microeco: an R package for data mining in microbial community ecology
ABSTRACT A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequen...
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| Veröffentlicht in: | FEMS microbiology ecology 2021-02, Vol.97 (2), p.1 |
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| container_title | FEMS microbiology ecology |
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| creator | Liu, Chi Cui, Yaoming Li, Xiangzhen Yao, Minjie |
| description | ABSTRACT
A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).
An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology. |
| doi_str_mv | 10.1093/femsec/fiaa255 |
| format | Article |
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A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).
An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.</description><identifier>ISSN: 1574-6941</identifier><identifier>ISSN: 0168-6496</identifier><identifier>EISSN: 1574-6941</identifier><identifier>DOI: 10.1093/femsec/fiaa255</identifier><identifier>PMID: 33332530</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Abundance ; Analysis ; Biological diversity ; Community ecology ; Computational Biology ; Data analysis ; Data Mining ; Data processing ; Ecological research ; Ecology ; Functional analysis ; Identification and classification ; Methods ; Microbiology ; Microbiomes ; Microbiota ; Microorganisms ; Network analysis ; Next-generation sequencing ; R (Programming language) ; Sequences ; Software ; Statistical analysis ; Taxa ; Taxonomy ; Venn diagrams</subject><ispartof>FEMS microbiology ecology, 2021-02, Vol.97 (2), p.1</ispartof><rights>The Author(s) 2020. Published by Oxford University Press on behalf of FEMS. 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press on behalf of FEMS.</rights><rights>COPYRIGHT 2021 Oxford University Press</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c535t-881ddb43d51afb25430bd4fc913919ff35479139ca146ad267ae9f91e4ae74233</citedby><cites>FETCH-LOGICAL-c535t-881ddb43d51afb25430bd4fc913919ff35479139ca146ad267ae9f91e4ae74233</cites><orcidid>0000-0001-7501-0519</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1604,27924,27925</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/femsec/fiaa255$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33332530$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Chi</creatorcontrib><creatorcontrib>Cui, Yaoming</creatorcontrib><creatorcontrib>Li, Xiangzhen</creatorcontrib><creatorcontrib>Yao, Minjie</creatorcontrib><title>microeco: an R package for data mining in microbial community ecology</title><title>FEMS microbiology ecology</title><addtitle>FEMS Microbiol Ecol</addtitle><description>ABSTRACT
A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).
An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.</description><subject>Abundance</subject><subject>Analysis</subject><subject>Biological diversity</subject><subject>Community ecology</subject><subject>Computational Biology</subject><subject>Data analysis</subject><subject>Data Mining</subject><subject>Data processing</subject><subject>Ecological research</subject><subject>Ecology</subject><subject>Functional analysis</subject><subject>Identification and classification</subject><subject>Methods</subject><subject>Microbiology</subject><subject>Microbiomes</subject><subject>Microbiota</subject><subject>Microorganisms</subject><subject>Network analysis</subject><subject>Next-generation sequencing</subject><subject>R (Programming language)</subject><subject>Sequences</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Taxa</subject><subject>Taxonomy</subject><subject>Venn diagrams</subject><issn>1574-6941</issn><issn>0168-6496</issn><issn>1574-6941</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkc1rGzEQxUVpSBw31x6LoJfmYEefK6u3EJwPCARCchazWsko3ZXc1e7B_33k2k1CCERz0DD83mOYh9B3SuaUaH7mXZedPfMBgEn5BU2oVGJWaUG_vumP0HHOT4RQyQU5REe8PCY5maBlF2yfnE2_MUR8j9dg_8DKYZ963MAAuAsxxBUOEf8j6wAttqnrxhiGDS7CNq0239CBhza7k_0_RY-Xy4eL69nt3dXNxfntzEouh9liQZumFryRFHzNpOCkboS3mnJNtfdcCrXtLVBRQcMqBU57TZ0ApwTjfIp-7XzXffo7ujyYLmTr2haiS2M2TKiiJHxBC_rzHfqUxj6W7QyTlFOtlKxeqRW0zoTo09CD3Zqa82rBpNKVYoWaf0CValw5SorOhzL_SFAulnPvvFn3oYN-Yygx29zMLjezz60Ifuy3HevONS_4_6AKcLoD0rj-zOwZ-fugfw</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Liu, Chi</creator><creator>Cui, Yaoming</creator><creator>Li, Xiangzhen</creator><creator>Yao, Minjie</creator><general>Oxford University Press</general><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>3V.</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7T7</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7501-0519</orcidid></search><sort><creationdate>20210201</creationdate><title>microeco: an R package for data mining in microbial community ecology</title><author>Liu, Chi ; Cui, Yaoming ; Li, Xiangzhen ; Yao, Minjie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c535t-881ddb43d51afb25430bd4fc913919ff35479139ca146ad267ae9f91e4ae74233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abundance</topic><topic>Analysis</topic><topic>Biological diversity</topic><topic>Community ecology</topic><topic>Computational Biology</topic><topic>Data analysis</topic><topic>Data Mining</topic><topic>Data processing</topic><topic>Ecological research</topic><topic>Ecology</topic><topic>Functional analysis</topic><topic>Identification and classification</topic><topic>Methods</topic><topic>Microbiology</topic><topic>Microbiomes</topic><topic>Microbiota</topic><topic>Microorganisms</topic><topic>Network analysis</topic><topic>Next-generation sequencing</topic><topic>R (Programming language)</topic><topic>Sequences</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Taxa</topic><topic>Taxonomy</topic><topic>Venn diagrams</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Chi</creatorcontrib><creatorcontrib>Cui, Yaoming</creatorcontrib><creatorcontrib>Li, Xiangzhen</creatorcontrib><creatorcontrib>Yao, Minjie</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>FEMS microbiology ecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Chi</au><au>Cui, Yaoming</au><au>Li, Xiangzhen</au><au>Yao, Minjie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>microeco: an R package for data mining in microbial community ecology</atitle><jtitle>FEMS microbiology ecology</jtitle><addtitle>FEMS Microbiol Ecol</addtitle><date>2021-02-01</date><risdate>2021</risdate><volume>97</volume><issue>2</issue><spage>1</spage><pages>1-</pages><issn>1574-6941</issn><issn>0168-6496</issn><eissn>1574-6941</eissn><abstract>ABSTRACT
A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).
An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>33332530</pmid><doi>10.1093/femsec/fiaa255</doi><orcidid>https://orcid.org/0000-0001-7501-0519</orcidid></addata></record> |
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| subjects | Abundance Analysis Biological diversity Community ecology Computational Biology Data analysis Data Mining Data processing Ecological research Ecology Functional analysis Identification and classification Methods Microbiology Microbiomes Microbiota Microorganisms Network analysis Next-generation sequencing R (Programming language) Sequences Software Statistical analysis Taxa Taxonomy Venn diagrams |
| title | microeco: an R package for data mining in microbial community ecology |
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