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Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories
Abstract Motivation The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to prov...
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Published in: | Bioinformatics (Oxford, England) England), 2023-01, Vol.39 (1) |
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creator | Banjac, Jelena Sprenger, Norbert Dogra, Shaillay Kumar |
description | Abstract
Motivation
The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
Results
Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory.
Availability and implementation
The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox
Supplementary information
Supplementary data are available at Bioinformatics online. |
doi_str_mv | 10.1093/bioinformatics/btac781 |
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Motivation
The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
Results
Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory.
Availability and implementation
The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox
Supplementary information
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4811</identifier><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btac781</identifier><identifier>PMID: 36469345</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Anomalies ; Applications Note ; Availability ; Bioinformatics ; Data analysis ; Data processing ; Intestinal microflora ; Metadata ; Microbiomes ; Microbiota ; Python ; Software ; Source code ; Statistical analysis</subject><ispartof>Bioinformatics (Oxford, England), 2023-01, Vol.39 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c484t-5cbe9f46d0a6b799d5eb5bd82afbc084eb8b2e1c214464865b84b4b9bc62d0133</citedby><cites>FETCH-LOGICAL-c484t-5cbe9f46d0a6b799d5eb5bd82afbc084eb8b2e1c214464865b84b4b9bc62d0133</cites><orcidid>0000-0003-4880-2750 ; 0000-0001-7373-4150 ; 0000-0002-2987-4313</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825749/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825749/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36469345$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Marschall, Tobias</contributor><creatorcontrib>Banjac, Jelena</creatorcontrib><creatorcontrib>Sprenger, Norbert</creatorcontrib><creatorcontrib>Dogra, Shaillay Kumar</creatorcontrib><title>Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
Results
Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory.
Availability and implementation
The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Anomalies</subject><subject>Applications Note</subject><subject>Availability</subject><subject>Bioinformatics</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Intestinal microflora</subject><subject>Metadata</subject><subject>Microbiomes</subject><subject>Microbiota</subject><subject>Python</subject><subject>Software</subject><subject>Source code</subject><subject>Statistical analysis</subject><issn>1367-4811</issn><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNkc1u3CAURlGUqkmTvkJkqZtspgEMGLKIFEX9k1J1ky4jBPg6w8j2dcEetX36Us00TbrqCiTOPfouHyFnjL5l1NQXPmIcO0yDm2PIF352odHsgByzWjUroRk7fHI_Iq9y3lBKJZXqJTmqlVCmFvKY3H-OIWGxDVDdIfYev19WA8xrbLHHhxhcX7lpSujCGnI1Y9VCiluo3NhW25gX18efUA1_LXNyGwgzpgj5lLzoXJ_h9f48IV_fv7u7-bi6_fLh08317SoILeaVDB5MJ1RLnfKNMa0EL32ruet8oFqA154DC5wJoYRW0mvhhTc-KN5SVtcn5GrnnRY_QBtgLCl6O6U4uPTDoov2-csY1_YBt9ZoLhthiuB8L0j4bYE82yHmAH3vRsAlW96IhlKujS7om3_QDS5pLOvZukSRnGvaFErtqPIvOSfoHsMwan83aJ83aPcNlsGzp6s8jv2prABsB-Ay_a_0F_ujskg</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Banjac, Jelena</creator><creator>Sprenger, Norbert</creator><creator>Dogra, Shaillay Kumar</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4880-2750</orcidid><orcidid>https://orcid.org/0000-0001-7373-4150</orcidid><orcidid>https://orcid.org/0000-0002-2987-4313</orcidid></search><sort><creationdate>20230101</creationdate><title>Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories</title><author>Banjac, Jelena ; Sprenger, Norbert ; Dogra, Shaillay Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c484t-5cbe9f46d0a6b799d5eb5bd82afbc084eb8b2e1c214464865b84b4b9bc62d0133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Anomalies</topic><topic>Applications Note</topic><topic>Availability</topic><topic>Bioinformatics</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Intestinal microflora</topic><topic>Metadata</topic><topic>Microbiomes</topic><topic>Microbiota</topic><topic>Python</topic><topic>Software</topic><topic>Source code</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Banjac, Jelena</creatorcontrib><creatorcontrib>Sprenger, Norbert</creatorcontrib><creatorcontrib>Dogra, Shaillay Kumar</creatorcontrib><collection>Open Access: Oxford University Press Open Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Banjac, Jelena</au><au>Sprenger, Norbert</au><au>Dogra, Shaillay Kumar</au><au>Marschall, Tobias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2023-01-01</date><risdate>2023</risdate><volume>39</volume><issue>1</issue><issn>1367-4811</issn><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
Results
Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory.
Availability and implementation
The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36469345</pmid><doi>10.1093/bioinformatics/btac781</doi><orcidid>https://orcid.org/0000-0003-4880-2750</orcidid><orcidid>https://orcid.org/0000-0001-7373-4150</orcidid><orcidid>https://orcid.org/0000-0002-2987-4313</orcidid><oa>free_for_read</oa></addata></record> |
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source | Open Access: Oxford University Press Open Journals; PubMed Central |
subjects | Anomalies Applications Note Availability Bioinformatics Data analysis Data processing Intestinal microflora Metadata Microbiomes Microbiota Python Software Source code Statistical analysis |
title | Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories |
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