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

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.

Keywords: Genomics; Proteomics; R package; Transcriptomics.

Publication types

  • Review
  • Letter

MeSH terms

  • DNA* / genetics
  • Humans
  • Proteins* / genetics
  • Proteins* / metabolism
  • RNA* / genetics
  • Software

Substances

  • RNA
  • DNA
  • Proteins