A concise guide to essential R packages for analyses of DNA, RNA, and proteins.
| Autor: | Chua EW; Centre for Drug and Herbal Development, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, 50300 Kuala Lumpur, Malaysia. Electronic address: cew85911@ukm.edu.my., Ooi J; Department of Preclinical Sciences, Faculty of Dentistry, MAHSA University, 42610 Jenjarom, Selangor, Malaysia., Nor Muhammad NA; Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Molecules and cells [Mol Cells] 2024 Nov; Vol. 47 (11), pp. 100120. Date of Electronic Publication: 2024 Oct 05. |
| DOI: | 10.1016/j.mocell.2024.100120 |
| Abstrakt: | 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. Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|