IncRna: The R Package for Optimizing lncRNA Identification Processes

J Comput Biol. 2023 Dec;30(12):1322-1326. doi: 10.1089/cmb.2023.0091. Epub 2023 Oct 25.

Abstract

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.

Keywords: R library; accuracy; error analysis; identification; lncRna.

MeSH terms

  • Computational Biology
  • RNA, Long Noncoding* / genetics
  • Software*

Substances

  • RNA, Long Noncoding