RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods

Volume 1097 of the series Methods in Molecular Biology pp 437-456


Computational Prediction of MicroRNA Genes

  • Jana HertelAffiliated withBioinformatics Group, Department of Computer Science, University of Leipzig
  • , David LangenbergerAffiliated withBioinformatics Group, Department of Computer Science, University of Leipzig
  • , Peter F. StadlerAffiliated withBioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of LeipzigMax Planck Institute for Mathematics in the SciencesFraunhofer Institute for Cell Therapy and ImmunologyInstitute for Theoretical Chemistry, University of ViennaCenter for non-coding RNA in Technology and Health, University of CopenhagenSanta Fe Institute

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The computational identification of novel microRNA (miRNA) genes is a challenging task in bioinformatics. Massive amounts of data describing unknown functional RNA transcripts have to be analyzed for putative miRNA candidates with automated computational pipelines. Beyond those miRNAs that meet the classical definition, high-throughput sequencing techniques have revealed additional miRNA-like molecules that are derived by alternative biogenesis pathways. Exhaustive bioinformatics analyses on such data involve statistical issues as well as precise sequence and structure inspection not only of the functional mature part but also of the whole precursor sequence of the putative miRNA. Apart from a considerable amount of species-specific miRNAs, the majority of all those genes are conserved at least among closely related organisms. Some miRNAs, however, can be traced back to very early points in the evolution of eukaryotic species. Thus, the investigation of the conservation of newly found miRNA candidates comprises an important step in the computational annotation of miRNAs.

Topics covered in this chapter include a review on the obvious problem of miRNA annotation and family definition, recommended pipelines of computational miRNA annotation or detection, and an overview of current computer tools for the prediction of miRNAs and their limitations. The chapter closes discussing how those bioinformatic approaches address the problem of faithful miRNA prediction and correct annotation.

Key words

miRNA Machine learning Homology Structure conservation