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EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study

Advances in next-generation sequencing (NGS) have revolutionized microbial studies in many fields, especially in clinical investigation. As the second human genome, microbiota has been recognized as a new approach and perspective to understand the biological and pathologic basis of various diseases....

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Autores principales: Liu, Bingdong, Huang, Liujing, Liu, Zhihong, Pan, Xiaohan, Cui, Zongbing, Pan, Jiyang, Xie, Liwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764268/
https://www.ncbi.nlm.nih.gov/pubmed/35058973
http://dx.doi.org/10.3389/fgene.2021.803627
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author Liu, Bingdong
Huang, Liujing
Liu, Zhihong
Pan, Xiaohan
Cui, Zongbing
Pan, Jiyang
Xie, Liwei
author_facet Liu, Bingdong
Huang, Liujing
Liu, Zhihong
Pan, Xiaohan
Cui, Zongbing
Pan, Jiyang
Xie, Liwei
author_sort Liu, Bingdong
collection PubMed
description Advances in next-generation sequencing (NGS) have revolutionized microbial studies in many fields, especially in clinical investigation. As the second human genome, microbiota has been recognized as a new approach and perspective to understand the biological and pathologic basis of various diseases. However, massive amounts of sequencing data remain a huge challenge to researchers, especially those who are unfamiliar with microbial data analysis. The mathematic algorithm and approaches introduced from another scientific field will bring a bewildering array of computational tools and acquire higher quality of script experience. Moreover, a large cohort research together with extensive meta-data including age, body mass index (BMI), gender, medical results, and others related to subjects also aggravate this situation. Thus, it is necessary to develop an efficient and convenient software for clinical microbiome data analysis. EasyMicroPlot (EMP) package aims to provide an easy-to-use microbial analysis tool based on R platform that accomplishes the core tasks of metagenomic downstream analysis, specially designed by incorporation of popular microbial analysis and visualization used in clinical microbial studies. To illustrate how EMP works, 694 bio-samples from Guangdong Gut Microbiome Project (GGMP) were selected and analyzed with EMP package. Our analysis demonstrated the influence of dietary style on gut microbiota and proved EMP package's powerful ability and excellent convenience to address problems for this field.
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spelling pubmed-87642682022-01-19 EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study Liu, Bingdong Huang, Liujing Liu, Zhihong Pan, Xiaohan Cui, Zongbing Pan, Jiyang Xie, Liwei Front Genet Genetics Advances in next-generation sequencing (NGS) have revolutionized microbial studies in many fields, especially in clinical investigation. As the second human genome, microbiota has been recognized as a new approach and perspective to understand the biological and pathologic basis of various diseases. However, massive amounts of sequencing data remain a huge challenge to researchers, especially those who are unfamiliar with microbial data analysis. The mathematic algorithm and approaches introduced from another scientific field will bring a bewildering array of computational tools and acquire higher quality of script experience. Moreover, a large cohort research together with extensive meta-data including age, body mass index (BMI), gender, medical results, and others related to subjects also aggravate this situation. Thus, it is necessary to develop an efficient and convenient software for clinical microbiome data analysis. EasyMicroPlot (EMP) package aims to provide an easy-to-use microbial analysis tool based on R platform that accomplishes the core tasks of metagenomic downstream analysis, specially designed by incorporation of popular microbial analysis and visualization used in clinical microbial studies. To illustrate how EMP works, 694 bio-samples from Guangdong Gut Microbiome Project (GGMP) were selected and analyzed with EMP package. Our analysis demonstrated the influence of dietary style on gut microbiota and proved EMP package's powerful ability and excellent convenience to address problems for this field. Frontiers Media S.A. 2022-01-04 /pmc/articles/PMC8764268/ /pubmed/35058973 http://dx.doi.org/10.3389/fgene.2021.803627 Text en Copyright © 2022 Liu, Huang, Liu, Pan, Cui, Pan and Xie. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Bingdong
Huang, Liujing
Liu, Zhihong
Pan, Xiaohan
Cui, Zongbing
Pan, Jiyang
Xie, Liwei
EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title_full EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title_fullStr EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title_full_unstemmed EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title_short EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study
title_sort easymicroplot: an efficient and convenient r package in microbiome downstream analysis and visualization for clinical study
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764268/
https://www.ncbi.nlm.nih.gov/pubmed/35058973
http://dx.doi.org/10.3389/fgene.2021.803627
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