Current progress of high-throughput microRNA differential expression analysis and random forest gene selection for model and non-model systems: an R implementation

J Integr Bioinform. 2016 Dec 22;13(5):306. doi: 10.2390/biecoll-jib-2016-306.


MicroRNAs are short non-coding RNA transcripts that act as master cellular egulators with roles in orchestrating virtually all biological functions. The recent affordability and widespread use of high-throughput microRNA profiling technologies has grown along with the advancement of bioinformatics tools available for analysis of the mounting data flow. While there are many computational resources available for the management of data from genome sequenced animals, researchers are often faced with the challenge of identifying the biological implications of the daunting amount of data generated from these high-throughput technologies. In this article, we review the current state of highthroughput microRNA expression profiling platforms, data analysis processes, and computational tools in the context of comparative molecular physiology. We also present RBioMIR and RBioFS, our R package implementations for differential expression analysis and random forest-based gene selection. Detailed installation guides are available at

MeSH terms

  • Algorithms*
  • Animals
  • Computational Biology
  • Gene Expression Profiling*
  • Gene Expression Regulation
  • Genes*
  • High-Throughput Screening Assays / methods*
  • Liver / metabolism
  • Marsupialia / genetics
  • MicroRNAs / genetics*
  • MicroRNAs / metabolism
  • Models, Biological*
  • Sequence Analysis, RNA


  • MicroRNAs