid><doi>10.1093/bioinformatics/btaa188</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0003-1779-7266</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1367-4803 |
ispartof | Bioinformatics, 2020-06, Vol.36 (11), p.3493-3498 |
issn | 1367-4803 1460-2059 1367-4811 |
language | eng |
recordid | cdi_proquest_miscellaneous_2377685731 |
source | Oxford Journals Open Access Collection |
title | M2IA: a web server for microbiome and metabolome integrative analysis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T18%3A18%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_TOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=M2IA:%20a%20web%20server%20for%20microbiome%20and%20metabolome%20integrative%20analysis&rft.jtitle=Bioinformatics&rft.au=Ni,%20Yan&rft.date=2020-06-01&rft.volume=36&rft.issue=11&rft.spage=3493&rft.epage=3498&rft.pages=3493-3498&rft.issn=1367-4803&rft.eissn=1460-2059&rft_id=info:doi/10.1093/bioinformatics/btaa188&rft_dat=%3Cproquest_TOX%3E2377685731%3C/proquest_TOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2377685731&rft_id=info:pmid/32176258&rft_oup_id=10.1093/bioinformatics/btaa188&rfr_iscdi=true |