使用R进行微生物组分析的最佳实践。

IF 13.6 1区 生物学 Q1 CELL BIOLOGY Protein & Cell Pub Date : 2023-10-25 DOI:10.1093/procel/pwad024
Tao Wen, Guoqing Niu, Tong Chen, Qirong Shen, Jun Yuan, Yong-Xin Liu
{"title":"使用R进行微生物组分析的最佳实践。","authors":"Tao Wen, Guoqing Niu, Tong Chen, Qirong Shen, Jun Yuan, Yong-Xin Liu","doi":"10.1093/procel/pwad024","DOIUrl":null,"url":null,"abstract":"<p><p>With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.</p>","PeriodicalId":20790,"journal":{"name":"Protein & Cell","volume":null,"pages":null},"PeriodicalIF":13.6000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599642/pdf/","citationCount":"0","resultStr":"{\"title\":\"The best practice for microbiome analysis using R.\",\"authors\":\"Tao Wen, Guoqing Niu, Tong Chen, Qirong Shen, Jun Yuan, Yong-Xin Liu\",\"doi\":\"10.1093/procel/pwad024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.</p>\",\"PeriodicalId\":20790,\"journal\":{\"name\":\"Protein & Cell\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.6000,\"publicationDate\":\"2023-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599642/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Protein & Cell\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/procel/pwad024\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Protein & Cell","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/procel/pwad024","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

随着测序技术的逐渐成熟,许多微生物组研究已经发表,推动了相关分析工具的出现和进步。R语言是广泛使用的微生物组数据分析平台,具有强大的功能。然而,数以万计的R包和无数类似的分析工具给许多研究人员探索微生物组数据带来了重大挑战。如何从众多的R包中选择合适、高效、方便、易于学习的工具,已经成为许多微生物组研究人员的一个问题。我们已经组织了324个用于微生物组分析的常见R包,并根据应用类别(多样性、差异性、生物标志物、相关性和网络、功能预测等)对其进行了分类,这可以帮助研究人员快速找到用于微生物组分析的相关R包。此外,我们系统地对用于微生物组分析的集成R包(phyloseq、microbiome、MicrobiomeAnalystR、Animalcules、microeco和Amplifcon)进行了分类,并总结了其优势和局限性,这将有助于研究人员选择合适的工具。最后,我们彻底回顾了微生物组分析的R包,总结了微生物组中大多数常见的分析内容,并形成了最适合微生物组的分析管道。本文附有GitHub中数百个包含10000行代码的示例,可以帮助初学者学习,也可以帮助分析师比较和测试不同的工具。本文系统地梳理了R在微生物组中的应用,为未来开发更好的微生物组工具提供了重要的理论依据和实践参考。所有代码都可以在GitHub GitHub.com/taowenmicro/EasyMicrobiomeR上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The best practice for microbiome analysis using R.

With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Protein & Cell
Protein & Cell CELL BIOLOGY-
CiteScore
24.00
自引率
0.90%
发文量
1029
审稿时长
6-12 weeks
期刊介绍: Protein & Cell is a monthly, peer-reviewed, open-access journal focusing on multidisciplinary aspects of biology and biomedicine, with a primary emphasis on protein and cell research. It publishes original research articles, reviews, and commentaries across various fields including biochemistry, biophysics, cell biology, genetics, immunology, microbiology, molecular biology, neuroscience, oncology, protein science, structural biology, and translational medicine. The journal also features content on research policies, funding trends in China, and serves as a platform for academic exchange among life science researchers.
期刊最新文献
Correction to: Oncogenic miR-19a and miR-19b co-regulate tumor suppressor MTUS1 to promote cell proliferation and migration in lung cancer. p21/Zbtb18 repress the expression of cKit to regulate the self-renewal of hematopoietic stem cells. Syn3, a newly developed cyclic peptide and BDNF signaling enhancer, ameliorates retinal ganglion cell degeneration in diabetic retinopathy. Integrative analysis of transcriptome, DNA methylome, and chromatin accessibility reveals candidate therapeutic targets in hypertrophic cardiomyopathy. Antiviral activity of lipoxygenase against severe fever with thrombocytopenia syndrome virus.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1