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用于DNA、RNA和蛋白质分析的必备R包简明指南。

A concise guide to essential R packages for analyses of DNA, RNA, and proteins.

作者信息

Chua Eng Wee, Ooi Der Jiun, Nor Muhammad Nor Azlan

机构信息

Centre for Drug and Herbal Development, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, 50300 Kuala Lumpur, Malaysia.

Department of Preclinical Sciences, Faculty of Dentistry, MAHSA University, 42610 Jenjarom, Selangor, Malaysia.

出版信息

Mol Cells. 2024 Nov;47(11):100120. doi: 10.1016/j.mocell.2024.100120. Epub 2024 Oct 5.

Abstract

R is widely regarded as unrivaled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative polymerase chain reaction (qPCR) and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve.

摘要

R被广泛认为在统计功能方面是其他高级编程语言无法比拟的。R作为一种统计语言的普及导致许多人忽视了它在统计领域之外的应用。在这篇简短的综述中,我们列出了一系列R包,用于支持涉及DNA、RNA和蛋白质分析的项目。这些R包涵盖了从常规定量聚合酶链反应(qPCR)和蛋白质免疫印迹到高通量测序和蛋白质组学等重要分子技术的范围,这些技术会生成非常大的数据集。R的文本挖掘能力也可用于促进文献综述以及预测未来的研究趋势和途径。鉴于R语言的多功能性及其简洁的语法能够简化初步学习曲线,我们鼓励研究人员在工作中充分利用R。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9989/11541695/d01ebb5ac32f/gr1.jpg

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